Introduction Parkinson’s Disease is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting more than 10 million people worldwide. Parkinson’s is characterized primarily by the deterioration of motor and cognitive ability. There is no single test which can be administered for diagnosis. Instead, doctors must perform a careful clinical analysis of the patient’s medical history. Unfortunately, this method of diagnosis is highly inaccurate. A study from the National Institute of Neurological Disorders finds that early diagnosis (having symptoms for 5 years or less) is only 53% accurate. This is not much better than random guessing, but an early diagnosis is critical to effective treatment. Because of these difficulties, I investigate a machine learning approach to accurately diagnose Parkinson’s, using a dataset of various speech features (a non-invasive yet characteristic tool) from the University of Oxford. Why speech features? Speech is very predictive and characteristic of Parkinson’s disease; almost every Parkinson’s patient experiences severe vocal degradation (inability to produce sustained phonations, tremor, hoarseness), so it makes sense to use voice to diagnose the disease. Voice analysis gives the added benefit of being non-invasive, inexpensive, and very easy to extract clinically. Background Parkinson's Disease Parkinson’s is a progressive neurodegenerative condition resulting from the death of the dopamine containing cells of the substantia nigra (which plays an important role in movement). Symptoms include: “frozen” facial features, bradykinesia (slowness of movement), akinesia (impairment of voluntary movement), tremor, and voice impairment. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. Performance Metrics TP = true positive, FP = false positive, TN = true negative, FN = false negative Accuracy: (TP+TN)/(P+N) Matthews Correlation Coefficient: 1=perfect, 0=random, -1=completely inaccurate Algorithms Employed Logistic Regression (LR): Uses the sigmoid logistic equation with weights (coefficient values) and biases (constants) to model the probability of a certain class for binary classification. An output of 1 represents one class, and an output of 0 represents the other. Training the model will learn the optimal weights and biases. Linear Discriminant Analysis (LDA): Assumes that the data is Gaussian and each feature has the same variance. LDA estimates the mean and variance for each class from the training data, and then uses properties of statistics (Bayes theorem , Gaussian distribution, etc) to compute the probability of a particular instance belonging to a given class. The class with the largest probability is the prediction. k Nearest Neighbors (KNN): Makes predictions about the validation set using the entire training set. KNN makes a prediction about a new instance by searching through the entire set to find the k “closest” instances. “Closeness” is determined using a proximity measurement (Euclidean) across all features. The class that the majority of the k closest instances belong to is the class that the model predicts the new instance to be. Decision Tree (DT): Represented by a binary tree, where each root node represents an input variable and a split point, and each leaf node contains an output used to make a prediction. Neural Network (NN): Models the way the human brain makes decisions. Each neuron takes in 1+ inputs, and then uses an activation function to process the input with weights and biases to produce an output. Neurons can be arranged into layers, and multiple layers can form a network to model complex decisions. Training the network involves using the training instances to optimize the weights and biases. Naive Bayes (NB): Simplifies the calculation of probabilities by assuming that all features are independent of one another (a strong but effective assumption). Employs Bayes Theorem to calculate the probabilities that the instance to be predicted is in each class, then finds the class with the highest probability. Gradient Boost (GB): Generally used when seeking a model with very high predictive performance. Used to reduce bias and variance (“error”) by combining multiple “weak learners” (not very good models) to create a “strong learner” (high performance model). Involves 3 elements: a loss function (error function) to be optimized, a weak learner (decision tree) to make predictions, and an additive model to add trees to minimize the loss function. Gradient descent is used to minimize error after adding each tree (one by one). Engineering Goal Produce a machine learning model to diagnose Parkinson’s disease given various features of a patient’s speech with at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9. Compare various algorithms and parameters to determine the best model for predicting Parkinson’s. Dataset Description Source: the University of Oxford 195 instances (147 subjects with Parkinson’s, 48 without Parkinson’s) 22 features (elements that are possibly characteristic of Parkinson’s, such as frequency, pitch, amplitude / period of the sound wave) 1 label (1 for Parkinson’s, 0 for no Parkinson’s) Project Pipeline pipeline Summary of Procedure Split the Oxford Parkinson’s Dataset into two parts: one for training, one for validation (evaluate how well the model performs) Train each of the following algorithms with the training set: Logistic Regression, Linear Discriminant Analysis, k Nearest Neighbors, Decision Tree, Neural Network, Naive Bayes, Gradient Boost Evaluate results using the validation set Repeat for the following training set to validation set splits: 80% training / 20% validation, 75% / 25%, and 70% / 30% Repeat for a rescaled version of the dataset (scale all the numbers in the dataset to a range from 0 to 1: this helps to reduce the effect of outliers) Conduct 5 trials and average the results Data a_o a_r m_o m_r Data Analysis In general, the models tended to perform the best (both in terms of accuracy and Matthews Correlation Coefficient) on the rescaled dataset with a 75-25 train-test split. The two highest performing algorithms, k Nearest Neighbors and the Neural Network, both achieved an accuracy of 98%. The NN achieved a MCC of 0.96, while KNN achieved a MCC of 0.94. These figures outperform most existing literature and significantly outperform current methods of diagnosis. Conclusion and Significance These robust results suggest that a machine learning approach can indeed be implemented to significantly improve diagnosis methods of Parkinson’s disease. Given the necessity of early diagnosis for effective treatment, my machine learning models provide a very promising alternative to the current, rather ineffective method of diagnosis. Current methods of early diagnosis are only 53% accurate, while my machine learning model produces 98% accuracy. This 45% increase is critical because an accurate, early diagnosis is needed to effectively treat the disease. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. With an earlier diagnosis, much of this degradation could have been slowed or treated. My results are very significant because Parkinson’s affects over 10 million people worldwide who could benefit greatly from an early, accurate diagnosis. Not only is my machine learning approach more accurate in terms of diagnostic accuracy, it is also more scalable, less expensive, and therefore more accessible to people who might not have access to established medical facilities and professionals. The diagnosis is also much simpler, requiring only a 10-15 second voice recording and producing an immediate diagnosis. Future Research Given more time and resources, I would investigate the following: Create a mobile application which would allow the user to record his/her voice, extract the necessary vocal features, and feed it into my machine learning model to diagnose Parkinson’s. Use larger datasets in conjunction with the University of Oxford dataset. Tune and improve my models even further to achieve even better results. Investigate different structures and types of neural networks. Construct a novel algorithm specifically suited for the prediction of Parkinson’s. Generalize my findings and algorithms for all types of dementia disorders, such as Alzheimer’s. References Bind, Shubham. "A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction." International Journal of Computer Science and Information Technologies 6 (2015): n. pag. International Journal of Computer Science and Information Technologies. 2015. Web. 8 Mar. 2017. Brooks, Megan. "Diagnosing Parkinson's Disease Still Challenging." Medscape Medical News. National Institute of Neurological Disorders, 31 July 2014. Web. 20 Mar. 2017. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) Hashmi, Sumaiya F. "A Machine Learning Approach to Diagnosis of Parkinson’s Disease."Claremont Colleges Scholarship. Claremont College, 2013. Web. 10 Mar. 2017. Karplus, Abraham. "Machine Learning Algorithms for Cancer Diagnosis." Machine Learning Algorithms for Cancer Diagnosis (n.d.): n. pag. Mar. 2012. Web. 20 Mar. 2017. Little, Max. "Parkinsons Data Set." UCI Machine Learning Repository. University of Oxford, 26 June 2008. Web. 20 Feb. 2017. Ozcift, Akin, and Arif Gulten. "Classifier Ensemble Construction with Rotation Forest to Improve Medical Diagnosis Performance of Machine Learning Algorithms." Computer Methods and Programs in Biomedicine 104.3 (2011): 443-51. Semantic Scholar. 2011. Web. 15 Mar. 2017. "Parkinson’s Disease Dementia." UCI MIND. N.p., 19 Oct. 2015. Web. 17 Feb. 2017. Salvatore, C., A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri, M. Lopez, G. Arabia, M. Morelli, M.c. Gilardi, and A. Quattrone. "Machine Learning on Brain MRI Data for Differential Diagnosis of Parkinson's Disease and Progressive Supranuclear Palsy."Journal of Neuroscience Methods 222 (2014): 230-37. 2014. Web. 18 Mar. 2017. Shahbakhi, Mohammad, Danial Taheri Far, and Ehsan Tahami. "Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine."Journal of Biomedical Science and Engineering 07.04 (2014): 147-56. Scientific Research. July 2014. Web. 2 Mar. 2017. "Speech and Communication." Speech and Communication. Parkinson's Disease Foundation, n.d. Web. 22 Mar. 2017. Sriram, Tarigoppula V. S., M. Venkateswara Rao, G. V. Satya Narayana, and D. S. V. G. K. Kaladhar. "Diagnosis of Parkinson Disease Using Machine Learning and Data Mining Systems from Voice Dataset." SpringerLink. Springer, Cham, 01 Jan. 1970. Web. 17 Mar. 2017.

This case study shows how to create a model for text analysis and classification and deploy it as a web service in Azure cloud in order to automatically classify support tickets. This project is a proof of concept made by Microsoft (Commercial Software Engineering team) in collaboration with Endava http://endava.com/en

