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1975 articles for “font meta- data” (ranking capped at the first 2,000 matches — narrow the search to see the rest)
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Evaluating Epidemiological and Physical Risk Factors in the Development of Chemotherapy-induced Peripheral Neuropathy (CIPN): An In-depth Systematic Review and Meta-analysis
Abstract: Background of study: Chemotherapy-induced peripheral neuropathy (CIPN) encompasses a range of adverse effects caused by various cytotoxic medications and stands as a primary source of pain among individuals who have survived cancer. In instances of pronounced and sudden CIPN, it might become necessary to diminish chemotherapy dosages or discontinue their application. Currently, no efficacious strategy exists for proactively preventing CIPN, managing established chronic CIPN is constrained, and the risk factors …
Published in International Journal of Oncological Nursing and Practices · Vol. 1, Issue 2, 2023 · pp. 10–21 Read article
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Optimizing Customer Care Centre Performance: A Data Analytics Approach
Abstract: Customer care centres are essential in today's competitive corporate environment for ensuring client loyalty and satisfaction. By utilizing data analytics approaches, one may gain important insights regarding performance overall, operational effectiveness, and customer interactions. Data analytics encompasses the analysing, interpretation, and extraction of valuable insights from data to aid decision-making and address intricate issues. Call centre analytics is the process of gathering and evaluating call data to assist companies in …
Published in Recent Trends in Programming languages · Vol. 11, Issue 1, 2024 · pp. 34–49 Read article
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Data Integration and Visualization in Bioinformatics: Techniques and Challenges
Abstract: Data integration and visualization play essential roles in bioinformatics, facilitating the thorough analysis, and interpretation of intricate biological datasets. In the field of bioinformatics, vast amounts of data are generated from various experimental platforms, such as genomic sequencing, proteomics, transcriptomics, and metabolomics. However, the heterogeneity of these datasets, coupled with their large scale and complexity, presents significant challenges in terms of integration, analysis, and visualization. Data integration techniques aim to …
Published in Research and Reviews : Journal of Computational Biology · Vol. 13, Issue 3, 2024 · pp. 1–8 Read article
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An Analysis of Graph Database in Data Modelling and Analysis for a Recommendation System
Abstract: This research work focuses on graph databases, mainly Neo4j databases, in recommendation systems for e-commerce websites. The importance of research is that it explains how graph databases efficiently handle the complex relationship between user-items, which is difficult for traditional databases. Sparsity, limited diversity, and high setup costs are the challenges traditional databases face. This research work overcomes these problems using Ne04j with Cypher query language and graph algorithms (PageRank, Shortest …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 3, 2025 · pp. 33–39 Read article
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A Data-Driven Analysis of Machine Learning Classification Models for Reliable Crop Yield Prediction
Abstract: The adoption of ML technologies in agriculture is reshaping farming practices, empowering producers to make informed, data-oriented decisions that improve yields, sustainability, and long-term resilience. In mango cultivation, ML analyzes data from weather, soil, and pests to optimize irrigation, fertilization, and pest control. Predictive analytics help forecast ideal farming practices, minimizing resource wastage and improving yield. Real-time monitoring and image-based disease detection allow timely interventions to maintain plant health and …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 15, Issue 1, 2026 · pp. 12–17 Read article
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A Knowledge Graph Approach for Breast Cancer Diagnosis and Data Sharing Platform Implementation in the Context of Human Papillomavirus Infection
Abstract: Background: Breast cancer remains among the most prevalent malignancies in women worldwide, and effective diagnosis and data integration continue to challenge clinical practice. Diagnostic reports from mammography and ultrasound contain rich clinical information that is often under-utilised due to heterogeneous formats and limited data-sharing infrastructure. In the context of human papillomavirus (HPV) infection, which may influence oncogenic pathways and data complexity, advanced computational methods offer new solutions to this problem. …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 15, Issue 1, 2026 Read article
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Develop the Design of Sustainable Polymer Materials: Applying Reinforcement Learning, IoT-Enabled Monitoring, and Data-Driven Manufacturing Approaches
