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471 articles for “integration complexities”
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SMART HOSPITAL MANAGEMENT SYSTEM:USING CLOUD, IOT & WEB DEVELOPMENT
Abstract: The Smart Hospital Management System (SHMS) leverages cloud computing, Internet of Things (IoT) technologies, and web development to address the inefficiencies in healthcare management, particularly in the context of the COVID-19 pandemic. With India’s doctor-to-patient ratio below WHO standards, there is a need for innovative solutions to enhance patient care and hospital resource management. This system integrates SpO2, temperature, and ECG sensors with the Raspberry Pi, enabling continuous and real-time …
Published in Journal of Telecommunication, Switching Systems and Networks Read article
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Noise Pollution in Healthcare Settings: Impacts on Patient Recovery and Staff Well-Being
Abstract: Healthcare environments frequently experience noise pollution, which has serious effects on both patient recovery and the well-being of healthcare staff. This extensive research study includes patient rooms, waiting spaces, and corridors as part of its effort to measure and categorize noise levels. The study explores the complex effects of noise pollution on patients and healthcare personnel by integrating quantitative measures, surveys, and qualitative evaluations. According to preliminary research, loud surroundings …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 1, Issue 1, 2023 · pp. 19–24 Read article
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Association Rule Mining for Predicting Heart Disease: Challenges and Opportunities
Abstract: The exponential growth of digital healthcare data has spurred innovative applications of data mining techniques in medical research and practice. Among these, association rule mining stands out for its ability to uncover meaningful correlations within diverse datasets, such as electronic health records, imaging data, and genetic information. This paper reviews the application of association rule mining in predicting heart diseases, emphasizing its potential to enhance early detection, risk stratification, and …
Published in Journal of Advanced Database Management & Systems · Vol. 11, Issue 3, 2024 · pp. 29–34 Read article
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Neuroinformatics and Its Impact on the Future of Brain-Computer Interface Technology
Abstract: Neuroinformatics, a multidisciplinary field combining neuroscience, information technology, and data science, plays a crucial role in advancing brain-computer interface (BCI) technology. By leveraging large-scale neural data, machine learning algorithms, and computational models, neuroinformatics enhances our understanding of brain function and improves the design and development of BCIs. The integration of neuroinformatics into BCI systems offers new possibilities for interpreting complex brain signals, facilitating real-time communication between the brain and external …
Published in Research and Reviews : Journal of Computational Biology · Vol. 13, Issue 3, 2024 · pp. 9–18 Read article
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Evaluating TRIZ Methodology in the Conceptual Design Phase of Industrial Products
Abstract: The Theory of Inventive Problem Solving (TRIZ) has emerged as a powerful systematic innovation methodology that enhances creativity and problem-solving efficiency in engineering design. This paper evaluates the application of TRIZ during the conceptual design phase of industrial products, where early-stage decisions critically influence functionality, cost, sustainability, and market competitiveness. TRIZ provides designers with structured tools—such as the 40 Inventive Principles, Contradiction Matrix, Su-Field Analysis, and Trends of Technological Evolution—that …
Published in International Journal of Industrial and Product Design Engineering · Vol. 3, Issue 2, 2025 · pp. 27–32 Read article
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High Performance Multi-Valued Logic (MVL)Gate Design Using FinFET
Abstract: CMOS scaling faces challenges such as leakage, power dissipation, and short channel effects. Multi-Valued Logic (MVL) offers higher information density and reduced interconnections. This work presents a FinFET-based MVL gate design that improves electrostatic control, switching speed, and reliability. Simulation results confirm reduced leakage power and enhanced performance compared to conventional logic. The proposed architecture demonstrates scalability for advanced technology nodes. It also shows potential for low-power applications in portable …
Published in Journal of Microelectronics and Solid State Devices · Vol. 13, Issue 1, 2026 · pp. 34–45 Read article
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Neuro-Symbolic Agentic AI for Autonomous Scientific Discovery: Integrating Deep Reinforcement Learning, Quantum Simulation, and XAI-Audited LLM Hypothesis Generation in Drug Target Identification
