Results for 'Machine learning'

989 found
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  1.  46
    Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration.Gopinathan Vimal Raja - 2022 - International Journal of Multidisciplinary Research in Science, Engineering and Technology 5 (8):1336-1339.
    This paper presents a machine learning-based framework for real-time short-term snowfall forecasting by integrating atmospheric and topographic data. The model uses real-time meteorological data such as temperature, humidity, and pressure, along with terrain data like elevation and land cover, to predict snowfall occurrence within a 12-hour forecast window. Random Forest (RF) and Support Vector Machine (SVM) models are employed to process these multi-source inputs, demonstrating a significant improvement in prediction accuracy over traditional methods. Experimental results show that (...)
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  2. Machine Learning-Based Diabetes Prediction: Feature Analysis and Model Assessment.Fares Wael Al-Gharabawi & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):10-17.
    This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed (...)
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  3. 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 (...)
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  4.  70
    Utilizing Machine Learning for Automated Data Normalization in Supermarket Sales Databases.Gopinathan Vimal Raja - 2025 - International Journal of Advanced Research in Education and Technology(Ijarety) 10 (1):9-12.
    Data normalization is a crucial step in database management systems (DBMS), ensuring consistency, minimizing redundancy, and enhancing query performance. Traditional methods of normalization in supermarket sales databases often demand significant manual effort and domain expertise, making the process time-consuming and prone to errors. This paper introduces an innovative machine learning (ML)-based framework to automate data normalization in supermarket sales databases. The proposed approach utilizes both supervised and unsupervised ML techniques to identify functional dependencies, detect anomalies, and suggest optimal (...)
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  5.  48
    Using Machine Learning tools to Calculate Multi Slice Multi Echo (MSME) Score for Alzheimer's Diagnosis.Yalamati Sreedhar - 2024 - International Journal of Innovations in Scientific Engineering 19 (1):49-67.
    Alzheimer's disease (AD) poses a significant public health challenge. The hippocampus is one of the most affected brain regions and a readily accessible biomarker for diagnosis through MRI imaging in machine learning applications. However, utilizing entire MRI image slices in machine learning for AD classification has shown reduced accuracy. This study introduces the novel 'select slices' method, which involves identifying and focusing on specific landmarks within the hippocampus region in MRI images. This approach aims to improve (...)
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  6. 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.
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  7. Machine Learning, Misinformation, and Citizen Science.Adrian K. Yee - 2023 - European Journal for Philosophy of Science 13 (56):1-24.
    Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.
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  8.  38
    Quantum Machine Learning: Harnessing Quantum Algorithms for Supervised and Unsupervised Learning.Mittal Mohit - 2022 - International Journal of Innovative Research in Science, Engineering and Technology 11 (9):11631-11637.
    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 (...)
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  9.  75
    Machine Learning for Characterization and Analysis of Microstructure and Spectral Data of Materials.Venkataramaiah Gude - 2023 - International Journal of Intelligent Systems and Applications in Engineering 12 (21):820 - 826.
    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. (...)
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  10. (1 other version)Machine Learning and Irresponsible Inference: Morally Assessing the Training Data for Image Recognition Systems.Owen C. King - 2019 - In Matteo Vincenzo D'Alfonso & Don Berkich, On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence. Springer Verlag. pp. 265-282.
    Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in (...)
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  11. Understanding from Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.
    Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models (...)
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  12.  77
    Breast Cancer Detection Using Machine Learning.Shifa A. M. Amrutha D. - 2024 - International Journal of Innovative Research in Science, Engineering and Technology 13 (11):19401-19406.
    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), (...)
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  13. The Use of Machine Learning Methods for Image Classification in Medical Data.Destiny Agboro - forthcoming - International Journal of Ethics.
    Integrating medical imaging with computing technologies, such as Artificial Intelligence (AI) and its subsets: Machine learning (ML) and Deep Learning (DL) has advanced into an essential facet of present-day medicine, signaling a pivotal role in diagnostic decision-making and treatment plans (Huang et al., 2023). The significance of medical imaging is escalated by its sustained growth within the realm of modern healthcare (Varoquaux and Cheplygina, 2022). Nevertheless, the ever-increasing volume of medical images compared to the availability of imaging (...)
