Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.

With the rapid evolution of information technology, malware has become an advanced cybersecurity threat, targeting computer systems, smart devices, and large-scale networks in real time. Traditional detection methods often fail to recognize emerging malware variants due to limitations in accuracy, adaptability, and response time. This paper presents a comprehensive review of machine learning algorithms for real-time malware detection, categorizing existing approaches based on their methodologies and effectiveness. The study examines recent advancements and evaluates the performance of various (...) machine learning techniques in detecting malware with minimal false positives and improved scalability. Additionally, key challenges, such as adversarial attacks, computational overhead, and real-time processing constraints, are discussed, along with potential solutions to enhance detection capabilities. An empirical evaluation is conducted to assess the effectiveness of different machine learning models, providing insights for future research in real-time malware detection. Keywords: Real-t. (shrink)

Electrocardiogram (ECG) signal analysis is a critical task in healthcare for diagnosing cardiovascular conditions such as arrhythmias, heart attacks, and other heart-related diseases. With the growth of Internet of Things (IoT) networks, real-time ECG monitoring has become possible through wearable devices and sensors, providing continuous patient health monitoring. However, real-time ECG signal analysis in IoT environments poses several challenges, including data latency, limited computational power of IoT devices, and energy constraints. This paper proposes a framework for Optimized Machine (...) class='Hi'>Learning Algorithms designed to analyze ECG signals in real time within IoT networks. The proposed system leverages lightweight machine learning models, including support vector machines (SVM) and convolutional neural networks (CNNs), optimized to run efficiently on low-power IoT devices while maintaining high accuracy. The system addresses the computational limitations of IoT devices by employing edge computing techniques that distribute the processing load between IoT devices and edge servers. Additionally, data compression and feature extraction techniques are applied to reduce the size of the data transmitted over the network, thereby minimizing latency and bandwidth usage. This paper reviews the current advancements in real-time ECG analysis, explores the challenges posed by IoT environments, and presents the optimized machine learning algorithms that enhance real-time monitoring of heart health. The system is evaluated for its performance in terms of accuracy, energy efficiency, and data transmission speed, showing promising results in improving real-time ECG signal analysis in resource-constrained IoT networks. (shrink)

The use of machine learning to medical image classification has seen significant development and implementation in the last several years. Computers can learn to identify patterns, make predictions, and use data to inform their judgements; this capability is known as machine learning, a branch of Artificial intelligence (AI). Classifying images according to their contents allows us to do things like identify the type of sickness, organ, or tissue depicted. Medical picture classification and interpretation using machine (...) learning algorithms has greatly improved the speed and accuracy of diagnosis and treatment. Machine learning algorithms can aid radiologists and doctors in diagnosing patients using medical pictures from X-rays, positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), and other scans. (shrink)

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP (...) experiments with the goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model. (shrink)

Quantum machine learning (QML) provides a transformative approach to data analysis by integrating the principles of quantum computing with classical machine learning methods. With the exponential growth of data and the increasing complexity of computational tasks, quantum algorithms offer tremendous advantages in terms of processing speed, memory efficiency, and the ability to resolve issues intractable for classical systems. In this work, the use of QML techniques for both supervised and unsupervised learning problems is explored. (...) Quantum-enhanced models such Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs) show outstanding performance in classification and regression tasks by using quantum kernels and entanglement in supervised learning. Moreover, hybrid quantum-classical solutions offer useful implementations on noisy intermediate-scale quantum (NISQ) devices, hence bridging the gap between present quantum technology and practical uses. By means of comparative analysis, this paper emphasizes the possible benefits and drawbacks of QML, thereby providing understanding of its future importance in sectors including material science, finance, and healthcare. In the end, QML opens the path for a new era of intelligent data processing and solves until unthinkable difficult challenges. (shrink)

Thyroid disorders are common endocrine conditions that affect millions of people worldwide. Accurately identifying these disorders is essential for effective treatment and management. Traditional diagnostic methods, such as laboratory tests and clinical evaluations, are reliable but can be time-consuming and sometimes affected by human error. With advancements in artificial intelligence and machine learning (ML), automated systems are now being developed to improve diagnostic accuracy and efficiency. This study aims to build a machine learning model that classifies (...) thyroid disorders using the thyroidDF.csv dataset. Several ML algorithms, including Random Forest, Artificial Neural Networks (ANN), and XGBoost, are applied to analyze patient data. Various data preprocessing techniques, such as feature selection, data balancing, and hyperparameter tuning, are used to enhance model performance. Among these models, XGBoost performs the best, showing higher accuracy, recall, and precision compared to others. Beyond classification, this research also addresses challenges like data imbalance and the need for model interpretability in medical applications. The proposed system is designed to be practical and reliable for clinical use, helping healthcare professionals make informed decisions for early detection and diagnosis. Future improvements could include expanding the dataset, incorporating explainable AI techniques, and developing real-time monitoring features to further improve accuracy and usability in real-world settings. This report is structured into chapters covering the motivation, methodology, experimental results, and future directions of this research. By integrating AI with medical diagnostics, this study contributes to improving healthcare solutions, enabling more precise diagnoses and better treatment strategies for thyroid disorders. (shrink)

