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911 articles for “Integrated modeling”
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From Theory to Practice: The Mathematical Foundations of Secure Blockchain Transactions
Abstract: The rise of blockchain technology has transformed the method of conducting secure and decentralized transactions across multiple industries. At its core, blockchain relies on a robust mathematical foundation to ensure data integrity, transparency, and immutability. This paper delves into the theoretical underpinnings that make secure blockchain transactions possible, including cryptographic algorithms, distributed consensus protocols, and mathematical proofs of security. We begin by exploring the role of cryptography, particularly public-key encryption, …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 1, 2025 · pp. 13–20 Read article
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Impact of Partially Observable Markov Decision Process in Next Generation Satellite for Remote Sensing
Abstract: The integration of Partially Observable Markov Decision Processes (POMDPs) in next- generation satellite systems represents a transformative advancement in remote sensing technology. This article explores how POMDP frameworks address the inherent uncertainties and incomplete observability challenges in satellite operations, including dynamic task scheduling, resource allocation, and adaptive sensing strategies. By modeling satellite decision-making under uncertainty, POMDPs enable autonomous systems to optimize mission objectives while managing constraints such as limited power, …
Published in International Journal of Satellite Remote Sensing · Vol. 3, Issue 2, 2025 · pp. 20–28 Read article
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Innovations in Mineral Science and Engineering for Sustainable Resource Development
Abstract: The growing global demand for mineral resources, coupled with increasing environmental and social concerns, has intensified the need for sustainable approaches in mineral science and engineering. Traditional mining and mineral processing practices, while essential for industrial development, are often associated with high energy consumption, resource depletion, and environmental degradation. In response, recent innovations in mineral science and engineering have focused on improving resource efficiency, minimizing environmental impact, and ensuring long-term …
Published in International Journal of Minerals · Vol. 3, Issue 1, 2026 · pp. 31–36 Read article
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Harnessing Biomass for Sustainable Insect Farming and Biotechnology: Ecological Roles, Industrial Applications, and Future Opportunities
Abstract: Biomass, derived from biological materials, such as plant residues, animal waste, and agricultural by-products, plays a pivotal role in ecological systems, including those involving insects. Insects interact with biomass at multiple levels, serving as decomposers, pollinators, and converters of organic matter into valuable resources. The integration of biomass into insect ecology and farming has garnered significant attention for its potential in sustainable agriculture, waste management, and biotechnology. This review highlights …
Published in International Journal of Insects · Vol. 2, Issue 1, 2025 · pp. 17–21 Read article
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Enhancing the User Experience of Asthma Inhalers: A Redesign Approach
Abstract: Asthma is a widespread chronic respiratory condition impacting millions globally, presenting significant challenges in its management and treatment. While conventional inhalers effectively administer medication, they often encounter usability issues, hindering patient adherence and treatment outcomes. This abstract delineates the development and potential impact of a redesigned asthma inhaler aimed at addressing these challenges. Incorporating human-centered design principles, the revamped inhaler prioritizes usability, portability, and effectiveness to enhance the overall management …
Published in International Journal of Solid State Innovations & Research · Vol. 1, Issue 2, 2023 · pp. 32–36 Read article
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Artificial Intelligence and Machine Learning Applications in Optimizing Air Conditioning Systems
Abstract: The growing demand for air conditioning systems, especially in the wake of climate change and increasing global temperatures, has led to a significant increase in energy consumption. This, in turn, contributes to the growing concerns of environmental sustainability and operational costs. As a result, there is a pressing need for innovative solutions to optimize the performance and energy efficiency of air conditioning (AC) systems. Artificial Intelligence (AI) and Machine Learning …
Published in Journal of Refrigeration, Air conditioning, Heating and ventilation · Vol. 12, Issue 1, 2025 · pp. 38–43 Read article
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Integral Sliding Mode Control: A Review of Applications
