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113 articles for “efficiency metrics”
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Experimental investigation of humidification-dehumidification desalination system
Abstract: The aim of present study is to investigate an H-DH desalination system experimentally. The system consists of a solar air heater, a humidifier, and a dehumidifier connected with an evaporative cooler. The proposed system is operated in a closed loop configuration, at two airflow rates. The performance metrics are assessed via the examination of energy and economic factors. The solar air heater has the ability to produce hot air with …
Published in Journal of Polymer & Composites · Vol. 12, Issue 2, 2024 · pp. 104–113 Read article
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Self-Healing Polymer Nanocomposites: A Comprehensive Review of Design Strategies, Mechanisms, and Emerging Applications
Abstract: Self-healing polymer nanocomposites (SHPNs) are a new class of materials that can autonomously repair themselves to prolong their lifetime and improve their application performance. Herein, we provide a comprehensive overview of the design strategies, healing mechanisms, and emerging applications of SHPNs. Incorporating nanofillers (nanoparticles, nanofibers, and/or nanotubes) into polymer matrices leads to requisite functionalization that significantly enhances mechanical properties and is essential in self-healable products including improved cross-linking, high dispersion, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 281–293 Read article
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Breast Cancer Detection Using Machine Learning: A Comparative Analysis of Supervised Learning Algorithms
Abstract: Globally, breast cancer remains a predominant cause of mortality among women, highlighting the urgent need for timely and precise diagnostic approaches. This research explores the application of machine learning algorithms—including Logistic Regression, SVM, Naïve Bayes, KNN, and Random Forest—on the Wisconsin Breast Cancer Dataset for effective tumor classification. Key pre-processing steps such as missing value handling, feature scaling, and dimensionality reduction were employed to improve model performance. The study evaluated …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 3, 2025 · pp. 46–52 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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Semantics Analysis of Expected Goals in Soccer Data Using Machine Learning
Abstract: In recent years, the increasing availability of soccer data has greatly enhanced the accuracy and depth of player performance evaluation. Soccer, being one of the most popular sports worldwide, attracts millions of fans due to its simple rules, minimal equipment requirements, and high entertainment value. However, analyzing an entire match manually can be time-consuming, leading to a growing demand for automated methods that can summarize and interpret game data efficiently. …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 2, 2025 · pp. 31–47 Read article
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A Meta-Analysis of the Role of Serverless Computing Models in Modern e-Healthcare Systems
Abstract: The integration of serverless computing models in e-healthcare systems represents a paradigm shift in healthcare technology infrastructure. This meta-analysis examines the role, benefits, and challenges of serverless architectures in modern healthcare applications, focusing on studies published between 2019 and 2025. Serverless computing offers unprecedented scalability, cost-efficiency, and operational flexibility, making it particularly suited for healthcare applications handling variable workloads such as medical imaging processing, real-time patient monitoring, and electronic health …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 3, 2025 · pp. 49–58 Read article
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Sentiment Analysis of E-Commerce Reviews using Machine Learning
Abstract: In e-commerce, sentiment pertains to the emotional responses, opinions, or perceptions that customers have about their online shopping experiences, including factors like product quality, service, and various processes such as ordering, shipping, and customer support. Sentiment analysis, which involves machine learning techniques, plays a crucial role in deciphering these sentiments. By using sentiment analysis, companies can obtain valuable insights from customer feedback from diverse online sources, including social media, surveys, …
Published in Journal of Operating Systems Development & Trends · Vol. 11, Issue 3, 2024 · pp. 25–37 Read article
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A Comparison of Different Generative AI Models
Abstract: Generative models have significantly advanced the field of artificial intelligence by allowing machines to produce complex and realistic outputs such as images, text, and other forms of data. Among the leading frameworks in this domain are generative adversarial networks (GANs), variational autoencoders (VAEs), and architectures based on Transformers. Each model offers specific benefits and drawbacks concerning design structure, training demands, and range of applications. This paper provides a detailed comparison …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 1, 2025 · pp. 16–22 Read article
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Effectiveness of Online Advertising in Reaching Target Audiences
Abstract: This study examines the effectiveness of online advertising in reaching target audiences. With the rapid growth of digital platforms, understanding the efficacy of online advertising has become paramount for marketers. Utilizing a combination of quantitative analysis and case studies, this research investigates the various strategies and channels employed in online advertising to reach specific demographic segments. By analyzing metrics such as click-through rates, conversion rates, and audience engagement, this study …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 14, Issue 2, 2024 · pp. 8–12 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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Advancements in K-Means Clustering: Boosting Algorithm Performance through Innovations