With the rapid evolution of information technology, malware has become an advanced cybersecurity threat, targeting computer systems, smart devices, and large-scale networks in real time. Traditional detection methods often fail to recognize emerging malware variants due to limitations in accuracy, adaptability, and response time. This paper presents a comprehensive review of machine learning algorithms for real-time malware detection, categorizing existing approaches based on their methodologies and effectiveness. The study examines recent advancements and evaluates the performance of various (...) machine learning techniques in detecting malware with minimal false positives and improved scalability. Additionally, key challenges, such as adversarial attacks, computational overhead, and real-time processing constraints, are discussed, along with potential solutions to enhance detection capabilities. An empirical evaluation is conducted to assess the effectiveness of different machine learning models, providing insights for future research in real-time malware detection. -/- . (shrink)

The rise of fake news on digital platforms has become a major societal issue, shaping public opinion and eroding trust in legitimate news sources. As misinformation spreads rapidly, it becomes increasingly difficult to discern fact from fiction, leading to harmful consequences in various domains such as politics, public health, and social relationships. To combat this, there is a growing need for advanced systems that can accurately detect and classify fake news. This project aims to develop a robust machine (...) class='Hi'>learning system, leveraging Natural Language Processing (NLP) techniques and machine learning algorithms, to identify deceptive information in textual data with high precision. By focusing on automation, this system promises to scale effectively and provide a timely solution to the growing problem of fake news. The proposed solution incorporates a pre-processing phase that includes Part-of-Speech (POS) tagging, a technique used to analyze the grammatical structure of the text. POS tagging helps in understanding the syntactic roles of words, which can significantly enhance the ability of the model to detect subtle linguistic patterns often associated with misinformation. The system will align these POS-tagged texts with their original content, ensuring that the underlying meaning is preserved while enabling further analysis. This pre-processing is a crucial step before integrating advanced models, such as General Pre-trained Transformers (GPT), which are known for their strong language modeling capabilities. The combination of these techniques will ensure the model achieves a high level of accuracy in detecting fake news. (shrink)

This article recapitulates and develops the attempts in the Nietzschean traditions to address and overcome the proliferation of nihilism that Nietzsche predicted to unfold in the next 200 years (WP 2). Nietzsche approached nihilism not merely as a psychology but as a labyrinthic and pervasive historical process whereby the highest values of culture and founding assumptions of philosophical thought prevented the further flourishing of life. Therefore, he thought nihilism had to be encountered and experienced on many, often opposing, fronts to (...) be fully consumed and left behind. Thus, just as Nietzsche captured the subtle reinventions of nihilism in new forms in his time, in the new doers assumed behind new deeds (WP 488), this article focuses on the contemporary tectonic shifts brought by digital technology and challenges subjectivation and narrativization of algorithmic will to power in human-like interfaces such as ChatGPT. Having identified philosophers and himself as the most advanced nihilists in their overvaluation of truth, in the 4th part of Thus Spoke Zarathustra, Nietzsche grappled with the difficult, perhaps impossible, temporality of post-nihilistic historicity that oscillates between highest creativity and highest truths, especially in the sections entitled The Shadow and At Noon whereby he explored new temporal techniques to evade the relapses into temporal nihilisms of the notions of linear progress or eternity (TSZ, 4). By drawing on this juncture, this article offers ways to address transfigurations of nihilism behind new technological performances of subjectivity. The article points toward creative temporalities beyond narrativity and subjectivity insofar as the statistical operations and probabilistic estimations of language-models exceed grammatical construction of meaning. This multifaceted application of his thought on the contemporary ontic reality is necessary to perceive our comet's incalculable movement as a veritable ray of sunshine. (shrink)

It is in recent times that the working conditions of the rural and farm workers have drawn special attention on account of escalating exposure to toxins like pesticides. Detection and diagnosis of pesticide poisonings at the initial stages become crucial to save the patient at the earliest stage of medical interventions and to forestall longterm complications. Even so, few rural communities still lack access to high-tech equipment and techniques in diagnostics. To resolve this problem, this project introduces a data-driven methodology (...) using Supervised Machine Learning (SML) methods for the efficient and accurate diagnosis of pesticide poisoning using clinical and biochemical markers. The main contribution of this research is the development and application of a systematic methodology called Data Refinement Cycle with Supervised Machine Learning (DRC–SML). This model was created to offer a complete, applicable, and reproducible methodology that directs a Data Science project from start to finish. It consists of phases like data collection, data preprocessing, missing value handling, encoding and scaling, model selection, performance metrics, and preparation for deployment. In contrast to most current methodologies, which only deal with isolated phases of the data pipeline, DRC–SML guarantees an overall workflow that facilitates a smooth transition between the steps and uniform results. (shrink)

As the adoption of cloud computing continues to increase, securing cloud environments has become an ever-growing concern. Traditional security models struggle to keep up with the evolving nature of cyber threats, making it essential for organizations to explore innovative approaches. This paper explores how artificial intelligence (AI) and machine learning (ML) can enhance cloud security by automating threat detection, response, and mitigation in real-time. Through the application of advanced ML algorithms, AI-driven security systems can identify and predict (...) security incidents, classify threats, and adapt to new attack strategies. The paper examines the various ML techniques, such as anomaly detection, supervised learning, and deep learning, used to enhance cloud security. It also explores the challenges, benefits, and potential future directions of AI-driven security in cloud computing. Case studies from industry leaders demonstrate the impact of these technologies in improving the robustness and efficiency of cloud security frameworks. (shrink)

