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328 articles for “Machine learning(ML)”
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Early Heart Disease Prediction Using Hybrid Machine Learning Techniques
Abstract: In the contemporary era, cardiovascular disease is one in all the most causes of death within the world. Estimating Heart problems i.e cardiopathy is a crucial challenge within the area of clinical data analysis. Large volumes of data produced by the healthcare sector have been proved to be useful for helping with decision-making and speculation, thanks to machine learning (ML).. Various studies help us to review and supply glimpse into …
Published in Journal of Microcontroller Engineering and Applications · Vol. 9, Issue 2, 2022 · pp. 35–41 Read article
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A Correlative Study of Machine Learning Algorithms
Abstract: Machine learning (ML) is the term used to define a systematic reading of algorithms and mathematical models of computer structure used to perform a specific task without precise programming. Learning algorithms in many programs we use every day. Nowadays, one of the reasons an online search engine like Google performs so effectively is because the learning algorithm has mastered the art of rating web sites. Additional uses for these algorithms …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 9, Issue 2, 2022 · pp. 30–35 Read article
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Educating Compilers to Learn: Utilizing Machine Learning for More Brilliant Code Optimization
Abstract: This study explores the use of machine learning (ML) approaches to compiler optimization. The now-traditional static compilation techniques are transformed into adaptive, dynamic systems capable of making context-specific advancements. Traditional compilers rely mostly on heuristic or rule-based optimization techniques. While these techniques work well in general cases, they consistently fail to adapt well within the limits of code structures that modern machines display. This limitation is especially acute in today's …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 50–54 Read article
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Integrating AI and ML in Tribology: A Review of Current Trends and Future Prospects
Abstract: This review paper explores the growing integration of artificial intelligence (AI) and machine learning (ML) within the field of tribology. Tribology, the study of friction, wear, and lubrication, is crucial for Improving the performance and longevity of mechanical systems. This review explores the role of AI and machine learning techniques, including artificial neural networks (ANNs), support vector machines (SVMs), and physics-informed machine learning (PIML)can be used to solve difficult tribological …
Published in Recent Trends in Fluid Mechanics · Vol. 12, Issue 3, 2025 · pp. 56–60 Read article
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Comparison of Machine Learning Classification Algorithms for Prediction of Early-stage Diabetes
Abstract: Diabetes is the most widespread and gruesome disease spread all around the globe. It is a chronic metabolic disease which has mild symptoms which are hard to spot at the initial stage. It is well known that diabetes causes a high blood sugar level. This is because insulin is not able to carry sugar from your blood into your cells in order to be stored or used for energy as …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 10, Issue 1, 2023 · pp. 1–5 Read article
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Deep Learning Applications in Bone Fracture Detection for Improved Radiographic Diagnostics
Abstract: Bone fracture detection is a critical aspect of medical diagnostics, traditionally relying on manual interpretation of radiographic images by experienced radiologists. This discipline has undergone a revolution with the introduction of machine learning (ML), which can improve accuracy, shorten diagnosis times, and lessen human error. This study investigates the use of different machine learning methods to enhance and automate the identification of bone fractures in radiography pictures. We utilized a …
Published in International Journal of Optical Innovations & Research · Vol. 2, Issue 2, 2024 · pp. 17–22 Read article
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Depression Detection Using Machine Learning: A Comprehensive Review
Abstract: Depression remains one of the most prevalent mental health conditions globally, yet it frequently goes undiagnosed due to the reliance on subjective evaluation methods. With the growing availability of digital behavioral data and significant progress in machine learning (ML), new possibilities have emerged for the automated detection of depression. This review offers a detailed examination of recent advancements in ML-driven approaches to identifying depressive symptoms. It covers a range of …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 3, 2025 · pp. 27–32 Read article
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A Review of Machine Learning Applications in Web Data Mining
Abstract: The rapid development of Internet technology has resulted in a rapidly changing and intricate digital environment that requires new methods for organizing and evaluating online data. This study examines the use of machine learning (ML) in web data mining, focusing on its ability to extract relevant insights from huge amounts of online data. Web data mining, which is divided into three categories: content mining, structure mining, and use mining, uses …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 1, 2025 · pp. 39–47 Read article
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A Survey on Ensemble Technique for Enhanced Cyberattack Detection
Abstract: It is now more difficult than ever to safeguard enterprises against cyberattacks due to their fast growth and growing sophistication. Stronger cyberattack detection systems are becoming more and more necessary as hostile strategies continue to evolve in order to safeguard information, preserve corporate trust, and protect sensitive data. An overview of contemporary detection techniques is given in this study, with a focus on integrating machine learning (ML) to increase efficacy. …
Published in Journal Of Network security · Vol. 13, Issue 3, 2025 · pp. 50–54 Read article
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Intelligent Systems: A study on AI and Machine learning
Abstract: Artificial Intelligence (AI) and Machine Learning (ML) are dynamic branches of computer science that focus on developing systems capable of executing tasks commonly associated with human intelligence. These activities encompass making choices, resolving issues, understanding language, identifying patterns, and learning through experience. Artificial Intelligence refers to the broad area of designing systems and frameworks that enable machines to perform tasks resembling human thought and behavior. This field integrates diverse technologies …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 1, 2025 Read article
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Using Machine Learning to Guess Photochemical Reaction Pathways
