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527 articles for “classification”
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Investigation of Pollution Status in River No. 2, Freetown, Sierra Leone, Using Physicochemical and Bacterial Indicators
Abstract: This study assessed the physicochemical and bacteriological quality of surface water in the River No. 2 watershed, Sierra Leone, through monthly sampling from March–August 2024 at upstream, midstream, and downstream sites. Analyses included temperature, turbidity, pH, electrical conductivity, total dissolved solids, ammonia, fluoride, sulfite, nitrate, lead, arsenic, chromium, and microbial indicators (Escherichia coli, fecal and non-fecal coliforms). Most physicochemical parameters met WHO drinking-water guidelines. pH (7.0–7.3), TDS (7–17 mg/L), turbidity …
Published in International Journal of Pollution: Prevention & Control · Vol. 4, Issue 1, 2026 · pp. 11–27 Read article
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Depression Detection Using AI with Chatbot Support
Abstract: Depression is a major global health concern and a significant contributor to suicide rates worldwide. India reports a high number of suicide cases, making the early detection of mental distress and depression essential for timely intervention. This research presents an AI-based system for depression detection that integrates deep learning, natural language processing (NLP), and a chatbot for user support. The system analyzes facial expressions using convolutional neural networks (CNNs) and …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 14, Issue 1, 2025 · pp. 01–08 Read article
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Petrographic and Geochemical Characteristics of Deccan Trap Basalts from Belagavi, Karnataka, India
Abstract: The Deccan Trap volcanic province represents one of the largest continental flood basalt provinces in the world and provides significant insights into magma generation and evolution processes. The present study focuses on the petrography and geochemistry of basalts from the Belagavi region, which forms part of the southern extension of the Deccan volcanic province. Petrographic analysis of thin sections reveals that the basalts are mainly composed of plagioclase feldspar and …
Published in International Journal of Minerals · Vol. 3, Issue 1, 2026 · pp. 52–61 Read article
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Stock Market Prediction Using Machine Learning: Techniques, Challenges, and Future Directions
Abstract: The continuous advancement of machine learning (ML) technologies has significantly transformed the field of financial forecasting, particularly in the area of stock market prediction. The ability to accurately forecast stock price movements and market trends plays a crucial role in supporting informed investment strategies and effective risk management. This paper provides a comprehensive review of recent developments in the application of ML techniques for predicting stock market behavior. It classifies …
Published in E-Commerce for Future & Trends · Vol. 13, Issue 1, 2026 · pp. 10–16 Read article
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Revolutionizing Gender Justice: The Intersection of AI and Third-Gender Rights in Indian Legal Systems
Abstract: Artificiаl intеlligеncе (AI) аnd third-gеndеr rights in Indiа convеrgе аt а рivotаl juncturе for аdvаncing еquitаblе justicе systems. A trаnsformаtivе cараcity emеrgеs whеn аlgorithmic tools аrе strаtеgicаlly alignеd with lеgаl frаmеworks, раrticulаrly in reducing systemic bаrriеrs through еnhаncеd judiciаl аccеssibility. This аnаlysis еvаluаtеs AI’s duаl rolе аs both cаtаlyst аnd chаllеngе within Indiа’s evolving рolicy lаndscаре, focusing on thrее domains: lеgislаtivе dеsign, рredictivе jurisрrudеncе, аnd rights-bаsеd govеrnаncе structurеs. Currеnt rеsеаrch …
Published in Recent Trends in Social Studies · Vol. 3, Issue 1, 2026 · pp. 25–31 Read article
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Image-Based Crack Morphology Characterisation for Electrical Failure Analysis in Conductive Polymer Composites
Abstract: Electrical performance in conductive polymer composites is strongly governed by crack-network evolution, yet failure analysis typically relies on qualitative image inspection or electrical anomaly detection in isolation. This work proposes an end-to-end framework that converts optical/SEM crack imagery into a standardised crack morphology signature and quantitatively links it to electrical degradation indicators. A two-stage learning strategy is adopted: crack-representation pretraining using the public Concrete Crack Images for Classification dataset, followed …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1375-1386 Read article
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Crop Disease Prediction Using Image Processing
Abstract: For any country in the world, its livelihood depends on agriculture. However, crop diseases affect the production and food supply of any country because we are unable to detect crop diseases. This paper presents a machine learning CNN (convolutional neural network) model, which uses images of crops to detect diseases. This model detects the diseases in the early stage and provides us with a solution to the crop diseases. It …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 15, Issue 2, 2026 · pp. 9–16 Read article