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest over a restful (...) Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model. (shrink)

The main objective of this work is to measure water quality using machine learning algorithms. A Water Quality Index (WQI) is a numeric expression used to evaluate the quality of a given water body. In this paper the following water quality parameters were used to evaluate the overall water quality in terms of the WQI. These parameters were as temp, dissolved oxygen (DO) (% sat), pH, conductivity, Biochemical oxygen demand (BOD), nitrates (NO3), faecal and total coli forms (...) (TC). These parameters are used as feature vector to represent the water quality. In paper five kinds of classification algorithms, namely Navie Bayes(NB), Decision Tree(DT), K-Nearest Neighbor (KNN), Support Vector Machine(SVM), and Random Forest(RF) were employed to predict the water quality class. Experiments were conducted using the real dataset containing the details from various places in Tamil Nadu as well as the synthetic dataset generated on the basis of parameters randomly. Based on the performance of five kinds of classifier, it was found out that the Random Forest classifier achieves some improved result compared to other classifiers. From the analysis it shows that machine learning techniques have the good ability to predict the water quality index . (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

This remains the case because cyberattacks are becoming more frequent and sophisticated, and as a result, the IDS must also be innovative and evolving to protect sensitive networks. Traditional intrusion detection techniques are replaced by more advanced and dynamic Methodologies based on Machine Learning (ML) algorithms. These dynamic systems capture significant data flows, search for potentially pathological patterns, and forecast threats in real time. By simulating and analyzing real-time cases, this paper discusses the architecture for ML-based IDS, (...) how this function is implemented, and its effectiveness. The primary issues of data quality, model scaling, and interpretability are presented with solutions to address them and support the reliable functioning of the system. (shrink)

Prediction-based decisions, which are often made by utilizing the tools of machine learning, influence nearly all facets of modern life. Ethical concerns about this widespread practice have given rise to the field of fair machine learning and a number of fairness measures, mathematically precise definitions of fairness that purport to determine whether a given prediction-based decision system is fair. Following Reuben Binns (2017), we take ‘fairness’ in this context to be a placeholder for a variety of (...) normative egalitarian considerations. We explore a few fairness measures to suss out their egalitarian roots and evaluate them, both as formalizations of egalitarian ideas and as assertions of what fairness demands of predictive systems. We pay special attention to a recent and popular fairness measure, counterfactual fairness, which holds that a prediction about an individual is fair if it is the same in the actual world and any counterfactual world where the individual belongs to a different demographic group (cf. Kusner et al. (2018)). (shrink)

Every year, millions of hectares of forests are destroyed by forest fires, which are a common and significant natural calamity that seriously endangers both human life and property. For the purpose of developing quick risk management plans and putting into practice efficient firefighting techniques, precise quantitative forecasting of forest fire spread is crucial. The Forest Fire Spread Behaviour Prediction (FFSBP) model, which includes two essential components—the Forest Fire Spread Process Prediction (FFSPP) model and the Forest Fire Spread Results Prediction (FFSRP) (...) model—is presented in this work in answer to this requirement. Cellular Automata and Wang Zhengfei models are combined to create the FFSPP model, which predicts the direction and rate of forest fire propagation. The FFSRP model, on the other hand, employs machine learning techniques to anticipate the size of the burnt area. An actual case study is conducted utilising the "3.29 Forest Fire" disaster in China to verify the effectiveness of the suggested models. This instance is used to verify the FFSPP model, while an actual fire dataset from Portugal's Montesinho National Forest Park is used to evaluate the FFSRP model. The validation process's findings show that the FFSPP model forecasts a burnt area of 286.81 hm2 with a corresponding relative error of 28.94% during the "3.29 Forest Fire's" natural growth phase. Compared to the Farsite and Prometheus fire behaviour simulation models, this relative inaccuracy is noticeably lower. Furthermore, the FFSRP model has excellent prediction performance, especially when considering small and medium-sized fire situations. These results highlight the FFSBP model's potential as a useful instrument for improving the accuracy of forest fire predictions and enabling more effective risk reduction and suppression tactics. (shrink)