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  14.  32
    Machine Learning for Improving water Quality Monitoring and Management.Mariam El-Sayed Ahmed Nour, Ahmed Mostafa - 2025 - International Journal of Multidisciplinary and Scientific Emerging Research 13 (2):914-917.
    Water is a fundamental resource for life, yet maintaining its quality remains a significant global challenge due to pollution from industrial, agricultural, and domestic sources. Traditional water quality monitoring methods are time-consuming and limited in scope. Machine Learning (ML) offers advanced capabilities for analyzing complex datasets, predicting pollution levels, and optimizing water resource management in real-time. This paper explores the integration of ML in water quality monitoring and management, reviewing current applications, methodologies, and future research directions. We present (...)
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  15.  67
    Machine Learning for Autonomous Systems: Navigating Safety, Ethics, and Regulation In.Madhu Aswathy - 2025 - International Journal of Advanced Research in Education and Technology 12 (2):458-463.
    Autonomous systems, powered by machine learning (ML), have the potential to revolutionize various industries, including transportation, healthcare, and robotics. However, the integration of machine learning in autonomous systems raises significant challenges related to safety, ethics, and regulatory compliance. Ensuring the reliability and trustworthiness of these systems is crucial, especially when they operate in environments with high risks, such as self-driving cars or medical robots. This paper explores the intersection of machine learning and autonomous systems, (...)
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  16. 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 outcome is a strong (...)
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  17.  52
    Machine Learning For Autonomous Systems: Navigating Safety, Ethics, and Regulation In.Saurav Choure Aswathy Madhu, Ankita Shinde - 2025 - International Journal of Innovative Research in Computer and Communication Engineering 13 (2):1680-1685.
    Autonomous systems, powered by machine learning (ML), have the potential to revolutionize various industries, including transportation, healthcare, and robotics. However, the integration of machine learning in autonomous systems raises significant challenges related to safety, ethics, and regulatory compliance. Ensuring the reliability and trustworthiness of these systems is crucial, especially when they operate in environments with high risks, such as self-driving cars or medical robots. This paper explores the intersection of machine learning and autonomous systems, (...)
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  18. Reliability in Machine Learning.Thomas Grote, Konstantin Genin & Emily Sullivan - 2024 - Philosophy Compass 19 (5):e12974.
    Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning – as far as they are concerned with reliability.
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  19.  64
    MACHINE LEARNING ALGORITHMS FOR REALTIME MALWARE DETECTION.Sharma Sidharth - 2017 - Journal of Artificial Intelligence and Cyber Security (Jaics) 1 (1):12-16.
    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 (...) 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)
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  20.  43
    Machine Learning Meets Network Management and Orchestration in Edge-Based Networking Paradigms": The Integration of Machine Learning for Managing and Orchestrating Networks at the Edge, where Real-Time Decision-Making is C.Odubade Kehinde Santhosh Katragadda - 2022 - International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 11 (4):1635-1645.
    Integrating machine learning (ML) into network management and orchestration has revolutionized edgebased networking paradigms, where real-time decision-making is critical. Traditional network management approaches often struggle with edge environments' dynamic and resource-constrained nature. By leveraging ML algorithms, networks at the edge can achieve enhanced efficiency, automation, and adaptability in areas such as traffic prediction, resource allocation, and anomaly detection (Wang et al., 2021). Supervised and unsupervised learning techniques facilitate proactive network optimization, reducing latency and improving quality of service (...)
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  21. 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 experiments (...)
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  22.  33
    Developing Machine Learning Models for Real-Time Fraud Detection in Online Transactions.R. S. Mandlo Mayuri Sunhare - 2025 - International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 14 (2):465-472.
    The increasing volume of online transactions has heightened the risk of fraud, making real-time fraud detection crucial for safeguarding financial systems. This paper explores the development and application of machine learning (ML) models for detecting fraudulent activities in real-time online transactions. The study investigates various ML algorithms, including supervised and unsupervised learning techniques, to identify patterns indicative of fraud. We evaluate the performance of different models based on accuracy, precision, recall, and F1-score. The results show that ensemble (...)