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http://www.seedpeer.me/details/4848277/TutsPlus---Build-Web-Apps-in-Node-and-Express.html http://www.seedpeer.me/details/5683153/Tutsplus---Catch-Up-with-Ruby-on-Rails-4.html http://www.seedpeer.me/details/4918947/TutsPlus---CodeIgniter-Essentials.html http://www.seedpeer.me/details/5069781/TutsPlus---Connected-to-the-Backbone.html http://www.seedpeer.me/details/5513056/Tutsplus---Designing-Professional-Resumes.html http://www.seedpeer.me/details/5706815/Tutsplus-Easier-JavaScript-Apps-with-AngularJS.html http://www.seedpeer.me/details/6462415/TutsPlus---Easier-JavaScript-with-TypeScript.html http://www.seedpeer.me/details/5868293/TutsPlus---Getting-Started-With-Windows-8-Development-Using-HTML,-CSS-&amp;-JavaScript-V413HAV.html http://www.seedpeer.me/details/6150521/TutsPlus-HTML5-Video-Essentials-PRODEV.html http://www.seedpeer.me/details/4841911/TutsPlus---JavaScript-Testing-With-Jasmine.html http://www.seedpeer.me/details/6593486/TutsPlus---Less-is-More.html http://www.seedpeer.me/details/6571637/TutsPlus---Modern-Testing-in-PHP-with-Codeception.html http://www.seedpeer.me/details/6095651/Tutsplus---Parallax-Scrolling-for-Web-Design.html http://www.seedpeer.me/details/6574591/TutsPlus---Say-Yo-to-Yeoman.html http://www.seedpeer.me/details/4811335/Tutsplus---Test-Driven-Development-in-Ruby.html http://www.seedpeer.me/details/6268980/TutsPlus-Test-Driven-Development-With-CoffeeScript-and-Jasmine.html http://www.seedpeer.me/details/6185755/TutsPlus---The-MVC-Mindser-Jeffery-Way---ICARUS.html http://www.seedpeer.me/details/5024493/TutsPlus---Venture-Into-Vim.html http://www.seedpeer.me/details/6286416/Tutsplus---Vim-for-Advanced-Users.html http://www.seedpeer.me/details/6585031/Tutsplus---WordPress-Hackers-Guide-to-the-Galaxy.html http://www.seedpeer.me/details/4848477/TutsPlus---Writing-Modular-JavaScript.html @giansalex Owner Author giansalex commented on 26 Feb 2018 • SOLID http://www.allitebooks.com/beginning-solid-principles-and-design-patterns-for-asp-net-developers/ @giansalex Owner Author giansalex commented on 7 Mar 2018 Udemy: AWS Arquitecto de Soluciones Certificado Asociado https://mega.co.nz/#!ZzhGWSAL!wuthFca0SdJBjmaP5lFX0QF6PeMsrdclKFXlZL1Rsi4 Pass: gratismas.org @giansalex Owner Author giansalex commented on 7 Mar 2018 Go lang Complete https://www.freetutorials.us/wp-content/uploads/2017/11/FreeTutorials.Us-Udemy-go-the-complete-developers-guide.torrent @GCPBigData GCPBigData commented on 15 Jul 2018 go books https://drive.google.com/open?id=1d6OsFAn8kpHCXtw0bcoYuyHqrAdGZva0 @freisrael freisrael commented on 14 Aug 2018 giansalex thanks for sharing. I am looking for learning phython with Joe Marini. It would be great if you post it. @FirstBoy1 FirstBoy1 commented on 25 May 2019 Can anyone provide this book "Getting started with Spring Framework: covers Spring 5" by " J Sharma (Author), Ashish Sarin ". Thanks in advance @okreka okreka commented on 31 May 2019 Can anyone provide "Windows Presentation Foundation Masterclass" course from Udemy. Thanks in advance @singhaltanvi singhaltanvi commented on 8 Aug 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. @kumarsreenivas051 kumarsreenivas051 commented on 9 Sep 2019 Can anyone provide "Programming languages A,B and C" course from Coursera. Thanks in advance. @BrunoMoreno BrunoMoreno commented on 11 Sep 2019 The link for the torrents in piratebay, now is .org to the correct url. @sany2k8 sany2k8 commented on 24 Sep 2019 Can anyone add this The Complete Hands-On Course to Master Apache Airflow @pharaoh1 pharaoh1 commented on 30 Sep 2019 can you pls add this course to your list https://www.udemy.com/course/advanced-python3/ @SushantDhote936 SushantDhote936 commented on 1 Oct 2019 Can you add Plural Sight CISSP @allayGerald allayGerald commented on 1 Oct 2019 open directive for lynda courses: https://drive.google.com/drive/folders/1zQan1cq1ZnqXmueRF5IqKoOtpFxl6Y4G @ezekielskottarathil ezekielskottarathil commented on 3 Oct 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. "wrong place boy" @pulkitd2699 pulkitd2699 commented on 8 Oct 2019 Does anyone has a link for 'Cyber security: Python and web applications' course? Thanks @mohanrajrc mohanrajrc commented on 19 Oct 2019 • Can anyone provide torrent file for Mastering React By Mosh Hamedani. Thanks https://codewithmosh.com/p/mastering-react @evilprince2009 evilprince2009 commented on 27 Oct 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 @nunusandio nunusandio commented on 30 Oct 2019 Can anyone post torrent file for ASP.NET Authentication: The Big Picture https://app.pluralsight.com/library/courses/aspdotnet-authentication-big-picture/table-of-contents @EslamElmadny EslamElmadny commented on 9 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 hay brother! do you have html5 game development with javascript course ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 This link is not working brother! http://www.seedpeer.me/details/4657790/Lynda.com-Building-Facebook-Applications-with-HTML-and-JavaScript.html @smithtuka smithtuka commented on 20 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? @AbdOoSaed AbdOoSaed commented on 22 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff @EslamElmadny EslamElmadny commented on 23 Dec 2019 • Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj @jedi2610 jedi2610 commented on 27 Dec 2019 Can anyone provide me with Code with Mosh's Ultimate Java Mastery Series link? plis @InnocentZaib InnocentZaib commented on 31 Dec 2019 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms @edward-teixeira edward-teixeira commented on 1 Jan 2020 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms Yea i'm looking for it too @kaneyxx kaneyxx commented on 1 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj could you please share the part-1 & part-3? @edward-teixeira edward-teixeira commented on 2 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? @ravisharmaa ravisharmaa commented on 7 Jan Please add this . https://www.letsbuildthatapp.com/course/AppStore-JSON-APIs @WaleedAlrashed WaleedAlrashed commented on 13 Jan This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ @Sopheakmorm Sopheakmorm commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 @EslamElmadny EslamElmadny commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 +1 @EslamElmadny EslamElmadny commented on 20 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses @mohanrajrc mohanrajrc commented on 22 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. @shihab122 shihab122 commented on 22 Jan Please give me the torrnet file or link to download The Ultimate Design Patterns @EslamElmadny EslamElmadny commented on 22 Jan • Please give me the torrnet file or link to download The Ultimate Design Patterns Waiting for it also :D @K-wachira K-wachira commented on 23 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. You are a saviour .. Altho i feel bad i cant really buy the course... its really good @msdyn95 msdyn95 commented 25 days ago • Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ @K-wachira K-wachira commented 23 days ago This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ Hey did you find this one? @edward-teixeira edward-teixeira commented 22 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? @msdyn95 msdyn95 commented 21 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. @edward-teixeira edward-teixeira commented 20 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. Found it ! https://vminhsang.name.vn/category/it-courses/codewithmosh/ @ZainA14 ZainA14 commented 16 days ago • Can someone please link me to this mosh course for torrent or direct download link https://codewithmosh.com/p/the-ultimate-full-stack-net-developer-bundle @khushiigupta khushiigupta commented 9 days ago Can any one please provide me link for jenkins so that I can learn as al as possible to join this conversation on GitHub. Already have an account? Sign in to comment © 2020 GitHub, Inc. Terms Privacy Security Status Help Contact GitHub Pricing API Training Blog About

Tumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.

According to a 2015 study on job seeking behavior by Pew Research Center, 79% of the job seekers utilized the online resources for their most recent employment (Aaron ,2015). This study result suggests that the online job boards become the major channel for job seekers in the digital era. However, another finding in the study indicates that most of the job seekers fail to match their experiences with the job requirements and spend hours on job board to apply job which is not seen to be suitable (Aaron, 2015). Additionally, Dr. John Sullivan conducted a similar research in 2013 which highlighted some interesting aspects: on average, 250 resumes are received for each job opening by the major organizations, more than 50% of the resumes does not meet the minimum requirement (John, 2013). This means the time our recruiter spends on these 50% of the resumes for each job is wasted. From both candidate and recruiter’s points of view, the phenomenon may suggest that the traditional online job board does not seem to simplify the job application process or reduce the effort required from both parties. With this challenge getting bigger and bigger, the demand to automate the resume - job matching process is getting increased as well. For instance, the content - based recommendation system (CBR) is introduced to analyze the job description to identify the potential area of interest to the job seekers (Shiqiang et al., 2016). To apply the concept in Singapore local context, our team has conducted a text mining project based on the data acquired from the major online job board in Singapore. The primary objective of this project is to create a machine learning model to accelerate the job - resume matching process. The detail of the text mining methodology and results are presented in the following sections.

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

Event Study package is an open-source python project created to facilitate the computation of financial event study analysis.

The Chat-GPT-Flashcards-To-Anki-Converter is a project that aims to revolutionize the way students study by simplifying the process of creating Anki flashcards from ChatGPT-generated content. By copying and pasting the text into Chat GPT, users can generate flashcards and then, with a single click, convert them into Anki-compatible format.

What is JavaScript and what does it do? Before you start learning something new, it’s important to understand exactly what it is and what it does. This is especially useful when it comes to mastering a new programming language. In simple terms, JavaScript is a programming language used to make websites interactive. If you think about the basic makeup of a website, you have HTML, which describes and defines the basic content and structure of the website, then you have CSS, which tells the browser how this HTML content should be displayed—determining things like color and font. With just HTML and CSS, you have a website that looks good but doesn’t actually do much. JavaScript brings the website to life by adding functionality. JavaScript is responsible for elements that the user can interact with, such as drop-down menus, modal windows, and contact forms. It is also used to create things like animations, video players, and interactive maps. Nowadays, JavaScript is an all-purpose programming language—meaning it runs across the entire software stack. The most popular application of JavaScript is on the client side (aka frontend), but since Node.js came on the scene, many people run JavaScript on the server side (aka backend) as well. When used on the client side, JavaScript code is read, interpreted, and executed in the user’s web browser. When used on the server side, it is run on a remote computer. You can learn more about the difference between frontend and backend programming here. JavaScript isn’t only used to create websites. It can also be used to build browser-based games and, with the help of certain frameworks, mobile apps for different operating systems. The creation of new libraries and frameworks is also making it possible to build backend programs with JavaScript, such as web apps and server apps. Is it still worth learning JavaScript in 2021? The world of web development is constantly moving. With so many new tools popping up all the time, it can be extremely difficult to know where you should focus your efforts. As an aspiring developer, you’ll want to make sure that what you’re learning is still relevant in today’s industry. If you’re having doubts about JavaScript, it’s important to know that, since its creation in 1995, JavaScript is pretty much everywhere on the web—and that’s not likely to change any time soon. According to the 2020 StackOverflow developer survey, JavaScript is the most commonly used programming language for the eighth year in a row. It is currently used by 94.5% of all websites and, despite originally being designed as a client-side language, JavaScript has now made its way to the server-side of websites (thanks to Node.js), mobile devices (thanks to React Native and Ionic) and desktop (courtesy of Electron). As long as people are interacting with the web, you can assume that JavaScript is highly relevant—there’s no doubt that this is a language worth knowing! With that in mind, let’s look at some of the key benefits of becoming a JavaScript expert. Why learn JavaScript? The most obvious reason for learning JavaScript is if you have hopes of becoming a web developer. Even if you haven’t got your heart set on a tech career, being proficient in JavaScript will enable you to build websites from scratch—a pretty useful skill to have in today’s job market! If you do want to become a web developer, here are some of the main reasons why you should learn JavaScript: JavaScript experts are versatile JavaScript is an extremely versatile language. Once you’ve mastered it, the possibilities are endless: you can code on the client-side (frontend) using Angular and on the server-side (backend) using Node.js. You can also develop web, mobile, and desktop apps using React, React Native, and Electron, and you can even get involved in machine learning. If you want to become a frontend developer, JavaScript is a prerequisite. However, that’s not the only career path open to you as a JavaScript expert. Mastering this key programming language could see you go on to work in full-stack development, games development, information security software engineering, machine learning, and artificial intelligence—to name just a few! Ultimately, if you want any kind of development or engineering career, proficiency in JavaScript is a must. JavaScript experts are in-demand (and well-paid) JavaScript is the most popular programming language in the world, so it’s no wonder that JavaScript is one of the most sought-after skills in the web development industry today. According to the Devskiller IT Skills and Hiring Report 2020, 72% of companies are looking to hire JavaScript experts. Enter the search term “JavaScript” on job site Indeed and you’ll find over 40,000 jobs requiring this skill (in the US). Run the same search on LinkedIn and the results are in excess of 125,000. At the same time, the global demand for JavaScript seems to outweigh the expertise available on the market. According to this 2018 HackerRank report, 48% of employers worldwide need developers with JavaScript skills, while only 42% of student developers claim to be proficient in JavaScript. And, in their most recent report for 2020, HackerRank once again reports that JavaScript is the most popular language that hiring mangers look for in a web developer candidate. Not only are JavaScript experts in demand—they are also well-paid. In the United States, JavaScript developers earn an average yearly salary of $111,953 per year. We’ve covered this topic in more detail in our JavaScript salary guide, but as you can see, learning JavaScript can really boost your earning potential as a developer. JavaScript is beginner-friendly Compared to many other programming languages, JavaScript offers one of the more beginner-friendly entry points into the world of coding. The great thing about JavaScript is that it comes installed on every modern web browser—there’s no need to set up any kind of development environment, which means you can start coding with JavaScript right away! Another advantage of learning JavaScript as your first programming language is that you get instant feedback; with a minimal amount of JavaScript code, you’ll immediately see visible results. There’s also a huge JavaScript community on sites like Stack Overflow, so you’ll find plenty of support as you learn. Not only is JavaScript beginner-friendly; it will also set you up with some extremely valuable transferable skills. JavaScript supports object-oriented, functional, and imperative styles of programming—skills which can be transferred to any new language you might learn later on, such as Python, Java, or C++. JavaScript provides a crucial introduction to key principles and practices that you’ll take with you throughout your career as a developer. Should you learn plain JavaScript first or can you skip to frameworks and libraries? When deciding whether or not to learn JavaScript, what you’re really asking is whether or not you should learn “vanilla” JavaScript. Vanilla JavaScript just means plain JavaScript without any libraries or frameworks. Let’s explore what this means in more detail now. What is meant by vanilla JavaScript, libraries, and frameworks? If you research the term “vanilla JavaScript”, you might run into some confusion; however, all you need to know is that vanilla JavaScript is used to refer to native, standards-based, non-extended JavaScript. There is no difference between vanilla JavaScript and JavaScript—it’s just there to emphasize the usage of plain JavaScript without the use of libraries and frameworks. So what are libraries and frameworks? JavaScript libraries and frameworks both contain sets of prewritten, ready-to-use JavaScript code—but they’re not the same thing. You can think of a framework as your blueprint for building a website: it gives you a structure to work from, and contains ready-made components and tools that help you to build certain elements much quicker than if you were to code them from scratch. Some popular JavaScript frameworks include Angular, React, Vue, and Node.js. Frameworks also contain libraries. Libraries are smaller than frameworks, and tend to be used for more specific cases. A JavaScript library contains sets of JavaScript code which can be called upon to implement certain functions and features. Let’s imagine you want to code a particular element into your website. You could write, say, ten lines of JavaScript from scratch—or you could take the condensed, ready-made version from your chosen JavaScript library. Some examples of JavaScript libraries include jQuery, Lodash, and Underscore. The easiest way to understand how frameworks and libraries work together is to imagine you are building a house. The framework provides the foundation and the structure, while the library enables you to add in ready-made components (like furniture) rather than building your own from scratch. You can learn more about the relationship between languages and libraries in this post explaining the main differences between JavaScript and jQuery. For now, let’s go back to our original question: How important is it to learn vanilla JavaScript? Should you learn vanilla JavaScript first? When it comes to learning JavaScript, it can be tempting to skip ahead to those time-saving frameworks and libraries we just talked about—and many developers do. However, there are many compelling arguments for learning plain JavaScript first. While JavaScript frameworks may help you get the job done quicker, there’s only so far you can go if you don’t understand the core concepts behind these frameworks. Frontend developer Abhishek Nagekar describes how not learning vanilla JavaScript came back to bite him when he started learning the JavaScript frameworks Node and Express: “As I went to write more and more code in Node and Express, I began to get stuck at even the tiniest problems. Suddenly, I was surrounded with words like callbacks, closures, event loop and prototype. It felt like I got a reintroduction to JavaScript, but this time, it was not a toddler playing in its cradle, it was something of a mysterious monster, challenging me on every other step for not having taken it seriously.” The above Tweet references a long-running joke within the developer community, and although it dates way back to 2015, it’s still highly relevant today. If you want to become a developer who can innovate, not just execute, you need to understand the underlying principles of the web—not just the shortcuts. This means learning vanilla JavaScript before you move on to frameworks. In fact, understanding plain JavaScript will help you later on when it comes to deciding whether to use a framework for a certain project, and if so, which framework to use. Why Study JavaScript? JavaScript is one of the 3 languages all web developers must learn: 1. HTML to define the content of web pages 2. CSS to specify the layout of web pages 3. JavaScript to program the behavior of web pages Learning Speed In this tutorial, the learning speed is your choice. Everything is up to you. If you are struggling, take a break, or re-read the material. Always make sure you understand all the "Try-it-Yourself" examples. The only way to become a clever programmer is to: Practice. Practice. Practice. Code. Code. Code ! Commonly Asked Questions How do I get JavaScript? Where can I download JavaScript? Is JavaScript Free? You don't have to get or download JavaScript. JavaScript is already running in your browser on your computer, on your tablet, and on your smart-phone. JavaScript is free to use for everyone.

