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53 articles for “Robust metrics”
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A Comprehensive survey of robust image quality metrics for satellite imagery
Abstract: Satellite imagery is essential for applications like environmental monitoring, urban development, precision agriculture, defence surveillance, and disaster response. The reliability of these applications is closely tied to the quality of the captured images, which may be compromised by atmospheric effects, sensor imperfections, compression artifacts, and transmission noise. As a result, accurate image quality assessment (IQA) is essential to ensure trustworthy analysis and informed decision-making in satellite-based systems. The distinctive properties …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 15, Issue 1, 2026 · pp. 7–20 Read article
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Structure–Property Modeling of Cement-Based Multi-Component Composites Using Ensemble Machine Learning and Explainable Feature Attribution
Abstract: Accurate prediction of compressive strength is central to structure–property optimization, quality control, and sustainability-driven design in cement-based composite materials. Cementitious systems represent heterogeneous multi-phase composites composed of reactive binder matrices and dispersed aggregate phases, whose macroscopic mechanical performance emerges from complex nonlinear interactions among constituents and curing-dependent microstructural evolution. This study develops a data-driven structure–property modeling framework to quantify the nonlinear dependence of compressive strength on multi-component composite composition and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 112–131 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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Review article on Quality Control in Clinical Trials
Abstract: Quality control (QC) is a critical component in the conduct of clinical trials, ensuring the accuracy, reliability, and credibility of data collected throughout the study. It encompasses a systematic set of procedures designed to monitor trial conduct and data integrity, thus safeguarding the rights, safety, and well-being of participants. This review explores the principles, implementation, and evolving practices of quality control in clinical trials, highlighting its importance across all phases …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 3, 2025 · pp. 01–07 Read article
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A Majority Function Based Full Subtractor
Abstract: In the present landscape of very large-scale integration (VLSI) technology, the imperative to implement Boolean functions with minimal gate count remains a cornerstone of efficient circuit design. This pursuit has only grown more critical with the evolution of low-power design strategies, which now offer significantly enhanced benefits compared to traditional approaches. The trifecta of performance, affordability, and dependability continue to drive innovation in this field, shaping the trajectory of technological …
Published in Research & Reviews : Journal of Physics · Vol. 13, Issue 2, 2024 · pp. 8–14 Read article
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MentaLLaMA: Advancing Mental Health Insights with Instruction-Finetuned Large Language Models
Abstract: The growing prevalence of mental health challenges in contemporary society has highlighted the urgent need for advanced, interpretable, and reliable artificial intelligence solutions that can support mental health assessment and intervention. In response to this need, this research introduces a novel collection of open-source, instruction-tuned large language models (LLMs) specifically designed to facilitate transparent and accurate mental health evaluations. Leveraging a newly developed dataset, which integrates multiple tasks and diverse …
Published in Recent Trends in Programming languages · Vol. 12, Issue 3, 2025 · pp. 08–15 Read article
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Leveraging Generative AI for Test Case Creation in Complex Systems
Abstract: Modern software systems exhibit increasing complexity, demanding sophisticated testing methodologies to ensure reliability and functionality. Traditional manual testing approaches often struggle to keep pace with this complexity, leading to inadequate test coverage and increased risk of unforeseen issues. This study explores the potential of Generative AI (GAI) in revolutionizing test case creation for complex systems. We delve into the practical application of GAI techniques, such as Variational Autoencoders (VAEs) and …
Published in Recent Trends in Programming languages · Vol. 12, Issue 3, 2025 · pp. 16–22 Read article
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Prediction of Customer Churn Using Machine Learning Classification Models
Abstract: Customer churn prediction is a critical task in both the telecommunication and medical industries, where retaining customers or patients is essential for ensuring long-term profitability and maintaining high-quality service. To address this, a range of machine learning models—including logistic regression, decision trees, random forests, gradient boosting machines, and support vector machines—were employed to accurately forecast churn behavior. Prior to model training, the dataset underwent thorough preprocessing, which included handling missing …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 86–92 Read article
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Photochemically Assisted LC–MS Method Development and Validation for Stability-Indicating Determination of Glasdegib in Rat Plasma
Abstract: A simple, precise, and cost-effective LC–MS method was successfully developed for the determination of Glasdegib in rat plasma. The method optimization was carried out by systematically varying key chromatographic parameters, including flow rate, injection volume, analyte concentration, and mobile phase composition, to achieve optimal sensitivity and resolution. A C18 Hypersil BDS column (150 mm × 4.6 mm, 3.5 µm particle size) was used for chromatographic separation, offering effective peak shape …
Published in International Journal of Photochemistry and Photochemical Research · Vol. 4, Issue 1, 2026 · pp. 11–20 Read article
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Intimate Mappings in Interval-Valued Fuzzy Metric Space: A Few Fixed-Point Findings
Abstract: The purpose of this study is to extend some previously established fixed point results for interval valued fuzzy metric space. For this objective, various contractive criteria with respect to an intimate mapping are applied. We employ intimate mapping in interval valued fuzzy metric space (IVFMS) to validate some well-known fixed point results. Our findings complement and generalize the recent findings of the common fixed point theorem for intimate mapping. The …
Published in Recent Trends in Mathematics · Vol. 1, Issue 1, 2024 · pp. 16–23 Read article
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Securing IoT Wilderness with VHDL
