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349 articles for “Machine Learning Optimization”
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A Systematic Review on Leukemia Detection and Classification Techniques Using Gene Expression
Abstract: Early diagnosis of genetic diseases is crucial for effective treatment, especially in the case of Leukemia, a type of blood cancer characterized by abnormal proliferation of white blood cells. This paper presents a systematic review of recent computational techniques for the detection and classification of Leukemia using gene expression data obtained from DNA microarray analysis. The study explores diverse methodologies including machine learning (ML), deep learning (DL), and bio-inspired algorithms …
Published in International Journal of Genetic Modifications and Recombinations · Vol. 3, Issue 2, 2025 Read article
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Optimizing Sentiment Analysis with Naïve Bayes and Random Forest Techniques: A Result-based Approach
Abstract: In the increased digitalization, the sentiment analysis and classification have evolved as an eminent area to determine the polarity of positive, negative, and neutral reviews of the customers and users on products. It is an integral application field that employs supervised learning, Machine Learning, and Natural Language Processing concepts. The proposed Semantic Analysis and Classification using Naive Bayes and Random Forest system accomplishes the sentiment polarity by classifying the user …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 46–57 Read article
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AI-Driven Robotics for Sustainable Solutions in Disaster Management
Abstract: Disasters, whether natural or man-made, present significant challenges to societies worldwide. Efficient response, recovery, and mitigation strategies are crucial to minimizing human suffering, loss of life, and economic damage. Traditional disaster management strategies, while effective to some degree, often face limitations related to human resources, response time, accessibility, and safety. The integration of artificial intelligence (AI) and robotics into disaster management offers transformative potential for overcoming these challenges. This paper …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 3, Issue 1, 2025 · pp. 24–30 Read article
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IoT-Based Adaptive Irrigation Solution for Smart Farming
Abstract: Facing escalating global challenges such as water scarcity and unpredictable weather conditions, precision farming emerges as a crucial solution for enhancing agricultural productivity and sustainability. This paper introduces a smart irrigation system which employs IoT-based technology, integrating sensors and a designed to enhance water efficiency and boost agricultural productivity. The system leverages live data related to soil moisture and temperature, cloud cover, and precipitation, coupled with a cloud-based infrastructure using …
Published in Journal of Microcontroller Engineering and Applications · Vol. 12, Issue 3, 2025 · pp. 17–24 Read article
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Iron Oxide/Chitosan Nanocomposite: Properties and Design for AI Enhanced Immunotherapy and Regenerative Medicine
Abstract: Biopolymers are valuable complex materials. They attract attentions of many scientists, engineers, and medical professionals’ due to their distinguished properties for various applications. In this research, emphasis is given to Fe3O4/Chitosan nanocomposite which has desirable biophysical properties compared to pure Chitosan nanoparticles. Following the green synthesis procedures and characterization methods including thermo gravimetric analysis, AI assisted biomedical application of; Fe3O4/Chitosan nanocomposite is presented. The effect of alkali typically, KOH, in …
Published in International Journal of Cheminformatics · Vol. 3, Issue 2, 2025 · pp. 22–32 Read article
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A Systematic Review on The Role of Artificial Intelligence in Assisted Reproductive Technology
Abstract: Artificial Intelligence (AI) has significantly transformed Assisted Reproductive Technology (ART) over the past five years, enhancing diagnostic accuracy, treatment personalization, and overall success rates. AI-driven algorithms and machine learning models have been integrated into various aspects of ART, including sperm selection, embryo grading, and predicting implantation success. Deep learning techniques have improved image-based embryo assessment, reduced human subjectivity and increased efficiency. Additionally, AI-powered predictive analytics have helped optimize ovarian stimulation …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 1, 2026 Read article
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Optimizing Mechanical and Durability Properties of Eco-Friendly Composite Materials Using Recycled Fillers and ML Techniques