Poole, Mackworth & Goebel 1998, p. 1. Russell & Norvig 2003, p. 55. Definition of AI as the study of intelligent agents: Poole, Mackworth & Goebel (1998), which provides the version that is used in this article. These authors use the term "computational intelligence" as a synonym for artificial intelligence.[1] Russell & Norvig (2003) (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field".[2] Nilsson 1998 Legg & Hutter 2007 Russell & Norvig 2009, p. 2. McCorduck 2004, p. 204 Maloof, Mark. "Artificial Intelligence: An Introduction, p. 37" (PDF). georgetown.edu. Archived (PDF) from the original on 25 August 2018. "How AI Is Getting Groundbreaking Changes In Talent Management And HR Tech". Hackernoon. Archived from the original on 11 September 2019. Retrieved 14 February 2020. Schank, Roger C. (1991). "Where's the AI". AI magazine. Vol. 12 no. 4. p. 38. Russell & Norvig 2009. "AlphaGo – Google DeepMind". Archived from the original on 10 March 2016. Allen, Gregory (April 2020). "Department of Defense Joint AI Center - Understanding AI Technology" (PDF). AI.mil - The official site of the Department of Defense Joint Artificial Intelligence Center. Archived (PDF) from the original on 21 April 2020. Retrieved 25 April 2020. Optimism of early AI: * Herbert Simon quote: Simon 1965, p. 96 quoted in Crevier 1993, p. 109. * Marvin Minsky quote: Minsky 1967, p. 2 quoted in Crevier 1993, p. 109. Boom of the 1980s: rise of expert systems, Fifth Generation Project, Alvey, MCC, SCI: * McCorduck 2004, pp. 426–441 * Crevier 1993, pp. 161–162,197–203, 211, 240 * Russell & Norvig 2003, p. 24 * NRC 1999, pp. 210–211 * Newquist 1994, pp. 235–248 First AI Winter, Mansfield Amendment, Lighthill report * Crevier 1993, pp. 115–117 * Russell & Norvig 2003, p. 22 * NRC 1999, pp. 212–213 * Howe 1994 * Newquist 1994, pp. 189–201 Second AI winter: * McCorduck 2004, pp. 430–435 * Crevier 1993, pp. 209–210 * NRC 1999, pp. 214–216 * Newquist 1994, pp. 301–318 AI becomes hugely successful in the early 21st century * Clark 2015 Pamela McCorduck (2004, p. 424) writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics ... and these with own sub-subfield—that would hardly have anything to say to each other." This list of intelligent traits is based on the topics covered by the major AI textbooks, including: * Russell & Norvig 2003 * Luger & Stubblefield 2004 * Poole, Mackworth & Goebel 1998 * Nilsson 1998 Kolata 1982. Maker 2006. Biological intelligence vs. intelligence in general: Russell & Norvig 2003, pp. 2–3, who make the analogy with aeronautical engineering. McCorduck 2004, pp. 100–101, who writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplished, and the other aimed at modeling intelligent processes found in nature, particularly human ones." Kolata 1982, a paper in Science, which describes McCarthy's indifference to biological models. Kolata quotes McCarthy as writing: "This is AI, so we don't care if it's psychologically real".[19] McCarthy recently reiterated his position at the AI@50 conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence".[20]. Neats vs. scruffies: * McCorduck 2004, pp. 421–424, 486–489 * Crevier 1993, p. 168 * Nilsson 1983, pp. 10–11 Symbolic vs. sub-symbolic AI: * Nilsson (1998, p. 7), who uses the term "sub-symbolic". General intelligence (strong AI) is discussed in popular introductions to AI: * Kurzweil 1999 and Kurzweil 2005 See the Dartmouth proposal, under Philosophy, below. McCorduck 2004, p. 34. McCorduck 2004, p. xviii. McCorduck 2004, p. 3. McCorduck 2004, pp. 340–400. This is a central idea of Pamela McCorduck's Machines Who Think. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition."[26] "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized."[27] "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction."[28] She traces the desire back to its Hellenistic roots and calls it the urge to "forge the Gods."[29] "Stephen Hawking believes AI could be mankind's last accomplishment". BetaNews. 21 October 2016. Archived from the original on 28 August 2017. Lombardo P, Boehm I, Nairz K (2020). "RadioComics – Santa Claus and the future of radiology". Eur J Radiol. 122 (1): 108771. doi:10.1016/j.ejrad.2019.108771. PMID 31835078. Ford, Martin; Colvin, Geoff (6 September 2015). "Will robots create more jobs than they destroy?". The Guardian. Archived from the original on 16 June 2018. Retrieved 13 January 2018. AI applications widely used behind the scenes: * Russell & Norvig 2003, p. 28 * Kurzweil 2005, p. 265 * NRC 1999, pp. 216–222 * Newquist 1994, pp. 189–201 AI in myth: * McCorduck 2004, pp. 4–5 * Russell & Norvig 2003, p. 939 AI in early science fiction. * McCorduck 2004, pp. 17–25 Formal reasoning: * Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 978-0-15-601391-8. OCLC 46890682. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Turing, Alan (1948), "Machine Intelligence", in Copeland, B. Jack (ed.), The Essential Turing: The ideas that gave birth to the computer age, Oxford: Oxford University Press, p. 412, ISBN 978-0-19-825080-7 Russell & Norvig 2009, p. 16. Dartmouth conference: * McCorduck 2004, pp. 111–136 * Crevier 1993, pp. 47–49, who writes "the conference is generally recognized as the official birthdate of the new science." * Russell & Norvig 2003, p. 17, who call the conference "the birth of artificial intelligence." * NRC 1999, pp. 200–201 McCarthy, John (1988). "Review of The Question of Artificial Intelligence". Annals of the History of Computing. 10 (3): 224–229., collected in McCarthy, John (1996). "10. Review of The Question of Artificial Intelligence". Defending AI Research: A Collection of Essays and Reviews. CSLI., p. 73, "[O]ne of the reasons for inventing the term "artificial intelligence" was to escape association with "cybernetics". Its concentration on analog feedback seemed misguided, and I wished to avoid having either to accept Norbert (not Robert) Wiener as a guru or having to argue with him." Hegemony of the Dartmouth conference attendees: * Russell & Norvig 2003, p. 17, who write "for the next 20 years the field would be dominated by these people and their students." * McCorduck 2004, pp. 129–130 Russell & Norvig 2003, p. 18. Schaeffer J. (2009) Didn't Samuel Solve That Game?. In: One Jump Ahead. Springer, Boston, MA Samuel, A. L. (July 1959). "Some Studies in Machine Learning Using the Game of Checkers". IBM Journal of Research and Development. 3 (3): 210–229. CiteSeerX 10.1.1.368.2254. doi:10.1147/rd.33.0210. "Golden years" of AI (successful symbolic reasoning programs 1956–1973): * McCorduck 2004, pp. 243–252 * Crevier 1993, pp. 52–107 * Moravec 1988, p. 9 * Russell & Norvig 2003, pp. 18–21 The programs described are Arthur Samuel's checkers program for the IBM 701, Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU. DARPA pours money into undirected pure research into AI during the 1960s: * McCorduck 2004, p. 131 * Crevier 1993, pp. 51, 64–65 * NRC 1999, pp. 204–205 AI in England: * Howe 1994 Lighthill 1973. Expert systems: * ACM 1998, I.2.1 * Russell & Norvig 2003, pp. 22–24 * Luger & Stubblefield 2004, pp. 227–331 * Nilsson 1998, chpt. 17.4 * McCorduck 2004, pp. 327–335, 434–435 * Crevier 1993, pp. 145–62, 197–203 * Newquist 1994, pp. 155–183 Mead, Carver A.; Ismail, Mohammed (8 May 1989). Analog VLSI Implementation of Neural Systems (PDF). The Kluwer International Series in Engineering and Computer Science. 80. Norwell, MA: Kluwer Academic Publishers. doi:10.1007/978-1-4613-1639-8. ISBN 978-1-4613-1639-8. Archived from the original (PDF) on 6 November 2019. Retrieved 24 January 2020. Formal methods are now preferred ("Victory of the neats"): * Russell & Norvig 2003, pp. 25–26 * McCorduck 2004, pp. 486–487 McCorduck 2004, pp. 480–483. Markoff 2011. 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Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Problem solving, puzzle solving, game playing and deduction: * Russell & Norvig 2003, chpt. 3–9, * Poole, Mackworth & Goebel 1998, chpt. 2,3,7,9, * Luger & Stubblefield 2004, chpt. 3,4,6,8, * Nilsson 1998, chpt. 7–12 Uncertain reasoning: * Russell & Norvig 2003, pp. 452–644, * Poole, Mackworth & Goebel 1998, pp. 345–395, * Luger & Stubblefield 2004, pp. 333–381, * Nilsson 1998, chpt. 19 Psychological evidence of sub-symbolic reasoning: * Wason & Shapiro (1966) showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive social intelligence, performance dramatically improves. (See Wason selection task) * Kahneman, Slovic & Tversky (1982) have shown that people are terrible at elementary problems that involve uncertain reasoning. (See list of cognitive biases for several examples). * Lakoff & Núñez (2000) have controversially argued that even our skills at mathematics depend on knowledge and skills that come from "the body", i.e. sensorimotor and perceptual skills. (See Where Mathematics Comes From) Knowledge representation: * ACM 1998, I.2.4, * Russell & Norvig 2003, pp. 320–363, * Poole, Mackworth & Goebel 1998, pp. 23–46, 69–81, 169–196, 235–277, 281–298, 319–345, * Luger & Stubblefield 2004, pp. 227–243, * Nilsson 1998, chpt. 18 Knowledge engineering: * Russell & Norvig 2003, pp. 260–266, * Poole, Mackworth & Goebel 1998, pp. 199–233, * Nilsson 1998, chpt. ≈17.1–17.4 Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts): * Russell & Norvig 2003, pp. 349–354, * Poole, Mackworth & Goebel 1998, pp. 174–177, * Luger & Stubblefield 2004, pp. 248–258, * Nilsson 1998, chpt. 18.3 Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): * Russell & Norvig 2003, pp. 328–341, * Poole, Mackworth & Goebel 1998, pp. 281–298, * Nilsson 1998, chpt. 18.2 Causal calculus: * Poole, Mackworth & Goebel 1998, pp. 335–337 Representing knowledge about knowledge: Belief calculus, modal logics: * Russell & Norvig 2003, pp. 341–344, * Poole, Mackworth & Goebel 1998, pp. 275–277 Sikos, Leslie F. (June 2017). Description Logics in Multimedia Reasoning. Cham: Springer. doi:10.1007/978-3-319-54066-5. ISBN 978-3-319-54066-5. S2CID 3180114. Archived from the original on 29 August 2017. Ontology: * Russell & Norvig 2003, pp. 320–328 Smoliar, Stephen W.; Zhang, HongJiang (1994). "Content based video indexing and retrieval". IEEE Multimedia. 1 (2): 62–72. doi:10.1109/93.311653. S2CID 32710913. Neumann, Bernd; Möller, Ralf (January 2008). "On scene interpretation with description logics". Image and Vision Computing. 26 (1): 82–101. doi:10.1016/j.imavis.2007.08.013. Kuperman, G. J.; Reichley, R. M.; Bailey, T. C. (1 July 2006). "Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations". Journal of the American Medical Informatics Association. 13 (4): 369–371. doi:10.1197/jamia.M2055. PMC 1513681. PMID 16622160. MCGARRY, KEN (1 December 2005). "A survey of interestingness measures for knowledge discovery". The Knowledge Engineering Review. 20 (1): 39–61. doi:10.1017/S0269888905000408. S2CID 14987656. Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies". MM '06 Proceedings of the 14th ACM international conference on Multimedia. 14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682. Qualification problem: * McCarthy & Hayes 1969 * Russell & Norvig 2003[page needed] While McCarthy was primarily concerned with issues in the logical representation of actions, Russell & Norvig 2003 apply the term to the more general issue of default reasoning in the vast network of assumptions underlying all our commonsense knowledge. Default reasoning and default logic, non-monotonic logics, circumscription, closed world assumption, abduction (Poole et al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning"): * Russell & Norvig 2003, pp. 354–360, * Poole, Mackworth & Goebel 1998, pp. 248–256, 323–335, * Luger & Stubblefield 2004, pp. 335–363, * Nilsson 1998, ~18.3.3 Breadth of commonsense knowledge: * Russell & Norvig 2003, p. 21, * Crevier 1993, pp. 113–114, * Moravec 1988, p. 13, * Lenat & Guha 1989 (Introduction) Dreyfus & Dreyfus 1986. Gladwell 2005. Expert knowledge as embodied intuition: * Dreyfus & Dreyfus 1986 (Hubert Dreyfus is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See Dreyfus' critique of AI) * Gladwell 2005 (Gladwell's Blink is a popular introduction to sub-symbolic reasoning and knowledge.) * Hawkins & Blakeslee 2005 (Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.) Planning: * ACM 1998, ~I.2.8, * Russell & Norvig 2003, pp. 375–459, * Poole, Mackworth & Goebel 1998, pp. 281–316, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Information value theory: * Russell & Norvig 2003, pp. 600–604 Classical planning: * Russell & Norvig 2003, pp. 375–430, * Poole, Mackworth & Goebel 1998, pp. 281–315, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning: * Russell & Norvig 2003, pp. 430–449 Multi-agent planning and emergent behavior: * Russell & Norvig 2003, pp. 449–455 Turing 1950. Solomonoff 1956. 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Archived from the original on 11 June 2020. Retrieved 11 June 2020. Machine perception: * Russell & Norvig 2003, pp. 537–581, 863–898 * Nilsson 1998, ~chpt. 6 Speech recognition: * ACM 1998, ~I.2.7 * Russell & Norvig 2003, pp. 568–578 Object recognition: * Russell & Norvig 2003, pp. 885–892 Computer vision: * ACM 1998, I.2.10 * Russell & Norvig 2003, pp. 863–898 * Nilsson 1998, chpt. 6 Robotics: * ACM 1998, I.2.9, * Russell & Norvig 2003, pp. 901–942, * Poole, Mackworth & Goebel 1998, pp. 443–460 Moving and configuration space: * Russell & Norvig 2003, pp. 916–932 Tecuci 2012. Robotic mapping (localization, etc): * Russell & Norvig 2003, pp. 908–915 Cadena, Cesar; Carlone, Luca; Carrillo, Henry; Latif, Yasir; Scaramuzza, Davide; Neira, Jose; Reid, Ian; Leonard, John J. (December 2016). "Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age". IEEE Transactions on Robotics. 32 (6): 1309–1332. arXiv:1606.05830. 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Retrieved 26 April 2018. Domingos 2015. Artificial brain arguments: AI requires a simulation of the operation of the human brain * Russell & Norvig 2003, p. 957 * Crevier 1993, pp. 271 and 279 A few of the people who make some form of the argument: * Moravec 1988 * Kurzweil 2005, p. 262 * Hawkins & Blakeslee 2005 The most extreme form of this argument (the brain replacement scenario) was put forward by Clark Glymour in the mid-1970s and was touched on by Zenon Pylyshyn and John Searle in 1980. Goertzel, Ben; Lian, Ruiting; Arel, Itamar; de Garis, Hugo; Chen, Shuo (December 2010). "A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures". Neurocomputing. 74 (1–3): 30–49. doi:10.1016/j.neucom.2010.08.012. Nilsson 1983, p. 10. Nils Nilsson writes: "Simply put, there is wide disagreement in the field about what AI is all about."[163] AI's immediate precursors: * McCorduck 2004, pp. 51–107 * Crevier 1993, pp. 27–32 * Russell & Norvig 2003, pp. 15, 940 * Moravec 1988, p. 3 Haugeland 1985, pp. 112–117 The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of perceptrons by Marvin Minsky and Seymour Papert in 1969. See History of AI, AI winter, or Frank Rosenblatt. Cognitive simulation, Newell and Simon, AI at CMU (then called Carnegie Tech): * McCorduck 2004, pp. 139–179, 245–250, 322–323 (EPAM) * Crevier 1993, pp. 145–149 Soar (history): * McCorduck 2004, pp. 450–451 * Crevier 1993, pp. 258–263 McCarthy and AI research at SAIL and SRI International: * McCorduck 2004, pp. 251–259 * Crevier 1993 AI research at Edinburgh and in France, birth of Prolog: * Crevier 1993, pp. 193–196 * Howe 1994 AI at MIT under Marvin Minsky in the 1960s : * McCorduck 2004, pp. 259–305 * Crevier 1993, pp. 83–102, 163–176 * Russell & Norvig 2003, p. 19 Cyc: * McCorduck 2004, p. 489, who calls it "a determinedly scruffy enterprise" * Crevier 1993, pp. 239–243 * Russell & Norvig 2003, p. 363−365 * Lenat & Guha 1989 Knowledge revolution: * McCorduck 2004, pp. 266–276, 298–300, 314, 421 * Russell & Norvig 2003, pp. 22–23 Frederick, Hayes-Roth; William, Murray; Leonard, Adelman. "Expert systems". AccessScience. doi:10.1036/1097-8542.248550. Embodied approaches to AI: * McCorduck 2004, pp. 454–462 * Brooks 1990 * Moravec 1988 Weng et al. 2001. Lungarella et al. 2003. Asada et al. 2009. Oudeyer 2010. Revival of connectionism: * Crevier 1993, pp. 214–215 * Russell & Norvig 2003, p. 25 Computational intelligence * IEEE Computational Intelligence Society Archived 9 May 2008 at the Wayback Machine Hutson, Matthew (16 February 2018). "Artificial intelligence faces reproducibility crisis". Science. pp. 725–726. Bibcode:2018Sci...359..725H. doi:10.1126/science.359.6377.725. Archived from the original on 29 April 2018. Retrieved 28 April 2018. Norvig 2012. Langley 2011. Katz 2012. The intelligent agent paradigm: * Russell & Norvig 2003, pp. 27, 32–58, 968–972 * Poole, Mackworth & Goebel 1998, pp. 7–21 * Luger & Stubblefield 2004, pp. 235–240 * Hutter 2005, pp. 125–126 The definition used in this article, in terms of goals, actions, perception and environment, is due to Russell & Norvig (2003). Other definitions also include knowledge and learning as additional criteria. Agent architectures, hybrid intelligent systems: * Russell & Norvig (2003, pp. 27, 932, 970–972) * Nilsson (1998, chpt. 25) Hierarchical control system: * Albus 2002 Lieto, Antonio; Lebiere, Christian; Oltramari, Alessandro (May 2018). "The knowledge level in cognitive architectures: Current limitations and possibile developments". Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Lieto, Antonio; Bhatt, Mehul; Oltramari, Alessandro; Vernon, David (May 2018). "The role of cognitive architectures in general artificial intelligence". Cognitive Systems Research. 48: 1–3. doi:10.1016/j.cogsys.2017.08.003. hdl:2318/1665249. S2CID 36189683. Russell & Norvig 2009, p. 1. White Paper: On Artificial Intelligence - A European approach to excellence and trust (PDF). Brussels: European Commission. 2020. p. 1. Archived (PDF) from the original on 20 February 2020. Retrieved 20 February 2020. CNN 2006. Using AI to predict flight delays Archived 20 November 2018 at the Wayback Machine, Ishti.org. N. Aletras; D. Tsarapatsanis; D. Preotiuc-Pietro; V. Lampos (2016). "Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective". PeerJ Computer Science. 2: e93. doi:10.7717/peerj-cs.93. "The Economist Explains: Why firms are piling into artificial intelligence". The Economist. 31 March 2016. Archived from the original on 8 May 2016. Retrieved 19 May 2016. Lohr, Steve (28 February 2016). "The Promise of Artificial Intelligence Unfolds in Small Steps". The New York Times. Archived from the original on 29 February 2016. Retrieved 29 February 2016. Frangoul, Anmar (14 June 2019). "A Californian business is using A.I. to change the way we think about energy storage". CNBC. Archived from the original on 25 July 2020. Retrieved 5 November 2019. Wakefield, Jane (15 June 2016). "Social media 'outstrips TV' as news source for young people". BBC News. Archived from the original on 24 June 2016. Smith, Mark (22 July 2016). "So you think you chose to read this article?". BBC News. Archived from the original on 25 July 2016. Brown, Eileen. "Half of Americans do not believe deepfake news could target them online". ZDNet. Archived from the original on 6 November 2019. Retrieved 3 December 2019. The Turing test: Turing's original publication: * Turing 1950 Historical influence and philosophical implications: * Haugeland 1985, pp. 6–9 * Crevier 1993, p. 24 * McCorduck 2004, pp. 70–71 * Russell & Norvig 2003, pp. 2–3 and 948 Dartmouth proposal: * McCarthy et al. 1955 (the original proposal) * Crevier 1993, p. 49 (historical significance) The physical symbol systems hypothesis: * Newell & Simon 1976, p. 116 * McCorduck 2004, p. 153 * Russell & Norvig 2003, p. 18 Dreyfus 1992, p. 156. Dreyfus criticized the necessary condition of the physical symbol system hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules."[206] Dreyfus' critique of artificial intelligence: * Dreyfus 1972, Dreyfus & Dreyfus 1986 * Crevier 1993, pp. 120–132 * McCorduck 2004, pp. 211–239 * Russell & Norvig 2003, pp. 950–952, Gödel 1951: in this lecture, Kurt Gödel uses the incompleteness theorem to arrive at the following disjunction: (a) the human mind is not a consistent finite machine, or (b) there exist Diophantine equations for which it cannot decide whether solutions exist. Gödel finds (b) implausible, and thus seems to have believed the human mind was not equivalent to a finite machine, i.e., its power exceeded that of any finite machine. He recognized that this was only a conjecture, since one could never disprove (b). Yet he considered the disjunctive conclusion to be a "certain fact". The Mathematical Objection: * Russell & Norvig 2003, p. 949 * McCorduck 2004, pp. 448–449 Making the Mathematical Objection: * Lucas 1961 * Penrose 1989 Refuting Mathematical Objection: * Turing 1950 under "(2) The Mathematical Objection" * Hofstadter 1979 Background: * Gödel 1931, Church 1936, Kleene 1935, Turing 1937 Graham Oppy (20 January 2015). "Gödel's Incompleteness Theorems". Stanford Encyclopedia of Philosophy. Archived from the original on 22 April 2016. Retrieved 27 April 2016. These Gödelian anti-mechanist arguments are, however, problematic, and there is wide consensus that they fail. Stuart J. Russell; Peter Norvig (2010). "26.1.2: Philosophical Foundations/Weak AI: Can Machines Act Intelligently?/The mathematical objection". Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall. ISBN 978-0-13-604259-4. even if we grant that computers have limitations on what they can prove, there is no evidence that humans are immune from those limitations. Mark Colyvan. An introduction to the philosophy of mathematics. Cambridge University Press, 2012. From 2.2.2, 'Philosophical significance of Gödel's incompleteness results': "The accepted wisdom (with which I concur) is that the Lucas-Penrose arguments fail." Iphofen, Ron; Kritikos, Mihalis (3 January 2019). "Regulating artificial intelligence and robotics: ethics by design in a digital society". Contemporary Social Science: 1–15. doi:10.1080/21582041.2018.1563803. ISSN 2158-2041. "Ethical AI Learns Human Rights Framework". Voice of America. Archived from the original on 11 November 2019. Retrieved 10 November 2019. Crevier 1993, pp. 132–144. In the early 1970s, Kenneth Colby presented a version of Weizenbaum's ELIZA known as DOCTOR which he promoted as a serious therapeutic tool.[216] Joseph Weizenbaum's critique of AI: * Weizenbaum 1976 * Crevier 1993, pp. 132–144 * McCorduck 2004, pp. 356–373 * Russell & Norvig 2003, p. 961 Weizenbaum (the AI researcher who developed the first chatterbot program, ELIZA) argued in 1976 that the misuse of artificial intelligence has the potential to devalue human life. Wendell Wallach (2010). Moral Machines, Oxford University Press. Wallach, pp 37–54. Wallach, pp 55–73. Wallach, Introduction chapter. Michael Anderson and Susan Leigh Anderson (2011), Machine Ethics, Cambridge University Press. "Machine Ethics". aaai.org. Archived from the original on 29 November 2014. Rubin, Charles (Spring 2003). "Artificial Intelligence and Human Nature". The New Atlantis. 1: 88–100. Archived from the original on 11 June 2012. Brooks, Rodney (10 November 2014). "artificial intelligence is a tool, not a threat". Archived from the original on 12 November 2014. "Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence". Observer. 19 August 2015. Archived from the original on 30 October 2015. Retrieved 30 October 2015. Chalmers, David (1995). "Facing up to the problem of consciousness". Journal of Consciousness Studies. 2 (3): 200–219. Archived from the original on 8 March 2005. Retrieved 11 October 2018. See also this link Archived 8 April 2011 at the Wayback Machine Horst, Steven, (2005) "The Computational Theory of Mind" Archived 11 September 2018 at the Wayback Machine in The Stanford Encyclopedia of Philosophy Searle 1980, p. 1. This version is from Searle (1999), and is also quoted in Dennett 1991, p. 435. Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states." [230] Strong AI is defined similarly by Russell & Norvig (2003, p. 947): "The assertion that machines could possibly act intelligently