Abstract: The exponential growth of multi-omics data and the increasing complexity of disease-associated protein interactomes have rendered conventional drug target identification pipelines computationally and epistemologically inadequate. This paper presents the Neuro-Symbolic Agentic AI for Scientific Discovery (NS-AASD) framework, a unified architecture that cohesively integrates deep reinforcement learning (DRL) exploration strategies, variational quantum simulation (VQS) of protein conformational dynamics, and XAI-audited large language model (LLM) hypothesis generation within an autonomous scientific discovery …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 Read article
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Graphene–Perovskite Hybrid Opto-Electronic Modulators for Ultra-Low Power Optical Communication
Abstract: This paper proposes a novel self-adaptive neuromorphic opto-electronic transceiver architecture designed to enhance the intelligence, adaptability, and efficiency of next-generation optical communication networks. The proposed system integrates neuromorphic computing principles with photonic signal processing to enable real-time learning, dynamic resource allocation, and autonomous compensation of channel impairments such as dispersion, nonlinearities, and noise. Unlike conventional transceivers, the developed model employs spiking neural networks embedded within opto-electronic circuits to mimic biological …
Published in Trends in Opto-electro & Optical Communication · Vol. 16, Issue 1, 2026 · pp. 41–52 Read article
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Designing Intelligent Agents for Effective Collaboration with Human in Complex Environment
Abstract: An intelligent agent is a self-governing system that can take action to accomplish its goals based on how it perceives its surroundings. This paper introduces a newly designed humanoid robot that demonstrates significant advancements in performance, reliability, durability, energy efficiency, and environmental sustainability. The design emphasizes a bio-inspired musculoskeletal system, enabling natural and flexible motion while improving operational lifespan. A self-repair system is integrated to allow the robot to address …
Published in Journal of Control & Instrumentation · Vol. 16, Issue 3, 2025 · pp. 1–7 Read article
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Integrative Machine Learning Approaches for Predicting the Rheological Behaviour of Soft Magnetorheological Elastomers
Abstract: Magnetorheological Elastomers (MREs) are advanced composite materials known for their ability to alter mechanical properties under external magnetic fields, making them highly valuable in adaptive damping systems, vibration control, and smart devices. The accurate prediction of rheological behavior in soft MREs remains a significant challenge due to the complex interplay between material composition and magnetic fields. To address this challenge, this study employs a multi-pronged approach that integrates traditional material …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 1083–1096 Read article
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AI Driven IoT Based Decision Making System for Brain wave study: KSK approach for Brain wave study
Abstract: The rapid convergence of Internet of Things (IoT) architectures and deep learning has unlocked unprecedented potential for real-time neuro-diagnostic monitoring. This paper presents a novel framework for an AI-driven IoT ecosystem designed to capture, transmit, and interpret human electroencephalography (EEG) signals with minimal latency. Traditional brain-computer interface (BCI) studies are often constrained by localized computing power and the high dimensionality of neural data. Our proposed architecture integrates low-power EEG sensors …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 Read article
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Integrated risk assessment framework for mixed use real estate development: Retail–office configuration
Abstract: Mixed-use real estate development is a multifaceted challenge that offers significant potential benefits, yet developers and urban planners face skepticism and uncertainty. Despite the promise of enhancing property values, promoting secure neighborhoods, stimulating economic vitality, and creating synergies, the associated risks demand a critical examination. This research seeks to address this gap by constructing a comprehensive risk assessment framework for mixed-use real estate projects. The literature review underscores the substantial …
Published in International Journal of Rural and Regional Development · Vol. 2, Issue 1, 2024 · pp. 22–36 Read article
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A Comprehensive Analysis of Various Payment Gateways for Web-based Food Ordering
Abstract: The digital revolution has ushered in profound transformations across industries, and the online food sector stands at the forefront of this evolution. With consumers increasingly prioritizing convenience and accessibility, the online food industry has witnessed unprecedented growth. In this dynamic landscape, payment gateways have emerged as indispensable components, facilitating seamless transactions and driving operational efficiency for online food restaurant websites. This research article endeavors to conduct a comprehensive analysis of …
Published in Journal of Web Engineering & Technology · Vol. 11, Issue 2, 2024 · pp. 1–6 Read article
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Cutting-edge Deep Learning Methods for Predicting and Detecting Cardiovascular Diseases