The Agricultural Portal is an innovative platform designed to improve crop production by providing farmers with easy access to agricultural information, resources, and tools. The portal offers a wide range of features including weather forecasts, crop shopping, crop prediction, yield prediction, crop stock and purchase History. This technical paper outlines the development and implementation of the Agricultural Portal, highlighting its features and functionalities. The paper also explores the benefits of the portal for farmers, including increased productivity, improved decision-making, and enhanced (...) profitability. Advanced machine learning models are integrated into the portal to improve its predictive capabilities. The portal is built on a robust technology platform that is scalable and adaptable to the needs of farmers of different sizes and geographies. The Agricultural Portal represents a significant step forward in the use of technology in agriculture. By providing farmers with easy access to information and resources, it has the potential to transform the way they farm and improve crop production across the globe by using different Machine Learning Algorithms. In the existing system There is only one feature but we are trying to add extra features and making into a single portal in the proposed system. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Abstract: Ensuring the proactive detection of transaction risks is paramount for financial institutions, particularly in the context of managing credit scores. In this study, we compare different machine learning algorithms to effectively and efficiently. The algorithms used in this study were: MLogisticRegressionCV, ExtraTreeClassifier,LGBMClassifier,AdaBoostClassifier, GradientBoostingClassifier,Perceptron,RandomForestClassifier,KNeighborsClassifier,BaggingClassifier, DecisionTreeClassifier, CalibratedClassifierCV, LabelPropagation, Deep Learning. The dataset was collected from Kaggle depository. It consists of 164 rows and 8 columns. The best classifier with unbalanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, (...) precession 100.0%,Recall100.0% and the F1-score 100.0%. However, the best classifier with balanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%, Recall 100.0% and the F1-score 100.0%. (shrink)

In the contemporary world, there is lot of research going on in creating novel nano materials that are essential for many industries including electronic chips and storage devices in cloud to mention few. At the same time, there is emergence of usage of machine learning (ML) for solving problems in different industries such as manufacturing, physics and chemical engineering. ML has potential to solve many real world problems with its ability to learn in either supervised or unsupervised means. (...) It is inferred from the state of the art that that it is essential to use ML methods for analysing imagery of nano materials so as to ascertain facts further towards characterization and analysis of microstructure and spectral data of materials. Towards this end, in this paper, we proposed a ML based methodology for STEM image analysis and spectral data analysis from STEM image of a nano material. We proposed an algorithm named Machine Learning for STEM Image Analysis (ML-SIA) for analysing STEM image of a nano material. We proposed another algorithm named Machine Learning for STEM Image Spectral Data Analysis (ML-SISDA) for analysing spectral data of STEM image of a nano material. We developed a prototype ML application to implement the algorithms and evaluate the proposed methodology. Experimental results revealed that the ML based approaches are useful for characterization of nano materials. Thus this research helps in taking this forward by triggering further work in the area of material analysis with artificial intelligence. (shrink)

The use of AI algorithms in criminal trials has been the subject of very lively ethical and legal debates recently. While there are concerns over the lack of accuracy and the harmful biases that certain algorithms display, new algorithms seem more promising and might lead to more accurate legal decisions. Algorithms seem especially relevant for bail decisions, because such decisions involve statistical data to which human reasoners struggle to give adequate weight. While getting the right legal (...) outcome is a strong desideratum of criminal trials, advocates of the relational theory of procedural justice give us good reason to think that fairness and perceived fairness of legal procedures have a value that is independent from the outcome. According to this literature, one key aspect of fairness is trustworthiness. In this paper, I argue that using certain algorithms to assist bail decisions could increase three diferent aspects of judges’ trustworthiness: (1) actual trustworthiness, (2) rich trustworthiness, and (3) perceived trustworthiness. (shrink)

Depression is a pervasive and serious mental health concern with far-reaching consequences for individuals. Early detection and intervention are crucial in mitigating its impact. This paper explores the application of machine learning, specifically the random forest algorithm, to analyze social media data for depression detection. Additionally, real-time data collected from students and parents are employed to predict suicidal ideation, making this research a multifaceted approach to addressing mental health issues. Using a random forest algorithm, this study achieved an (...) 86.45% accuracy rate in classifying social media posts as indicative of depression. Furthermore, the research employed an XGBoost algorithm to categorize real-time data into five depression severity stages, achieving an accuracy rate of 83.87%. These results highlight the potential of machine learning in identifying individuals at risk of depression and suicidal ideation. The proposed system offers several advantages, including scalability for analyzing extensive social media datasets, cost-effectiveness, and the ability to provide real-time depression detection, enabling early intervention. Despite challenges associated with noisy and unreliable social media data, as well as the need for models adaptable to diverse populations and contexts, recent advancements in random forest algorithms have shown promise in improving depression detection accuracy. In the future, research in this area should focus on refining machine learning models for more robust and precise depression detection, exploring their applicability across various populations and settings, and developing supportive tools for individuals grappling with depression. (shrink)

In this paper, we present the design and implementation of a machine learning-based movie recommender system. This system suggests movies to users based on their preferences, using a combination of similarity metrics and data from The Movie Database (TMDb) API. The recommender system is deployed as a web application using the Streamlit framework, providing an intuitive interface for users to interact with. The results demonstrate the effectiveness of the recommendation algorithm in suggesting relevant movies.