A repository to keep all open sources projects that created by individuals or study groups of Microsoft ML Scholarship

This project was built as a result of a deepening of the studies discussed on the blog farlley.com with a greater focus on Domain Driven Design (DDD) architecture. In this work you will find a simple template for creating microservices, as well as a use case (which will still be implemented according to the Roadmap found in this same document) applying the theories and studies presented in the books Implementing Domain-Driven Design - Vaughn Vernon, Domain-Driven Design: Attacking the Complexities at the Heart of Software - Eric Evans, and Building Microservices: Designing Fine-Grained Systems - Sam Newman, the bibliography consulted will be added to the end of this document in accordance with the application roadmap. The template was written for the python language in its version 3.9.0, using the FastApi framework.

Due to the increasingly development of network technology recently, there are various cyber-attacks posed the huge threats to different fields around the world. Many studies and researches about cyber-security are carried out by experts in order to construct a safe network environment for people. The aim of the work is to build the detection models for classifying the attack data. Hence, we applied the UNSW-NB15 network data set which combines both normal and modern low-level attacks because we would like to create the experimental scenario close to the real world. Two classifiers are logistic regression and decision tree model for binary classification in the work. The deployed technique for decision tree achieved the highest result with 99.99% of testing accuracy compare to the 78.15% of logistic regression classifier. On the other hand, the KNN model is used for categorizing the multi-class in the project, and the averaged accuracy for testing is around 23% for ten categories classification.