Abstract: Integral Sliding Mode Control (ISMC) has emerged as a robust and efficient method for handling nonlinear systems with uncertainty, turbulence and external disturbances. This study provides a detailed review of ISMC and its design foundations, design methods, practical application aspects and recent developments are included. ISMC design methodology is explored, to be extended with various design methods with different design methods. Recent advances in research, chatter reduction techniques, applications in …
Published in International Journal of Advanced Control and System Engineering · Vol. 2, Issue 1, 2024 · pp. 25–35 Read article
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Remote Sensing in Atmospheric Studies: Enhancing Understanding of Climate Dynamics and Air Quality, Atmospheric Monitoring and Analysis; Emerging Technologies and Future Directions
Abstract: Remote sensing has emerged as a transformative tool in atmospheric sciences, providing detailed and comprehensive insights into various atmospheric phenomena. Its advanced capabilities have revolutionized our understanding of climate dynamics, air quality, and atmospheric composition, enabling more accurate monitoring and analysis. This paper reviews the critical role of remote sensing technologies in enhancing knowledge of atmospheric processes and their practical applications in areas such as climate change monitoring, air pollution …
Published in International Journal of Atmosphere · Vol. 2, Issue 1, 2025 · pp. 1–5 Read article
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AI-Enhanced Interpretation of Cardiac Troponins: Toward Predictive Precision in Myocardial Injury
Abstract: Background: Cardiac troponins (cTn) represent the gold standard biomarkers for myocardial injury detection, yet their interpretation remains challenging due to various confounding factors and clinical contexts. Artificial intelligence (AI) technologies provide remarkable possibilities to improve the interpretation of troponin levels by utilizing pattern recognition, predictive modeling, and clinical decision-making support. Objective: This review examines the current state and future potential of AI-enhanced cardiac troponin interpretation, focusing on machine learning applications, …
Published in Research and Reviews: A Journal of Medicine · Vol. 15, Issue 3, 2025 · pp. 1–9 Read article
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Optimizing Green Fodder Availability: Strategic Roadmaps for Sustainable Dairy Development in the Tropics
Abstract: The growing demand for dairy products in tropical regions underscores the urgent need to optimize green fodder availability for sustainable dairy farming.The growing demand for dairy products in tropical regions underscores the urgent need to optimize green fodder availability for sustainable dairy farming. Green fodder serves as a vital source of nutrition for dairy cows, directly influencing milk yield, animal health, and overall farm profitability. However, challenges such as limited …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 14, Issue 3, 2025 · pp. 11–30 Read article
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Offloading Computation to the Cloud
Abstract: Mobile devices are increasingly relied upon for complex and resource-intensive applications such as real-time video processing, augmented reality, and machine learning. However, their limited computational power, storage capacity, and battery life pose significant challenges. Computation offloading to the cloud has emerged as a promising solution to overcome these limitations by transferring demanding tasks from mobile devices to remote cloud servers. This approach enables improved performance, reduced energy consumption, and enhanced …
Published in International Journal of Mobile Computing Technology · Vol. 3, Issue 2, 2025 · pp. 27–37 Read article
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Power Estimation Approach for Artix 7 FPGA using Machine Learning Technique
Abstract: This paper presents the power estimation approach using a suitable machine learning technique. Artix7 FPGA has been chosen as the target FPGA (Field Programmable Gate Arrays) platform for understanding the methodology of power estimation. There are various approaches of power estimation for FPGAs that have been given in the literature viz. probabilistic, statistical, and LUT- based, etc. In the past few years, the demand for hand-handled devices like smartphones, tabs, …
Published in Research & Reviews: A Journal of Embedded System & Applications Read article
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Advancing Healthcare Systems: A Machine Learning Approach to Multi-Disease Prediction
Abstract: The integration of machine learning algorithms in healthcare has revolutionized the way we approach disease prediction and diagnosis. An attempt to employ machine learning techniques to forecast numerous diseases is presented in this study. A diverse dataset containing patient records, medical history, and relevant features for various diseases was used to develop predictive models. Feature selection and normalization were among the preprocessing methods used to clean and prepare the data. …
Published in Journal of Electronic Design Technology · Vol. 16, Issue 1, 2025 · pp. 1–6 Read article