Abstract: Cardiovascular diseases (CVDs) remain a major global health issue, highlighting the need for improved early detection and risk assessment methods. This research investigates the efficacy of both deep learning and traditional machine learning methods in forecasting cardiovascular diseases (CVDs). We evaluate a variety of models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Multilayer Perceptrons (MLPs), Long Short-Term Memory (LSTM) networks, as well as Logistic Regression (LR), Decision Trees …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 2, 2024 · pp. 36–42 Read article
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Harmony Through Ayurveda: A Holistic Guide to Optimal Health Through Diet and Lifestyle
Abstract: Ayurveda is an eternal system of medicine that delineates conventional methods to treat the diseased conditions and encourage perseverance of optimal health, thus maintaining a healthy life. This objective of health maintenance can be achieved with the help of affirmative food and lifestyle alterations in harmony with the quotidian cycle and respective seasons. Ahara and vihara have pivotal role in governing the healthy state of body and mind. In the …
Published in Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy · Vol. 13, Issue 2, 2024 · pp. 54–60 Read article
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Ethical Risks of Generative AI in Education: Challenges, Implications, and a Responsible Use Framework
Abstract: The rapid diffusion of generative artificial intelligence (AI) technologies in educational settings is reshaping how teaching, learning, and assessment are designed and enacted. Large language models and related generative systems offer powerful capabilities for content creation, personalized feedback, and instructional support, promising gains in efficiency and learner engagement. However, their growing use also introduces a complex set of ethical risks that challenge foundational educational values such as integrity, equity, transparency, …
Published in International Journal of Education Sciences · Vol. 3, Issue 1, 2026 · pp. 23–30 Read article
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Fibre Orientation and Void Distribution Analysis in Polymer Composite Structures Using Image Processing
Abstract: Fibre orientation and void distribution are critical microstructural features that govern the mechanical performance and reliability of polymer composite materials. Accurate and simultaneous characterisation of these features remains challenging due to their complex spatial interactions and dependence on processing conditions. In this work, an integrated image-processing framework is proposed for the quantitative analysis of fiber orientation and void distribution in polymer composite structures. High-resolution composite microstructure images are processed through …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 925–933 Read article
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Spectral Intuitionistic Fuzzy Hypergraph Operators and Dominance Kernels for Resilient Discrete Network Design
Abstract: A new discrete-mathematical framework is developed for resilient network design on intuitionistic fuzzy hypergraphs, where uncertainty is explicitly represented through membership, non-membership, and hesitation degrees associated with both vertices and hyperedges. These three components are systematically integrated into an effective incidence operator that captures the underlying uncertain relationships within complex hypergraph structures. Based on this operator, both un-normalised and normalized Laplacian matrices are formulated to characterize the spectral properties and …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 41–48 Read article
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The Tapper Approach: An Integrated Framework for Land Degradation, Restoration, and Climate-Conflict Dynamics
Abstract: Land systems across the globe are increasingly exposed to multiple and interacting pressures, including land degradation, climate change, biodiversity loss, unsustainable land-use practices, rapid population growth, and socio-economic conflicts. These challenges not only reduce ecosystem productivity and resilience but also threaten food security, water availability, rural livelihoods, and long-term environmental sustainability. Despite the growing recognition of these interconnected issues, most existing conceptual and analytical frameworks continue to address them in …
Published in Research & Reviews : Journal of Ecology · Vol. 15, Issue 2, 2026 Read article
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A Comprehensive Study of Risk-Adaptive Access Control in Advanced Database Management Systems
Abstract: The current control methods for accession of a crucial resource often struggle to provide adequate security in dynamic and complex advanced database management systems (DBMS). These static models lack the flexibility to adapt to evolving threats and contextual changes, leaving potential vulnerabilities. Risk-Adaptive Access Control (RadAC) emerges as a sophisticated solution, integrating real-time risk assessment into authorization decisions to dynamically adjust access permissions. This review article provides a comprehensive study …
Published in Journal of Advanced Database Management & Systems · Vol. 13, Issue 1, 2026 Read article