​ ​#​ ​Ukraine-Cyber-Operations ​Curated Intelligence is working with analysts from around the world to provide useful information to organisations in Ukraine looking for additional free threat intelligence. Slava Ukraini. Glory to Ukraine. ([​Blog​](https://www.curatedintel.org/2021/08/welcome.html) | [​Twitter​](https://twitter.com/CuratedIntel) | [​LinkedIn​](https://www.linkedin.com/company/curatedintelligence/)) ​ ​ ​###​ ​Analyst Comments: ​-​ 2022-02-25 ​  ​-​ Creation of the initial repository to help organisations in Ukraine ​  ​-​ Added [​Threat Reports​](https://github.com/curated-intel/Ukraine-Cyber-Operations#threat-reports) section ​  ​-​ Added [​Vendor Support​](https://github.com/curated-intel/Ukraine-Cyber-Operations#vendor-support) section ​-​ 2022-02-26 ​  ​-​ Additional resources, chronologically ordered (h/t Orange-CD) ​  ​-​ Added [​Vetted OSINT Sources​](https://github.com/curated-intel/Ukraine-Cyber-Operations#vetted-osint-sources) section  ​  ​-​ Added [​Miscellaneous Resources​](https://github.com/curated-intel/Ukraine-Cyber-Operations#miscellaneous-resources) section ​-​ 2022-02-27 ​  ​-​ Additional threat reports have been added ​  ​-​ Added [​Data Brokers​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/README.md#data-brokers) section ​  ​-​ Added [​Access Brokers​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/README.md#access-brokers) section ​-​ 2022-02-28 ​  ​-​ Added Russian Cyber Operations Against Ukraine Timeline by ETAC ​  ​-​ Added Vetted and Contextualized [​Indicators of Compromise (IOCs)​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/ETAC_Vetted_UkraineRussiaWar_IOCs.csv) by ETAC ​-​ 2022-03-01 ​  ​-​ Additional threat reports and resources have been added ​-​ 2022-03-02 ​  ​-​ Additional [​Indicators of Compromise (IOCs)​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/ETAC_Vetted_UkraineRussiaWar_IOCs.csv#L2011) have been added ​  ​-​ Added vetted [​YARA rule collection​](https://github.com/curated-intel/Ukraine-Cyber-Operations/tree/main/yara) from the Threat Reports by ETAC ​  ​-​ Added loosely-vetted [​IOC Threat Hunt Feeds​](https://github.com/curated-intel/Ukraine-Cyber-Operations/tree/main/KPMG-Egyde_Ukraine-Crisis_Feeds/MISP-CSV_MediumConfidence_Filtered) by KPMG-Egyde CTI (h/t [​0xDISREL​](https://twitter.com/0xDISREL)) ​    ​-​ IOCs shared by these feeds are ​`LOW-TO-MEDIUM CONFIDENCE`​ we strongly recommend NOT adding them to a blocklist ​    ​-​ These could potentially be used for ​`THREAT HUNTING`​ and could be added to a ​`WATCHLIST` ​    ​-​ IOCs are generated in ​`MISP COMPATIBLE`​ CSV format ​-​ 2022-03-03 ​  ​-​ Additional threat reports and vendor support resources have been added ​  ​-​ Updated [​Log4Shell IOC Threat Hunt Feeds​](https://github.com/curated-intel/Log4Shell-IOCs/tree/main/KPMG_Log4Shell_Feeds) by KPMG-Egyde CTI; not directly related to Ukraine, but still a widespread vulnerability. ​  ​-​ Added diagram of Russia-Ukraine Cyberwar Participants 2022 by ETAC ​  ​-​ Additional [​Indicators of Compromise (IOCs)​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/ETAC_Vetted_UkraineRussiaWar_IOCs.csv#L2042) have been added ​####​ ​`Threat Reports` ​| Date | Source | Threat(s) | URL | ​| --- | --- | --- | --- | ​| 14 JAN | SSU Ukraine | Website Defacements | [ssu.gov.ua](https://ssu.gov.ua/novyny/sbu-rozsliduie-prychetnist-rosiiskykh-spetssluzhb-do-sohodnishnoi-kiberataky-na-orhany-derzhavnoi-vlady-ukrainy)| ​| 15 JAN | Microsoft | WhisperGate wiper (DEV-0586) | [microsoft.com](https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/) | ​| 19 JAN | Elastic | WhisperGate wiper (Operation BleedingBear) | [elastic.github.io](https://elastic.github.io/security-research/malware/2022/01/01.operation-bleeding-bear/article/) | ​| 31 JAN | Symantec | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [symantec-enterprise-blogs.security.com](https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/shuckworm-gamaredon-espionage-ukraine) | ​| 2 FEB | RaidForums | Access broker "GodLevel" offering Ukrainain algricultural exchange | RaidForums [not linked] | ​| 2 FEB | CERT-UA | UAC-0056 using SaintBot and OutSteel malware | [cert.gov.ua](https://cert.gov.ua/article/18419) | ​| 3 FEB | PAN Unit42 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [unit42.paloaltonetworks.com](https://unit42.paloaltonetworks.com/gamaredon-primitive-bear-ukraine-update-2021/) | ​| 4 FEB | Microsoft | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [microsoft.com](https://www.microsoft.com/security/blog/2022/02/04/actinium-targets-ukrainian-organizations/) | ​| 8 FEB | NSFOCUS | Lorec53 (aka UAC-0056, EmberBear, BleedingBear) | [nsfocusglobal.com](https://nsfocusglobal.com/apt-retrospection-lorec53-an-active-russian-hack-group-launched-phishing-attacks-against-georgian-government) | ​| 15 FEB | CERT-UA | DDoS attacks against the name server of government websites as well as Oschadbank (State Savings Bank) & Privatbank (largest commercial bank). False SMS and e-mails to create panic | [cert.gov.ua](https://cert.gov.ua/article/37139) | ​| 23 FEB | The Daily Beast | Ukrainian troops receive threatening SMS messages | [thedailybeast.com](https://www.thedailybeast.com/cyberattacks-hit-websites-and-psy-ops-sms-messages-targeting-ukrainians-ramp-up-as-russia-moves-into-ukraine) | ​| 23 FEB | UK NCSC | Sandworm/VoodooBear (GRU) | [ncsc.gov.uk](https://www.ncsc.gov.uk/files/Joint-Sandworm-Advisory.pdf) | ​| 23 FEB | SentinelLabs | HermeticWiper | [sentinelone.com]( https://www.sentinelone.com/labs/hermetic-wiper-ukraine-under-attack/ ) | ​| 24 FEB | ESET | HermeticWiper | [welivesecurity.com](https://www.welivesecurity.com/2022/02/24/hermeticwiper-new-data-wiping-malware-hits-ukraine/) | ​| 24 FEB | Symantec | HermeticWiper, PartyTicket ransomware, CVE-2021-1636, unknown webshell | [symantec-enterprise-blogs.security.com](https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/ukraine-wiper-malware-russia) | ​| 24 FEB | Cisco Talos | HermeticWiper | [blog.talosintelligence.com](https://blog.talosintelligence.com/2022/02/threat-advisory-hermeticwiper.html) | ​| 24 FEB | Zscaler | HermeticWiper | [zscaler.com](https://www.zscaler.com/blogs/security-research/hermetic-wiper-resurgence-targeted-attacks-ukraine) | ​| 24 FEB | Cluster25 | HermeticWiper | [cluster25.io](https://cluster25.io/2022/02/24/ukraine-analysis-of-the-new-disk-wiping-malware/) | ​| 24 FEB | CronUp | Data broker "FreeCivilian" offering multiple .gov.ua | [twitter.com/1ZRR4H](https://twitter.com/1ZRR4H/status/1496931721052311557)| ​| 24 FEB | RaidForums | Data broker "Featherine" offering diia.gov.ua | RaidForums [not linked] | ​| 24 FEB | DomainTools | Unknown scammers | [twitter.com/SecuritySnacks](https://twitter.com/SecuritySnacks/status/1496956492636905473?s=20&t=KCIX_1Ughc2Fs6Du-Av0Xw) | ​| 25 FEB | @500mk500 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [twitter.com/500mk500](https://twitter.com/500mk500/status/1497339266329894920?s=20&t=opOtwpn82ztiFtwUbLkm9Q) | ​| 25 FEB | @500mk500 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [twitter.com/500mk500](https://twitter.com/500mk500/status/1497208285472215042)| ​| 25 FEB | Microsoft | HermeticWiper | [gist.github.com](https://gist.github.com/fr0gger/7882fde2b1b271f9e886a4a9b6fb6b7f) | ​| 25 FEB | 360 NetLab | DDoS (Mirai, Gafgyt, IRCbot, Ripprbot, Moobot) | [blog.netlab.360.com](https://blog.netlab.360.com/some_details_of_the_ddos_attacks_targeting_ukraine_and_russia_in_recent_days/) | ​| 25 FEB | Conti [themselves] | Conti ransomware, BazarLoader | Conti News .onion [not linked] | ​| 25 FEB | CoomingProject [themselves] | Data Hostage Group | CoomingProject Telegram [not linked] | ​| 25 FEB | CERT-UA | UNC1151/Ghostwriter (Belarus MoD) | [CERT-UA Facebook](https://facebook.com/story.php?story_fbid=312939130865352&id=100064478028712)| ​| 25 FEB | Sekoia | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com/sekoia_io](https://twitter.com/sekoia_io/status/1497239319295279106) | ​| 25 FEB | @jaimeblascob | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com/jaimeblasco](https://twitter.com/jaimeblascob/status/1497242668627370009)| ​| 25 FEB | RISKIQ | UNC1151/Ghostwriter (Belarus MoD) | [community.riskiq.com](https://community.riskiq.com/article/e3a7ceea/) | ​| 25 FEB | MalwareHunterTeam | Unknown phishing | [twitter.com/malwrhunterteam](https://twitter.com/malwrhunterteam/status/1497235270416097287) | ​| 25 FEB | ESET | Unknown scammers | [twitter.com/ESETresearch](https://twitter.com/ESETresearch/status/1497194165561659394) | ​| 25 FEB | BitDefender | Unknown scammers | [blog.bitdefender.com](https://blog.bitdefender.com/blog/hotforsecurity/cybercriminals-deploy-spam-campaign-as-tens-of-thousands-of-ukrainians-seek-refuge-in-neighboring-countries/) | ​| 25 FEB | SSSCIP Ukraine | Unkown phishing | [twitter.com/dsszzi](https://twitter.com/dsszzi/status/1497103078029291522) | ​| 25 FEB | RaidForums | Data broker "NetSec"  offering FSB (likely SMTP accounts) | RaidForums [not linked] | ​| 25 FEB | Zscaler | PartyTicket decoy ransomware | [zscaler.com](https://www.zscaler.com/blogs/security-research/technical-analysis-partyticket-ransomware) | ​| 25 FEB | INCERT GIE | Cyclops Blink, HermeticWiper | [linkedin.com](https://www.linkedin.com/posts/activity-6902989337210740736-XohK) [Login Required] | ​| 25 FEB | Proofpoint | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com/threatinsight](https://twitter.com/threatinsight/status/1497355737844133895?s=20&t=Ubi0tb_XxGCbHLnUoQVp8w) | ​| 25 FEB | @fr0gger_ | HermeticWiper capabilities Overview | [twitter.com/fr0gger_](https://twitter.com/fr0gger_/status/1497121876870832128?s=20&t=_296n0bPeUgdXleX02M9mg) ​| 26 FEB | BBC Journalist | A fake Telegram account claiming to be President Zelensky is posting dubious messages | [twitter.com/shayan86](https://twitter.com/shayan86/status/1497485340738785283?s=21) | ​| 26 FEB | CERT-UA | UNC1151/Ghostwriter (Belarus MoD) | [CERT_UA Facebook](https://facebook.com/story.php?story_fbid=313517477474184&id=100064478028712) | ​| 26 FEB | MHT and TRMLabs | Unknown scammers, linked to ransomware | [twitter.com/joes_mcgill](https://twitter.com/joes_mcgill/status/1497609555856932864?s=20&t=KCIX_1Ughc2Fs6Du-Av0Xw) | ​| 26 FEB | US CISA | WhisperGate wiper, HermeticWiper | [cisa.gov](https://www.cisa.gov/uscert/ncas/alerts/aa22-057a) | ​| 26 FEB | Bloomberg | Destructive malware (possibly HermeticWiper) deployed at Ukrainian Ministry of Internal Affairs & data stolen from Ukrainian telecommunications networks | [bloomberg.com](https://www.bloomberg.com/news/articles/2022-02-26/hackers-destroyed-data-at-key-ukraine-agency-before-invasion?sref=ylv224K8) | ​| 26 FEB | Vice Prime Minister of Ukraine | IT ARMY of Ukraine created to crowdsource offensive operations against Russian infrastructure | [twitter.com/FedorovMykhailo](https://twitter.com/FedorovMykhailo/status/1497642156076511233) | ​| 26 FEB | Yoroi | HermeticWiper | [yoroi.company](https://yoroi.company/research/diskkill-hermeticwiper-a-disruptive-cyber-weapon-targeting-ukraines-critical-infrastructures) | ​| 27 FEB | LockBit [themselves] | LockBit ransomware | LockBit .onion [not linked] |  ​| 27 FEB | ALPHV [themselves] | ALPHV ransomware | vHUMINT [closed source] | ​| 27 FEB | Mēris Botnet [themselves] | DDoS attacks | vHUMINT [closed source] | ​| 28 FEB | Horizon News [themselves] | Leak of China's Censorship Order about Ukraine | [TechARP](https://www-techarp-com.cdn.ampproject.org/c/s/www.techarp.com/internet/chinese-media-leaks-ukraine-censor/?amp=1)| ​| 28 FEB | Microsoft | FoxBlade (aka HermeticWiper) | [Microsoft](https://blogs.microsoft.com/on-the-issues/2022/02/28/ukraine-russia-digital-war-cyberattacks/?preview_id=65075) | ​| 28 FEB | @heymingwei | Potential BGP hijacks attempts against Ukrainian Internet Names Center | [https://twitter.com/heymingwei](https://twitter.com/heymingwei/status/1498362715198263300?s=20&t=Ju31gTurYc8Aq_yZMbvbxg) | ​| 28 FEB | @cyberknow20 | Stormous ransomware targets Ukraine Ministry of Foreign Affairs | [twitter.com/cyberknow20](https://twitter.com/cyberknow20/status/1498434090206314498?s=21) |  ​| 1 MAR | ESET | IsaacWiper and HermeticWizard | [welivesecurity.com](https://www.welivesecurity.com/2022/03/01/isaacwiper-hermeticwizard-wiper-worm-targeting-ukraine/) | ​| 1 MAR | Proofpoint | Ukrainian armed service member's email compromised and sent malspam containing the SunSeed malware (likely TA445/UNC1151/Ghostwriter) | [proofpoint.com](https://www.proofpoint.com/us/blog/threat-insight/asylum-ambuscade-state-actor-uses-compromised-private-ukrainian-military-emails) | ​| 1 MAR | Elastic | HermeticWiper | [elastic.github.io](https://elastic.github.io/security-research/intelligence/2022/03/01.hermeticwiper-targets-ukraine/article/) | ​| 1 MAR | CrowdStrike | PartyTicket (aka HermeticRansom), DriveSlayer (aka HermeticWiper) | [CrowdStrike](https://www.crowdstrike.com/blog/how-to-decrypt-the-partyticket-ransomware-targeting-ukraine/) | ​| 2 MAR | Zscaler | DanaBot operators launch DDoS attacks against the Ukrainian Ministry of Defense | [zscaler.com](https://www.zscaler.com/blogs/security-research/danabot-launches-ddos-attack-against-ukrainian-ministry-defense) | ​| 3 MAR | @ShadowChasing1 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [twitter.com/ShadowChasing1](https://twitter.com/ShadowChasing1/status/1499361093059153921) | ​| 3 MAR | @vxunderground | News website in Poland was reportedly compromised and the threat actor uploaded anti-Ukrainian propaganda | [twitter.com/vxunderground](https://twitter.com/vxunderground/status/1499374914758918151?s=20&t=jyy9Hnpzy-5P1gcx19bvIA) | ​| 3 MAR | @kylaintheburgh | Russian botnet on Twitter is pushing "#istandwithputin" and "#istandwithrussia" propaganda (in English) | [twitter.com/kylaintheburgh](https://twitter.com/kylaintheburgh/status/1499350578371067906?s=21) | ​| 3 MAR | @tracerspiff | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com](https://twitter.com/tracerspiff/status/1499444876810854408?s=21) | ​####​ ​`Access Brokers` ​| Date | Threat(s) | Source | ​| --- | --- | --- | ​| 23 JAN | Access broker "Mont4na" offering UkrFerry | RaidForums [not linked] | ​| 23 JAN | Access broker "Mont4na" offering PrivatBank | RaidForums [not linked] | ​| 24 JAN | Access broker "Mont4na" offering DTEK | RaidForums [not linked] | ​| 27 FEB | KelvinSecurity Sharing list of IP cameras in Ukraine | vHUMINT [closed source] | ​| 28 FEB | "w1nte4mute" looking to buy access to UA and NATO countries (likely ransomware affiliate) | vHUMINT [closed source] | ​####​ ​`Data Brokers` ​| Threat Actor    | Type            | Observation                                                                                               | Validated | Relevance                     | Source                                                     | ​| --------------- | --------------- | --------------------------------------------------------------------------------------------------------- | --------- | ----------------------------- | ---------------------------------------------------------- | ​| aguyinachair    | UA data sharing | PII DB of ukraine.com (shared as part of a generic compilation)                                           | No        | TA discussion in past 90 days | ELeaks Forum \[not linked\]                                | ​| an3key          | UA data sharing | DB of Ministry of Communities and Territories Development of Ukraine (minregion\[.