Abstract: Photochemical reactions are crucial to many activities in the fields of energy conversion, environmental cleanup, and synthetic chemistry. However, predicting their causes and results effectively is still very hard since they entail excited electronic states, nonadiabatic transitions, and complicated potential energy surfaces. Machine learning (ML) has been a powerful technique to go along with classic quantum chemistry methods in the last few years. It offers better prediction capability and lower …
Published in International Journal of Photochemistry and Photochemical Research · Vol. 3, Issue 2, 2025 · pp. 01–12 Read article
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Machine Learning Classification of Normal Versus Age-Related Macular Degeneration OCT Images
Abstract: The macula is a small area of the retina which is especially very important for good eyesight. The age related macular degeneration (AMD) is a type of visual impairment that can cause central vision blur or even loss of visionblindness or even loss of eyesight. AMD was a dangerous and progressing chronic disease which affects people over the age of 60. One of the most common symptoms of this condition …
Published in Journal of Control & Instrumentation · Vol. 13, Issue 3, 2022 · pp. 1–8 Read article
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Detection of Breast Cancer with Python
Abstract: Global cancer data confirms more than 2 million women diagnosed with breast cancer each year reflecting majority of new cancer cases and related deaths, making it significant public health concern. But fortunately, it is also the curable cancer in its early stage. Early diagnosis of breast cancer with timely and effective treatment services improves the prognosis and survival of patients. During classifying tumors, there are significant chances of error and …
Published in Research & Reviews : Journal of Statistics · Vol. 9, Issue 3, 2020 · pp. 5–22 Read article
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Polymer Composite Nodes for Smart IoT Environmental Monitoring: Applying ML-Calibrated RF Sensors for Exceptionally Low-Power, Flexible, and Dependable Performance
Abstract: The quick usage of the Internet of Things (IoT) for monitoring the environment needs sensor platforms that are accurate, responsive, adaptable, energy-efficient, and able to work in many different conditions. This work presents polymer composite nodes equipped with machine learning (ML)-calibrated radio-frequency (RF) sensors that adhere to stringent criteria. Sensor substrates built of flexible polymer matrices with conductive fillers are strong, light, and flexible enough to fit on surfaces that …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1205–1221 Read article
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Stock Market Prediction Using Machine Learning: Techniques, Challenges, and Future Directions
Abstract: The continuous advancement of machine learning (ML) technologies has significantly transformed the field of financial forecasting, particularly in the area of stock market prediction. The ability to accurately forecast stock price movements and market trends plays a crucial role in supporting informed investment strategies and effective risk management. This paper provides a comprehensive review of recent developments in the application of ML techniques for predicting stock market behavior. It classifies …
Published in E-Commerce for Future & Trends · Vol. 13, Issue 1, 2026 · pp. 10–16 Read article
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Empowering Communication: A Review of Sign Language Translation Systems Powered by Machine Learning
Abstract: This research study offers a fresh solution to the communication gap between the hearing population and the deaf and hard-of-hearing community: the creation of a machine learning-based sign language translator. By utilizing cutting-edge K Nearest Neighbour (K-NN), the system effectively converts sign language motions into text and vice versa, facilitating smooth communication between sign language users and well-read people. The basis of the project is thorough data collection and careful …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 1, 2024 · pp. 25–31 Read article
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AI and ML-Driven Immersive Technologies: A New Era in Education
Abstract: The very fast adoption of Artificial Intelligence (AI) and Machine Learning (ML) in education has transformed contemporary teaching and learning ecosystems driven by advances in immersive technologies and the growing engagement of global technology leaders with virtual environments. AI-powered educational platforms enable adaptive and personalized learning pathways by dynamically adjusting content, pace and instructional strategies to learners’ preferences, abilities and learning styles by improving engagement, retention and academic outcomes. Deep …
Published in International Journal of Education Sciences · Vol. 3, Issue 1, 2026 · pp. 116–123 Read article
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Open-Source Software Empowering Artificial Intelligence, Machine Learning, and Cyber Security: A Comprehensive Research Study
Abstract: Open-source software (OSS) has become a foundational pillar for rapid innovation across artificial intelligence (AI), machine learning (ML), and cybersecurity. This paper delivers a comprehensive, journal-length analysis of OSS-driven ecosystems, emphasizing collaborative development, transparency, and accelerated deployment. By providing freely available libraries, tools, and frameworks, OSS makes it easier for developers and researchers to experiment, build models, and deploy solutions quickly. This study examines how OSS can be combined with …
Published in Journal of Open Source Developments · Vol. 13, Issue 1, 2026 · pp. 08–15 Read article
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Alzheimer disorders diagnosis system design using machine learning for EEG signal
Abstract: The diagnosis of Alzheimer's disorders (AD), a prevalent neurological disorder, can created by utilising a range of therapeutic methods, including the electroencephalogram (EEG), which has been especially successful in the past. The objective for this study is to develop a computer-aided diagnosis tool which may recognize AD from EEG data. The EEG information was cleaned up with a band-pass elliptic digital filter to remove any interference or disruptions. The filtered …
Published in Journal of Control & Instrumentation · Vol. 14, Issue 1, 2023 · pp. 9–22 Read article
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EMERGING TECHNOLOGIES RESHAPING INDUSTRIES
Abstract: Emerging technologies such as Artificial Intelligence (AI), machine learning and 5 G technology are reshaping industries rapidly. This paper contains knowledge about some of the key emerging technologies and their real-world applications in various sectors like health care, education, agriculture, transportation, finance. This includes some of the challenges and considerations arising due to the widespread use of these emerging technologies. Also exploring about the future directions due to adopting these …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 12, Issue 3, 2025 · pp. 1–10 Read article