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332 articles for “data driven model”
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Statistical and AI Approaches to Measure Sustainability Performance of Enterprises
Abstract: Measuring sustainability performance has become a critical priority for enterprises facing increasing regulatory pressure, stakeholder expectations, and global sustainability challenges. Traditional assessment methods, largely based on static indicators and manual reporting, often struggle to capture the multidimensional, dynamic, and data-intensive nature of sustainability. This study explores the integration of statistical and artificial intelligence (AI) approaches to evaluate and enhance the sustainability performance of enterprises in a more robust, accurate, and …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 · pp. 30–36 Read article
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Machine Learning Driven Mobile Price Prediction Using Feature Selection and Parameter Optimization
Abstract: Machine learning calculations are utilized in many fields like money, training, industry, medication, and online business. Machine learning calculations show execution contrasts relying upon the dataset and handling steps. Picking the right calculation, preprocessing and post-handling techniques have incredible significance in accomplishing great outcomes. The Random Forest classifier, K-nearest neighbor classifier, and support vector machine methods are evaluated to forecast mobile phone price categories. The “prediction” dataset which is taken …
Published in Current Trends in Information Technology · Vol. 14, Issue 3, 2024 · pp. 18–25 Read article
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Revolutionizing Artificial Organs: Next-Generation Membrane Technologies for Precision Medicine and Global Health
Abstract: Membrane technology has emerged as a cornerstone in the advancement of artificial organ systems, offering critical functionalities in selective molecular filtration, tissue scaffolding, and controlled therapeutic delivery. Recent innovations have propelled the field beyond traditional polymeric membranes to include nanostructured, biomimetic, and stimuli-responsive materials, significantly enhancing biocompatibility, selectivity, and durability. The integration of smart technologies, such as AI-driven membrane design, bioelectronic sensors, and personalized fabrication via 3D printing, is ushering …
Published in International Journal of Membranes · Vol. 2, Issue 2, 2025 · pp. 29–39 Read article
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Entomo-Analytics: Insect Behavioral Intelligence for Climate-Smart Environmental Monitoring Systems
Abstract: Rapid environmental change driven by climate variability, urbanization, and ecological degradation has intensified the need for innovative monitoring systems capable of providing real-time ecological intelligence. Traditional environmental monitoring methods often rely on satellite imaging and stationary sensors, which may lack fine-scale biological sensitivity. In contrast, insects—due to their abundance, ecological diversity, and rapid responsiveness to environmental shifts—offer a powerful yet underutilized source of bio-sensing data. This paper introduces the concept …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 15, Issue 2, 2026 · pp. 17–26 Read article
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Today’s Status of Digital Resources in Medical College Libraries
Abstract: Digital resources have become integral to the advancement of medical education and research, enabling access to current scientific evidence, clinical guidelines, e-books, e-journals, and multimedia learning tools. Medical college libraries worldwide are transitioning from traditional print repositories to hybrid digital knowledge hubs. This transformation is driven by the evolution of Information and Communication Technology (ICT), rising expectations of learners and educators, institutional mandates for evidence-based practice, and the diffusion of …
Published in Journal of Advancements in Library Sciences · Vol. 13, Issue 1, 2026 · pp. 85–94 Read article
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Machine Learning in Nuclear Medical Applications: A Review of Research Frontiers
Abstract: Nuclear medicine, encompassing PET, SPECT, and targeted radionuclide therapy, generates high-dimensional, quantitative data uniquely suited for machine learning (ML) analysis. This review synthesizes current research applications of ML across six key domains. Positron emission tomography (PET), single-photon emission computed tomography (SPECT), and targeted radionuclide therapy are examples of nuclear medicine modalities that generate high- dimensional, quantitative datasets that are particularly well-suited for machine learning (ML)-driven analysis. These imaging methods provide …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 1, 2026 · pp. 19–24 Read article
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Leveraging Large Language Models for Personalized Document Summarization and Question Answering: An Architecture for Stoner-Friendly Chatbots
Abstract: This study presents a detailed framework for developing personalized chatbots that utilize large language models (LLMs) to process and extract information from extensive documents while effectively responding to user inquiries. The proposed system is designed to mitigate information overload by employing advanced natural language processing techniques, leveraging technologies such as OpenAI, LangChain, and Streamlit. By integrating these tools, the framework enhances knowledge retrieval, simplifies document comprehension, and improves overall productivity. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 88–93 Read article
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VIKSIT BHARAT: Empowering Indians in the Area of Caring Society Culture for National Development
Abstract: This paper examines the vision of Viksit Bharat (Developed India) by focusing on the empowerment of citizens through the cultivation of a “Caring Society Culture.” It argues that sustainable national development in India must go beyond economic indicators and be rooted in societal values such as empathy, mutual respect, and collective responsibility. The study highlights the interplay between social responsibility and national growth, asserting that a society driven by compassion …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 15, Issue 3, 2025 · pp. 37–47 Read article
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Review on Strengthening & Retrofitting of RCC & Masonry Bridge Structure with Analytical Modelling of Sensor Data for the Durability of Bridge Structure
Abstract: In later a long time, monstrous advancement in Structural Health Monitoring (SHM) of bridges makes a difference address the life span and unwavering quality of bridge structure at differentiating stages of their benefit life. This article gives a point-by-point understanding of bridge observing, and it centers on sensors utilized and all sorts of harm location (strain, Displacement, acceleration, and temperature) concurring to bridge nature (scour, suspender failure, disconnection of bolt …
Published in Trends in Transport Engineering and Applications · Vol. 9, Issue 3, 2022 · pp. 16–23 Read article
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A Review Study on CPU-Optimized Parameter-Efficient Fine-Tuning for Large Language Models to Increase Accuracy Using LoRA