This work explores the potential of big data analytics, natural language processing (NLP), and machine learning (ML) techniques in predicting cyberbullying on social media. By analyzing large-scale datasets consisting of user comments, posts, and interactions, the study aims to detect harmful content patterns, abusive language, and behavioral trends that indicate cyberbullyingThe rapid proliferation of social media has transformed communication and interaction, but it has also led to an alarming rise in cyberbullying incidents. Cyberbullying, characterized by repeated and intentional (...) harassment through digital platforms, has significant psychological and social consequences for victims, often leading to anxiety, depression, and even self-harm. Traditional methods of identifying and mitigating cyberbullying are often reactive and inefficient due to the vast volume of data generated across multiple platforms. Therefore, predictive models leveraging big data and artificial intelligence (AI) offer a proactive approach to detecting and preventing online harassment. Sentiment analysis, text classification, and deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are employed to enhance the accuracy of cyberbullying predictions. Additionally, graph-based techniques are utilized to examine social network structures and identify potential perpetrators and victims based on interaction patterns. Despite the promising potential of big datadriven approaches, challenges such as data privacy, ethical concerns, and algorithmic bias must be addressed to ensure fair and responsible implementation. The study also highlights the importance of real-time monitoring systems that can alert platform administrators or authorities when cyberbullying behavior is detected. The findings demonstrate that integrating AI with big data analytics significantly improves the accuracy and efficiency of cyberbullying detection, enabling early intervention and fostering a safer digital environment. Future research will focus on refining detection models to handle multilingual data, cultural nuances, and evolving forms of cyberbullying across diverse social media platforms. (shrink)

Typically, fair machine learning research focuses on a single decision maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does partial compliance and the consequent strategic behavior of decision subjects affect the (...) allocation outcomes? If k% of employers were to voluntarily adopt a fairness-promoting intervention, should we expect k% progress (in aggregate) towards the benefits of universal adoption, or will the dynamics of partial compliance wash out the hoped-for benefits? How might adopting a global (versus local) perspective impact the conclusions of an auditor? In this paper, we propose a simple model of an employment market, leveraging simulation as a tool to explore the impact of both interaction effects and incentive effects on outcomes and auditing metrics. Our key findings are that at equilibrium: (1) partial compliance by k% of employers can result in far less than proportional (k%) progress towards the full compliance outcomes; (2) the gap is more severe when fair employers match global (vs local) statistics; (3) choices of local vs global statistics can paint dramatically different pictures of the performance vis-a-vis fairness desiderata of compliant versus non-compliant employers; (4) partial compliance based on local parity measures can induce extreme segregation. Finally, we discuss implications for auditors and insights concerning the design of regulatory frameworks. (shrink)

Earthquakes are among the most destructive natural disasters, yet accurately predicting them remains one of science’s greatest challenges. Traditional seismological approaches struggle to interpret complex patterns from vast seismic datasets. Recently, machine learning (ML) has shown promise in seismology by identifying hidden patterns, detecting microseismic activities, and forecasting earthquake probabilities. This paper explores the integration of ML into earthquake prediction, reviewing current models, methodologies, and challenges. It also proposes a data- driven framework for improving seismic event forecasting using (...) supervised and unsupervised ML algorithms. (shrink)

The proliferation of the Internet of Things (IoT) has transformed various industries by enabling smart environments and improving operational efficiencies. However, this expansion has introduced numerous security vulnerabilities, making IoT systems prime targets for cyberattacks. This paper proposes a machine learning-based intrusion detection framework tailored to the unique characteristics of IoT environments. The framework leverages feature engineering, advanced machine learning algorithms, and real-time anomaly detection to identify and mitigate security threats effectively. Experimental results demonstrate the (...) efficacy of the proposed approach in detecting diverse IoT-specific attacks, including denial-of-service (DoS), man-in-the-middle (MITM), and malware-based attacks. The Internet of Things (IoT) has revolutionized modern living by interconnecting devices and enabling seamless communication. However, the increasing reliance on IoT systems has exposed significant vulnerabilities, making them a prime target for security attacks. This paper proposes a machine learning-based intrusion detection framework to detect and mitigate security attacks in IoT environments. The framework integrates diverse machine learning algorithms to identify abnormal behavior and potential threats. Through comprehensive experiments and evaluations, this research demonstrates the efficacy of the proposed framework in terms of accuracy, scalability, and robustness. (shrink)

Objectives: Machine learning (ML) has the potential to facilitate “continual learning” in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such “adaptive” ML systems in medicine that have, thus far, been neglected in the literature. -/- Target audience: The target audiences for (...) this tutorial are the developers of ML AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. -/- Scope: Discussions of adaptive ML systems to date have overlooked the distinction between 2 sorts of variance that such systems may exhibit—diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites)—and underestimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems. (shrink)

We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to (...) explore overlapping causal patterns of variable granularity and scope. We characterise the conditions under which such a game is almost surely guaranteed to converge on a optimal explanation surface in polynomial time, and highlight obstacles that will tend to prevent the players from advancing beyond certain explanatory thresholds. The game serves a descriptive and a normative function, establishing a conceptual space in which to analyse and compare existing proposals, as well as design new and improved solutions. (shrink)

It is crucial to actively detect the risks of transactions in a financial company to improve customer experience and minimize financial loss. In this study, we compare different machine learning algorithms to effectively and efficiently predict the legitimacy of financial transactions. The algorithms used in this study were: MLP Repressor, Random Forest Classifier, Complement NB, MLP Classifier, Gaussian NB, Bernoulli NB, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Bagging Classifier, Decision Tree Classifier and (...) Deep Learning. The dataset was collected from Kaggle depository. It consists of 6362620 rows and 10 columns. The best classifier with unbalanced dataset was the Random Forest Classifier. The Accuracy 99.97%, precession 99.96%, Recall 99.97% and the F1-score 99.96%. However, the best classifier with balanced dataset was the Bagging Classifier. The Accuracy 99.96%, precession 99.95%, Recall 99.98% and the F1-score 99.96%. (shrink)

