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260 articles for “Data availability”
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A Comprehensive Review of CNN-Based Framework for Multi-Sign Detection of Diabetic Retinopathy in Fundus Images Using Public Datasets
Abstract: Diabetic retinopathy (DR) is one of the main causes of vision impairment. Blindness prevention and effective treatment depend on early detection. A thorough deep learning-based framework for the automatic segmentation and simultaneous detection of exudates, hemorrhages, and microaneurysms – three important DR indicators – from retinal fundus images is presented in this work. These three pathological signs’ corresponding annotated image patches, along with background (no-sign) areas, were used to train …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 14–23 Read article
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SGLT2 Inhibitors in Heart Failure and Chronic Kidney Disease: Expanding Therapeutic Horizons beyond Glycemic Control
Abstract: Heart failure (HF) and chronic kidney disease (CKD) are closely related clinical diseases that significantly increase morbidity and death worldwide. The co-occurrence of both conditions is frequently referred to as the "cardio-renal syndrome," in which the failure of one organ hastens the decline of the other. The original purpose of sodium–glucose cotransporter-2 (SGLT2) inhibitors was to treat type 2 diabetic mellitus (T2DM) by reducing blood sugar levels. Nevertheless, new data …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 16, Issue 2, 2026 Read article
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Ligand Based Drug Virtual Screening in Computer Aided Drug Design: A Review
Abstract: Computer-Aided Drug Design (CADD) has revolutionized the drug discovery process by providing efficient and cost-effective methods for identifying potential drug candidates. Within CADD, ligandbased virtual screening techniques play a pivotal role in the early stages of drug discovery. This review provides a comprehensive overview of ligand-based drug virtual screening, focusing on its principles, applications, challenges, and future directions. Fundamentals of ligand-based virtual screening, including pharmacophore-based methods, similarity searching, and machine …
Published in Research & Reviews: A Journal of Drug Formulation, Development and Production · Vol. 11, Issue 2, 2024 · pp. 1–8 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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Accelerating Drug Discovery with AI: Transforming the Pharmaceutical Pipeline
Abstract: The revolutionary potential of artificial intelligence (AI) is examined in this essay the pharmaceutical industry, highlighting its application across the drug development lifecycle. Artificial Intelligence, specifically via deep learning models and machine learning (ML) such as GANs, RNNs, and transformers, enhances drug discovery, formulation, toxicity prediction, and clinical trials. It streamlines processes like identification of targets, virtual screening, modelling of structure-activity relationships, and medication repurposing. AI is also employed in …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 2, 2025 · pp. 77–84 Read article
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An Empirical Study of Hyperparameter Impact on Deep Learning Models for Cardamom Leaf Disease Classification
Abstract: Recent advancements in deep learning models like convolutional neural networks and self- attention mechanisms have achieved great success in the field of plant disease classification. This study investigates the efficacy of two pre-trained models, ConvNeXT-Tiny and Swin Transformer-Tiny, for leaf disease classification in cardamom using a publicly available dataset constituting three categories of leaves, namely Healthy, Colletotrichum Blight and Phyllosticta Leaf Spot. The effectiveness of the models highly depends on …
Published in Current Trends in Information Technology · Vol. 15, Issue 3, 2025 · pp. 48–60 Read article
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AI-Powered Solutions for Sustainable Waste Management in Construction Projects
Abstract: The construction industry is a significant contributor to global waste, posing challenges to sustainability and environmental health. This research explores AI-powered solutions for sustainable waste management in construction projects, focusing on optimizing waste reduction, recycling, and resource efficiency. By integrating machine learning algorithms and IoT-enabled sensors, real-time monitoring of waste generation and segregation can be achieved. Predictive analytics and AI-driven decision-making tools are employed to enhance material reuse and minimize …
Published in Recent Trends in Civil Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 1–5 Read article
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Cybersecurity in Web Automation: A Machine Learning Approach to Lightweight Intrusion Detection
Abstract: Launch-Attack is a lightweight and practical threat-detection framework designed specifically for smaller web-automation environments, including setups that rely on tools such as Selenium. Rather than aiming to replace large enterprise-grade security platforms, the framework focuses on offering an accessible option for developers, testers, and researchers who need real-time monitoring without the heavy resource demands of traditional systems. The model relies on machine-learning techniques implemented through Scikit-learn, enabling it to detect …
Published in Journal of Web Engineering & Technology · Vol. 13, Issue 1, 2026 · pp. 34–40 Read article
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Synthesis, Characterization and Dielectric Properties of Polymer Materials for Microwave Communication Applications
Abstract: The rapid advancement in wireless communication technologies has necessitated the development of advanced materials with tailored dielectric properties. Polymer materials, because of their flexibility, lightweight nature, and processing ease, are currently being used in initiatives to formulate low-dielectric materials specifically tailored for microwave communication applications. In the current investigation, the authors concentrate on the synthesis, characterization, and dielectric properties of five distinct novel polymer materials. The synthesized polymers are characterized …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1482–1495 Read article
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Advanced AI based Energy Monitoring and Demand Prediction with Theft Detection
Abstract: This paper presents a study on an AI-based energy management system, which is designed for real-time monitoring of energy consumption for theft detection and energy demand prediction. Our energy management system has voltage and current sensors for energy consumption measurement and provides real- time data on voltage (V), current (mA), and energy units. We have implemented Machine Learning algorithm SVM to improve the process of theft detection by identifying anomalies …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 17, Issue 1, 2026 Read article
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Kenaf Reinforced Polyamide Composite Materials: A Systematic Review
