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485 articles for “machine learning prediction”
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Assessing the Robustness of Machine Learning Models for Wireless Intrusion Detection Under Adversarial Traffic Perturbations
Abstract: As the Internet of Things (IoT) devices and wireless communication networks continue to grow rapidly, protecting systems from cyber threats has become increasingly important. Machine learning–based intrusion detection systems (IDS) have shown strong potential in detecting abnormal and malicious network activities, yet their effectiveness and resilience when facing adversarial attacks are still not sufficiently explored. This research evaluates Machine Learning (ML) models–XGBoost, random forest, and multi-layer perceptron (MLP)—in detecting attacks …
Published in International Journal of Wireless Security and Networks · Vol. 4, Issue 1, 2026 · pp. 29–34 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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Implement Explainable Machine Learning to Improve Conductivity in Polymer-CNT Nanocomposites: Supporting Adaptive, Flexible, and Long-Lasting IoT Wrap-Around Electronics Applications
Abstract: The rapid growth of Internet of Things (IoT) technologies requires electronic components that are adaptable, lightweight, and durable, and that can continue to function well in diverse contexts and circumstances. Polymer–carbon nanotube (CNT) nanocomposites have become interesting choices for these kinds of uses because they are more flexible, conduct electricity better, and can be made to fit specific needs. However, improving conductivity in these heterogeneous systems remains a major challenge …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 238–254 Read article
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Advancing Asthma Management: The Synergy of Systems Biology, Artificial Intelligence, and Next-Generation Therapeutics
Abstract: Asthma is an inflammatory disorder of the respiratory tract that is chronic and heterogeneous in nature and has various effects on millions of people. Being a chronic inflammatory disease, asthma remains incurable and the major conventional treatments offer limited success due to the mask nature of its pathophysiology. Systems biology/(AI), and next-generation has greatly enhanced knowledge and the management of asthma. The approaches based on gene, transcript, protein, and metabolite …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 2, 2024 · pp. 1–12 Read article
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Viscoelastic Behavior and Wrinkle Formation in Cotton- Polyester Garments: A Data-Driven Approach for Textile Care
Abstract: This study investigates the wrinkle behavior of cotton-polyester blended fabrics by analyzing data from over 1,200 store-handled garments. Integrating concepts from polymer chemistry and computer vision, it aims to establish a smart textile care framework based on fiber-specific wrinkle characteristics. The research identifies how cotton’s hydrophilic and non-elastic structure results in increased wrinkling, while polyester’s thermoplastic and crystalline properties enhance wrinkle resistance. Elastomeric fibers like Lycra contribute to wrinkle recovery …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 50–60 Read article
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Pothole Detection utilising Machine Learning: A Review
Abstract: Potholes must be found and fixed quickly in order to maintain infrastructure, maximize transportation systems, and guarantee road safety. Using the Sequential API and the Keras library, this study presents a neural network model for pothole detection. Convolutional layers with ReLU activation, global average pooling, dense layers with dropout, and softmax activation for binary classification make up the model architecture. Image loading, resizing, array conversion, labeling, shuffling, normalization, and one-hot …
Published in Journal of Control & Instrumentation · Vol. 16, Issue 1, 2025 · pp. 35–43 Read article
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Develop a Data Science Approach for Optimizing Energy Consumption
Abstract: Optimizing energy consumption has become a critical challenge in the era of sustainability and increasing energy demand. Efficient energy management is essential to address environmental concerns, reduce costs, and ensure resource availability for future generations. This project leverages data science techniques to evaluate and improve energy consumption across diverse sectors, including residential, industrial, and commercial domains. By integrating advanced analytics, machine learning models, and real-time data processing, the project aims …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 31–44 Read article
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A Supervised Learning Approach for Toxic Comment Detection on Social Media Platforms
Abstract: Nowadays everyone uses social media platforms like X (formerly Twitter), Instagram, Facebook, etc. for various purposes. With the help of this, we share our opinions, ideas, and feelings. Generally, the datasets obtained from the internet are constructive; however, there is a significant proportion of toxic ones. The datasets are filtered to remove noise, and noise is removed in post-processing. The study initiates with the upload and preprocessing of a toxic …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 11, Issue 2, 2024 · pp. 7–14 Read article
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Early Autism Diagnosis: Machine Learning Models and Their Effectiveness
Abstract: Diagnosis is of utmost importance for timely intervention and support. However, traditional diagnosis methods, which are based on subjective assessment, are delayed. This project explores the role that machine learning techniques might play in enhancing the accuracy and effectiveness of ASD detection. Several state-of-the-art classification algorithms were benchmarked using a dataset from Kaggle. Logistic Regression, XG Boost, Random Forest, Decision Tree, and Gradient Boosting were taken into consideration. Other performance …
Published in Journal of Instrumentation Technology & Innovations · Vol. 14, Issue 2, 2024 Read article
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AI-Designed Functionally Graded Polymer Composites for Multifunctional Thin Films
Abstract: The design of multifunctional polymer composite thin films requires simultaneous optimization of mechanical, optical, barrier, and thermal properties—objectives often in conflict when using conventional homogeneous materials. This study presents an artificial intelligence-driven framework for designing functionally graded material (FGM) architectures in polymer nanocomposite thin films. We integrated machine learning with physics-based modeling to optimize compositional gradients across film thickness, achieving superior performance compared to homogeneous and discrete multilayer alternatives. A …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1026–1041 Read article
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Greener 3D Printing: The Role of Artificial Intelligence in Sustainable Polymer and Composite Manufacturing
