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1946 articles for “Friday AI-2.0” (ranking capped at the first 2,000 matches — narrow the search to see the rest)
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Ai-Driven Healthcare System for Enhanced Diagnosis and Patient Interaction
Abstract: Deep learning techniques are used in an AI-driven healthcare system to improve disease identification and medical picture analysis. Data collection, preprocessing, model training, and evaluation are all part of the system's systematic workflow. Various deep learning architectures, such as ResNet50, VGG-16, and U-Net, are employed for precise classification and segmentation of medical images. The approach incorporates advanced techniques such as optimization, transfer learning, and data augmentation to significantly enhance the …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 2, 2025 Read article
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Creating a ‘Sustainable Future’ through Secured-AI
Abstract: It is imperative in the present world that we need to figure out a way to move to a ‘Sustainable future’. However, a ‘Sustainable future’ from an energy standpoint can only be built by a ‘Sustainably intelligent’ society. But the individuals who come together to form a society tend to neglect any discussion around ‘Sustainability’ considering it as something to be driven in top-down manner. In reality, with the massive …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 12, Issue 3, 2025 Read article
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AI-Driven Micro-Expression Recognition for Early Mental Health Disorder
Abstract: Mental health conditions like anxiety and depression are often undiagnosed because the usual diagnostic methods based on basic regular instruments like questionnaires and clinical interviews have some limitations in them. They are not objective often and may not catch the initial signs of psychological distress. Micro-expressions have become valid measures of repressed or unconscious emotions and can provide greater insight into someone's mental condition. Also, identification and interpretation of these …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 12, Issue 3, 2025 · pp. 40–49 Read article
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DR. REVIVE: An AI-Powered Medical Recommendation System for Optimised Resources and Improved Patient Care
Abstract: Dr. Revive is an AI-powered medical recommendation system designed to enhance virtual healthcare interactions by connecting patients, doctors, and healthcare stakeholders. Leveraging advanced machine learning algorithms, it analyses user-reported symptoms to provide initial medical recommendations, serving as a reliable first point of guidance. With access to a comprehensive medical database, the platform delivers accurate and timely advice, empowering patients while supporting healthcare professionals with data-driven decision-making. By offering a complete …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 3, 2024 · pp. 1–10 Read article
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AI-Powered Drug Delivery: Revolutionizing Formulation Science
Abstract: Artificial Intelligence (AI) is emerging as a groundbreaking tool in revolutionizing Drug Delivery Systems (DDS), offering promising advancements in precision, efficiency, and personalized treatment strategies. The integration of AI technologies into pharmaceutical research and development is transforming how drugs are formulated, delivered, and monitored in real time. By leveraging machine learning algorithms and data analytics, researchers can design drug delivery models that are not only more effective but also tailored …
Published in Trends in Drug Delivery · Vol. 13, Issue 1, 2026 · pp. 48–61 Read article
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Aura Pulse: An AI-Powered System for Real-Time Emotional Support and Personalized Recommendations
Abstract: Aura Pulse is a cutting-edge AI-powered platform developed to provide real-time emotional support, tackling the growing challenges of stress, anxiety, and burnout in today’s fast-moving digital era. Utilizing advanced facial expression analysis, Aura Pulse interprets visual cues to create a comprehensive emotional profile of the user. This instant emotional evaluation enables the platform to deliver personalized suggestions aligned with the user’s mood and mental state, promoting overall well- being and …
Published in Journal of Advancements in Robotics · Vol. 13, Issue 1, 2026 · pp. 18–25 Read article
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Multi-Layered AI-Driven Paradigm Shift in IoT Ecosystem Security
Abstract: As the Internet of Things (IoT) continues to weave itself into the fabric of modern life – from smart homes and industrial automation to healthcare and urban infrastructure – the associated security vulnerabilities have become increasingly apparent. Traditional security mechanisms, often built on static rules and perimeter-based defenses, struggle to keep pace with the scale, heterogeneity, and dynamic nature of IoT ecosystems. In response, artificial intelligence (AI) has emerged as …
Published in Journal of Communication Engineering & Systems · Vol. 16, Issue 1, 2026 · pp. 13–21 Read article
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Green Hydrogen Production Using Air-Conditioner Condensate Wastewater: A Review of Solar-Powered Electrolysis Systems
Abstract: As the world moves toward sustainable and clean energy solutions, growing attention has been directed toward alternative energy carriers such as Hydrogen (H 2 ). Hydrogen is considered an attractive energy option due to its high-density energy and ecofriendly properties. However, most conventional H 2 production methods rely on fossil-fuel-based processes such as steam methane reforming and coal gasification, which contribute significantly to greenhouse gas emissions and environmental degradation. As …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 17, Issue 2, 2026 Read article
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Green AI-Enabled Opto-Electronic Communication Systems for Carbon-Neutral Digital Networks
Abstract: The rapid expansion of digital communication infrastructure, driven by cloud computing, Internet of Things (IoT), 6G networks, and artificial intelligence applications, has significantly increased the energy consumption and carbon footprint of modern communication systems. Conventional optical communication networks often rely on static resource allocation and energy-intensive signal processing mechanisms, resulting in inefficient utilization of network resources and elevated operational costs. This study proposes a Green Artificial Intelligence (Green AI)-Enabled Opto-Electronic …
Published in Trends in Opto-electro & Optical Communication · Vol. 16, Issue 2, 2026 Read article
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Reduction of Air Pollutants of Urban Canyons through Management of Particulate Matters 2.5 in the Streets
