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273 articles for “methodological techniques”
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Advancements in Drug Design Technology and Its Impact on COVID-19 Treatment
Abstract: The deadly coronavirus disease 19 (COVID-19) pandemic has recently spread, raising concerns about global health. The search for novel therapeutic compounds is made more necessary by the persistent problem of the absence of licensed medications or vaccinations. By saving money and time, computer-aided drug design has sped up the process of finding and developing new drugs. The structured-based and ligand-based drug discovery subcategories of computer-aided drug design (CADD) are the …
Published in International Journal of Virus Studies · Vol. 1, Issue 1, 2024 · pp. 1–15 Read article
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Aerodynamic Optimization of UAV Wings Using Machine Learning
Abstract: Unmanned Aerial Vehicles (UAVs) are increasingly deployed across defense, transportation, agriculture, and environmental monitoring, demanding improved aerodynamic efficiency to enhance endurance, stability, and payload capacity. Traditional aerodynamic optimization approaches, relying on computational fluid dynamics (CFD) simulations and wind tunnel experiments, are often time-consuming and computationally expensive. This study proposes a machine learning (ML)-driven framework for the aerodynamic optimization of UAV wing geometries, aiming to significantly reduce design cycles while improving …
Published in International Journal on Drones · Vol. 2, Issue 1, 2026 · pp. 1–7 Read article
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Enhancements in Titanium Matrix Composites: A Multifaceted Exploration of Various Structural Ceramics Reinforcement
Abstract: This research study delves into the comprehensive exploration of Titanium Matrix Composites (TMCs) reinforced with Silicon Carbide (SiC), Alumina (Al2O3), and Boron Nitride (BN). The investigation is structured into three key phases, each addressing essential aspects of TMCs: Phase 1 involves materials and manufacturing, where TMC coupons were fabricated through the stir casting technique with varying compositions of aluminum and silicon carbide. Phase 2 focuses on characterization and defect detection, …
Published in Journal of Polymer & Composites · Vol. 12, Issue 1, 2024 · pp. 83–94 Read article
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Therapeutic Efficacy of Artificial Intelligence based Counselling for Anxiety and Depression: A Systematic Review
Abstract: Background: The occurrence of the COVID-19 pandemic resulted in a notable rise in the prevalence of mental health issues, majorly anxiety and depression. Treatments like cognitive behavioural therapy (CBT) conducted in a face-to-face setting, have consistently demonstrated their effectiveness. Though not always accessible or reasonably priced for everyone, face-to- face treatment environments offer great assistance. AI-based counselling is starting to be a good replacement for those in need of real-time, …
Published in Recent Trends in Social Studies · Vol. 2, Issue 1, 2025 · pp. 8–16 Read article
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Chemiluminescence: Recent Developments and Photochemical Applications in Analytical Chemistry
Abstract: Chemiluminescence (CL) is the observable production of electromagnetic radiation from a chemical reaction, typically in the form of ultraviolet, visible, or infrared light. According to subjective yield, CL has either been directly emitted from electronically excited intermediate products, or it has been supported by another molecule to emit CL. Since the 1950s, CL has been supported as a valid analytical tool, with the descriptive issued scope being limited to content …
Published in International Journal of Photochemistry and Photochemical Research · Vol. 2, Issue 1, 2024 · pp. 20–25 Read article
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Artificial Intelligence in Entomology: Global Advances, Applications, and Future Directions in Insect Research and Pest Management
Abstract: Artificial Intelligence (AI) is transforming entomology by enabling scalable, data-driven approaches to insect identification, ecological monitoring, and sustainable pest management. This review synthesizes recent global advances in AI applications across taxonomy, behavioral ecology, predictive modeling, and precision agriculture. Machine learning and deep learning techniques—including convolutional neural networks, acoustic classification models, and ensemble predictive algorithms—have demonstrated high classification accuracies (often exceeding 90% under controlled conditions) and improved early detection of pest …
Published in International Journal of Insects · Vol. 3, Issue 1, 2026 · pp. 29–40 Read article
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A Split and Merge UNet: A Deep Learning Assisted UNet Model to Segment Corpus Callosum of Brain for Automatic Autism Detection
