Classification of BCI competition VI dataset 2a using ANN by applying WPD and CSP for feature extraction

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

I used six classification techniques, artificial neural network (ANN), Support Vector Machine (SVM), Decision tree (DT), random forest (RF), Logistics Regression (LR) and Naïve Bayes (NB)

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Pytorch code for the paper "Deep Active Learning for Joint Classification and Segmentation with Weak Annotator"

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:chicken: An face-detection platform based on Gabor filters, with classifications done via ANN and Naive Bayes.

This repository consists of all the work done regarding Heart sound classification employing ANN, CNN and other methods, Android Application deployed with predictive model is also included.

Aim was to develop a machine learning model which can analyze sentiments on twitter and to predict the winner of Lok Sabha Elections 2019. Web scraping was used for comments and then applied feature extraction, TF-IDF, Word2Vec, ANN and LSTM to improve the accuracy of model. Softmax function was used for multiclass classification. Learning curve was used to select the epochs and batch size in ANN and LSTM. Word cloud was made to select the most important features in the dataset. Random forest classification was selected as the best classifier with the help of PR curve by using micro average for average precision and recall.

Rehabilitation of people afflicted with elbow joint ailments is quite challenging. Studies reveal that rehabilitation through robotic devices exhibits promising results, in particular exoskeleton robots. In this work, 1 degree of freedom active upper-limb exoskeleton robot with artificial intelligence aided myoelectric control system has been developed for elbow joint rehabilitation. The raw surface electromyogram (sEMG) signals from seventeen different subjects for five different elbow joint angles were acquired using the Myo armband. Time-domain statistical features such as waveform length, root mean square, variance, and a number of zero crossings were extracted and the most advantageous feature was investigated for Artificial Neural Network (ANN) – a backpropagation neural network with Levenberg-Marquardt training algorithm and Support Vector Machine (SVM) – with Gaussian kernel. The results show that waveform length consumes the least amount of computation time. With waveform length as an input feature, ANN and SVM exhibited an average overall classification accuracy of 91.33% and 91.03% respectively. Moreover, SVM consumed 36% more time than ANN or classification.

ANN based electrical fault detection and classification using line and phase currents and voltages.

ANN to SNN conversion on land cover and land use classification problem for increased energy efficiency.

Machine Learning, EDA, Binary Classification task weather dataset, ANN, SVM, LR

Cervical cancer is the second most common type of cancer that is found in the women worldwide. Generally, cancer caused due to irregular growth of cells in a particular area that or have the potential to spread to the other parts of the body as well. Identification of a cervical cancer test is an examination of the tissue taken from a particular region, which might contain cancerous cells through biopsy, is exceptionally challenging because these types of cells does not offer unusual color or texture variants from the standard cells. To identify the abnormalities in human cell the high-level digital image processing technologies are already present in the market which very costly concerning the money. Therefore, we are proposing the model which going to classify whether a female patient has cervical cancer or not. We are using various attributes from real-life and performing a feature selection algorithm Recursive Feature Elimination (RFE). Afterward, making classification models using three machine-learning algorithms like K-Nearest Neighbor (KNN), Random Forest and Multilayer Perceptron (MLP), MLP is a type of the Artificial Neural Network (ANN) algorithm whereas KNN and Random Forest is a supervised type of algorithm.

Medical image fusion is the process of combining two different modality images into a single image. The resultant image can help the physicians to extract features that may not be easily identifiable in an individual modality images. This paper aims to demonstrate an efficient method for detection of brain tumor from CT and MRI images of the brain, by applying image fusion, segmentation, feature extraction and classification. Initially, the source images are decomposed into low-level sub-band and high level sub-band by Discrete Wavelet Transform (DWT). The fused low level sub-band and high level sub-band are reconstructed to form the final fused image using Inverse Discrete Wavelet Transform (IDWT). Parameter analysis is done on the fused image. The fused image is then segmented using Otsu’s thresholding operation and the texture features are extracted forms the Grey Level Co-occurrence Matrix (GLCM) technique. Finally, the extracted feature is provided to Adaptive Neural Network (ANN) classifier to identify and predict the nature of the tumor. Further this proposed method gives an accuracy of 93.5% for 12 samples of MRI and CT images each.