Abstract: The increasing demand for sustainable construction materials has intensified the exploration of recycled fillers as partial or full replacements for natural aggregates in composite materials. This study investigates the mechanical and durability performance of polymer matrix composites incorporating processed recycled fillers derived from construction and demolition (C&D) waste. Three distinct processing methods were employed to prepare the recycled fillers: untreated (URF), single processed (SPRF), and double processed (DPRF), with replacement …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 269–309 Read article
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Pharmacovigilance and drug safety current trends and challenges
Abstract: Pharmacovigilance and drug safety are critical areas of focus in healthcare, aimed at monitoring the safety and efficacy of pharmaceutical products throughout their lifecycle. This review explores current trends, emerging technologies, and challenges in pharmacovigilance, emphasizing their impact on public health and regulatory practices. With the increasing complexity of global pharmaceutical markets, pharmacovigilance systems are evolving to integrate real-time data analytics, artificial intelligence (AI), and machine learning (ML) to detect …
Published in Research & Reviews: A Journal of Drug Formulation, Development and Production · Vol. 12, Issue 1, 2025 · pp. 19–27 Read article
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Exploring the Development of AI Models Using Open-Source Tools to Predict Patient Outcomes and Optimize Treatment Plans
Abstract: Integrating artificial intelligence (AI) into healthcare offers a transformative opportunity to enhance patient care and clinical decision-making. Through the use of predictive analytics, AI can significantly enhance the accuracy of outcome predictions and assist in developing personalized treatment plans that cater to each patient’s specific needs. This paper delves into the development of AI models using open-source tools, which are increasingly favored for their accessibility, collaborative nature, and capacity for …
Published in Journal of Open Source Developments · Vol. 11, Issue 3, 2024 · pp. 37–49 Read article
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Adaptive Task Scheduling And Resource Optimization Using Ai Middleware
Abstract: Modern distributed and heterogeneous computing systems face significant challenges in dealing with dynamically changing workloads, resource fragmentation, and changing latencies; existing traditional, or rule-based, schedulers are no longer useful in achieving the best system performance. Such limitations highlight the importance of the adaptive scheduling paradigms that are able to learn, to forecast and reaction to the real red conditions in the system. The middleware of artificial-intelligence is also an attractive …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 Read article
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A Thorough Examination of How Artificial Intelligence is Affecting the Transformation of Agriculture in India and Throughout the World
Abstract: By providing creative ways to increase crop yields, maximize resource usage, and advance sustainability, artificial intelligence (AI) is revolutionizing agriculture. AI technologies, such as machine learning, computer vision, and robotics, are being increasingly used in precision farming, crop monitoring, disease detection, and decision-making as the global agricultural sector faces pressing challenges like food security, population growth, and climate change. AI enables farmers to make data-driven decisions, optimize irrigation systems, monitor …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 3, 2025 · pp. 39–45 Read article
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Computer and Commerce – Relationship for The Future
Abstract: The relationship between computers and commerce has evolved dramatically over the past few decades, transforming the way businesses operate and how consumers interact with markets. This synergy continues to grow and holds significant potential for the future. Computers, through advancements in artificial intelligence (AI), machine learning, cloud computing, and big data analytics, have revolutionized commerce by enhancing efficiency, improving decision-making, and fostering innovation. In the future, we can expect even …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 2, 2025 · pp. 42–59 Read article
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Fitness Fusion: Maximizing Health Through Exercise and Calorie Control
Abstract: The delicate balance between improving physical fitness and managing calorie intake has gained increasing significance as individuals aim to achieve their fitness goals while maintaining a healthy weight. This approach highlights the strategic integration of exercise, such as high-intensity interval training (HIIT), with controlled dietary practices to effectively manage weight, enhance physical performance, and support overall health. HIIT, known for its time-efficient ability to burn calories and improve cardiovascular fitness, …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 113–118 Read article