Abstract: Sustainable polymer materials development is a must due to resource constraints, environmental concerns, and the demand for designed materials with high performance. When it comes to material optimization, energy utilization, process unpredictability, and lifecycle sustainability, traditional polymer production methods have their challenges. Reinforcement Learning (RL), Internet of Things (IoT) monitoring, and data-driven production are utilized in the design and manufacturing of sustainable polymer materials. It is recommended to use Internet …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1207–1231 Read article
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Adoption to Big Data Analytics: Privacy Issues and Integration of PPDM to overcome it
Abstract: AbstractIn this emerging era of technology, the amount of data being generated is massive. To deal with such a huge amount of data, development of big data analytics to extract valuable information for making marketing decision is gaining popularity. This data can be either structured, semi-structured or unstructured, so it is very difficult to process such rapidly growing and changing data with the conventional database techniques. As big data is …
Published in Journal of Computer Technology & Applications · Vol. 8, Issue 2, 2017 · pp. 33–38 Read article
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Design and Performance Assessment of Light Weight Data Security System for Secure Data Transmission in IoT
Abstract: The Internet of Things (IoT) is expected to provide an interface for future technologies’ small processing tools. It is expected to provide more communication data and information security canrisky. Data pinnacles and information security can be a risk. This size of the gadget in this engineering is essentially little,lowpowerutilization. Many rounds of encryption are essentially a misuse of requirements Gadget vitality. Less convoluted calculation, be that as it may, conceivable …
Published in Journal Of Network security · Vol. 9, Issue 1, 2021 · pp. 29–41 Read article
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Indirect Treatment Comparison in Meta-Analysis Using Three Methods for Rheumatoid Arthritis
Abstract: Background: The data were extracted from the comparative effectiveness review (CER) on pharmacological treatments for rheumatoid arthritis (RA) developed by the International University of North Carolina Evidence Based Practice Center (UNC EPC). The included studies enrolled patients with active RA despite oral disease-modifying antirheumatic drugs (DMARD) therapy. The outcome measures of choice were American College of Rheumatology (ACR) 20/50/70 response rates.Objective: To compare and find the best treatment for RA …
Published in Research and Reviews : Journal of Computational Biology · Vol. 7, Issue 1, 2018 · pp. 22–27 Read article
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Anti-hyperglycaemic Effect of Psidium guajava Extracts in Diabetes Mellitus: Systematic Review and Meta-analysis
Abstract: Introduction: Psidium guajava belonging to family Myrtaceae is traditionally used plant for the treatment of diabetes in many countries of the world. This systematic review was designed to determine level of evidence of on the anti-hyperglycaemic effect of Psidium guajava extracts from experimental trials. Methods: An electronic literature search from the database inception was performed since December 2014 with specified terms and defined outcome. We included only experimental study designs …
Published in Research and Reviews : Journal of Computational Biology · Vol. 8, Issue 1, 2019 · pp. 26–33 Read article
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Role of Organizational Culture in Mediating AI-Induced Social Alienation: A Meta-Analysis
Abstract: The fast adoption of artificial intelligence (AI) in the workplace has raised worries about its influence on employee well-being, particularly social alienation. Social alienation is characterised by feelings of detachment and estrangement at work. It can harm job satisfaction, staff engagement, and organisational performance. Existing literature suggests that organizational culture is crucial in shaping employee experiences with AI technologies. This meta-analysis investigates how organizational culture mediates the relationship between AI …
Published in Recent Trends in Social Studies · Vol. 2, Issue 1, 2025 · pp. 28–33 Read article
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Predicting Multiple Diseases Using Machine Learning: A Data-Driven Approach
Abstract: The increasing prevalence of chronic and life-threatening diseases highlights the need for innovative healthcare solutions that enable early detection and proactive management. The Multiple Disease Prediction Platform is a web-based system utilizing machine learning (ML) and deep learning (DL) algorithms to analyze user-inputted health data, generating real-time predictions of potential health risks. By leveraging Python’s Streamlit library, the platform provides an interactive and accessible diagnostic experience, eliminating the need for …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 16–35 Read article
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Modeling Dispersed Count Data: Evaluating the Conway–Maxwell–Poisson Regression with COVID-19 Mortality Data
