Results for 'Machine Learning Algorithms'

979 found
Order:
  1. Clinical applications of machine learning algorithms: beyond the black box.David S. Watson, Jenny Krutzinna, Ian N. Bruce, Christopher E. M. Griffiths, Iain B. McInnes, Michael R. Barnes & Luciano Floridi - 2019 - British Medical Journal 364:I886.
    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.
    Download  
     
    Export citation  
     
    Bookmark   17 citations  
  2.  75
    Leveraging Machine Learning Algorithms for Medical Image Classification Introduction.Ugochukwu Llodinso - manuscript
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  3. Efficient Machine Learning Algorithm for Future Gold Price Prediction.A. Ravikumar - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-18.
    The project titled "Efficient Machine Learning Algorithm for Future Gold Price Prediction" focuses on the development of a machine learning model that can accurately predict future gold prices using historical data and various economic indicators. Gold has long been regarded as a safe-haven asset, and its price is influenced by multiple factors, including global economic conditions, inflation rates, interest rates, and geopolitical events. This research aims to design and implement a robust machine learning model (...)
    Download  
     
    Export citation  
     
    Bookmark  
  4.  63
    OPTIMIZING CONSUMER BEHAVIOUR ANALYTICS THROUGH ADVANCED MACHINE LEARNING ALGORITHMS.Yoheswari S. - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):362-370.
    Consumer behavior analytics has become a pivotal aspect for businesses to understand and predict customer preferences and actions. The advent of machine learning (ML) algorithms has revolutionized this field by providing sophisticated tools for data analysis, enabling businesses to make data-driven decisions. However, the effectiveness of these ML algorithms significantly hinges on the optimization techniques employed, which can enhance model accuracy and efficiency. This paper explores the application of various optimization techniques in consumer behaviour analytics using (...)
    Download  
     
    Export citation  
     
    Bookmark  
  5. Machine learning in scientific grant review: algorithmically predicting project efficiency in high energy physics.Vlasta Sikimić & Sandro Radovanović - 2022 - European Journal for Philosophy of Science 12 (3):1-21.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark   2 citations  
  6. OPTIMIZING CONSUMER BEHAVIOUR ANALYTICS THROUGH ADVANCED MACHINE LEARNING ALGORITHMS.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):360-368.
    Consumer behavior analytics has become a pivotal aspect for businesses to understand and predict customer preferences and actions. The advent of machine learning (ML) algorithms has revolutionized this field by providing sophisticated tools for data analysis, enabling businesses to make data-driven decisions. However, the effectiveness of these ML algorithms significantly hinges on the optimization techniques employed, which can enhance model accuracy and efficiency. This paper explores the application of various optimization techniques in consumer behaviour analytics using (...)
    Download  
     
    Export citation  
     
    Bookmark  
  7. At Noon: (Post)Nihilistic Temporalities in The Age of Machine-Learning Algorithms That Speak.Talha Issevenler - 2023 - The Agonist : A Nietzsche Circle Journal 17 (2):63–72.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  8.  77
    Advanced Driver Drowsiness Detection Model Using Optimized Machine Learning Algorithms.S. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):396-402.
    Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an (...)
    Download  
     
    Export citation  
     
    Bookmark  
  9.  12
    Crime Type and Occurrence Prediction Using Machine Learning Algorithm.R. Senthilkumar - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-18.
    This project aims to develop a predictive system capable of identifying crime types and predicting their occurrences based on historical crime data. The system uses advanced machine learning techniques to analyze factors such as geographic location, time, and other socio-economic variables, enabling authorities to better understand crime patterns and trends. By training models on vast datasets of past criminal activities, the system predicts not only the likely occurrence of specific crime types but also identifies high-risk locations and times, (...)
    Download  
     
    Export citation  
     
    Bookmark  
  10. Sarcasm Detection in Headline News using Machine and Deep Learning Algorithms.Alaa Barhoom, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (4):66-73.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark   12 citations  
  11. OPTIMIZED CARDIOVASCULAR DISEASE PREDICTION USING MACHINE LEARNING ALGORITHMS.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):350-359.
    Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and (...)
    Download  
     
    Export citation  
     
    Bookmark  
  12. Implementation of Data Mining on a Secure Cloud Computing over a Web API using Supervised Machine Learning Algorithm.Tosin Ige - 2022 - International Journal of Advanced Computer Science and Applications 13 (5):1 - 4.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  13. Human Stress Detection Based on Sleeping Habits Using Machine Learning Algorithms.S. Venkatesh - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-15.
    Stress has become a significant concern in today’s fast-paced world, affecting individuals’ physical and mental well-being. This project, titled Human Stress Detection Based on Sleeping Habits Using Machine Learning Algorithms, aims to address this issue by leveraging data-driven insights to identify stress levels. The proposed system analyzes sleeping patterns, including sleep duration, interruptions, and quality, to classify stress levels effectively. By utilizing advanced machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression, and (...)
    Download  
     