Abstract: The fast proliferation of large language models (LLMs) has increased the need to optimize the process of fine-tuning, but the existing workflows that require a graphics processing unit (GPU) are still expensive, intensive, and unavailable to most researchers. This paper is driven by the desire to have a more cost-efficient and democratized version by examining a CPU-efficient implementation of parameter-efficient fine-tuning (PEFT) based on low-rank adaptation (LoRA). The major purpose …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 · pp. 32–38 Read article
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Collaborative Robotics and Smart Automation: Enhancing Human–Robot Synergy in Industry 5.0
Abstract: Industry 5.0 marks a paradigm shift from efficiency-centric automation to a human-centred, sustainable, and collaborative production environment . In this context, collaborative robots, commonly referred to as cobots, play a central role by enabling direct and safe interaction between humans and machines within shared workspaces. These systems are designed to support human operators by undertaking repetitive, precision-intensive, and physically demanding tasks, thereby allowing humans to focus on supervisory control, problem-solving, …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 4, Issue 1, 2026 · pp. 22–29 Read article
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Leveraging Full Stack Data Science for Healthcare Transformation: An Exploration of the Microsoft Intelligent Data Platform
Abstract: The rapid progress of the Fourth Industrial Revolution has been largely driven by the evolution of artificial intelligence (AI), with notable contributions from technologies such as Generative Pre-trained Transformers (GPT). This revolution has seen the convergence of physical, digital, and biological technologies, leading to transformative impacts across various sectors. Data science, serving as a crucial enabler, has enabled the development of intelligent value chains. However, the application of data science …
Published in Journal of Advanced Database Management & Systems · Vol. 11, Issue 3, 2024 · pp. 1–8 Read article
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Artificial Intelligence in Trigonometry: Innovations, Applications, and Future Prospects
Abstract: Artificial Intelligence (AI) has transformed numerous scientific fields, yet its integration with classical mathematics such as trigonometry is still emerging. This paper explores how AI enhances trigonometric problem solving, learning, and real-world applications. We analyse AI-driven tools for teaching trigonometry, AI in geometric and spatial reasoning, usage in robotics and computer vision, and future directions for research. Key challenges, methodologies, and case studies are discussed to provide a comprehensive overview …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 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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Explainable GeoAI-Based Multi-Temporal Remote Sensing Framework for Early Detection of Climate-Induced Land Cover Transformation
Abstract: Climate change has emerged as one of the primary drivers of rapid land cover transformation, affecting ecosystems, agricultural productivity, biodiversity, and regional sustainability. Traditional remote sensing approaches often face challenges in detecting subtle and early-stage land cover changes due to limitations in temporal analysis and model interpretability. This study proposes an Explainable GeoAI-based multi-temporal remote sensing framework for the early detection of climate-induced land cover transformation using multi-source satellite imagery …
Published in International Journal of Satellite Remote Sensing · Vol. 4, Issue 2, 2026 Read article
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Statistical Modeling for Weld Quality Assessment using AI SAW Welding of Mild Steel
Abstract: The main issue to the industries that apply Submerged Arc Welding (SAW) is quality assurance since the structural integrity dictates safety and the performance of the industry. The existing system of checking manuals is not only time consuming but also has human errors that make it mandatory to deploy automated intelligent systems. This study carries out an extensive comparison of the leading approaches based on the use of Artificial Intelligence …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 892–907 Read article
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Design of Secure Biometric-based Access Mechanism for Cloud Services
Abstract: In our data-driven society, the demand for remote information storage and computation services is increasing exponentially, as is the need for secure access to such information and services. during this project, we have a tendency to style a replacement biometric-based authentication protocol to produce secure access to an overseas (cloud) server. Within the planned approach, we have a tendency to think about biometric information of a user as a secret …
Published in Journal of Experimental & Applied Mechanics Read article
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Studies of Mathematics: A Review of Research Trends, Themes and Implications
Abstract: This review examines contemporary research trends, thematic developments, and emerging implications within the field of mathematics education and mathematical studies. Drawing on a synthesis of recent scholarly literature, it explores how mathematics as both a discipline and a pedagogical practice continues to evolve in response to technological advancements, interdisciplinary applications, and changing educational paradigms. Major research trends reveal a growing emphasis on problem-based learning, mathematical modeling, and the integration of …
Published in Recent Trends in Mathematics · Vol. 2, Issue 2, 2025 · pp. 19–24 Read article
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Ethical and Responsible AI: A Comprehensive Review of Principles, Methods, and Tools
Abstract: Quick development of artificial intelligence (AI) has revolutionized a number of industries, including healthcare, banking, and government, by providing creative answers to challenging issues. However, there are serious ethical issues with growing integration of AI into crucial decision-making processes, including prejudice, a lack of transparency, abuses of data privacy, and accountability gaps. A systematic strategy that incorporates technical solutions, legal frameworks, and ethical standards is needed to address these issues. …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 1, 2026 · pp. 23–34 Read article
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Environmental Impact Assessment of Ocean Energy Converters Using Quantum Machine Learning
Abstract: The accelerating deployment of ocean energy converters (OECs) across tidal, wave, osmotic, and thermal domains necessitates rigorous, data-intensive environmental impact assessment (EIA) frameworks capable of modelling multi-stressor marine ecosystems in real time. Classical machine learning approaches, while operationally mature, encounter scalability bottlenecks and feature correlation limitations when applied to the high-dimensional, non-linear datasets characteristic of offshore monitoring networks. This paper presents a comprehensive quantum machine learning (QML) framework for the …
Published in Journal of Energy, Environment & Carbon Credits · Vol. 16, Issue 1, 2026 · pp. 22–31 Read article