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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

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2020 was a roller coaster of major, world-shaking events. We all couldn't wait for the year to end. But just as 2020 was about to close, it pulled another fast one on us: the SolarWinds hack, one of the biggest cybersecurity breaches of the 21st century. The SolarWinds hack was a major event not because a single company was breached, but because it triggered a much larger supply chain incident that affected thousands of organizations, including the U.S. government. What is SolarWinds? SolarWinds is a major software company based in Tulsa, Okla., which provides system management tools for network and infrastructure monitoring, and other technical services to hundreds of thousands of organizations around the world. Among the company's products is an IT performance monitoring system called Orion. As an IT monitoring system, SolarWinds Orion has privileged access to IT systems to obtain log and system performance data. It is that privileged position and its wide deployment that made SolarWinds a lucrative and attractive target. What is the SolarWinds hack? The SolarWinds hack is the commonly used term to refer to the supply chain breach that involved the SolarWinds Orion system. In this hack, suspected nation-state hackers that have been identified as a group known as Nobelium by Microsoft -- and often simply referred to as the SolarWinds Hackers by other researchers -- gained access to the networks, systems and data of thousands of SolarWinds customers. The breadth of the hack is unprecedented and one of the largest, if not the largest, of its kind ever recorded. More than 30,000 public and private organizations -- including local, state and federal agencies -- use the Orion network management system to manage their IT resources. As a result, the hack compromised the data, networks and systems of thousands when SolarWinds inadvertently delivered the backdoor malware as an update to the Orion software. SolarWinds customers weren't the only ones affected. Because the hack exposed the inner workings of Orion users, the hackers could potentially gain access to the data and networks of their customers and partners as well -- enabling affected victims to grow exponentially from there. Orion Platform hack compromised networks of thousands of SolarWinds customers Hackers compromised a digitally signed SolarWinds Orion network monitoring component, opening a backdoor into the networks of thousands of SolarWinds government and enterprise customers. How did the SolarWinds hack happen? The hackers used a method known as a supply chain attack to insert malicious code into the Orion system. A supply chain attack works by targeting a third party with access to an organization's systems rather than trying to hack the networks directly. The third-party software, in this case the SolarWinds Orion Platform, creates a backdoor through which hackers can access and impersonate users and accounts of victim organizations. The malware could also access system files and blend in with legitimate SolarWinds activity without detection, even by antivirus software. SolarWinds was a perfect target for this kind of supply chain attack. Because their Orion software is used by many multinational companies and government agencies, all the hackers had to do was install the malicious code into a new batch of software distributed by SolarWinds as an update or patch. The SolarWinds hack timeline Here is a timeline of the SolarWinds hack: September 2019. Threat actors gain unauthorized access to SolarWinds network October 2019. Threat actors test initial code injection into Orion Feb. 20, 2020. Malicious code known as Sunburst injected into Orion March 26, 2020. SolarWinds unknowingly starts sending out Orion software updates with hacked code According to a U.S. Department of Homeland Security advisory, the affected versions of SolarWinds Orion are versions are 2019.4 through 2020.2.1 HF1. More than 18,000 SolarWinds customers installed the malicious updates, with the malware spreading undetected. Through this code, hackers accessed SolarWinds's customer information technology systems, which they could then use to install even more malware to spy on other companies and organizations. Who was affected? According to reports, the malware affected many companies and organizations. Even government departments such as Homeland Security, State, Commerce and Treasury were affected, as there was evidence that emails were missing from their systems. Private companies such as FireEye, Microsoft, Intel, Cisco and Deloitte also suffered from this attack. The breach was first detected by cybersecurity company FireEye. The company confirmed they had been infected with the malware when they saw the infection in customer systems. FireEye labeled the SolarWinds hack "UNC2452" and identified the backdoor used to gain access to its systems through SolarWinds as "Sunburst." Microsoft also confirmed that it found signs of the malware in its systems, as the breach was affecting its customers as well. Reports indicated Microsoft's own systems were being used to further the hacking attack, but Microsoft denied this claim to news agencies. Later, the company worked with FireEye and GoDaddy to block and isolate versions of Orion known to contain the malware to cut off hackers from customers' systems. They did so by turning the domain used by the backdoor malware used in Orion as part of the SolarWinds hack into a kill switch. The kill switch here served as a mechanism to prevent Sunburst from operating further. Nonetheless, even with the kill switch in place, the hack is still ongoing. Investigators have a lot of data to look through, as many companies using the Orion software aren't yet sure if they are free from the backdoor malware. It will take a long time before the full impact of the hack is known. Why did it take so long to detect the SolarWinds attack? With attackers having first gained access to the SolarWinds systems in September 2019 and the attack not being publicly discovered or reported until December 2020, attackers may well have had 14 or more months of unfettered access. The time it takes between when an attacker is able to gain access and the time an attack is actually discovered is often referred to as dwell time. According to a report released in January 2020 by security firm CrowdStrike, the average dwell time in 2019 was 95 days. Given that it took well over a year from the time the attackers first entered the SolarWinds network until the breach was discovered, the dwell time in the attack exceeded the average. The question of why it took so long to detect the SolarWinds attack has a lot to do with the sophistication of the Sunburst code and the hackers that executed the attack. "Analysis suggests that by managing