Abstract: The Internet of Things (IoT) has revolutionized connectivity by integrating billions of devices and reshaping industries. However, this vast network also brings substantial security concerns. From compromised sensors to hijacked industrial control systems, the vulnerabilities within IoT devices can have far-reaching consequences. Hardware Security Modules (HSMs) provide a reliable and secure environment for performing cryptographic operations and safeguarding sensitive data. This article explores the crucial role of VHDL (VHSIC Hardware …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 3, Issue 1, 2025 · pp. 29–40 Read article
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Nano‑Enabled CT for Cancer Imaging: From Molecular Targeting to Image‑Guided Therapy
Abstract: Nano‑enabled computed tomography (CT) exploits high‑atomic‑number (high‑Z) nanomaterials engineered with targeting ligands and therapeutic payloads to enhance contrast, enable molecular imaging, and support image‑guided interventions in oncology. Tumor‑specific nanoprobes such as RGD‑modified gold nanorods, polymer‑coated bismuth nanoparticles, and peptide, antibody, or aptamer‑functionalized platforms can intensify tumor conspicuity, allow early lesion detection and staging, and provide real‑time treatment monitoring. Integration of CT visibility with photothermal, photodynamic, chemo‑, radio‑, and immunotherapeutic functions …
Published in Nano Trends – A Journal of Nano Technology & Its Applications · Vol. 28, Issue 1, 2025 · pp. 40–49 Read article
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Strategy for Improving Software Maintenance Using Machine Learning for Security Requirements: A Review
Abstract: Within the area of software technical education, the significance of software defect discovery has increased as a research focus to enhance program reliability. By maximizing testing resources and assisting developers in identifying potential problems using program defect predictions, program dependability is increased. Applying software engineering (SE) techniques to critical and intricate systems, like networking and security systems, is imperative. Traditional methods of predicting software maintainability have limitations, particularly in balancing …
Published in International Journal of Information Security Engineering · Vol. 2, Issue 2, 2024 · pp. 36–48 Read article
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GreenDiagnosis: Intelligent Crop Disease Detection Using Deep Learning Algorithm
Abstract: Agriculture in parts of India relies on labour-intensive traditions, maintaining disease-free crops is crucial. Manual methods can be inaccurate, driving farmers towards AI-based solutions. AI offers a proactive approach to address real-time farming challenges. Among these is the invasion of pests, which diminishes crop quality. Combating pest-related diseases poses a challenge, prompting innovation. Effective surveillance and early detection of crop diseases play a pivotal role in ensuring global food security …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 2, 2025 · pp. 8–18 Read article
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A Supervised Learning Approach for Toxic Comment Detection on Social Media Platforms
Abstract: Nowadays everyone uses social media platforms like X (formerly Twitter), Instagram, Facebook, etc. for various purposes. With the help of this, we share our opinions, ideas, and feelings. Generally, the datasets obtained from the internet are constructive; however, there is a significant proportion of toxic ones. The datasets are filtered to remove noise, and noise is removed in post-processing. The study initiates with the upload and preprocessing of a toxic …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 11, Issue 2, 2024 · pp. 7–14 Read article
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A Comparative Analysis of Machine Learning Techniques for Fruit Defect Detection Systems
Abstract: With evolving technologies in machine learning, significant advancements have been made in the livestock industry, helping to reduce waste, increase yield, achieve cost savings, and improve competitiveness in the marketplace. Fruit defect detection models support precision agriculture by providing valuable data for decision-making and enhancing overall efficiency through automated inspection processes. This study implements and comparatively evaluates machine learning models including MobileNetV2, a custom-designed convolutional neural network (CNN) model, ResNet50, …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 3, 2024 · pp. 37–47 Read article
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Comparative Analysis of Supervised Learning Algorithms
Abstract: Supervised learning is a fundamental and widely used branch of machine learning in which models are trained on labeled datasets, meaning that each input is associated with a known output. Supervised learning algorithms develop predictive capability by understanding the mapping between input variables and corresponding output labels, enabling them to accurately forecast outcomes for previously unseen data. Due to this capability, supervised learning has found extensive applications across diverse domains …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 25–30 Read article
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(𝛅,Ü ) Convex Structure on Partial B-Metric Space Concerning Quasi Contraction and Fixed-Point Results
Abstract: This work introduces the concept of (δ,Ü )– Convex Partial b-Metric Spaces using convex structure. Motivated by this approach, we demonstrated fixed point results and their uniqueness, as well as quasi contraction, and provided some supporting instances for the established results. Our findings expand prior fixed-point results to a novel concept (δ,Ü )– Convex Partial b-Metric Spaces. To support our theoretical findings, we provide several instances that exemplify the established …
Published in Recent Trends in Mathematics · Vol. 1, Issue 1, 2024 Read article
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An Investigative Study of Competition-Aware Incentive Mechanisms in Mobile Ad Hoc Networks
Abstract: Mobile ad hoc networks (MANETs) present a unique communication paradigm characterized by their decentralized and dynamic nature, where nodes rely on each other for packet forwarding and network maintenance. However, the inherent selfishness of individual nodes and resource constraints often leads to non-cooperative behaviors, significantly degrading network performance. This investigative study delves into the critical role of competition-aware incentive mechanisms in fostering sustainable cooperation within MANETs. Competition, a portmanteau of …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 13, Issue 1, 2026 · pp. 08–15 Read article
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Comparative Analysis of Data Augmentation Techniques in CNN-based Classification of Atelectasis
Abstract: This research delves into the critical issue of atelectasis, its causes, and potential complications if left untreated. Leveraging deep learning algorithms, particularly convolutional neural networks (CNN), the paper explores their application in medical image analysis, focusing on the detection of atelectasis using the “chestX-ray8” database. The study compares various data augmentation techniques for improved accuracy, showcasing the importance of augmentation in enhancing model generalization. Through meticulous experimentation and evaluation, the …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 3, 2024 · pp. 1–8 Read article