    Export citation  
     
    Bookmark  
  14. Egalitarian Machine Learning.Clinton Castro, David O’Brien & Ben Schwan - 2023 - Res Publica 29 (2):237–264.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark   4 citations  
  15. Prediction of Heart Disease Using a Collection of Machine and Deep Learning Algorithms.Ali M. A. Barhoom, Abdelbaset Almasri, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (4):1-13.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark   7 citations  
  16. Fair machine learning under partial compliance.Jessica Dai, Sina Fazelpour & Zachary Lipton - 2021 - In Jessica Dai, Sina Fazelpour & Zachary Lipton (eds.), Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. pp. 55–65.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  17. Machine learning in bail decisions and judges’ trustworthiness.Alexis Morin-Martel - 2023 - AI and Society:1-12.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark   3 citations  
  18. MACHINE LEARNING IMPROVED ADVANCED DIAGNOSIS OF SOFT TISSUES TUMORS.M. Bavadharani - 2022 - Journal of Science Technology and Research (JSTAR) 3 (1):112-123.
    Delicate Tissue Tumors (STT) are a type of sarcoma found in tissues that interface, backing, and encompass body structures. Due to their shallow recurrence in the body and their extraordinary variety, they seem, by all accounts, to be heterogeneous when seen through Magnetic Resonance Imaging (MRI). They are effortlessly mistaken for different infections, for example, fibro adenoma mammae, lymphadenopathy, and struma nodosa, and these indicative blunders have an extensive unfavorable impact on the clinical treatment cycle of patients. Analysts have proposed (...)
    Download  
     
    Export citation  
     
    Bookmark  
  19. Fraudulent Financial Transactions Detection Using Machine Learning.Mosa M. M. Megdad, Samy S. Abu-Naser & Bassem S. Abu-Nasser - 2022 - International Journal of Academic Information Systems Research (IJAISR) 6 (3):30-39.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark   26 citations  
  20. Machine Learning-Based Intrusion Detection Framework for Detecting Security Attacks in Internet of Things.Jones Serena - manuscript
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  21. Credit Score Classification Using Machine Learning.Mosa M. M. Megdad & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (5):1-10.
    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%, (...)
    Download  
     
    Export citation  
     
    Bookmark  
  22. (2 other versions)The explanation game: a formal framework for interpretable machine learning.David S. Watson & Luciano Floridi - 2020 - Synthese 198 (10):1–⁠32.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark   17 citations  
  23. Diachronic and synchronic variation in the performance of adaptive machine learning systems: the ethical challenges.Joshua Hatherley & Robert Sparrow - 2023 - Journal of the American Medical Informatics Association 30 (2):361-366.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  24. Consequences of unexplainable machine learning for the notions of a trusted doctor and patient autonomy.Michal Klincewicz & Lily Frank - 2020 - Proceedings of the 2nd EXplainable AI in Law Workshop (XAILA 2019) Co-Located with 32nd International Conference on Legal Knowledge and Information Systems (JURIX 2019).
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  25.  96
    Machine Learning-Based Cyberbullying Detection System with Enhanced Accuracy and Speed.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):421-429.
    The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify (...)
    Download  
     
    Export citation  
     
    Bookmark  
  26. Movie Recommendation System using Machine Learning Techniques.G. H. Ram Ganesh - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-20.
    The Movie Recommendation System using Machine Learning Techniques is a data-driven approach designed to provide personalized movie suggestions based on user preferences and historical data. This system leverages advanced machine learning algorithms, including collaborative filtering, content-based filtering, and hybrid methods, to predict the most relevant movies for individual users. The system's primary goal is to enhance user experience by recommending movies that align with their tastes, thereby promoting user engagement and satisfaction. The recommendation process starts (...)
    Download  
     
    Export citation  
     
    Bookmark  
  27.  12
    Crime Prediction Using Machine Learning and Deep Learning.S. Venkatesh - 2024 - Journal of Science Technology and Research (JSTAR) 6 (1):1-13.
    Crime prediction has emerged as a critical application of machine learning (ML) and deep learning (DL) techniques, aimed at assisting law enforcement agencies in reducing criminal activities and improving public safety. This project focuses on developing a robust crime prediction system that leverages the power of both ML and DL algorithms to analyze historical crime data and predict potential future incidents. By integrating a combination of classification and clustering techniques, our system identifies crime-prone areas, trends, and (...)
    Download  
     