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Design and Optimization of Domain-Specific Languages for High-Performance Computing Applications
Abstract: The accelerating demand for computational power in scientific, engineering, and data-intensive domains has driven High-Performance Computing (HPC) systems toward unprecedented levels of parallelism and architectural complexity. Contemporary HPC platforms integrate multicore CPUs, many-core GPUs, accelerators, and deep memory hierarchies, creating significant challenges for software development and performance optimization. Traditional general-purpose programming languages and parallel programming frameworks provide low-level control over hardware resources but require extensive manual tuning, resulting in poor …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 Read article
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Comparison of Models of Machine Learning and Hyperparameter optimization methods on various datasets
Abstract: The most likely phase in achieving powerful and robust machine learning models is probably the hyperparameters tuning step. The traditional exhaustive methods of search (Grid Search and others) ensure that the search space is covered, but are computationally very inexpensive; random search is less expensive and can still miss good regions; and lastly, the modern model-based and population-based methods (Bayesian Optimization, Tree-structured Parzen Estimator (TPE), Genetic Algorithms) are thought to …
Published in Recent Trends in Programming languages · Vol. 13, Issue 1, 2026 Read article
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A Low-Cost Multi-Sensor IoT System for Real-Time Segregation of Polymer Waste
Abstract: Segregation of solid waste is a critical aspect of waste management, especially in settings where technical and financial constraints limit the adoption of sophisticated technologies. The proposed low cost, sensor-driven smart waste sorting system combines a variety of sensing technologies with an integrated decision-making system. The system employs an inductive sensor, moisture sensor and capacitive sensor to measure the physical properties of waste items, allowing segregation into metal, wet and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 90–`107 Read article
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Physicochemical Transitions and Polymerization Dynamics in Multi-Generational Dentin Adhesives: A Critical Review of the Resin-Dentin Composite Interface
Abstract: Adhesive dentistry has undergone a transformative refinement over the past three decades, transitioning from technique-sensitive, multi-step etch-and-rinse protocols to streamlined universal formulations. This narrative review critically synthesizes evidence from thirty peer-reviewed investigations to evaluate the evolution of dentin bonding agents from the fourth through the eighth generations. The analysis places particular emphasis on the physicochemical dynamics of the resin-dentin interface, including interfacial bond strength metrics, marginal integrity, and microleakage behavior. …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 209–228 Read article
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A Study on High-Risk Pregnancy and Its Management: Role of Obstetric Nurses in Improving Maternal and Fetal Outcomes
Abstract: High-risk pregnancy is a major challenge in maternal and neonatal healthcare and is associated with increased morbidity and mortality among mothers and new-borns, particularly in developing regions where access to quality healthcare services remains uneven, this article examines the concept, classification, risk factors, pathophysiology, and management of high-risk pregnancy with a strong focus on the role of obstetric nurses in improving maternal and fetal outcomes, obstetric nurses play a central …
Published in International Journal of Midwifery Nursing And Practices · Vol. 4, Issue 1, 2026 · pp. 26–43 Read article
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Assessing Taila Bindu Pariksha as a Diagnostic and Prognostic test for Diabetes Mellitus
Abstract: Ayurveda has consistently emphasised on not just treatment of a disease but also how to diagnose it and further assess the prognosis. Ayurveda is blessed with different diagnostic tests which is broadly classified into Roga and Rogi Pariksha such as Ashtavidha Pariksha, Dashavidha Pariksha and Dwadashavidha Pariksha. These tests are used very rarely by just few Ayurvedic practioners as an important diagnostic and prognostic methods in different diseases. Taila Bindu …
Published in Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy · Vol. 15, Issue 2, 2026 Read article
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A Review on Artificial Intelligence Techniques for Analyzing Deforestation and Illegal Logging Using Satellite Imagery