the intrusion through multiple servers based in the United States and mimicking legitimate network traffic, the attackers were able to circumvent threat detection techniques employed by both SolarWinds, other private companies, and the federal government," SolarWinds said in its analysis of the attack. FireEye, which was the first firm to publicly report the attack, conducted its own analysis of the SolarWinds attack. In its report, FireEye described in detail the complex series of action that the attackers took to mask their tracks. Even before Sunburst attempts to connect out to its command-and-control server, the malware executes a number of checks to make sure no antimalware or forensic analysis tools are running. What was the purpose of the hack? The purpose of the hack remains largely unknown. Still, there are many reasons hackers would want to get into an organization's system, including having access to future product plans or employee and customer information held for ransom. It is also not yet clear what information, if any, hackers stole from government agencies. But the level of access appears to be deep and broad. There are speculations that many enterprises might be collateral damage, as the main focus of the attack was government agencies that make use of the SolarWinds IT management systems. Who was responsible for the hack? Federal investigators and cybersecurity agents believe a Russian espionage operation -- mostly likely Russia's Foreign Intelligence Service -- is behind the SolarWinds attack. The Russian government has denied any involvement in the attack, releasing a statement that said, "Malicious activities in the information space contradicts the principles of the Russian foreign policy, national interests and understanding of interstate relations." They also added that "Russia does not conduct offensive operations in the cyber domain." Contrary to experts in his administration, then-President Donald Trump hinted at around the time of the discovery of the SolarWinds hack that Chinese hackers might be behind the cybersecurity attack. However, he did not present any evidence to back up his claim. Shortly after his inauguration, President Joe Biden vowed that his administration intended to hold Russia accountable, through the launch of a full-scale intelligence assessment and review of the SolarWinds attack and those behind it. The president also created the position of deputy national security adviser for cybersecurity as part of the National Security Council. The role, held by veteran intelligence operative Anne Neuberger, is part of an overall bid by the Biden administration to refresh the federal government's approach to cybersecurity and better respond to nation-state actors. Naming the attack: What is Solorigate, Sunburst and Nobelium? The SolarWinds attack has a number of different names associated with it. While the attack is often referred to simply as the SolarWinds attack, that isn't the only name to know. Sunburst. This is the name of the actual malicious code injection that was planted by hackers into the SolarWinds Orion IT monitoring system code. Both SolarWinds and CrowdStrike generally refer to the attack as Sunburst. Solorigate. Microsoft initially dubbed the actual threat actor group behind the SolarWinds attack as Solorigate. It's a name that stuck and was adopted by other researchers as well as media. Nobelium. In March 2021, Microsoft decided that the primary designation for the threat actor behind the SolarWinds attack should actually be Nobelium -- the idea being that the group is active against multiple victims -- not just SolarWinds -- and uses more malware than just Sunburst. The China connection to the SolarWinds attack While it is suspected that the initial Sunburst code and the attack against SolarWinds and its users came from a threat actor based in Russia, other nation-state threat actors have also used SolarWinds in attacks. According to a Reuters report, suspected nation-state hackers based in China exploited SolarWinds during the same period of time the Sunburst attack occurred. The suspected China-based threat actors targeted the National Finance Center, which is a payroll agency within the U.S. Department of Agriculture. It is suspected that the China-based attackers did not use Sunburst, but rather a different malware that SolarWinds identifies as Supernova. Why is the SolarWinds hack important? The SolarWinds supply chain attack is a global hack, as threat actors turned the Orion software into a weapon gaining access to several government systems and thousands of private systems around the world. Due to the nature of the software -- and by extension the Sunburst malware -- having access to entire networks, many government and enterprise networks and systems face the risk of significant breaches. The hack could also be the catalyst for rapid, broad change in the cybersecurity industry. Many companies and government agencies are now in the process of devising new methods to react to these types of attacks before they happen. Governments and organizations are learning that it is not enough to build a firewall and hope it protects them. They have to actively seek out vulnerabilities in their systems, and either shore them up or turn them into traps against these types of attacks. Since the hack was discovered, SolarWinds has recommended customers update their existing Orion platform. The company has released patches for the malware and other potential vulnerabilities discovered since the initial Orion attack. SolarWinds also recommended customers not able to update Orion isolate SolarWinds servers and/or change passwords for accounts that have access to those servers. The greater White House cybersecurity focus will be crucial, some industry experts have said. But organizations should consider adopting modern software-as-a-service tools for monitoring and collaboration. While the cybersecurity industry has significantly advanced in the last decade, these kinds of attacks show that there is still a long way to go to get really secure systems. The Nobelium group continues to attack targets The suspected threat actor group behind the SolarWinds attack has remained active in 2021 and hasn't stopped at just targeting SolarWinds. On May 27, 2021, Microsoft reported that Nobelium, the group allegedly behind the SolarWinds attack, infiltrated software from email marketing service Constant Contact. According to Microsoft, Nobelium targeted approximately 3,000 email accounts at more than 150 different organizations. The initial attack vector appears to be an account used by USAID. From that initial foothold, Nobelium was able to send out phishing emails in an attempt to get victims to click on a link that would deploy a backdoor Trojan designed to steal user information.

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.

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