    Export citation  
     
    Bookmark  
  28. Heart Disease Prediction Using Machine Learning Techniques.D. Devendran - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-17.
    Heart disease remains one of the leading causes of mortality worldwide. Early prediction and diagnosis are critical in preventing severe outcomes and improving the quality of life for patients. This project focuses on developing a robust heart disease prediction system using machine learning techniques. By analyzing a comprehensive dataset consisting of various patient attributes such as age, sex, blood pressure, cholesterol levels, and other medical parameters, the system aims to predict the likelihood of a patient having heart disease. (...)
    Download  
     
    Export citation  
     
    Bookmark  
  29. Exploring Machine Learning Techniques for Coronary Heart Disease Prediction.Hisham Khdair - 2021 - International Journal of Advanced Computer Science and Applications 12 (5):28-36.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  30. SMS Spam Detection using Machine Learning.R. T. Subhalakshmi - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-19.
    SMS spam has become a widespread issue, leading to significant inconvenience and security risks for users. Detecting and filtering out such spam messages is crucial for enhancing the user experience and ensuring privacy. TThe dataset used for training and testing the model consists of labeled SMS messages, which are processed using feature extraction techniques such as TF-IDF and word tokenization. Several machine learning algorithms, including Naive Bayes, Support Vector Machine (SVM), and Random Forest, are evaluated to (...)
    Download  
     
    Export citation  
     
    Bookmark  
  31. Medical Image Classification with Machine Learning Classifier.Destiny Agboro - forthcoming - Journal of Computer Science.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  32.  82
    Secure Cloud Storage with Machine Learning-Optimized Attribute-Based Access Control Protocols.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):420-435.
    This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. Exploring sophisticated machine learning algorithms for (...)
    Download  
     
    Export citation  
     
    Bookmark  
  33. Automated Cyberbullying Detection Framework Using NLP and Supervised Machine Learning Models.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):421-432.
    The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify (...)
    Download  
     
    Export citation  
     
    Bookmark  
  34. Neutrosophic speech recognition Algorithm for speech under stress by Machine learning.Florentin Smarandache, D. Nagarajan & Said Broumi - 2023 - Neutrosophic Sets and Systems 53.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  35. Widening Access to Applied Machine Learning With TinyML.Vijay Reddi, Brian Plancher, Susan Kennedy, Laurence Moroney, Pete Warden, Lara Suzuki, Anant Agarwal, Colby Banbury, Massimo Banzi, Matthew Bennett, Benjamin Brown, Sharad Chitlangia, Radhika Ghosal, Sarah Grafman, Rupert Jaeger, Srivatsan Krishnan, Maximilian Lam, Daniel Leiker, Cara Mann, Mark Mazumder, Dominic Pajak, Dhilan Ramaprasad, J. Evan Smith, Matthew Stewart & Dustin Tingley - 2022 - Harvard Data Science Review 4 (1).
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  36. OPTIMIZED DRIVER DROWSINESS DETECTION USING MACHINE LEARNING TECHNIQUES.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):395-400.
    Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an (...)
    Download  
     
    Export citation  
     
    Bookmark  
  37. OPTIMIZED CYBERBULLYING DETECTION IN SOCIAL MEDIA USING SUPERVISED MACHINE LEARNING AND NLP TECHNIQUES.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):421-435.
    The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify (...)
    Download  
     
    Export citation  
     
    Bookmark  
  38.  30
    Network Intrusion Detection using Machine Learning.B. Ravinder Reddy - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (4):1-15.
    With the growing sophistication and frequency of cyberattacks, there is a critical need for effective systems that can detect and prevent breaches in real time. The AI/ML-based Network Intrusion Detection System (NIDS) addresses this need by analyzing traffic patterns to identify security breaches in firewalls, routers, and network infrastructures. By integrating machine learning algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest—the system is able to detect both known cyber threats and previously unseen attack vectors. (...)
    Download  
     
    Export citation  
     
    Bookmark  
  39. Big Data Analytics in Healthcare: Exploring the Role of Machine Learning in Predicting Patient Outcomes and Improving Healthcare Delivery.Federico Del Giorgio Solfa & Fernando Rogelio Simonato - 2023 - International Journal of Computations Information and Manufacturing (Ijcim) 3 (1):1-9.
    Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to (...)
    Download  
     
    Export citation  
     
    Bookmark  
  40.  79
    Intelligent Driver Drowsiness Detection System Using Optimized Machine Learning Models.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):397-405.
    : Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates (...)
    Download  
     