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  23. Fair machine learning under partial compliance.Jessica Dai, Sina Fazelpour & Zachary Lipton - 2021 - In Jessica Dai, Sina Fazelpour & Zachary Lipton, 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 (...)
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  24. 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 (...)
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  25.  73
    Harnessing Machine Learning to Predict Chronic Kidney Disease Risk.M. Arulselvan - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-16.
    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 algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD.
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  26. Autonomy and Machine Learning as Risk Factors at the Interface of Nuclear Weapons, Computers and People.S. M. Amadae & Shahar Avin - 2019 - In Vincent Boulanin, The Impact of Artificial Intelligence on Strategic Stability and Nuclear Risk: Euro-Atlantic Perspectives. Stockholm: SIPRI. pp. 105-118.
    This article assesses how autonomy and machine learning impact the existential risk of nuclear war. It situates the problem of cyber security, which proceeds by stealth, within the larger context of nuclear deterrence, which is effective when it functions with transparency and credibility. Cyber vulnerabilities poses new weaknesses to the strategic stability provided by nuclear deterrence. This article offers best practices for the use of computer and information technologies integrated into nuclear weapons systems. Focusing on nuclear command and (...)
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  27.  28
    Machine Learning Solutions for Cyberbullying Detection and Prevention on Social Media.Baditha Yasoda Krishna Gandi Pranith - 2025 - International Journal of Advanced Research in Education and Technology 12 (2):721-729.
    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 (...)
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  28.  78
    Machine Learning-Based Customer Churn Prediction Analysis.D. M. Manasa - 2024 - International Journal of Innovative Research in Computer and Communication Engineering 12 (5):8178-8183.
    Customer churn prediction is a critical challenge for businesses in retaining their customer base and optimizing their marketing strategies. Machine learning (ML) techniques offer a powerful approach to predict customer churn by analyzing historical customer behavior, demographic information, and usage patterns. This paper provides an overview of machine learning-based models used for predicting customer churn, including classification algorithms such as logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks. We explore how businesses (...)
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  29.  31
    Machine Learning in Seismology for Earthquake Prediction.Jack Martin George Evans, Lily Harris - 2025 - International Journal of Multidisciplinary and Scientific Emerging Research 13 (2):887-890.
    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 (...)
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  30.  16
    Machine Learning Driven Agricultural Portal Enhancing Crop Production and Decision-Making.Shaik Khasim Vali G. Nivetha Sri, Sayeedha Firdouse Khan, Rotte Sachin, Shaik Asif, Sangem Ruthvik - 2025 - International Journal of Innovative Research in Science Engineering and Technology 14 (4):8853-8861.
    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 (...)
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  31.  71
    Wine Quality Prediction using Machine Learning.Abhishek Rathor Prajwal Wadghule - 2025 - International Journal of Multidisciplinary Research in Science, Engineering and Technology 8 (2):986-989.
    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, (...)
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  32. 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 by (...)
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  33. Machine Learning and Job Posting Classification: A Comparative Study.Ibrahim M. Nasser & Amjad H. Alzaanin - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 4 (9):06-14.
    In this paper, we investigated multiple machine learning classifiers which are, Multinomial Naive Bayes, Support Vector Machine, Decision Tree, K Nearest Neighbors, and Random Forest in a text classification problem. The data we used contains real and fake job posts. We cleaned and pre-processed our data, then we applied TF-IDF for feature extraction. After we implemented the classifiers, we trained and evaluated them. Evaluation metrics used are precision, recall, f-measure, and accuracy. For each classifier, results were summarized (...)
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  34. Human Induction in Machine Learning: A Survey of the Nexus.Petr Spelda & Vit Stritecky - 2021 - ACM Computing Surveys 54 (3):1-18.
    As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target (...)
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  35. 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%, precession 100.0%,Recall100.0% (...)
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  36.  49
    Optimized Machine Learning Algorithms for Real-Time ECG Signal Analysis in IoT Networks.P. Selvaprasanth - 2024 - Journal of Theoretical and Computationsl Advances in Scientific Research (Jtcasr) 8 (1):1-7.
    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 (...) 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)
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  37.  31
    Intelligent Data Transition in Automotive Manufacturing Systems Using Machine Learning.Gopinathan Vimal Raja - 2024 - International Journal of Multidisciplinary and Scientific Emerging Research 12 (2):515-518.