Abstract: Count data are prevalent in diverse fields such as biology, healthcare, psychology, and marketing, characterized by non-negativity and inherent heteroskedasticity, often exhibiting overdispersion or underdispersion. Traditional Poisson regression, which assumes equal mean and variance, is inadequate for such dispersed data. To address this, various generalized linear models (GLMs) and their extensions, including negative binomial (NB) and Conway–Maxwell–Poisson (CMP) regressions, are utilized. This study evaluates the performance of CMP regression compared …
Published in Research & Reviews : Journal of Statistics · Vol. 13, Issue 3, 2024 · pp. 18–26 Read article
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Big Data in Chemistry: Problems and Answers
Abstract: The rapid growth of experimental and computational chemistry data, researchers now have access to vast datasets, presenting both significant opportunities and challenges. This paper explores the primary challenges associated with managing, processing, and utilizing big data in chemistry, including data heterogeneity, integration across various scales and systems, lack of standardized formats, and the need for advanced tools for data analysis. Additionally, the paper discusses the ethical concerns of data ownership, …
Published in International Journal of Cheminformatics · Vol. 2, Issue 1, 2024 · pp. 9–14 Read article
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Metagenome-Informed Engineering of Monoclonal Antibodies: Mining Microbial Immunoglobulin-Like Domains for Ultra-Stable Therapeutics
Abstract: Metagenome-informed engineering represents an emerging strategy for improving the stability and functionality of monoclonal antibodies (mAbs) by leveraging the vast diversity of microbial immunoglobulin-like (Ig-like) domains (10,24). These domains, derived from uncultured bacteria and bacteriophages, exhibit exceptional thermostability, protease resistance, and structural resilience due to adaptation to extreme environments (29,30). This review highlights advances in metagenomic mining, structural characterization, and protein engineering that enable the integration of microbial Ig-like scaffolds …
Published in Research and Reviews : A Journal of Biotechnology · Vol. 16, Issue 1, 2026 · pp. 70–83 Read article
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Data-Driven Digital Twin Model for Real-Time Strength Estimation in Polymeric Materials
Abstract: The real-time prediction of mechanical properties in polymeric materials is essential for ensuring quality, consistency, and operational efficiency in modern manufacturing systems. As industrial processes become increasingly complex, traditional trial-and-error approaches to material characterization are no longer sufficient to meet the demands of high-throughput production environments. This study introduces a digital twin-integrated machine learning approach for the real-time estimation of tensile strength in polymeric materials by combining simulation-driven insights with …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 246–257 Read article
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Characterization of Aluminum Metal-Matrix Composite with SiC and B4C Reinforcement with Different Weight Percentage
Abstract: Metal Matrix Composites (MMCs) have been prepared by compo casting method. The effect of matrix material on the wettability/compatibility of refinement with matrix is studied. Silicon and boron ceramic particles were used as reinforcement in aluminum matrices. The study of density, micro-hardness and the tensile strength has been done in order to characterize the same. It is found that Al-SiC MMC shows an increase in density with increasing weight percentage …
Published in Journal of Experimental & Applied Mechanics · Vol. 6, Issue 1, 2015 · pp. 1–6 Read article
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Metal Foam by Powder Metallurgy Routes: A Review Paper
Abstract: The aim of study is to evaluate and optimize the process parameters and several control factors of foaming because of their physical and mechanical properties such as, high stiffness, high compressive strength, and good energy absorption. Powder metallurgy is a method in which aluminum and aluminum alloy are mixed with TiH2 or other blowing agents for foam making process. Powder mixed is consolidated to obtain a high dense foam compact …
Published in Journal of Production Research & Management · Vol. 6, Issue 1, 2016 · pp. 9–15 Read article
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Data-Driven Material Design and Performance Improvement: Constructing Sustainable Polymer Nanocomposites Using Deep Learning
Abstract: In the formation of sustainable polymer nanocomposites, the effective material techniques are required to balance the mechanical qualities, environmental compatibility and processing efficiency. The optimization of polymer matrix, nanofiller loading, processing conditions and material properties is typically time consuming, resource intensive and highly dependent on trial-error methodology using standard experimental techniques. The present work provides a data-driven approach that combines deep learning with sustainable polymer nanocomposite design for predicting and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article