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Advancing Brain Tumor MRI Segmentation
Abstract: Segmentation of brain tumors in MRI scans is an integral part of neuroimaging carried out for diagnostic and therapeutic interventions. Given that manual segmentation is cumbersome and highly variable, there arises a need for automated, more precise segmentation solutions. This project, ‘Machine Learning and Deep Neural Networks to Advance Brain Tumor MRI Segmentation’ will develop a better, efficient, and accurate segmentation model to help clinicians identify brain tumors with greater …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 2, 2025 · pp. 28–33 Read article
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Gear-Related Stress Analysis and Comparison Between the Fem and Agma Standards
Abstract: In many different devices, gears enable the efficient transfer of motion and torque. They are an essential component of modern mechanical power transmission systems. It has been demonstrated that bending and surface contact stresses at the gear tooth are the primary causes of gear failure, despite their widespread use. Too much stress can lead to tooth wear, pitting, or breakage, which can ultimately reduce the operating life and reliability of …
Published in Trends in Mechanical Engineering & Technology · Vol. 15, Issue 3, 2025 · pp. 13–18 Read article
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Machine Learning Assisted Timing Violation Prediction in Sub-7nm VLSI Physical Design
Abstract: The continuous scaling of semiconductor technology into the sub-7nm regime has introduced significant challenges in timing closure due to process variability, interconnect delay, power density, and manufacturing uncertainties. Conventional static timing analysis techniques often require extensive computational resources and iterative optimization cycles, resulting in increased design complexity and longer turnaround time. This research proposes a Machine Learning Assisted Timing Violation Prediction framework for sub-7nm VLSI physical design to improve early-stage …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 4, Issue 1, 2026 Read article
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Virtual Method to Predict Dental Disease
Abstract: The integration of technology and medicine in the healthcare domain has led to the emergence of inventive strategies to improve patient care and diagnostics. One such groundbreaking methodology is the utilization of Convolutional Neural Networks (CNNs) within the domain of deep learning, particularly for image recognition and processing tasks. In this paper, we propose a novel approach to image recognition that employs state-of-the-art deep learning algorithms to create a user-friendly …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 8–15 Read article
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Analysis of Bioinformatics Software Applied in Computer-Aided Drug Design
Abstract: The integration of bioinformatic tools with computational methods has revolutionized the field of Computer-aided Drug Design (CADD), enabling researchers to expedite the discovery and optimization of new therapeutics. This review provides an in-depth analysis of the bioinformatic tools utilized in CADD, encompassing molecular docking, molecular dynamics simulation, virtual screening, homology modelling, and molecular visualization. We go over the tenets, approaches, and uses of these instruments, emphasizing their value in expediting …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 11, Issue 2, 2024 · pp. 1–10 Read article
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A Study of Cloud-Enabled Deep Learning for Monitoring and Predicting Soil Health in Agriculture
Abstract: Soil health is a critical factor in ensuring sustainable agricultural practices and food security. Traditional methods for soil health assessment are often time-consuming, localized, and lack scalability. This study explores the integration of cloud-enabled deep learning techniques to monitor and predict soil health efficiently. Leveraging data from IoT sensors, satellite imagery, and lab-based analyses, a cloud-based framework is proposed to process and analyze soil health parameters such as pH, moisture …
Published in Journal of Remote Sensing & GIS · Vol. 16, Issue 2, 2025 · pp. 8–16 Read article
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Quasar: Quantum-Accelerated Sustainable Anomaly Recognition in Climate Systems
Abstract: Accurate detection of climate anomalies is vital for disaster alleviation and policy making in a sustainable manner, but customary detection methods face the challenges of computational inefficiency and physical inconsistency. In this study, we propose a novel approach called Quantum-Optimized Fuzzy Physics-Informed Neural Networks (QFuzzy-PINNs), which integrates quantum computing, fuzzy logic, and physics-informed deep learning. As a first step, we employ quantum annealing for conventional optimization to adjust multiple Gaussian …
Published in International Journal of Climate Conditions · Vol. 2, Issue 2, 2025 · pp. 18–27 Read article