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Advances in Stimuli-Responsive Smart Drug Delivery Systems: A Systematic Review
Abstract: Imagine a world where medicines work smarter, not harder. Stimuli-responsive drug delivery systems (SRDDS) are transforming healthcare by releasing therapeutic agents in response to specific triggers. These triggers can be internal, like changes in pH or temperature, or external, like light or magnetic fields. By leveraging these triggers, SRDDS can optimize treatment and minimize side effects. The potential applications of SRDDS are vast, from cancer treatment to gene delivery and …
Published in Trends in Drug Delivery · Vol. 13, Issue 2, 2026 Read article
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Advancements in K-Means Clustering: Boosting Algorithm Performance through Innovations
Abstract: K-Means clustering is a widely used unsupervised learning algorithm for partitioning a dataset into distinct clusters. Despite its popularity and simplicity, K-Means has several limitations, such as sensitivity to initial centroids, convergence to local minima, and inefficiency with large datasets. This paper reviews recent advancements aimed at addressing these challenges and enhancing the performance of the K-Means algorithm. Innovations include improved initialization methods, such as K-Means++, which significantly reduce the …
Published in International Journal of Solid State Innovations & Research · Vol. 3, Issue 1, 2025 · pp. 30–37 Read article
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Assessment of Milk Quality Optimization of Yogurt Fermentation
Abstract: In the creation of new products, yogurt makers need to prioritize consumer preferences to enhance their market share. An advanced prediction method will help in grasping the fundamental connection between consumer preferences and sensory attributes. This study introduces a new deep learning approach that employs an autoencoder to derive product features from expert-scored sensory attributes. These sensory features are then analyzed through support vector machine regression to align them with …
Published in Research and Reviews : Journal of Dairy Science and Technology · Vol. 13, Issue 2, 2024 · pp. 6–12 Read article
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Performance Analysis And Battery Management System Optimization In Electric Vehicles
Abstract: The rapid electrification of the automotive industry has led to an increased need for battery management systems that are not only efficient and safe but also intelligent. BMS is the device that guarantees the best use of the battery, prolongs its life, and allows its safe operation even under different environmental and load conditions. Through the synthesis of literature and industry practices, this paper acts as a tribute to the …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 1, 2026 Read article
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Accelerating Unsupervised Feature Learning: Parallelized Training of Denoising Autoencoders
Abstract: Unsupervised representation learning has become a cornerstone of contemporary machine learning, enabling algorithms to extract informative features from un-labelled, high-dimensional data. This work investigates the efficacy of stacked denoising autoencoders (SDAEs) trained via parallelized stochastic gradient descent (SGD) as a scalable approach to feature extraction. By strategically leveraging multi-threaded computation, our study systematically examines the trade-offs between increased parallelism, training efficiency, and the preservation of model accuracy. Experiments on the …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 56–64 Read article
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AI-Based Intelligent Traffic Signal Management System: A Review
Abstract: Traffic congestion is a growing problem in urban areas worldwide, leading to economic losses, increased pollution, and commuter frustration. Traditional traffic management systems rely on fixed timing cycles and lack adaptability to real-time traffic conditions. Intelligent traffic light control systems based on artificial intelligence (AI) have become a viable substitute for traditional techniques. These systems are able to evaluate large volumes of traffic data in real time, identify patterns, and …
Published in International Journal of Electronics Automation · Vol. 3, Issue 2, 2025 · pp. 1–5 Read article
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Money Laundering Transaction with Machine Learning
Abstract: This study discusses the use of machine learning algorithms to discover firms that are prone to money laundering. The purpose of this research is to develop, describe, and test a machine learning model for determining which bank transactions should be physically scrutinized for money laundering activities. To train a supervised machine learning model, three categories of historical data are required: legitimate "normal" transactions, transactions flagged as suspicious by the bank's …
Published in Current Trends in Information Technology · Vol. 14, Issue 2, 2024 · pp. 1–15 Read article