This is where my projects for classification, clustering, linear regression, random forest, ANN, deeplearning(PyTorch) and KNN are placed

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A collection of basic artificial neural network (ANN) training examples for classification and regression problems, providing a starting point for understanding and implementing ANN models.

This repo contains examples of binary classification with ANN and hyper-parameter tuning with grid search.

This research addresses the imperative challenges in power systems by proposing a comprehensive framework for Fault Detection, Classification, and Location Estimation utilizing Artificial Neural Networks (ANNs). The study acknowledges the evolving nature of power systems and introduces novel methodologies to enhance adaptability and precision.

I qt worked on corona virus tweet streams mam With hashtags #covid19,#indiafightscorona,#lockdown I did generate the dastset from the stream and procesed according to the working of deep learning algorithms work flow. I reframed my datset with 2 parameters-- tweets full text and sentiment score and worked on 4 algorithms mam. SET 1- DEEP LEARNING ALGOTITHMS: 1.CNN -(used 1csv with train_test_split method ) Accuracy-0.73368 2.LSTM- (used 2csv file seperate for trainingand testing) Training accuracy-0.9457,loss-0.1605 Testing accuracy-0.6557,loss-0.3442 3.FFNN-( used 2csv file seperate for trainingand testing) Training accuracy-0.28,loss-622.3 Testing accuracy-0.14893,loss-141.82 4.ANN with TFIDF Vectorizer(used 1 csv wth train_test_split) The different Ann epoches and models with different learning rate and different drop out value ,Training accuracy ranged btween 0.4752 to 0.6270 and the Validation accuracy ranged 0.2353 constantly On comparing the above 4 algorithms I came to a conclusiom with my understanding Sentiment analysis in tweets can be done efficiently in this order. CNN > LSTM > ANN > FFNN. SET 2-MACHINE LEARNING I did try with Linear Support vector Classifier --1 csv train_test_split method Training accuracy - 0.6666 Testing accuracy(f1score)-0.59471 And with Naive bayes classifier--1 csv train_test_split method Training accuracy - 0.64 Test accuracy -0.5486 SET 3- MODEL CLASSIFICATIONS: I compared my datasets efficiency with 4 models . The accuracies of the model classificatiom are: 1.Baseline Model - 62.86% 2.Reduces Model-65.71% 3.Regularized Model-66.86% 4.Dropout Model-67.43% Efficient modeling order for tweet data-set Dropout model > Regularized model > Reduced model > Baseline model .

Dive into the world of Artificial Neural Networks (ANN) through a curated collection of projects, all implemented in Python. From foundational concepts to advanced applications, these projects showcase my exploration of neural networks for solving diverse problems, including classification, regression, and more.

Deep learning model (ANN) for the classification of Satellite Image. The image is from Landsat (kathmandu) in this example.

Music genres is the taste, style and relax giving flow of a music. The genre of music refers to multiple types and categorization of music. The different types of famous music genre that we widely known are rock, jazz, reggae, classical, folk, blues, R & B, metal, dubstep, techno, country music, electro and pop. The key success of music in music industry is the genres of classified music that becomes a significant part of communicating music that provides bonding with relatively to human and masses of people. In contrast, the genre that falls under top-level style of rock are punk, indie, shoegaze, AOR and metal. They are basically subgenre of a music classification and it is important describing music to other people. In practical life, music is often used for multiple purposes due to physiological and social effects. Companies like Spotify, Soundcloud, Apple Music, Wynk & products like Shazam use music classification to provide their customers different flavour of music by recommending music they prefer to listen. we use python libraries such as Librosa and PyAudio library for audio processing in Python. We apply and use GTZAN dataset that is composed of 1000 audio tracks each 30-second-long representing 10 genres with 22050Hz mono audio file of 16bit in .au format for dataset. The functionality and working of music genre classification determine the help of Machine Learning algorithms. The algorithm such as KNN and artificial neural network (ANN) analyses and find out the similar similarity of genre features of music and classify it.

Using OpenCV for Image Classification for Android Devices using ANN

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Designed for different deep_learning projects, like-text, image classification and audio analysis. It consists CNN,ANN and also NLP based projects.

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UCI Thyroid Classification - Python, Keras, scikit-learn, ANN

Convert multi-label classification annotation data from .csv to .json for CreateML