Abstract: Deforestation and illegal logging remain critical environmental threats, driving biodiversity loss, climate change, and socio-economic disruption. Conventional monitoring techniques frequently do not yield real-time, large-scale insights. Recent developments in Artificial Intelligence (AI), especially in deep learning and computer vision, have revolutionized the ability to analyze high-resolution satellite images for detecting deforestation and monitoring illegal logging. This review synthesizes recent developments in AI-driven approaches, highlighting convolutional neural networks (CNNs), anomaly detection …
Published in International Journal of Satellite Remote Sensing · Vol. 4, Issue 1, 2026 · pp. 1–9 Read article
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Time Multiplexed Binary Offset Carrier (TMBOC) Transmitter with Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR)
Abstract: This paper provides insights into Time Multiplexed Binary Offset Carrier (TMBOC), a modulation technique employed in satellite navigation systems, specifically designed for GPS L1C. TMBOC improves signal correlation properties by time-multiplexing Binary Offset Carrier (BOC) (1, 1) and (6, 1). The text discusses various TMBOC models, including spectral representations and power distributions. Performance analysis reveals the potential of TMBOC signals in achieving superior tracking accuracy and interference resistance compared to …
Published in International Journal of Satellite Remote Sensing · Vol. 4, Issue 1, 2026 · pp. 34–49 Read article
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An AI-Driven IoT Framework for Autonomous Quality Assurance in Optical Lens Manufacturing
Abstract: The evolution of high-precision optics—ranging from smartphone micro-lenses to high-end astronomical glass—demands unprecedented accuracy in manufacturing. Traditional inspection methods, reliant on manual sampling or static automated optical inspection (AOI), often fail to bridge the gap between high-speed production and the detection of microscopic surface aberrations. This paper introduces an integrated architecture combining the Internet of Things (IoT) and Deep Learning-based decision-making systems to revolutionize lens quality control. By deploying an …
Published in International Journal of Optical Innovations & Research · Vol. 4, Issue 1, 2026 · pp. 36–41 Read article
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AI Driven IoT Based Decision Making System for Brain wave study: KSK approach for Brain wave study
Abstract: The rapid convergence of Internet of Things (IoT) architectures and deep learning has unlocked unprecedented potential for real-time neuro-diagnostic monitoring. This paper presents a novel framework for an AI-driven IoT ecosystem designed to capture, transmit, and interpret human electroencephalography (EEG) signals with minimal latency. Traditional brain-computer interface (BCI) studies are often constrained by localized computing power and the high dimensionality of neural data. Our proposed architecture integrates low-power EEG sensors …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 Read article
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Non-Small Cell Lung Cancer: Types, Pathogenesis, Diagnosis, and Novel Therapeutic Strategies
Abstract: Non-small cell lung cancer (NSCLC) is the most prevalent type of lung cancer, accounting for over 85% of all cases globally. It remains one of the primary causes of cancer-related death due to its rapid progression, few early symptoms, and late detection. The three main forms of non-small cell lung cancer (NSCLC) are adenocarcinoma, squamous cell carcinoma, and giant cell carcinoma; each has a unique histology, prognosis, and response to …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 16, Issue 2, 2026 Read article
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Lightining Future With Non Degradable Waste
Abstract: This project focuses on addressing the growing problem of non-biodegradable waste, which creates major environmental and management issues. It proposes a smart system for waste segregation and energy production using the Arduino UNO R4 platform. The system classifies waste into three types dry, wet, and metal by using various sensors such as proximity, moisture, infrared, temperature, and weight sensors. After segregation, dry waste is sent to an incineration chamber where …
Published in Journal of Energy, Environment & Carbon Credits · Vol. 16, Issue 2, 2026 Read article
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Machine Learning-Based Quantification of Polymer Structure Property Relationships for Predictive Material Design
Abstract: Polymer structures exhibit complex, hierarchical arrangements that strongly influence macroscopic properties, yet consistent quantification remains challenging due to nonlinear interactions and limited unified modeling strategies. Existing approaches inadequately capture generalized structure–property mappings across diverse polymer systems. This research aims to establish a machine learning-based quantification model for polymer structure–property relationships to support predictive material design. A Polymer Structure Property Dataset of 5,000 polymer samples includes structural descriptors and experimentally measured …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 737–754 Read article