\]gov\[.\]ua)           | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| an3key          | UA data sharing | DB of Ukrainian Ministry of Internal Affairs (wanted\[.\]mvs\[.\]gov\[.\]ua)                              | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (40M) of PrivatBank customers (privatbank\[.\]ua)                                                  | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | DB of "border crossing" DBs of DPR and LPR                                                                | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (7.5M) of Ukrainian passports                                                                      | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB of Ukrainian car registration, license plates, Ukrainian traffic police records                    | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (2.1M) of Ukrainian citizens                                                                       | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (28M) of Ukrainian citizens (passports, drivers licenses, photos)                                  | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (1M) of Ukrainian postal/courier service customers (novaposhta\[.\]ua)                             | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (10M) of Ukrainian telecom customers (vodafone\[.\]ua)                                             | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (3M) of Ukrainian telecom customers (lifecell\[.\]ua)                                              | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (13M) of Ukrainian telecom customers (kyivstar\[.\]ua)                                             | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| danieltx51      | UA data sharing | DB of Ministry of Foreign Affairs of Ukraine (mfa\[.\]gov\[.\]ua)                                         | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| DueDiligenceCIS | UA data sharing | PII DB (63M) of Ukrainian citizens (name, DOB, birth country, phone, TIN, passport, family, etc)          | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Featherine      | UA data sharing | DB of Ukrainian 'Diia' e-Governance Portal for Ministry of Digital Transformation of Ukraine              | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of Ministry for Internal Affairs of Ukraine public data search engine (wanted\[.\]mvs\[.\]gov\[.\]ua)  | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of Ministry for Communities and Territories Development of Ukraine (minregion\[.\]gov\[.\]ua)          | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of Motor Insurance Bureau of Ukraine (mtsbu\[.\]ua)                                                    | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | PII DB of Ukrainian digital-medicine provider (medstar\[.\]ua)                                            | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of ticket.kyivcity.gov.ua                                                                              | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of id.kyivcity.gov.ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of my.kyivcity.gov.ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of portal.kyivcity.gov.ua                                                                              | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of anti-violence-map.msp.gov.ua                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dopomoga.msp.gov.ua                                                                                 | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of e-services.msp.gov.ua                                                                               | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of edu.msp.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of education.msp.gov.ua                                                                                | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of ek-cbi.msp.gov.ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mail.msp.gov.ua                                                                                     | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of portal-gromady.msp.gov.ua                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of web-minsoc.msp.gov.ua                                                                               | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of wcs-wim.dsbt.gov.ua                                                                                 | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of bdr.mvs.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of motorsich.com                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dsns.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mon.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of minagro.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of zt.gov.ua                                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of kmu.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mvs.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dsbt.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of forest.gov.ua                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of nkrzi.gov.ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dabi.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of comin.gov.ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dp.dpss.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of esbu.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mms.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mova.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mspu.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of nads.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of reintegration.gov.ua                                                                                | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of sies.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of sport.gov.ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mepr.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mfa.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of va.gov.ua                                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mtu.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of cg.mvs.gov.ua                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of ch-tmo.mvs.gov.ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of cp.mvs.gov.ua                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of cpd.mvs.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of hutirvilnij-mrc.mvs.gov.ua                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dndekc.mvs.gov.ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of visnyk.dndekc.mvs.gov.ua                                                                            | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dpvs.hsc.gov.ua                                                                                     | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of odk.mvs.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of e-driver\[.\]hsc\[.\]gov\[.\]ua                                                                     | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of wanted\[.\]mvs\[.\]gov\[.\]ua                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of minregeion\[.\]gov\[.\]ua                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of health\[.\]mia\[.\]solutions                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mtsbu\[.\]ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of motorsich\[.\]com                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of kyivcity\[.\]com                                                                                    | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of bdr\[.\]mvs\[.\]gov\[.\]ua                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of gkh\[.\]in\[.\]ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of kmu\[.\]gov\[.\]ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mon\[.\]gov\[.\]ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of minagro\[.\]gov\[.\]ua                                                                              | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mfa\[.\]gov\[.\]ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| Intel\_Data     | UA data sharing | PII DB (56M) of Ukrainian Citizens                                                                        | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Kristina        | UA data sharing | DB of Ukrainian National Police (mvs\[.\]gov\[.\]ua)                                                      | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| NetSec          | UA data sharing | PII DB (53M) of Ukrainian citizens                                                                        | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Psycho\_Killer  | UA data sharing | PII DB (56M) of Ukrainian Citizens                                                                        | No        | TA discussion in past 90 days | Exploit Forum .onion \[not linked\]                        | ​| Sp333           | UA data sharing | PII DB of Ukrainian and Russian interpreters, translators, and tour guides                                | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Vaticano        | UA data sharing | DB of Ukrainian 'Diia' e-Governance Portal for Ministry of Digital Transformation of Ukraine \[copy\]     | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Vaticano        | UA data sharing | DB of Ministry for Communities and Territories Development of Ukraine (minregion\[.\]gov\[.\]ua) \[copy\] | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​####​ ​`Vendor Support` ​| Vendor | Offering | URL | ​| --- | --- | --- | ​| Dragos | Access to Dragos service if from US/UK/ANZ and in need of ICS cybersecurity support | [twitter.com/RobertMLee](https://twitter.com/RobertMLee/status/1496862093588455429) | ​| GreyNoise |  Any and all `Ukrainian` emails registered to GreyNoise have been upgraded to VIP which includes full, uncapped enterprise access to all GreyNoise products | [twitter.com/Andrew___Morris](https://twitter.com/Andrew___Morris/status/1496923545712091139) | ​| Recorded Future | Providing free intelligence-driven insights, perspectives, and mitigation strategies as the situation in Ukraine evolves| [recordedfuture.com](https://www.recordedfuture.com/ukraine/) | ​| Flashpoint | Free Access to Flashpoint’s Latest Threat Intel on Ukraine | [go.flashpoint-intel.com](https://go.flashpoint-intel.com/trial/access/30days) | ​| ThreatABLE | A Ukraine tag for free threat intelligence feed that's more highly curated to cyber| [twitter.com/threatable](https://twitter.com/threatable/status/1497233721803644950) | ​| Orange | IOCs related to Russia-Ukraine 2022 conflict extracted from our Datalake Threat Intelligence platform. | [github.com/Orange-Cyberdefense](https://github.com/Orange-Cyberdefense/russia-ukraine_IOCs)| ​| FSecure | F-Secure FREEDOME VPN is now available for free in all of Ukraine | [twitter.com/FSecure](https://twitter.com/FSecure/status/1497248407303462960) | ​| Multiple vendors | List of vendors offering their services to Ukraine for free, put together by [@chrisculling](https://twitter.com/chrisculling/status/1497023038323404803) | [docs.google.com/spreadsheets](https://docs.google.com/spreadsheets/d/18WYY9p1_DLwB6dnXoiiOAoWYD8X0voXtoDl_ZQzjzUQ/edit#gid=0) | ​| Mandiant | Free threat intelligence, webinar and guidance for defensive measures relevant to the situation in Ukraine. | [mandiant.com](https://www.mandiant.com/resources/insights/ukraine-crisis-resource-center) | ​| Starlink | Satellite internet constellation operated by SpaceX providing satellite Internet access coverage to Ukraine | [twitter.com/elonmusk](https://twitter.com/elonmusk/status/1497701484003213317) | ​| Romania DNSC | Romania’s DNSC – in partnership with Bitdefender – will provide technical consulting, threat intelligence and, free of charge, cybersecurity technology to any business, government institution or private citizen of Ukraine for as long as it is necessary. | [Romania's DNSC Press Release](https://dnsc.ro/citeste/press-release-dnsc-and-bitdefender-work-together-in-support-of-ukraine)| ​| BitDefender | Access to Bitdefender technical consulting, threat intelligence and both consumer and enterprise cybersecurity technology | [bitdefender.com/ukraine/](https://www.bitdefender.com/ukraine/) | ​| NameCheap | Free anonymous hosting and domain name registration to any anti-Putin anti-regime and protest websites for anyone located within Russia and Belarus | [twitter.com/Namecheap](https://twitter.com/Namecheap/status/1498998414020861953) | ​| Avast | Free decryptor for PartyTicket ransomware | [decoded.avast.io](https://decoded.avast.io/threatresearch/help-for-ukraine-free-decryptor-for-hermeticransom-ransomware/) |  ​####​ ​`Vetted OSINT Sources` ​| Handle | Affiliation | ​| --- | --- | ​| [@KyivIndependent](https://twitter.com/KyivIndependent) | English-language journalism in Ukraine | ​| [@IAPonomarenko](https://twitter.com/IAPonomarenko) | Defense reporter with The Kyiv Independent | ​| [@KyivPost](https://twitter.com/KyivPost) | English-language journalism in Ukraine | ​| [@Shayan86](https://twitter.com/Shayan86) | BBC World News Disinformation journalist | ​| [@Liveuamap](https://twitter.com/Liveuamap) | Live Universal Awareness Map (“Liveuamap”) independent global news and information site | ​| [@DAlperovitch](https://twitter.com/DAlperovitch) | The Alperovitch Institute for Cybersecurity Studies, Founder & Former CTO of CrowdStrike | ​| [@COUPSURE](https://twitter.com/COUPSURE) | OSINT investigator for Centre for Information Resilience | ​| [@netblocks](https://twitter.com/netblocks) | London-based Internet's Observatory | ​####​ ​`Miscellaneous Resources` ​| Source | URL | Content | ​| --- | --- | --- | ​| PowerOutages.com | https://poweroutage.com/ua | Tracking PowerOutages across Ukraine | ​| Monash IP Observatory | https://twitter.com/IP_Observatory | Tracking IP address outages across Ukraine | ​| Project Owl Discord | https://discord.com/invite/projectowl | Tracking foreign policy, geopolitical events, military and governments, using a Discord-based crowdsourced approach, with a current emphasis on Ukraine and Russia | ​| russianwarchatter.info | https://www.russianwarchatter.info/ | Known Russian Military Radio Frequencies |