Abstract: The integration of sustainable materials with additive manufacturing (AM) technologies marks a significant step towards environmentally responsible production. Biodegradable polymers, recycled thermoplastics, and bio-based composites, when used in 3D printing, offer the potential to reduce the ecological footprint of manufacturing. However optimizing the interplay between material properties process parameters, and product performance remains a complex challenge. This review examines how artificial intelligence (AI) is being applied to address these challenges …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 288–300 Read article
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Trends and Applications of Artificial Intelligence in Mechanical Engineering: A Review
Abstract: Artificial Intelligence (AI) has become a revolutionary force across various fields, including mechanical engineering, where it is redefining traditional approaches to design, manufacturing, maintenance, and overall system optimization. This review aims to provide a comprehensive introduction to AI and explore its diverse applications within the domain of mechanical engineering. The study begins with a foundational overview of AI, including key concepts such as machine learning, neural networks, deep learning, and …
Published in Journal of Mechatronics and Automation · Vol. 12, Issue 3, 2025 · pp. 30–35 Read article
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Detection of Pneumonia in COVID-19 Patients Using X-ray Images
Abstract: This study explores the use of chest X-ray image analysis and deep learning methods to identify pneumonia in COVID-19 patients. Due to the pandemic, Proper as well as immediate examination of COVID-19 is now essential for patient care and disease control. This study proposes a novel approach that uses convolutional neural networks (CNNs) to automatically predict pneumonia in COVID-19 patients using chest X-ray images. In this study, an X-ray of …
Published in International Journal of Radio Frequency Innovations · Vol. 1, Issue 1, 2023 · pp. 13–23 Read article
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The Role of AI in Modern Circuit Design and Simulation
Abstract: The integration of artificial intelligence (AI) in circuit design and simulation is revolutionizing the electronics industry by enabling faster, more efficient, and innovative design processes. This article explores the transformative role of AI in automating tasks traditionally reliant on manual expertise, such as schematic generation, component optimization, and fault detection. It highlights how machine learning algorithms and generative AI tools are improving design accuracy, reducing time-to-market, and enabling cost-effective prototyping. …
Published in Journal of Semiconductor Devices and Circuits · Vol. 12, Issue 1, 2025 · pp. 9–14 Read article
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AI-Driven Multi-Objective Optimization of Conductive Polymer Composites for High-Performance Flexible Electronics
Abstract: The development of conductive polymer composites (CPCs) is critical for advancing flexible and wearable electronic technologies. However, the conventional trial-and-error approach to material formulation is time-consuming and often inefficient due to the high-dimensional nature of the design space. This study introduces a novel AI-driven framework that integrates machine learning (ML) with multi-objective optimization to accelerate the discovery of high-performance CPCs. A dataset of 1,000 experimentally reported formulations was compiled, capturing …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 734–745 Read article
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Strategic Integration of Machine Learning in Polymer Composite Development: A Framework for R&D Portfolio Management and Technological Adoption
Abstract: The progress of advanced polymer composites is slow, costly and unpredictable due to traditional methods of trial-and-error research. As materials informatics and data-driven modeling speed up the process of discovering technology, there exists a huge disconnect between computational predictions on one hand and strategic decision-making on the other in research and development (R&D). To solve this issue, this paper presents the Agile Materials-Intelligence (AMI) Framework, a systematic combined methodology that …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1272–2286 Read article
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Multivariant Disease Detection from Different Plant Leaves and Classification
Abstract: Agricultural growth is significant in Indian GDP which is based on yield of crops, quality of the plants and procedure of the plants taken. To maintain good quality of plant, the plant diseases should be identified and then given proper suggestions to farmers for specific fertilizers and pesticides to be used. The use of specific fertilizers or pesticides makes plant more health with good quality so that farmers can get …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 15, Issue 2, 2026 · pp. 27–35 Read article
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The Role of Artificial Intelligence and Machine Learning in Redefining Global Healthcare Systems and Advancing Medical Innovation
Abstract: Health Services are being revolutionized with AI and ML through improved accuracy, efficiency and accessibility in the delivery of health care. With AI and ML, it is now possible for health care professionals to assess varying amounts of complex clinical data in a relatively short amount of time, therefore, creating opportunities for early detection of disease, increasing the odds of accurate diagnosis, and improving the ability to make informed clinical …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 4, Issue 1, 2026 · pp. 14–19 Read article
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Alzheimer’s Disease Detection Using ML Algorithm
Abstract: A degenerative neurological state of affairs, Alzheimer's disease (AD) gradually impairs cognitive and functional capacities, especially in people over 65. Early AD detection is crucial for efficient management and treatment prep. This study delves into novel approaches for the early detection of AD using non-invasive methods. We've implemented a blend of neuroimaging data analysis and machine learning algorithms to pinpoint markers indicative of the disease during its initial phases. Our …
Published in Journal of Experimental & Applied Mechanics · Vol. 15, Issue 3, 2024 · pp. 53–57 Read article
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The Scientific Foundations of Programming Languages: Bridging Theory and Practical Application
Abstract: The study of programming languages within computer science is fundamental to the development of efficient, reliable, and scalable software systems. However, the degree to which these languages adhere to scientific principles remains a topic of debate. This paper explores the scientific nature of computer science languages by examining their theoretical foundations, design principles, and practical applications. It evaluates how programming languages are grounded in mathematical logic, formal semantics, and computational …
Published in International Journal of Computer Science Languages · Vol. 3, Issue 1, 2025 · pp. 32–41 Read article