Search
1474 articles for “learning”
-
AI for Cybersecurity: Deploying Machine Learning for Network Traffic Anomaly Detection
Abstract: The growing sophistication of cyberattacks and the growth of network traffic necessitate sophisticated anomaly detection methods. This study overviews the use of artificial intelligence (AI) and machine learning (ML) to counter these challenges, as noted in current studies. It analyses supervised learning (SVM, Decision Trees), unsupervised learning (K-means, DBSCAN), and deep learning (CNNs, RNNs, Auto-encoders) approaches, considering their strengths and weaknesses. The research integrates current developments in AI/ML-based network anomaly …
Published in International Journal of Computer Science Languages · Vol. 3, Issue 2, 2025 · pp. 1–10 Read article
-
Fertiledata: Advanced Strategies For Crop Optimization Through Machine Learning Processing
Abstract: The venture, titled "Fertile Data: Advanced Strategies for Crop Optimization Through Machine Learning processing" is created utilizing HTML, CSS, and JavaScript for the front conclusion, and Python for the back conclusion. In a nation like India, where a noteworthy parcel of the populace depends on agribusiness for their vocation, joining progressed advances such as Machine Learning and Profound Learning into cultivating hones can revolutionize the industry. This venture presents a …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 2, 2025 · pp. 25–35 Read article
-
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
-
Machine Learning for Finding Materials for Membranes
Abstract: Traditionally, finding and improving membrane materials has depended on trial-and-error experiments, which can take a long time, cost a lot of money, and only cover a small area. Recent improvements in machine learning (ML) have the potential to change the way membrane materials are designed by making it possible to make predictions about performance, selectivity, and stability based on data. ML algorithms can find hidden links between the structure, composition, …
Published in International Journal of Membranes · Vol. 3, Issue 1, 2026 · pp. 1–7 Read article
-
Multimodal Data Fusion with Hybrid Machine Learning for Enhanced Prediction of Li-Ion Battery Remaining Useful Life and State of Charge
Abstract: Lithium-ion battery materials used in modern energy storage systems are required to exhibit high reliability, safety, and long lifecycle performance under varying operational and environmental conditions. Accurate prediction of Remaining Useful Life (RUL) and State of Charge (SoC) is therefore essential for understanding material degradation behavior, improving manufacturing quality, and enabling effective lifecycle management. However, nonlinear electrochemical aging, load variability, and thermal uncertainty significantly complicate accurate estimation of these parameters. …
Published in International Journal of Electro-Mechanics and Material Behaviour · Vol. 4, Issue 1, 2026 · pp. 1–5 Read article
-
Explainable Machine Learning for Process Parameter Optimization in Gradient 3D-Printed Polymer Composites
Abstract: The explainable machine learning-based structure may be employed to achieve a favorable process parameter of the graduate 3D-printed polymer composite structures to improve the mechanical and thermal properties without compromising the transparency of the decisions made during the fabrication process. Gradient composite specimens were made by systematically varied process parameters like nozzle temperature, raster orientation, deposition speed, gradient transition rate and fused filament fabrication. A predictive model of tensile strength …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 847–866 Read article
-
Environmental Impact Assessment of Ocean Energy Converters Using Quantum Machine Learning
Abstract: The accelerating deployment of ocean energy converters (OECs) across tidal, wave, osmotic, and thermal domains necessitates rigorous, data-intensive environmental impact assessment (EIA) frameworks capable of modelling multi-stressor marine ecosystems in real time. Classical machine learning approaches, while operationally mature, encounter scalability bottlenecks and feature correlation limitations when applied to the high-dimensional, non-linear datasets characteristic of offshore monitoring networks. This paper presents a comprehensive quantum machine learning (QML) framework for the …
Published in Journal of Energy, Environment & Carbon Credits · Vol. 16, Issue 1, 2026 · pp. 22–31 Read article
-
Machine Learning Assisted Design and Analysis of Polymer Composite Materials for Sustainable Renewable Energy Systems
Abstract: Accurate prediction and optimization of polymer composite properties is of paramount importance in the design of these lightweight, durable, and sustainable materials within renewable energy technologies. This work will provide a holistic machine learning-assisted framework that unites materials informatics with domain-specific features and state-of-the-art ML methodologies in the prediction of the mechanical properties of polymer composites, such as tensile strength. This includes embedding several ensemble models, including Random Forest and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 391–402 Read article
-
Smart Wearable Health Pendant Using IoT and Machine Learning for Heart Rate Monitoring and Detecting Falls
Abstract: Cardiovascular illnesses and accidental falls frequently occur among individuals and at-risk populations. This is the reason we genuinely require a device that can monitor their well-being continuously. We are referring to a health pendant designed for seniors and groups at risk can wear. This health pendant leverages Internet of Things technology and machine learning to monitor heart rate and identify falls. Our health pendant system incorporates an ESP32 microcontroller, a …
Published in Journal of Instrumentation Technology & Innovations · Vol. 16, Issue 2, 2026 Read article
-
Autonomous Calibration of Medical Devices Using Synthetic Biosignals and Adaptive Learning
Abstract: The accuracy and reliability of modern biomedical diagnostic devices are critically dependent on effective calibration mechanisms capable of handling dynamic physiological and environmental variations. Conventional calibration approaches, which rely on static reference signals and manual adjustments, are inadequate in addressing challenges such as sensor drift, noise interference, motion artifacts, and long-term performance degradation. To overcome these limitations, this research proposes an innovative AI-driven adaptive biosignal simulation and calibration architecture for …