Abstract: In recent years, deep learning techniques have shown remarkable performance in various image analysis applications, particularly in the domain of medical image processing. Among these, image segmentation plays a critical role, as it helps in isolating and analyzing specific regions within medical images. The proposed study focuses on segmenting the corpus callosum, a vital structure in the human brain, using a novel optimization technique known as the Split and Merge …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 11, Issue 3, 2024 · pp. 1–9 Read article
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Intelligent Aquaculture System for Fish Disease Detection Using Machine Learning
Abstract: Aquaculture is one of the key factors for global food security, but fish diseases bring about heavy economic losses and jeopardize sustainability. One of the most important aspects of global food security is aquaculture, but fish infections endanger sustainability and cause significant financial losses. Early diagnosis is not possible since traditional disease detection techniques are laborious and necessitate expert intervention. To effectively detect fish infections, this study suggests an Intelligent …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 12, Issue 2, 2025 · pp. 30–37 Read article
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Hybrid Quantum-Classical Reinforcement Learning Enabled Thermal-Aware Electronic Design Automation Framework for Energy-Efficient Next-Generation VLSI Systems Applications
Abstract: Modern Very Large-Scale Integration (VLSI) systems are becoming more complicated, which has increased need for sophisticated Electronic Design Automation (EDA) frameworks that can concurrently optimise thermal behaviour, power consumption, and performance. This study proposes a Hybrid Quantum-Classical Reinforcement Learning (HQCRL) Enabled Thermal-Aware EDA Framework for next-generation energy- efficient VLSI systems. The proposed framework integrates quantum-inspired optimization techniques with classical reinforcement learning algorithms to address the challenges of placement, routing, and …
Published in Journal of Electronic Design Technology · Vol. 17, Issue 2, 2026 Read article
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Experimental and Numerical Investigation of Corrosion-Induced Failures in Copper-Tin Alloy (Cu-Sn) and Aluminum-Magnesium Alloy (Al-Mg) Connectors: A Stress–Corrosion Coupling Analysis
Abstract: The utilization of an integrated experimental and finite element modelling (FEM) methodology, this study investigates the degradation and failure mechanisms in polymer composite electrical connectors exposed to aggressive environmental conditions. Epoxy- and polyamide-based composites, reinforced with carbon and glass fibers, were subjected to accelerated salt spray and humidity–temperature cycles to simulate prolonged outdoor exposure. Electrochemical and environmental aging experiments revealed that chloride ions and moisture ingress were responsible for matrix …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 366–379 Read article
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Physico-Chemical Soil Analysis: A Scientific Approach to Life on Land and Terrestrial Ecosystem Conservation for Supporting SDG 15 Goals
Abstract: Soil health and fertility evaluation are foundational elements in the sustainable management of ecosystems and the enhancement of agricultural productivity. Accurate assessment of soil properties, especially Carbon (C), Nitrogen (N), and Phosphorus (P), is critical for informed decision-making in agriculture and environmental conservation. However, traditional laboratory methods for analyzing these essential nutrients often involve significant resource allocation and time commitments, making them less practical for large-scale or time-sensitive applications. To …
Published in Journal of Geotechnical Engineering · Vol. 12, Issue 1, 2025 · pp. 25–23 Read article
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Applying Text Analysis Methods for Emotion Recognition
Abstract: This article presents a comprehensive study of sentiment analysis, a vital task in the realms of natural language processing (NLP) and artificial intelligence (AI). Sentiment analysis involves the extraction and classification of subjective information from textual data, determining whether the sentiment expressed is positive or negative. This paper investigates different approaches and methodologies used in sentiment analysis, encompassing machine learning models as well. Additionally, it discusses the challenges faced in …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 11, Issue 2, 2024 · pp. 12–22 Read article
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Implementation of Robotics in Pharmacy System
Abstract: Implementation of robotics are the essential aspects for development of the pharmacy sector. This replaces the medication (or) industrial process with new technology which results in significance of accurate and quality results. This technology aims to detect and ensure quality of finished products with new methodology which increases in productivity and it also identifies potential problems and rectify them which gives a great impact on pharmacy system. The main purpose …
Published in Journal of Instrumentation Technology & Innovations · Vol. 14, Issue 1, 2024 · pp. 1–15 Read article
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A Comprehensive Investigation of Bagging-Based Ensemble Methods for Improving Machine Learning Model Robustness