    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. (...)
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  38. 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 and (...)
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  39.  29
    Leveraging Azure AI and Machine Learning For Predictive Analytics and Decision Support Systems IN.Vishnuvardhan S. Venkatapathi S. - 2024 - International Journal of Multidisciplinary Research in Science, Engineering and Technology 7 (6):11631-11636.
    In today's data-driven business environment, organizations increasingly rely on advanced analytics and decision support systems to gain a competitive edge. Azure AI and Machine Learning (ML) provide powerful tools for predictive analytics, enabling businesses to forecast trends, optimize processes, and make more informed decisions. By leveraging the capabilities of Microsoft Azure, businesses can integrate AI and ML into their decision-making processes, enhancing productivity and improving strategic outcomes. This paper explores how Azure's AI and ML tools can be applied (...)
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  40. Building Scalable MLOps: Optimizing Machine Learning Deployment and Operations.Vijayan Naveen Edapurath - 2024 - International Journal of Scientific Research in Engineering and Management 8 (10):1-5.
    As machine learning (ML) models become increasingly integrated into mission-critical applications and production systems, the need for robust and scalable MLOps (Machine Learning Operations) practices has grown significantly. This paper explores key strategies and best practices for building scalable MLOps pipelines to optimize the deployment and operation of machine learning models at an enterprise scale. It delves into the importance of automating the end-to-end lifecycle of ML models, from data ingestion and model training to (...)
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  41. Crop Recommender System Using Machine Learning Approach.A. Ravikumar - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-19.
    Agriculture plays a crucial role in the economic stability of many nations, and optimizing crop selection is essential for enhancing agricultural productivity and sustainability. The "Crop Recommender System Using Machine Learning Approach" aims to leverage machine learning techniques to provide precise crop recommendations based on various environmental and soil conditions. By incorporating factors such as soil composition, pH level, temperature, humidity, rainfall, and geographic location, this system suggests the most suitable crops for a given area. The (...)
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  42. 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 determine (...)
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  43. 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 (...)
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  44. A Machine Learning Approach to Chronic Kidney Disease Prediction.M. Sheik Dawood - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-15.
    The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of this project can (...)
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  45. Crop Price Prediction Using Machine Learning.P. Aparna - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (5):1-15.
    Ensuring agricultural profitability is a vital issue in developing countries like India, where over a third of the population earns their income directly or indirectly through agriculture. Estimating and evaluating crop yields is done globally to achieve high yields and appropriate pricing. However, there is no accurate procedure in place to provide farmers with insights on which crops should be grown. This project aims to predict crop prices by analysing historical data, such as precipitation, temperature, market prices, land area, and (...)
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  46. 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 efficacy (...)
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  47. Leveraging Machine Learning for Early Detection of Chronic Kidney Disease.A. Manoj Prabaharan - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-18.
    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 algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques (...)
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  48.  80
    AI Healthcare ChatBot_ using Machine Learning (13th edition).Brahmtej B. Bargali Akash S. Shinde, - 2024 - International Journal of Innovative Research in Science, Engineering and Technology 13 (12):20832-20837. Translated by Akash S Shinde.
    The rapid advancement of artificial intelligence (AI) and machine learning (ML) has led to significant innovations in the healthcare sector. One such development is AI-powered healthcare chatbots, which assist patients and medical professionals by providing medical guidance, symptom assessment, and appointment scheduling. This paper presents the design and implementation of an AI healthcare chatbot using machine learning techniques. The chatbot leverages natural language processing (NLP) and deep learning models to understand and respond to user queries (...)
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  49. 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. (...)
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  50. Machines learning values.Steve Petersen - 2020 - In S. Matthew Liao, Ethics of Artificial Intelligence. Oxford University Press.
    Whether it would take one decade or several centuries, many agree that it is possible to create a *superintelligence*---an artificial intelligence with a godlike ability to achieve its goals. And many who have reflected carefully on this fact agree that our best hope for a "friendly" superintelligence is to design it to *learn* values like ours, since our values are too complex to program or hardwire explicitly. But the value learning approach to AI safety faces three particularly philosophical puzzles: (...)
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