Published in Journal of Instrumentation Technology & Innovations · Vol. 16, Issue 2, 2026 Read article
-
Diabetes Risk Prediction from Survey Data Using Machine Learning Algorithms
Abstract: Diabetes mellitus represents one of the most significant global health challenges, affecting millions worldwide and leading to severe complications if left undiagnosed or poorly managed. Early detection and risk assessment are crucial for preventing the progression of this chronic condition. This research presents a comprehensive machine learning approach for predicting diabetes risk using survey-based health parameters. The study implements and compares four prominent classification algorithms: Logistic Regression, K-Nearest Neighbors (KNN), …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
-
Predicting Student Placement Readiness: A Machine Learning Approach Using Coding Activities and Multi-Dimensional Performance Indicators
Abstract: In the modern information-driven academic world, identifying student employability and placement preparedness has predicted. be made a part and parcel of academic planning and career. development. This study provides a machine learning-based. structure to evaluate and forecast student placement pre-paredness by combining various performance aspects-academic achieve- ment, coding activity, aptitude and behavioral engage-ment metrics. Multi-source was gathered and preprocessed in the study. student information, such as student records (CGPA, attendance), …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 2026 Read article
-
Early Lung Cancer Prediction using deep Learning
Abstract: Lung cancer is a global killer because it’s often found late. Finding it early is key to treatment and survival so computer assisted diagnostics are essential. This research uses deep learning to spot early stage lung cancer from CT scans. We trained and fine-tuned three convolutional neural networks—ResNet50, Dense Net 201 and EfficientNet-B0—using transfer learning. We preprocessed the lung CT images by resizing, normalizing and augmenting them to enhance the …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 15, Issue 2, 2026 Read article
-
GenChrome-ML: A Machine Learning Framework for Early Detection of Chromosomal Disorders Using Genomic Data
Abstract: The increasing burden of chronic disease and cancer demands innovative, more rapid and effective diagnostic tools in the field of healthcare. The majority of current diagnostic tools are dependent upon clinical symptomology and manual evaluation, leading to delays in early detection and treatment. The development of artificial intelligence (AI) and machine learning (ML), in recent years, has offered opportunities for the enhancement of disease prediction, diagnosis and personalization of treatment …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
-
Hybrid Quantum–Machine Learning Framework for Nonlinear Rheological Modeling of Polymer and Composite Materials
Abstract: In polymer and composite materials, a major challenge lies in predicting their nonlinear rheological response, owing to complex multiscale interactions that are not captured by traditional constitutive laws or conventional machine learning approaches. In this study, a hybrid Quantum Machine Learning (QML) model comprising Quantum Support Vector Machine (QSVM) and Quantum Neural Network (QNN) architectures is proposed for viscosity prediction without requiring any specific rheological equation. To train and test …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
-
A Hybrid Mathematical Model for Epidemic Outbreak Forecasting Using Machine Learning and Cloud Computing
Abstract: The increasing frequency of infectious disease outbreaks has emphasized the necessity for intelligent epidemic surveillance systems capable of predicting disease spread at an early stage. Conventional outbreak detection approaches rely heavily on delayed statistical reporting and manual monitoring techniques, resulting in reduced responsiveness during critical periods. This paper presents a mathematical predictive framework for epidemic outbreak detection using machine learning and cloud computing technologies. The proposed framework integrates the Susceptible–Infected–Recovered …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 13, Issue 2, 2026 · pp. 01–06 Read article
-
Neuromorphic Self-Learning Polymer–MXene Photovoltaic Composites with Embedded Memristive Energy Routing for Adaptive Solar Energy Harvesting
Abstract: This dynamic and fast-growing intelligent renewable energy system requires photovoltaic materials that can autonomously adapt to fast-changing environmental conditions. In this study, a novel system is proposed for adaptive harvesting of solar energy based on Neuromorphic Self-Learning Polymer–MXene Photovoltaic Composites (NSPMPCs) with embedded memristive energy routing networks. To boost the charge generation and charge transport in the polymer–MXene heterostructure, the flexibility and processability of conductive polymers are integrated with the …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
-
Using Machine Learning to Analyse Football Teams and Predict the Outcome of a Football Match
Abstract: Football, as one of the most popular sports on the planet, has always attracted a large number of fans. Over 150 million men and women of all ages play it in over 200 countries. Modern football has seen a paradigm shift from being just one of the most physical sports to now being one of the most complex sports due to the involvement of multiple new factors such as Home …
Published in Journal of Communication Engineering & Systems Read article
-
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 Read article
-
Reinforcement Learning in Real World Application: A Study on Robotics; Autonomous Vehicles and Industrial Automation
Abstract: This research paper investigates the practical application of reinforcement learning (RL) in three critical domains: robotics, autonomous vehicles, and industrial automation. The study delves into the implementation of RL algorithms to enhance decision-making, adaptability, and autonomy in these real-world scenarios. Through a comprehensive review of existing literature, methodologies, and case studies, the paper addresses the challenges faced and the successes achieved in deploying RL in each domain. The findings offer …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 1, Issue 3, 2023 · pp. 1–15 Read article