: The integration of Machine Learning (ML) in Real-Time Biomedical Signal Processing has unlocked new possibilities in the field of telemedicine, especially when combined with the high-speed, low-latency capabilities of 5G networks. As telemedicine grows in importance, particularly in remote and underserved areas, real-time processing of biomedical signals such as ECG, EEG, and EMG is essential for accurate diagnosis and continuous monitoring of patients. Machine learning algorithms can be used to analyze large volumes of biomedical (...) data, enabling faster and more precise detection of anomalies. This paper proposes a novel system for machine learning-based real-time biomedical signal processing that leverages the capabilities of 5G networks to enhance the transmission, processing, and analysis of critical medical data in telemedicine applications. The system integrates convolutional neural networks (CNNs) for signal classification, anomaly detection, and predictive analysis, ensuring that patients receive timely and accurate medical feedback. Additionally, the 5G network’s low latency and high bandwidth provide seamless data transmission, improving remote diagnostics and enabling high-quality teleconsultations. This paper evaluates the current challenges in real-time biomedical signal processing in telemedicine, discusses the potential of machine learning and 5G networks, and presents an innovative solution for improving healthcare delivery through this integrated approach. (shrink)

Wine quality prediction is a significant task in the wine industry, as it helps producers and consumers determine the quality of a wine based on its chemical properties. Traditional methods of evaluating wine quality are subjective and time-consuming, relying on human tasters. However, with the advancement of machine learning (ML), it is now possible to predict wine quality in a more objective, scalable, and efficient manner. This paper explores various machine learning algorithms for predicting wine (...) quality, evaluates their performance, and demonstrates how these models can be applied to improve wine classification systems. (shrink)

Artificial intelligence technology that recognizes, learns, infers, and responds to external stimuli has recently attracted a lot of research interest. Information in a variety of domains by fusing big data, machine learning algorithms, and computing technologies. Nowadays, practically every industry uses artificial intelligence technology, and a large number of machine learning specialists are attempting to standardize and integrate different machine learning tools so that non-experts can use them with ease in their field. In (...) order to standardize the concepts of machine learning, the researchers are also investigating autonomous machine learning and ontology construction. In this paper, we present a problem solving process and categorize common steps in autonomous machine learning problem solving as tasks. We suggest a way to model self-learning machines using a workflow of machine learning tasks. Our proposed machine learning model based on task ontology, sets up a way to group UML activities by task. It will also create and grow machine learning models on its own using rules to change common parts and structures (how elements connect and work together). (shrink)

This paper provides an analysis of the way in which two foundational principles of medical ethics–the trusted doctor and patient autonomy–can be undermined by the use of machine learning (ML) algorithms and addresses its legal significance. This paper can be a guide to both health care providers and other stakeholders about how to anticipate and in some cases mitigate ethical conflicts caused by the use of ML in healthcare. It can also be read as a road map (...) as to what needs to be done to achieve an acceptable level of explainability in an ML algorithm when it is used in a healthcare context. (shrink)

Phishing sites which expects to take the victims confidential data by diverting them to surf a fake website page that resembles a honest to goodness one is another type of criminal acts through the internet and its one of the especially concerns toward numerous areas including e-managing an account and retailing. Phishing site detection is truly an unpredictable and element issue including numerous components and criteria that are not stable. On account of the last and in addition ambiguities in arranging (...) sites because of the intelligent procedures programmers are utilizing, some keen proactive strategies can be helpful and powerful tools can be utilized, for example, fuzzy, neural system and data mining methods can be a successful mechanism in distinguishing phishing sites. We applied Random Forest (RF), one of the different types of machine learning based algorithms used for detection of Phishing websites. Finally we measured and compared the performance of the classifier in terms of accuracy. (shrink)

In the era of exponential data growth, the efficient migration of data in automotive manufacturing systems is a critical challenge for enterprises. Traditional approaches are often time-intensive and error-prone. This paper proposes an intelligent data transition framework leveraging machine learning algorithms to automate, optimize, and ensure the reliability of data migration processes in automotive manufacturing databases. By integrating supervised learning and reinforcement learning techniques, the framework identifies optimal migration paths, predicts potential bottlenecks, and ensures minimal (...) downtime. Experimental results demonstrate significant improvements in data transfer efficiency and accuracy compared to traditional methods. (shrink)

Predicting university student graduation is a beneficial tool for both students and institutions. With the help of this predictive capacity, students may make well-informed decisions about their academic and career paths, and institutions can proactively identify students who may not graduate and offer tailored support to ensure their success. The use of machine learning for predicting university student graduation has drawn more attention in recent years. Large datasets of student academic performance data can be used to train (...) class='Hi'>machine learning algorithms to identify patterns that are applicable in predicting future outcomes. In accordance with some studies, this approach predicts student graduation with an accuracy rate as high as 90%. Many systematic literature reviews (SLRs) have been conducted in this field, but there are still limitations, including not discussing the predictive models and algorithms used, a lack of coverage of the machine learning algorithms applied, small database coverage, keyword selection that does not cover all synonyms relevant to the investigation, and less specific data collection transparency. (shrink)