Abstract: Machine learning models such as Decision Trees, Logistic Regression, and K-Nearest Neighbors are widely used for classification tasks due to their simplicity and interpretability. However, these models often suffer from high variance, overfitting, and poor generalization when applied to real-world datasets, particularly those that are small, noisy, or imbalanced, as commonly encountered in healthcare, finance, and cybersecurity applications. To address these limitations, this research proposes a Bagging (Bootstrap Aggregating)-based ensemble …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 24–34 Read article
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A New Separation Technique for Method, Development and Validation of Aclidinium Bromide and Formoterol Fumarate in Its Pure and Pharmaceutical Dosage Form by Using Rp-HPLC
Abstract: A new simple, precise, selective and accurate, a new method of development and validation using Rp-HPLC for the estimation of Aclidinium bromide and Formoterol Fumarate in its pure and pharmaceutical dosage form. Chromatogram was run through DIKMA Spursil, C18 segment (4.6×150 mm, 5µ0). Mobile phases contain 0.1% OPA: Acetonitrile (30:70) with the use of the 0.1% OPA buffer, stream rate of 1 ml/min and measured in 280 nm. The run …
Published in Research and Reviews: A Journal of Pharmaceutical Science · Vol. 15, Issue 3, 2024 · pp. 18–36 Read article
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AI-Assisted Gain Scheduling for Real-Time Temperature Control in Chemical Reactors
Abstract: Temperature control in continuous stirred-tank reactors (CSTR) represents a critical challenge in chemical process industries due to inherent nonlinearities, time-varying dynamics, and parametric uncertainties. Conventional proportional-integral-derivative (PID) controllers with fixed gains often fail to maintain optimal performance across varying operating conditions, leading to temperature excursions that compromise product quality and safety. This paper presents a novel AI-assisted gain scheduling framework that integrates artificial neural networks (ANN) with adaptive PID control …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 1, 2026 · pp. 24–33 Read article
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Record Linkage in Knowledge Discovery Process Using Angle Based Machine Learning
Abstract: Record linkage is a critical data cleansing step in the knowledge discovery process, aimed at identifying and resolving inconsistencies across datasets. This study proposes an enhanced record linkage framework tailored for uncertain and large-scale data using a combination of distance measurement, probabilistic modeling, and semantic reasoning. A novel angle-based distance measurement technique is introduced to optimize matching between candidate records. To further boost match accuracy, a Finite Mixture Model (FMM) …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1157–1170 Read article
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Numerical Investigation of Heat Transfer Enhancement in Micro-Channel Cooling Using Finite Element Analysis
Abstract: Due to the enormous heat fluxes emitted by modern electronic chips, there is a persistent need to enhance the efficiency of cooling systems. This study's focus is on optimizing heat transfer in micro-channel heat sinks that use liquid cooling. Geometric changes and the use of nano-fluids as coolants in place of water are performed to achieve this goal with little energy consumption. Numerical analysis of pipe micro-channel fluid flow and …
Published in International Journal of Energy and Thermal Applications · Vol. 1, Issue 1, 2023 · pp. 36–44 Read article
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Study of Algebraic Structures in Discrete Mathematics and Its Applications
Abstract: Algebraic structures such as groups, rings, fields, semi groups, and lattices form the foundational framework of discrete mathematics. These structures are defined by specific sets and operations that follow algebraic laws, enabling a systematic approach to problem-solving in various domains. This paper explores the theoretical principles of these algebraic systems and highlights their vital role in computer science, cryptography, automata theory, coding theory, and software engineering. By examining their properties …
Published in Emerging Trends in Symmetry · Vol. 1, Issue 1, 2025 · pp. 35–40 Read article
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Need For EPDS in Indian High-Rise Constructions for Reduced Greenwashing and Sustainable Future with Green Polymer and Composites
Abstract: Environmental product declarations (EPDs) are a globally recognized means of communicating the environmental impacts of a product or service across its entire lifecycle. They evaluate various items' environmental performance and pinpoint enhancement chances. EPDs are very useful in the construction industry since they may be used to choose environmentally friendly building materials and techniques. India is undergoing a swift construction surge, with high-rise towers becoming progressively prevalent.. However, the construction …
Published in Journal of Polymer & Composites · Vol. 12, Issue 7, 2024 · pp. 54–66 Read article