Abstract: K-Means clustering is a widely used unsupervised learning algorithm for partitioning a dataset into distinct clusters. Despite its popularity and simplicity, K-Means has several limitations, such as sensitivity to initial centroids, convergence to local minima, and inefficiency with large datasets. This paper reviews recent advancements aimed at addressing these challenges and enhancing the performance of the K-Means algorithm. Innovations include improved initialization methods, such as K-Means++, which significantly reduce the …
Published in International Journal of Solid State Innovations & Research · Vol. 3, Issue 1, 2025 · pp. 30–37 Read article
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Lightweight Models for Per-PC Energy Consumption Forecasting: Comparative Study with ML and DL Approaches
Abstract: We have collected primary data from automated logging of parameters like CPU utilization, estimated power, active or idle state, user logging activity, and the type of day. Additionally, survey data showed user awareness, energy-saving behaviour, and PC usage patterns. The data is pre-processed and merged by applying processes such as data cleaning, normalization, and feature extraction, i.e., determining the peak active timings and downtime. Developed lightweight prediction models based on …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 17, Issue 1, 2026 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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Advancing Sustainability: A Comprehensive Review of Environmental, Social, and Governance (ESG) Practices
Abstract: Environmental, Social, and Governance (ESG) frameworks have emerged as critical pillars guiding sustainable business practices and responsible investment decisions. This review explores the multifaceted role of ESG principles in promoting environmental stewardship, advancing social equity, and ensuring ethical governance. Environmentally, ESG focuses on combating climate change, conserving biodiversity, and managing resources through renewable energy adoption, waste reduction, and carbon emission control. Socially, it emphasizes employee well-being, diversity, community engagement, and …
Published in Journal of Thermal Engineering and Applications · Vol. 12, Issue 2, 2025 · pp. 1–9 Read article
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RTL-to-GDS Implementation of a High-Speed On-Chip 32:1 Serializer Using Open-Source Tools
Abstract: Exploring the RTL-to-GDS implementation of a high-speed on-chip 32:1 serializer, this study investigates the integration of open-source tools within VLSI design, emphasizing sustainable practices in the semiconductor industry while operating at the nanometer scale. Addressing methodologies for optimizing power consumption and chip area utilization, particularly focusing on efficient use of non-renewable resources. The study is set against the backdrop of advanced 7nm FinFET technology, critical for enabling efficient data transmission …
Published in Journal of VLSI Design Tools and Technology · Vol. 14, Issue 3, 2024 · pp. 1–10 Read article
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E-Empire: Brake & Acceleration In E-Cycle
Abstract: As e-cycles continue to gain momentum as an eco-friendly and efficient mode of transportation, understanding their key mechanical and electronic systems becomes even more vital. The report delves deeply into the nuances of braking mechanisms and acceleration technologies, emphasizing their roles in enhancing both safety and performance. By comparing mechanical disc brakes with hydraulic systems, it highlights the distinct advantages of each. For instance, hydraulic brakes, known for their superior …
Published in Journal of Automobile Engineering and Applications · Vol. 11, Issue 3, 2024 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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Hybrid Best-Response Algorithms for Mobile Computing Offloading: A Comprehensive Review
Abstract: The exponential growth of mobile applications with intensive computational requirements has necessitated innovative offloading strategies in mobile computing ecosystems. This comprehensive review examines hybrid best-response offloading algorithms integrated with game-theoretic optimization frameworks to address resource allocation challenges in mobile edge computing (MEC) environments. The proliferation of Internet of Things (IoT) devices and bandwidth-intensive applications has created unprecedented demands on mobile network infrastructure, compelling researchers to develop sophisticated offloading mechanisms that …
Published in International Journal of Mobile Computing Technology · Vol. 3, Issue 2, 2025 · pp. 20–26 Read article
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OBD-II Big Data–Driven ML and AI-Based Virtual Sensing for Fuel Economy, Component Health, and Carbon Intelligence
Abstract: The rapid growth of connected vehicles has led to the large-scale availability of high-frequency On-Board Diagnostics II (OBD-II) data; however, much of this data remains underutilised, as existing studies and commercial systems typically address fuel economy, maintenance, or emissions in isolation or rely on additional physical sensors. Such fragmented and sensor-dependent approaches limit scalability and increase system cost, particularly in high-volume and resource-constrained vehicle markets. To address this gap, this …
Published in Journal of Automobile Engineering and Applications · Vol. 13, Issue 1, 2026 · pp. 39–50 Read article
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House Price Estimation Using Linear Regression: A Machine Learning Perspective
Abstract: House price prediction plays a crucial role in the real estate industry, helping buyers, sellers, and investors make well-informed decisions. Accurate estimation of property values enables stakeholders to assess market trends, plan investments, and minimize financial risks. This study focuses on the application of linear regression, a fundamental and widely used machine learning algorithm, to predict house prices based on multiple influencing factors. These factors include location, property size, number …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 Read article