An important problem with machine learning is that when label number n>2, it is very difficult to construct and optimize a group of learning functions, and we wish that optimized learning functions are still useful when prior distribution P(x) (where x is an instance) is changed. To resolve this problem, the semantic information G theory, Logical Bayesian Inference (LBI), and a group of Channel Matching (CM) algorithms together form a systematic solution. MultilabelMultilabel A semantic channel (...) in the G theory consists of a group of truth functions or membership functions. In comparison with likelihood functions, Bayesian posteriors, and Logistic functions used by popular methods, membership functions can be more conveniently used as learning functions without the above problem. In Logical Bayesian Inference (LBI), every label’s learning is independent. For Multilabel learning, we can directly obtain a group of optimized membership functions from a big enough sample with labels, without preparing different samples for different labels. A group of Channel Matching (CM) algorithms are developed for machine learning. For the Maximum Mutual Information (MMI) classification of three classes with Gaussian distributions on a two-dimensional feature space, 2-3 iterations can make mutual information between three classes and three labels surpass 99% of the MMI for most initial partitions. For mixture models, the Expectation-Maxmization (EM) algorithm is improved and becomes the CM-EM algorithm, which can outperform the EM algorithm when mixture ratios are imbalanced, or local convergence exists. The CM iteration algorithm needs to combine neural networks for MMI classifications on high-dimensional feature spaces. LBI needs further studies for the unification of statistics and logic. (shrink)

Edge computing, combined with machine learning (ML), is emerging as a transformative paradigm for handling the data deluge generated by the Internet of Things (IoT) devices. Traditional cloud computing is often inadequate for the low-latency, high-throughput demands of IoT applications, especially in real-time analytics. By processing data locally at the edge of the network, edge computing reduces latency, enhances privacy, and alleviates the bandwidth burden on centralized cloud servers. The integration of ML algorithms into edge devices further (...) augments the decision-making process by enabling real-time data analytics, improving operational efficiency, and delivering immediate insights. This paper explores the synergy between edge computing, ML, and IoT, discussing how real-time data analytics at the edge can transform industries such as smart cities, healthcare, and industrial automation. We also present challenges, future directions, and practical applications of machine learning-based real-time analytics for edge computing in IoT ecosystems. (shrink)

Coronary Heart Disease (CHD) is one of the leading causes of death nowadays. Prediction of the disease at an early stage is crucial for many health care providers to protect their patients and save lives and costly hospitalization resources. The use of machine learning in the prediction of serious disease events using routine medical records has been successful in recent years. In this paper, a comparative analysis of different machine learning techniques that can accurately predict the (...) occurrence of CHD events from clinical data was performed. Four machine learning classifiers, namely Logistic Regression, Support Vector Machine (SVM), K- Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) Neural Networks were identified and applied to a dataset of 462 medical instances and 9 features as well as the class feature from the South African Heart Disease data retrieved from the KEEL repository. The dataset consists of 302 records of healthy patients and 160 records of patients who suffer from CHD. In order to handle the imbalanced classification problem, the K-means algorithm along with Synthetic Minority Oversampling TEchnique (SMOTE) was used in this study. The empirical results of applying the four machine learning classifiers on the oversampled dataset have been very promising. The results reported using different evaluation metrics showed that SVM has achieved the highest overall prediction performance. (shrink)

Agriculture contributes a significant amount to the economy of India due to the dependence on humanbeings for their survival. The main obstacle to food security is population expansion leading to rising demand for food. Farmers must produce more on the same land to boost the supply. Through crop yield prediction, technology can assist farmers in producing more. This paper’s primary goal is to predict crop yield utilizing the variables of rainfall, crop, meteorological conditions, area, production, and yield that have posed (...) a serious threat to the long-term viability of agriculture. Crop yield prediction is a decision-support tool that uses machine learning and deep learning that can be used to make decisions about which crops to produce and what to do in the crop’s growing season. It can decide which crops to produce and what to do in the crop’s growing season. Regardless of the distracting environment, machine learning and deep learning algorithms are utilized in crop selection to reduce agricultural yield output losses. To estimate the agricultural yield, machine learning techniques: decision tree, random forest, and XGBoost regression; deep learning techniques- convolutional neural network and long-short term memory network have been used. Accuracy, root mean square error, mean square error, mean absolute error, standard deviation, and losses are compared. Other machine learning and deep learning methods fall short compared to the random forest and convolutional neural network. The random forest has a maximum accuracy of 98.96%, mean absolute error of 1.97, root mean square error of 2.45, and standard deviation of 1.23. The convolutional neural network has been evaluated with a minimum loss of 0.00060. Consequently, a model is developed that, compared to other algorithms, predicts the yield quite well. The findings are then analyzed using the root mean square error metric to understand better how the model’s errors compare to those of the other methods. (shrink)