A repository to keep all open sources projects that created by individuals or study groups.

These are some of the projects I have worked on using vanila js In these projects, I learned a lot after studying I have created some of these projects with different instructor and gained many ideas from each different instructor the language

In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice. AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work. We will help you master Deep Learning, understand how to apply it, and build a career in AI. Created by: Deeplearning.AI

Summarization systems often have additional evidence they can utilize in order to specify the most important topics of document(s). For example, when summarizing blogs, there are discussions or comments coming after the blog post that are good sources of information to determine which parts of the blog are critical and interesting. In scientific paper summarization, there is a considerable amount of information such as cited papers and conference information which can be leveraged to identify important sentences in the original paper. How text summarization works In general there are two types of summarization, abstractive and extractive summarization. Abstractive Summarization: Abstractive methods select words based on semantic understanding, even those words did not appear in the source documents. It aims at producing important material in a new way. They interpret and examine the text using advanced natural language techniques in order to generate a new shorter text that conveys the most critical information from the original text. It can be correlated to the way human reads a text article or blog post and then summarizes in their own word. Input document → understand context → semantics → create own summary. 2. Extractive Summarization: Extractive methods attempt to summarize articles by selecting a subset of words that retain the most important points. This approach weights the important part of sentences and uses the same to form the summary. Different algorithm and techniques are used to define weights for the sentences and further rank them based on importance and similarity among each other. Input document → sentences similarity → weight sentences → select sentences with higher rank. The limited study is available for abstractive summarization as it requires a deeper understanding of the text as compared to the extractive approach. Purely extractive summaries often times give better results compared to automatic abstractive summaries. This is because of the fact that abstractive summarization methods cope with problems such as semantic representation, inference and natural language generation which is relatively harder than data-driven approaches such as sentence extraction. There are many techniques available to generate extractive summarization. To keep it simple, I will be using an unsupervised learning approach to find the sentences similarity and rank them. One benefit of this will be, you don’t need to train and build a model prior start using it for your project. It’s good to understand Cosine similarity to make the best use of code you are going to see. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. Since we will be representing our sentences as the bunch of vectors, we can use it to find the similarity among sentences. Its measures cosine of the angle between vectors. Angle will be 0 if sentences are similar. All good till now..? Hope so :) Next, Below is our code flow to generate summarize text:- Input article → split into sentences → remove stop words → build a similarity matrix → generate rank based on matrix → pick top N sentences for summary.

Created as Android study project for http://code.tutsplus.com/tutorials/create-a-weather-app-on-android--cms-21587

This GitHub repository has been created for the research project titled "Improving Aerial Targeting Precision: A Study on Point Cloud Semantic Segmentation with Advanced Deep Learning Algorithms."