Abstract: Sustainable development has become the global target to prevent exploitation and degradation of our environment in the pursuit for industrial growth. Almost all sectors including engineering, dental, medical, aerospace, etc use synthetic polymers in daily basis. The growing need for sustainable natural alternatives has encouraged research on natural fibres reinforced polymers like kenaf reinforced polyamide composites. Comprehensive evaluation of existing studies is crucial to understand the scope in this field …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 158–169 Read article
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Effect of Local Application of Phenytoin on Wound Healing in Surgical Wounds: A Scientific Review
Abstract: Surgical wound healing is a dynamic biological process influenced by local tissue response, vascularity, infection control, and systemic patient factors. Delayed wound healing contributes significantly to postoperative morbidity and healthcare costs. Phenytoin, a hydantoin derivative traditionally used as an antiepileptic agent, has demonstrated unexpected wound-healing properties when applied topically. This review critically evaluates experimental and clinical evidence regarding the role of locally applied phenytoin in enhancing wound healing in surgical …
Published in Research and Reviews : Journal of Surgery · Vol. 15, Issue 1, 2026 · pp. 15–19 Read article
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Optimizing Mango Harvest Timing in the Nasik Region (Maharashtra, India) by CNNs (Residual Network 101)
Abstract: The determination of optimal harvest timing is one of the most critical decisions in mango production, directly affecting postharvest quality, market value, transportation resilience, and export readiness. In regions such as Nashik, Maharashtra—one of India’s major fruit- producing belts—the climatic variability, cultivar differences, monsoon patterns, and market- driven pressures make accurate harvest timing essential. Traditional maturity assessment relies on subjective visual inspection, specific gravity, or destructive testing, each of which …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 Read article
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Seasonal Dynamics of Coastal Landscapes: A Critical Review Using Remote Sensing and GIS
Abstract: Coastal landscapes are among the most dynamic environments on Earth, undergoing continuous transformation due to both natural processes and anthropogenic activities. In India, particularly along the southern coastal regions of Andhra Pradesh, Tamil Nadu, and Kerala, shoreline morphology and sediment transport patterns are significantly influenced by seasonal monsoons, cyclones, storm surges, waves, tides, and changing river discharges. These factors contribute to varying rates of coastal erosion, accretion, inundation, and land- …
Published in Journal of Remote Sensing & GIS · Vol. 17, Issue 2, 2026 Read article
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Interest Level Prediction in Rental Properties Using Data Science
Abstract: A key component of forecasting home prices and rental patterns is real estate market analysis. Data science, data mining methodologies, and statistical models are some of the strategies that have been created in recent years to solve this problem. A few problems are still required to be resolved, such as the obstacles caused by the availability and quality of the data; the presence of outliers, missing values, and inconsistent formats …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 1, 2024 · pp. 28–34 Read article
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Automated Crop Disease Detection Using Convolutional Neural Networks
Abstract: Crop diseases contribute to major losses in agricultural production worldwide generating enormous economic costs. This study investigates the possibility of Convolutional Neural Networks (CNN) imaging techniques to auto-detect diseases associated with plants through image processing. A model was developed and trained on a publicly available plant disease dataset containing labeled images of several diseases. The CNN could classify various plant diseases with accuracy of 95%, precision of 92%, and recall …
Published in International Journal of Cheminformatics · Vol. 3, Issue 2, 2025 · pp. 7–15 Read article
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Transformative Breakthroughs: Revolutionizing Potato Disease Detection Through Machine Learning
Abstract: Advancements in agricultural technology and the integration of artificial intelligence for diagnosing plant and leaf diseases are crucial for sustainable agricultural development. Conditions like early blight and late blight exert a notable influence on both the quality and quantity of potato harvests. Identifying these leaf diseases manually demands significant labor and a considerable level of expertise. Therefore, efficient, and automated methods for disease detection are essential to improve potato production. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 54–62 Read article
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Integrative Network Biology Analysis of GSE6011 Uncovers Molecular Signatures in Duchenne Muscular Dystrophy
Abstract: Duchenne muscular dystrophy (DMD) is a rare, severe neuromuscular disorder demonstrated by progressive skeletal muscle deterioration and premature mortality. Despite advances in supportive care, no definitive cure exists, highlighting the need to explore novel molecular targets. The current study aimed to uncover key dysregulated genes and molecular pathways in DMD through a dataset-specific network biology approach. Publicly available microarray data (GSE6011) from DMD quadriceps muscle biopsies of 22 patients and …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 36–48 Read article
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Design and Implementation of an IoT-based Traffic and Parking Management System Integrated with GIS for Urban Environments
Abstract: As urbanization accelerates, managing traffic flow and parking availability has become increasingly challenging. This article presents the design and implementation of an internet of things (IoT)-based traffic and parking management system integrated with geographic information systems (GIS) to address these challenges in urban environments. The proposed system utilizes a network of IoT sensors to monitor traffic flow, congestion levels, and parking space availability in real time. The data collected by …
Published in Trends in Transport Engineering and Applications · Vol. 11, Issue 3, 2024 · pp. 23–32 Read article
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A Comprehensive Review on Federated Learning in Disease Detection
Abstract: Healthcare data, which is frequently dispersed among various organisations, has enormous potential to improve predictive analytics and illness identification. However, there are substantial privacy & legal obstacles to sharing this private data for centralised model training. Federated Learning is a paradigm shift that allows several organisations to work together to build a global model without disclosing raw patient information. Federated Learning uses a larger dataset to provide more reliable insights …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 1, 2026 · pp. 1–21 Read article