As more people utilise online banking services, the large losses that banks and other financial institutions sustained because of new bank account (NBA) fraud are concerning. Machine learning (ML) models have faced significant challenges because to the intrinsic skewness and rarity of NBA fraud cases. This occurs when the number of non-fraud instances exceeds the number of fraud instances, causing the ML models to miss and mistakenly regard fraud as non-fraud instances. Customers' confidence and trust may be damaged (...) by such mistakes. Existing research addresses the skewness of fraud datasets by considering fraud patterns rather than possible losses of NBA fraud risk characteristics. This study suggests NBA fraud detection in the framework of value-at-risk, a risk metric that views fraud cases as the worst-case situation. Value-at-risk models risk features as a skewed tail distribution and estimates possible losses of such attributes using historical simulation. ML was used to classify the risk-return characteristics derived from value-at-risk on the bank account fraud (BAF) dataset. The value-at-risk assigns weight to the skewed NBA fraud cases by managing the fraud skewness with an adjusted threshold probability range. The effectiveness of the fraud detection algorithm was assessed using a unique detection rate (DT) metric that takes risk fraud characteristics into account. A K-nearest neighbour with a true positive (TP) rate of 0.95 and a DT rate of 0.9406 is used to create an enhanced fraud detection model. Value-at-risk offers a clever way to create data-driven standards for fraud risk management within a reasonable loss tolerance in the banking industry. (shrink)

In contemporary healthcare, medical image categorization is essential for illness prediction, diagnosis, and therapy planning. The emergence of digital imaging technology has led to a significant increase in research into the use of machine learning (ML) techniques for the categorization of images in medical data. We provide a thorough summary of recent developments in this area in this review, using knowledge from the most recent research and cutting-edge methods.We begin by discussing the unique challenges and opportunities associated with (...) medical image classification, including the complexity of anatomical structures, variability in imaging modalities, and the need for interpretability and reliability in clinical settings. Subsequently, we survey a wide range of ML algorithms and techniques employed for medical image classification, including traditional methods such as support vector machines and k-nearest neighbors, as well as deep learning approaches like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Furthermore, we examine key considerations in dataset preparation, feature extraction, and model evaluation specific to medical image classification tasks. We highlight the importance of large, annotated datasets, transfer learning, and data augmentation techniques in enhancing model performance and generalization. (shrink)

It is well known that the unpredictable speech production brought on by stress from the task at hand has a significant negative impact on the performance of speech processing algorithms. Speech therapy benefits from being able to detect stress in speech. Speech processing performance suffers noticeably when perceptually produced stress causes variations in speech production. Using the acoustic speech signal to objectively characterize speaker stress is one method for assessing production variances brought on by stress. Real-world complexity and ambiguity (...) make it difficult for decision-makers to express their conclusions with clarity in their speech. In particular, the Neutrosophic speech algorithm is used to encode the language variables because they cannot be computed directly. Neutrosophic sets are used to manage indeterminacy in a practical situation. Existing algorithms are used except for stress on Neutrosophic speech recognition. The creation of algorithms that calculate, categorize, or differentiate between different stress circumstances. Understanding stress and developing strategies to combat its effects on speech recognition and human-computer interaction system are the goals of this recognition. (shrink)

The demands of fair machine learning are often expressed in probabilistic terms. Yet, most of the systems of concern are deterministic in the sense that whether a given subject will receive a given score on the basis of their traits is, for all intents and purposes, either zero or one. What, then, can justify this probabilistic talk? We argue that the statistical reference classes used in fairness measures can be understood as defining the probability that hypothetical persons, who (...) are representative of social roles, will receive certain goods. We call these hypothetical persons “representative individuals.” We claim that what we owe to actual, concrete individuals—whose individual chances of receiving the good in the system might be extreme (i.e., either zero or one)—is that their representative individual has an appropriate probability of receiving the good in question. While less immediately intuitive than other approaches, we argue that the representative individual approach has important advantages over other ways of making sense of this probabilistic talk in the context of fair machine learning. (shrink)

Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML (...) leverages low-cost and globally accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies. (shrink)

Machine learning (ML) has emerged as a powerful tool for transforming data-driven decision-making across various industries. By leveraging large volumes of data and advanced algorithms, machine learning models can uncover insights, make predictions, and enable businesses to make more informed decisions. This paper explores how machine learning is revolutionizing decision-making processes, enhancing efficiency, accuracy, and predictive capabilities. It also examines the key challenges, opportunities, and future directions for the integration of machine (...) class='Hi'>learning into decision-making frameworks. (shrink)

Breast cancer is one of the leading causes of death among women worldwide. Early detection plays a crucial role in improving survival rates, and machine learning (ML) provides powerful tools for identifying cancerous tumors in medical imaging and diagnostic data. This paper explores various machine learning techniques used for breast cancer detection, with a particular focus on the Wisconsin Breast Cancer Dataset (WBCD). We evaluate several classification models, including Logistic Regression (LR), Support Vector Machine (SVM), (...) k-Nearest Neighbors (k-NN), and Random Forest (RF), for their ability to accurately classify benign and malignant tumors. The goal of this paper is to demonstrate the use of supervised learning techniques to enhance early breast cancer detection. (shrink)