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1978 articles for “CLA” (ranking capped at the first 2,000 matches — narrow the search to see the rest)
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Secure Forge: Deepfake Image Detection Using Vision Transformers
Abstract: Deepfake technologies have become a major risk to the credibility and trustworthiness of digital visual information. Using powerful generative models like GANs and autoencoders, deepfakes can generate highly realistic fake videos and images, resulting in misinformation, identity theft, and public loss of trust in digital media. Classic Convolutional Neural Networks (CNNs) while being highly effective in initial-stage, deepfake detection tend to be limited by their local receptive fields and dependency …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 32–45 Read article
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Fault Detection in Solar PV Systems Integrated with the Power Grid: Evaluating Logistic Regression through Confusion Matrix Analysis
Abstract: This paper proposes a method for failure detection in grid-integrated solar photovoltaic (PV) systems using logistic regression and real-time sensor data. The approach effectively classifies and identifies seven distinct fault types. The developed model demonstrates a high fault identification accuracy, ranging from 93% to 96.5% across various fault types and operational conditions. By leveraging logistic regression, the system utilizes key independent variables that significantly influence the classification process. Additionally, the …
Published in Journal of Power Electronics and Power Systems · Vol. 15, Issue 2, 2025 · pp. 45–52 Read article
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Neurophysiological Effects of Chanting the Gayatri Mantra on Students of Allied Health Sciences: A Study among 1200 Students at Paramedical College, Desh Bhagat University
Abstract: Background: The Gayatri Mantra, a sacred Vedic chant, is traditionally believed to offer numerous benefits, including mental clarity, stress reduction, and cognitive enhancement. Despite anecdotal evidence supporting these claims, scientific research on its neurophysiological effects, especially within educational settings, remains limited. This study investigates the impact of chanting the Gayatri Mantra on brain activity, stress levels, and cognitive performance among 1200 students in the Allied Health Sciences program at Paramedical …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 15, Issue 2, 2025 Read article
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Evaluating Delay Impacts: From Root Cause Analysis to Dispute Resolution
Abstract: Construction projects inherently involve complex coordination among multiple stakeholders, interdependent tasks, and unpredictable external challenges, which frequently cause schedule overruns and disputes. Traditional contractual mechanisms—such as extensions of time (EoT), liquidated damages, and force majeure clauses—serve to define responsibilities, allocate risk, and establish formal dispute pathways when delays arise. For example, EoT provisions protect contractors from penalties when delays are beyond their control, provided proper notice and evidence are submitted …
Published in Journal of Structural Engineering and Management · Vol. 12, Issue 3, 2025 · pp. 11–25 Read article
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Polymer Chemistry in Flexible Dental Assemblies: Designing a Polymer-Based Composite Arm for Bio-Fluid Extraction Tube Support
Abstract: Polymer chemistry offers innovative solutions for designing flexible, durable assemblies in medical applications. This study explores an assembly for holding a bio-fluid extraction tube, featuring a flexible arm interpreted as a polymer composite, aimed at enhancing dental procedures. The assembly comprises a gooseneck-style arm—hypothesized as a rubber or fiber-reinforced polymer—connecting a first clamp (for anchoring to an object, e.g., dental chair) and a second clamp (securing the extraction tube). The …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 285–292 Read article
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Ai-Driven Healthcare System for Enhanced Diagnosis and Patient Interaction
Abstract: Deep learning techniques are used in an AI-driven healthcare system to improve disease identification and medical picture analysis. Data collection, preprocessing, model training, and evaluation are all part of the system's systematic workflow. Various deep learning architectures, such as ResNet50, VGG-16, and U-Net, are employed for precise classification and segmentation of medical images. The approach incorporates advanced techniques such as optimization, transfer learning, and data augmentation to significantly enhance the …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 2, 2025 Read article
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AI-Assisted Defect Detection in Polymer Composite Insulators Using an Optimised Ensemble Deep Learning Framework for Structural Health Monitoring