Navio is a program to create and manage linux containers. This project exists only for study purposes, so feel free to contribute :)

This project was created by Avanade Team to study

This project is an AI-powered Learning Management System (AI LMS) built using Next.js, Clerk for authentication, Inngest for function orchestration, Drizzle ORM for database interaction, and Google Gemini for AI-powered content generation. It allows users to create personalized study materials for various learning goals,

Using-Deep-Learning-Techniques-perform-Fracture-Detection-Image-Processing Using Different Image Processing techniques Implementing Fracture Detection on X rays Images on 8000 + images of dataset Description About Project: Bones are the stiff organs that protect vital organs such as the brain, heart, lungs, and other internal organs in the human body. There are 206 bones in the human body, all of which has different shapes, sizes, and structures. The femur bones are the largest, and the auditory ossicles are the smallest. Humans suffer from bone fractures on a regular basis. Bone fractures can happen as a result of an accident or any other situation in which the bones are put under a lot of pressure. Oblique, complex, comminute, spiral, greenstick, and transverse bone fractures are among the many forms that can occur. X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and other types of medical imaging techniques are available to detect various types of disorders. So we design the architecture of it using Neural Networks different models, compare the accuracy, and get a result of which model works better for our dataset and which model delivers correct results on a specific related dataset with 10 classes. Basically our main motive is to check that which model works better on our dataset so in future reference we all get an idea that which model gives better type of accuracy for a respective dataset . Proposed Method for Project: we decided to make this project because we have seen a lot of times that report that are generated by computer produce error sometimes so we wanted to find out which model gives good accuracy and produce less error so we start to research over image processing nd those libraries which are used in image processing like Keras , Matplot lib , Image Generator , tensor flow and other libraries and used some of them and implement it on different image processing algorithm like as CNN , VGG-16 Model ,ResNet50 Model , InceptionV3 Model . and then find the best model which gives best accuracy for that we generate classification report using predefined libraries in python such as precision , recall ,r2score , mean square error etc by importing Sklearn. Methodology of Project: Phase 1: Requirement analysis: • Study concepts of Basic Python programming. • Study of Tensor flow, keras and Python API interface . • Study of basic algorithms of Image Processing and neural network And deep learning concepts. • Collect the dataset from different resources and describe it into Different classes(5 Fractured + 5 non fractured). Phase 2: Designing and development: The stages of design and development are further segmented. This step starts with data from the Requirement and Analysis phase, which will lead to the model construction phase, where a model will be created and an algorithm will be devised. After the algorithm design phase is completed, the focus will shift to algorithm analysis and implementation in this project. Phase 3: Coding Phase: Before real coding begins, the task is divided into modules/units and assigned to team members once the system design papers are received. Because code is developed during this phase, it is the developers' primary emphasis. The most time-consuming aspect of the project will be this. This project's implementation begins with the development of a program in the relevant programming language and the production of an error-free executable program. Phase 4: Testing Phase: When it comes to the testing phase, we may test our model based on the classification report it generates, which contains a variety of factors such as accuracy, f1score, precision, and recall, and we can also test our model based on its training and testing accuracy. Phase 5: Deployment Phase: One of our goals is to bring all of the previous steps together and put them into practice. Another goal is to deploy our model into a python-based interface application after comparing the classification reports and determining which model is best for our dataset.

1-Project Title “Library Management System (L_I_B_R_A)” The “Library Management System” has been developed to override the problems prevailing in the practicing manual system. This software is supported to eliminate and in some cases reduce the hardship faced by this existing system. Moreover this system is designed for the particular need of the institution to carry out operating in a smooth and effective manner. 2-Domain Library management institutional management non-profitable organization. 3-Problem of Statement In our existing system all the transaction of books are done manually, so taking more time for a transaction like borrowing a book or returning a book and also for searching of member and books. Another major disadvantage is that to preparing the list of book borrowed and the available book in the library will take more time, currently it is doing as a day process for verifying all records. So after conducting he feasibility study we decided to make the manual library management system to be computerised. Proposed system is an automated library management system. Through our software user can add member, edit information, borrow and return books in quick time. Some of the problems being faced in faced in manual system are as follows: • Fast report generation is not possible. • Tracing a book is difficult. • Information about issue/return of the books is not properly maintained. • No central database can be created as information is not available in database. All the manual difficulty in managing the Library have been rectified by implementing computerization. This application is reduced as much as possible to avoid errors while entering the data. It also provides error message while entering invalid data. No, formal knowledge is needed for the user to use this system. Thus by this all it is user-friendly. Library Management System as described above can lead error free, secure, reliable and fast management system. It can assist the user to concentrate on other activities rather to concentrate on the record keeping. Thus it will help organisation in better utilization of resources. So that’s why I can choose this topic to make it simple. is a sub-discipline of issues faced by libraries and library management professionals. Library management encompasses normal managerial tasks, as well as intellectual that focuses on specific freedom and fundraising responsibilities. Issues faced in library management frequently overlap with those faced in managing Title: L_I_B_R_A Page 5 of 16 TMU-CCSIT Version 1. 4-Project Description The software to be produced is on Library Management System. A library card will also be provided to the customers who visit daily. A person can also borrow the book for particular days. All the information will be entered in the system. If the person doesn’t return the due book. Admin has the authority to add, delete or modify the details of the book available to/from the system. He also has the authority to provide username and password for the receptionist. He can also add the details of the book purchased from shops along with the shop name. Project plan Requirement Design Process description implementation STATE DIAGRAM : LIBRARIAN OBJECT Title: L_I_B_R_A Page 6 of 16 TMU-CCSIT Version 1. 4.1-Scope of the Work This project is helpful to track all the book and library information and to rate the maximum number of books, the students wished to allot books. The software will be able to handle all the necessary information related to the library. From a librarian perspective, the Library Management System Project enhanced searchable database for the search books, managing library members, issuing and receiving books . • Search Books, Managing Library Members, Issuing and Receiving Books: An enhanced atomized system is developed to maintain Books, Authors, Issuing and Receiving Books and maintain the history of transaction. • To utilize resources in an efficient manner by increasing their productivity through automation. • It satisfies the user requirement. Title: L_I_B_R_A Page 7 of 16 TMU-CCSIT Version 1. 4.2-Project Modules • Books: This module consist the details of the books available in library and their categories. Title: L_I_B_R_A Page 8 of 16 TMU-CCSIT Version 1. • Member Account details To issue an book from the library, one should have a account in the library. The registration contains all the details about the member like registration number, name, address, contact number etc.. • Book Request: This module is used by the member to request a book from the library. The search can be performed by using name of the book, author name, and subject name. Title: L_I_B_R_A Page 9 of 16 TMU-CCSIT Version 1. • Issue of books: This module is used by the librarian to issue a book based on the request made by the members. • Returning Books: In this module the librarian maintains the details of the books returned by the member, which also includes the fine details, damage book details, lost book details. • History: In this module the member can view the details about the previous issued books, requested books and returned books etc. • Reports: This module includes the details about the issued books, returned books, member reports, fine reports, or any damage to the book or details of the book which are not returned. Title: L_I_B_R_A Page 10 of 16 TMU-CCSIT Version 1. 5-Implementation Methodology In this I am trying to give an Idea of “How I can implement the library management system” . FUNCTIONAL DECOMPOSITION OF LIBRARY MANAGEMENT SYSTEM CLASS DIAGRAM OF LIBRARY MANAGEMENT SYSTEM Title: L_I_B_R_A Page 11 of 16 TMU-CCSIT Version 1. DFD OF LIBRARY MANAGEMENT SYSTEM ER DIAGRAM OF LIBRARY MANAGEMENT SYSYTEM Title: L_I_B_R_A Page 12 of 16 TMU-CCSIT Version 1. DATABASE OF LIBRARY MANAGEMENT SYSTEM Title: L_I_B_R_A Page 13 of 16 TMU-CCSIT Version 1. 6-Technologies to be used 6.1 -Software Platform a) Front-end ----python (3.8) ----tk-inter (GUI) b) Back-end -----sqlite (database) 6.2 -Hardware Platform RAM — 8 GB Hard Disk — not used OS — Mac OS (Mojave-10.14.6) Editor — idle (Available with python package) Processor — 1.8 GHz intel core i5 6.3 -Tools No tool used. 7-Advantages of this Project Our proposed system has the following advantages. User friendly interface Fast access to database Less error More storage capacity Search facility Look and feel environment Quick transaction 8-Future Scope and further enhancement of the Project o In future we can make this application online so that members will be able to search the book from any places as well as can send book request. o Book reading facility can be provided through on-line. o In the area of data security and system security. o Provide more online tips and help. o Implementation of ISBN BAR code reader Title: L_I_B_R_A Page 14 of 16 TMU-CCSIT Version 1. 9-Project Repository Location S# Project Artifacts (softcopy) Location Verified by Project Guide Verified by Lab In-Charge 1. Project Synopsis Report (Final Version) https://s.docworkspace.com/d/AEsSC- 7eqLpR6Z6S_OSdFA Tushar mehrotra Name and Signature 2. Project Progress updates Name and Signature Name and Signature 3. Project Requirement specifications Name and Signature Name and Signature 4. Project Report (Final Version) Name and Signature Name and Signature 5. Test Repository Name and Signature Name and Signature 6. Any other document, give details Name and Signature Name and Signature 10-Team Details Project Name Course Name Student ID Student Name Role Signature LIBRARY MANAGEMENT SYSTEM INDUSTRIAL TRAINING(PYTHON) (ECS 509) TCA1809026 UTTKARSH PARMAR Developer 11-Conclusion “Library Management System” allows the user to store the book details and the customer details. This software package allows storing the details of all the data related to library. The system is strong enough to withstand regressive yearly operations under conditions where the Title: L_I_B_R_A Page 15 of 16 TMU-CCSIT Version 1. database is maintained and cleared over a certain time of span. The implementation of the system in the organization will considerably reduce data entry, time and also provide readily calculated reports. 12-References • Website http://www.wikipedia.com http://www.sololearn.com And also my mentor from Ducat (Noida) • Book Python-basic-handbook ( writer- vivek Krishnamoorthy, jay Parmar, mario pisa pena)

The repository is created to support a Capstone project on the topic of "Study and Implementation of Sound Source Localization Techniques"

This project aims to create new tweets and carry out social engineering attacks by using the messages that people sent from social media accounts. Deep learning methods are used in this project. Besides, the model is trained with the social media messages of the people and new messages are created with this model. Malicious links are designed for people's interests. The study was implemented as a project developed only in the graduate course of ethical hacking. It is for educational purposes.

Climate change is a controversial topic of debate, where many of us do not believe in anthropogenic climate change. Because its consequences are predicted to be dire, such as mass ocean extinction and frequent extreme weather events, it is important to learn what causes the warming in order to better combat it. This project’s main objective is to predict weather and analyze various atmospheric gases that are causing harm to global warming up until now. In this study, the first challenge dwells on how to construct reliable statistical models based on climate data of few hundred years and accurately capture the relationship between temperature and potential factors such as concentrations of carbon dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), nitrous oxide (N2O), hydrofluorocarbon (HFC) and methane (CH4). We compared the performance of several mainstream machine learning algorithms on our data, which includes linear regression, random forest, and Autoregressive Integrated Moving Average (ARIMA) model to build the state-of-theart model to verify the warming of the earth and identifying factors contributing to global warming. We found that random forest outperforms other algorithms to create accurate climate models which use features including various different versions of temperature to precisely predict global temperature. But the problem that arises in the current random forest model is that it can’t forecast temperature in future so we’ve used the Autoregressive Integrated Moving Average (ARIMA) model to fill that gap. It was found that CO2 is the largest contributor to temperature change, followed by CH4, HFC, SO2 and then by CO. They all had some sorts of impact, though, meaning their release into the atmosphere should all be controlled to help restrain temperature increase, and help prevent climate change’s potential ramifications.

In this project we are creating a SQL case study. By doing this we'll learn concepts in SQL from basic to advanced including joins, window functions, subqueries and more.

IT Step Website is a personal web development project created as part of my studies at IT Academy STEP in Georgia. It showcases a modern, responsive design built using HTML, CSS, and JavaScript. This project reflects the skills I’ve gained while studying web development and serves as a portfolio piece for future projects.