Abstract: Urban canyons are long and high sky-scrappers closely to narrow streets result in very different microclimate challenges. These spaces often trap pollutants and restrict air circulation and intensify more retention of heat making them very uncomfortable for pedestrians. In order to resolve this issue a strong set of design guidelines and frameworks were needed which can balance out the human comfort and environmental aspects. This research studies strategies to improve …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 3, Issue 1, 2025 · pp. 11–23 Read article
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Assessment of Finerenone Safety and Efficacy in Individuals with Type 2 Diabetes and Chronic Kidney Disease
Abstract: Background: In this work, we provide strong evidence for the safety and efficacy of finerenone in people with type 2 diabetes and chronic kidney disease. Our goal is to improve patients' quality of life and general well-being through our research. Aim: Finerenone exhibited a significant decrease in the likelihood of renal and cardiovascular (CV) events in individuals with both type 2 diabetes and chronic kidney disease. This exploratory subgroup analysis …
Published in International Journal of Antibiotics · Vol. 1, Issue 2, 2024 · pp. 06–09 Read article
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AI-Driven Psychological Profiling on Social Media: Mechanisms, Ethical Breaches, and Regulatory Challenges in Data Inference
Abstract: This literature review examines AI-driven psychological profiling on social media, analyzing 21 academic studies that focus on machine learning techniques such as supervised learning, deep neural networks, sentiment analysis, and natural language processing. These methodologies infer mental health indicators—such as depression, anxiety, and stress—from users' digital footprints, encompassing linguistic patterns, engagement metrics, and temporal behaviors. While these tools offer potential for early detection of psychological distress, they also raise significant …
Published in Recent Trends in Social Studies · Vol. 2, Issue 1, 2025 · pp. 1–7 Read article
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An Evaluation of the Effectiveness of a Self-Instructional Module on First Aid and Safety Measures for School Children (Aged 11–14 years) at PDR VVP Vidyalaya, Loni
Abstract: Background: School children are active youngsters. India is home to nearly 500 million young individuals, with approximately 370 million being children under 15 years old, highlighting their crucial role as the future of the country. Young children often exhibit naughty, defiant, and impulsive behavior. According to the World Health Organization’s Global report, in the South-East Asia Region, road traffic accidents, drowning, burns, and other injuries are leading causes of child …
Published in Journal of Nursing Science & Practice · Vol. 15, Issue 1, 2025 · pp. 17–22 Read article
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Unmanned Aerial Vehicle Using AI-ML
Abstract: Remotely piloted aircraft systems (RPAS), commonly known as drones, have evolved significantly in recent years, revolutionizing various industries and domains. This article provides an overview of the key aspects of RPAS technology, their applications, and the impact they have had on society. RPAS are autonomous or semi-autonomous aerial vehicles that can be controlled remotely, offering diverse capabilities, from data collection and surveillance to cargo delivery and recreational activities. This abstract …
Published in International Journal of Satellite Remote Sensing · Vol. 3, Issue 1, 2025 · pp. 11–19 Read article
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Harnessing Hydrolgeological Parametrs: Prediction of Water Probability and Levels for Water Well Construction Using Ai-Enabled Models
Abstract: The AI-Based Decision Support System for Water Well Construction utilizes data from the National Aquifer Mapping and Management System (NAQUIM) and employs advanced AI techniques like regression analysis, decision trees, and neural networks. This system predicts crucial parameters for water well construction, including location suitability, water-bearing zone depths, and groundwater quality. By integrating large datasets such as lithology, geophysical logs, and aquifer maps provided by the Central Ground Water Board …
Published in Journal of Water Resource Engineering and Management · Vol. 12, Issue 1, 2025 · pp. 16–28 Read article
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Bridging the Theory-Practice Gap in Nursing Education in India: Exploring the Role of Simulation-Based Learning and AI-Driven Education
Abstract: The theory-practice gap in nursing education is a persistent issue that hinders the effective application of theoretical knowledge in clinical practice, ultimately affecting the clinical competence and decision-making skills of nursing graduates. This study explores the factors contributing to the theory-practice gap in nursing education in India and evaluates the potential of simulation-based learning (SBL) and artificial intelligence (AI)-driven education in bridging this gap. A mixed-methods approach was employed, involving …
Published in Journal of Nursing Science & Practice · Vol. 15, Issue 2, 2025 Read article
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Study of Social Trends Prediction Using AI
Abstract: AI (Artificial Intelligence) has fundamentally changed the ability to analyze social trends by using large datasets to develop predictions about human behavior, public sentiment, and global events. Using methodologies such as Natural Language Processing (NLP), Time-Series Forecasting, and Graph-Based Social Network Analysis, AI is able to find hidden correlations in a variety of available datasets, from social media to economic indicators to public records, and fundamentally changes decision-making based on …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 19–29 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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AI-Driven Lightning Strike Prediction Using Polymer-Integrated Sensor Platforms for Climate-Resilient Energy Systems in India
Abstract: Lightning strikes are a major climate-related threat to India, resulting in severe human injuries as well as regular damages to the power transmission network and renewable energy infrastructure. This research aims to introduce the concept of an AI-based lightning strike prediction and mitigation system with the integration of polymers for making climate-resilient energy infrastructure. Multidata are collected based on satellite images, climate variables, as well as surface-based sensing modules, and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 234–242 Read article
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CIPHER Intelligence: AI-Powered Global Military Expenditure Analysis and Predictive Modeling
Abstract: Military expenditure analysis has emerged as a critical component of economic and geopolitical intelligence in the modern era. This paper presents CIPHER Intelligence, a comprehensive AI-powered platform for analyzing and predicting global military spending patterns across 211 countries spanning54 years (1970-2024). We employ advanced machine learning techniques, particularly Random Forest regression models, to achieve 99.5% prediction accuracy for military expenditure forecasting based on economic indicators. The platform integrates data from …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 Read article