Abstract: Polymer composite insulators, particularly those made from silicone rubber and epoxy resins, are increasingly adopted in high-voltage transmission systems due to their superior electrical insulation, lightweight design, hydrophobicity, and environmental durability. Despite their advantages, these materials are susceptible to surface degradation, mechanical cracking, and flashover under prolonged exposure to environmental pollutants, thermal stress, and electrical aging. Accurate, real-time condition assessment of these composite insulators is critical for ensuring operational safety, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 253–261 Read article
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Optical Image Sensing and Analysis of Iron Ore Pellets: A Machine Learning Approach
Abstract: The present work is aimed to improve quality control in steel production using SEM imaging and machine learning. High-resolution SEM images of iron ore pellets, primarily composed of hematite and magnetite, are analyzed to understand their microstructural features, which significantly impact pellet performance during reduction processes. Traditional microstructure analysis is manual, time- consuming, and prone to inconsistencies. This study proposes an automated approach using K-Means Clustering, Canny Edge Detection, DBSCAN, …
Published in Journal of Instrumentation Technology & Innovations · Vol. 15, Issue 3, 2025 · pp. 7–18 Read article
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Malicious Network Traffic Detection Using Hybrid Feature Selection with Ensemble Neural Network
Abstract: The detection of malicious network traffic is a critical aspect of cybersecurity, aiming to protect sensitive data and maintain the integrity of network systems. This study introduces a novel approach that combines hybrid feature selection with ensemble neural networks to enhance the accuracy and efficiency of malicious network traffic detection. The dataset used in this study was obtained from Kaggle and offers a wide-ranging and varied collection of network traffic …
Published in Nano Trends – A Journal of Nano Technology & Its Applications · Vol. 27, Issue 3, 2025 Read article
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AI-Powered ECG Prediction System for Detecting Cardiovascular Disease
Abstract: The proposed AI-powered CardioSmart Analyzer, an electrocardiogram (ECG) prediction system, presents an innovative and scientifically rigorous approach to the real-time automated analysis of ECG signals for diagnosing various heart conditions. This research focused on building a predictive model to identify cardiovascular diseases (CVD) using ECG data. A dataset comprising 2,840 12-lead ECG recordings was gathered from medical facilities in Gazipur, Bangladesh, over the period from June to August 2024. The …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 3, 2025 · pp. 51–85 Read article
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Progress in Renal Tumor Surgery: The Role of 3D Surgical Planning in Partial Nephrectomy
Abstract: Renal cell carcinoma is the most common form of kidney cancer, representing 2–3% of global cases, with the highest incidence in Western Europe. For small renal tumors, partial nephrectomy is the preferred treatment, where the tumor is surgically removed. This procedure does not affect oncological outcomes, allowing part of the kidney to remain functional. During tumor removal, the surgeon minimizes excessive bleeding and improves visibility by cutting off the arterial …
Published in Research and Reviews : Journal of Surgery · Vol. 14, Issue 3, 2025 · pp. 35–40 Read article
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The Role of Symmetry in Topological Insulators and Superconductors
Abstract: Topological insulators and superconductors constitute a class of quantum materials characterized by insulating bulks and symmetry-protected conducting boundaries. Symmetry principles, notably time-reversal (TRS), particle-hole (PHS), and chiral symmetry, play a fundamental role in determining the topological phases and their classification within the Altland-Zirnbauer scheme. TRS protects gapless surface states in topological insulators, while PHS stabilizes Majorana modes in topological superconductors. Symmetry-protected topology extends this framework by considering partial symmetry breaking, …
Published in Emerging Trends in Symmetry · Vol. 1, Issue 2, 2025 · pp. 01–05 Read article
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EMOTION RECOGNITION FROM ELECTROENCEPHALOGRAM SIGNAL AND EYE MOVEMENT BASED ON DEEP LEARNING
Abstract: Emotion recognition from electroencephalogram (EEG) signals has gained significant attention due to its potential in human- computer interaction (HCI), mental health monitoring, and personalized content delivery. This paper presents the use of Convolutional neural networks (CNNs) to classify emotions such as happiness, sadness, fear, neutral, and disgust by leveraging a fusion of EEG signals and eye movements data. Compared to conventional methods of emotion detection, such as those that rely …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 2, 2025 · pp. 8–15 Read article