    Export citation  
     
    Bookmark  
  41. Semantic Information G Theory and Logical Bayesian Inference for Machine Learning.Chenguang Lu - 2019 - Information 10 (8):261.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark   3 citations  
  42. The Use and Misuse of Counterfactuals in Ethical Machine Learning.Atoosa Kasirzadeh & Andrew Smart - 2021 - In Atoosa Kasirzadeh & Andrew Smart (eds.), ACM Conference on Fairness, Accountability, and Transparency (FAccT 21).
    The use of counterfactuals for considerations of algorithmic fairness and explainability is gaining prominence within the machine learning community and industry. This paper argues for more caution with the use of counterfactuals when the facts to be considered are social categories such as race or gender. We review a broad body of papers from philosophy and social sciences on social ontology and the semantics of counterfactuals, and we conclude that the counterfactual approach in machine learning fairness (...)
    Download  
     
    Export citation  
     
    Bookmark   3 citations  
  43.  25
    CYBERBULLYING DETECTION AND ANALYSIS USING MACHINE LEARNING.Shailaja DrK - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (4):1-14.
    Increased online use and allowing users to engage with groups such as digital networking have contributed to the growth of hacking. Online abuse is a new type of harassment that has lately become more prevalent as online communities have grown in popularity. It tends to send messages which included defamatory claims or vocally harassing someone while in the internet group. Only if modern civilization recognizes harassment as it truly is, countless of hidden sufferers may continue to suffer. There have been (...)
    Download  
     
    Export citation  
     
    Bookmark  
  44. Privacy and Machine Learning- Based Artificial Intelligence: Philosophical, Legal, and Technical Investigations.Haleh Asgarinia - 2024 - Dissertation, Department of Philisophy, University of Twente
    This dissertation consists of five chapters, each written as independent research papers that are unified by an overarching concern regarding information privacy and machine learning-based artificial intelligence (AI). This dissertation addresses the issues concerning privacy and AI by responding to the following three main research questions (RQs): RQ1. ‘How does an AI system affect privacy?’; RQ2. ‘How effectively does the General Data Protection Regulation (GDPR) assess and address privacy issues concerning both individuals and groups?’; and RQ3. ‘How can (...)
    Download  
     
    Export citation  
     
    Bookmark  
  45.  12
    A Deep Prediction of Chronic Kidney Disease by Employing Machine Learning Method.R. Senthilkumar - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-20.
    Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning (...)
    Download  
     
    Export citation  
     
    Bookmark  
  46. An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey. [REVIEW]Tosin Ige, Christopher Kiekintveld & Aritran Piplai - forthcoming - Proceedings of the IEEE:11.
    To secure computers and information systems from attackers taking advantage of vulnerabilities in the system to commit cybercrime, several methods have been proposed for real-time detection of vulnerabilities to improve security around information systems. Of all the proposed methods, machine learning had been the most effective method in securing a system with capabilities ranging from early detection of software vulnerabilities to real-time detection of ongoing compromise in a system. As there are different types of cyberattacks, each of the (...)
    Download  
     
    Export citation  
     
    Bookmark  
  47. Fake Profile Detection on Social Networking Websites using Machine Learning.R. T. Subhalakshmi - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-18.
    With the increasing popularity of social networking websites, the problem of fake profiles has become a significant concern. Fake profiles, often created by malicious actors for fraudulent purposes, pose threats to user privacy, security, and trustworthiness of online platforms. This project proposes a machine learning-based approach to detect fake profiles on social networking websites. By analyzing various features such as user activity patterns, profile attributes, and network connections, the model identifies potential fake profiles with high accuracy. The system (...)
    Download  
     
    Export citation  
     
    Bookmark  
  48.  73
    Hybrid Cloud-Machine Learning Framework for Efficient Cardiovascular Disease Risk Prediction and Treatment Planning.Kannan K. S. - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):460-480.
    Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such (...)
    Download  
     
    Export citation  
     
    Bookmark  
  49. Prognostic System for Heart Disease using Machine Learning: A Review.R. Senthilkumar - 2021 - Journal of Science Technology and Research (JSTAR) 2 (1):33-38.
    In today’s world it became difficult for daily routine check-up. The Heart disease system is an end user support and online consultation project. Here the motto behind it is to make a person to know about their heart related problem and according to it formulate them how much vital the disease is. It will be easy to access and keep track of their respective health. Thus, it’s important to predict the disease as earliest. Attributes such as Bp, Cholesterol, Diabetes are (...)
    Download  
     
    Export citation  
     
    Bookmark  
  50.  58
    Machine Learning-Driven Optimization for Accurate Cardiovascular Disease Prediction.Yoheswari S. - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):350-359.
    The research methodology involves data preprocessing, feature engineering, model training, and performance evaluation. We employ optimization methods such as Genetic Algorithms and Grid Search to fine-tune model parameters, ensuring robust and generalizable models. The dataset used includes patient medical records, with features like age, blood pressure, cholesterol levels, and lifestyle habits serving as inputs for the ML models. Evaluation metrics, including accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC), assess the model's predictive power.
    Download  
     
    Export citation  
     
    Bookmark  
1 — 50 / 979