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A Dual-Model Deep Learning Framework for Early Alzheimer’s Detection Using Clinical Data and Neuroimaging with Architectural Performance Analysis
Abstract: Alzheimer’s disease (AD) poses a significant global health challenge due to its increasing prevalence and the absence of definitive cures. Early diagnosis is crucial for effective intervention and management. This study presents a dual-model deep learning framework for the early detection and classification of AD using both structured clinical data and neuroimaging datasets. Model 1 utilizes a greedy layer-wise autoencoder approach applied to structured data, achieving optimal binary classification accuracy …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 1, 2026 · pp. 1–12 Read article
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Management of Kosthasakhashrita Kamala W.S.R. to Hepatocellular Jaundice – A Case Study
Abstract: Kosthasakhāśrit Kamala is a well-recognized clinical entity in Ayurveda and is widely correlated with modern hepatocellular jaundice. The condition arises primarily due to the vitiation of Pitta dosha within the Kostha (gastrointestinal tract), which subsequently spreads to the Sakha (peripheral tissues). Classical texts describe hallmark features such as Haridra Netra (yellowish discoloration of the eyes), yellowish hue of twak and nakha, pitta varṇa of mutra and sakrit (yellow urine and …
Published in Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy · Vol. 15, Issue 1, 2026 · pp. 1–8 Read article
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Evaluation on Bioremediation Kinetics of Petroleum- Contaminated Soils Using Plant-Based Amendments
Abstract: The effectiveness of bioremediation processes is strongly influenced by environmental conditions, reactor design parameters, microbial characteristics, and pollutant properties. This study investigates the combined effects of environmental-related factors, reactor design considerations, organism- related characteristics, and pollutant properties on the degradation of total petroleum hydrocarbons (TPH) in swampy and clay soils amended. Laboratory-scale remediation experiments were conducted over an 84-day period using amendment dosages ranging from 20 to 100 g. The …
Published in Emerging Trends in Chemical Engineering · Vol. 13, Issue 1, 2026 · pp. 24–30 Read article
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A Study on “Clean" in Beauty: A Machine LearningApproach to Ingredient Transparency and ConsumerTrust
Abstract: The burgeoning "clean beauty" market, while driven by consumer demand for safer and more sustainable products, is plagued by ambiguous definitions and the pervasive challenge of "greenwashing". This ambiguity hinders informed consumer choices and complicates brand authenticity. This study addresses these complexities by developing a novel machine learning (ML) framework designed to objectively analyze cosmetic ingredient lists, classify products based on their "cleanliness" profile, and identify key ingredient attributes that …
Published in Recent Trends in Cosmetics · Vol. 3, Issue 1, 2026 · pp. 1–12 Read article
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Particle Swarm Optimization Framework for Accurate Battery State-of-Charge and Remaining Useful Life Estimation
Abstract: Accurate estimation of the State of Charge (SOC) and State of Health (SOH) of a battery is key to safe and efficient management of batteries in electric vehicles and energy-storage systems. However, it is challenging due to high nonlinearity, varying operating conditions, measurement noise, and limited access to comprehensive electrochemical parameters. Traditional data-driven models often generalize poorly and require heavy tuning, which can produce unstable predictions. To address these problems, …
Published in Journal of Automobile Engineering and Applications · Vol. 13, Issue 1, 2026 · pp. 53–64 Read article
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The Muse as Co-Creator: Rani Mrignayani and the Evolution of the Gwalior Dhrupad Tradition
Abstract: This study challenges the old history books that treat women in Indian music simply as romantic inspirations rather than active creators. Looking back at the bustling royal court of 15th-century Gwalior, the research proves that Queen Mrignayani was far more than just a muse; she was an essential co-creator of modern North Indian classical music. The research paper explores how King Man Singh Tomar’s royal support perfectly combined with Mrignayani’s …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 16, Issue 1, 2026 Read article
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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