Daily Archives: April 29, 2025

Deep Learning Approaches for Natural Disaster Prediction and Response Planning

Authors:-Manju.M

Abstract-Natural disasters, including earthquakes, hurricanes, wildfires, and floods, have devastating impacts on human life, infrastructure, and the environment. Effective prediction and response to these events are essential for minimizing damage and ensuring public safety. Deep learning, a subset of artificial intelligence (AI), has shown immense potential in improving natural disaster prediction, early warning systems, and disaster response planning. This paper explores various deep learning techniques, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), applied to the prediction and mitigation of natural disasters. The paper highlights the use of satellite imagery, sensor data, and meteorological models in disaster forecasting and emergency management. It also examines the role of deep learning in post-disaster recovery, from damage assessment to resource allocation. Through case studies and real-world applications, the paper demonstrates how deep learning is transforming natural disaster prediction and response, contributing to enhanced resilience and preparedness.

DOI: 10.61137/ijsret.vol.11.issue2.385

Published by: vikaspatanker

AI-Powered Personalization in E-Commerce: Transforming Consumer Experience Through Data Insights

Authors:-Ravi .M

Abstract-The e-commerce industry has rapidly evolved in recent years, with personalization becoming a central aspect of enhancing customer satisfaction and driving sales. With the vast amount of consumer data available, artificial intelligence (AI) plays a pivotal role in creating personalized shopping experiences. By analyzing customer behavior, preferences, and past interactions, AI enables e-commerce platforms to deliver tailored product recommendations, dynamic pricing, and targeted marketing strategies. This paper explores the application of AI in e-commerce personalization, highlighting key technologies such as machine learning, natural language processing (NLP), and recommendation systems. It examines how AI-driven personalization benefits both consumers and businesses, leading to increased customer engagement, loyalty, and ultimately, revenue growth. Additionally, the paper discusses the challenges and ethical considerations associated with data privacy and the future potential of AI in revolutionizing the online shopping experience.

DOI: 10.61137/ijsret.vol.11.issue2.384

Published by: vikaspatanker

AI-Enabled Real-Time Health Monitoring for Elderly Care: A Smart Solutions Approach

Authors:-Rajesh.S

Abstract-The aging global population presents unique challenges to healthcare systems worldwide, particularly in the realm of elderly care. With the increasing number of elderly individuals suffering from chronic conditions and requiring long-term care, the demand for innovative solutions to monitor their health in real-time has never been more urgent. Artificial intelligence (AI) and machine learning (ML) offer promising technologies that can revolutionize elderly care by enabling continuous health monitoring, early detection of health issues, and personalized interventions. This paper explores the role of AI in real-time health monitoring for elderly care, focusing on wearable devices, sensors, and AI-powered analytics. By combining real-time data collection with predictive analytics, AI systems can alert caregivers to potential health risks, such as heart attacks, falls, or medication non-adherence, allowing for timely interventions. The paper discusses various applications of AI in elderly care, challenges related to data privacy and security, and the future potential of AI in supporting independent living for seniors.

DOI: 10.61137/ijsret.vol.11.issue2.383

Published by: vikaspatanker

AI-Driven Optimization of Supply Chain Processes: Enhancing Efficiency and Reducing Costs

Authors:-Chandana P

Abstract-In today’s fast-paced global economy, supply chain optimization is crucial for enhancing operational efficiency, reducing costs, and ensuring seamless service delivery. Artificial Intelligence (AI) has emerged as a transformative tool, providing innovative solutions to traditional supply chain challenges. This paper explores the role of AI in optimizing supply chain processes, focusing on key areas such as demand forecasting, inventory management, logistics, and supplier relationship management. By leveraging machine learning, predictive analytics, and real-time data processing, AI can enhance decision-making, minimize inefficiencies, and support proactive problem-solving. Through case studies and industry applications, the paper illustrates the practical benefits of AI in supply chains and examines potential challenges, such as data quality, implementation costs, and ethical concerns. The paper concludes by discussing future trends and opportunities for AI in supply chain management, emphasizing its potential to reshape the future of global commerce.

DOI: 10.61137/ijsret.vol.11.issue2.382

Published by: vikaspatanker

Smart Elderly Care with Predictive AI Analytics

Authors:-Srinivas H S

Abstract-The growing elderly population worldwide presents significant challenges for healthcare systems, caregivers, and policymakers. With aging comes a higher risk of chronic conditions, cognitive decline, mobility issues, and social isolation. Traditional models of elder care are increasingly strained, leading to the need for intelligent, scalable, and proactive approaches. Predictive Artificial Intelligence (AI) analytics has emerged as a transformative solution in smart elderly care, leveraging data from various sources such as wearable sensors, home monitoring systems, electronic health records, and behavioral data to predict health events and enable timely interventions. This paper explores how predictive AI is reshaping elderly care by enhancing disease prevention, enabling fall detection and prediction, improving medication management, supporting cognitive health, and facilitating independent living. It also addresses ethical considerations, data privacy, system design challenges, and the future potential of AI in fostering a more responsive and dignified aging experience.

DOI: 10.61137/ijsret.vol.11.issue2.381

Published by: vikaspatanker

Augmented Reality and AI for Medical Training Simulators

Authors:-Mamatha U

Abstract-The evolution of medical education has witnessed significant transformations with the integration of emerging technologies. Among the most transformative are Augmented Reality (AR) and Artificial Intelligence (AI), which together are redefining the landscape of medical training. AR creates immersive learning environments by overlaying digital information onto the physical world, while AI adds an intelligent layer that adapts to learner needs, assesses performance, and offers personalized feedback. This paper explores the convergence of AR and AI in medical training simulators, detailing how this synergy is reshaping anatomical learning, surgical skill acquisition, patient interaction scenarios, and emergency response training. It discusses the pedagogical advantages, the technological architectures underpinning these systems, challenges in implementation, and the future trajectory of intelligent simulation platforms. Through predictive analytics, adaptive interfaces, and real-time feedback, AR and AI are equipping medical students and professionals with the experiential knowledge and confidence required in high-stakes clinical environments.

DOI: 10.61137/ijsret.vol.11.issue2.380

Published by: vikaspatanker

AI in Continuous Blood Glucose Monitoring Systems
Authors:-Nagesh M S

Abstract-Continuous Blood Glucose Monitoring (CGM) systems have revolutionized diabetes management by providing real-time insights into glucose fluctuations, enabling patients and healthcare providers to take proactive measures. The integration of Artificial Intelligence (AI) into CGM systems has significantly enhanced their efficiency, accuracy, and predictive capabilities. AI algorithms analyze complex and voluminous glucose data to identify patterns, predict future trends, and offer personalized recommendations. This paper explores the applications of AI in CGM, examining how machine learning and deep learning models are being used for improved glycemic control, early detection of glucose anomalies, behavior prediction, and adaptive insulin therapy. It also discusses the impact of AI-driven CGMs on patient engagement, remote monitoring, and clinical decision-making. Ethical concerns, data privacy, and technological limitations are also addressed. This comprehensive analysis underscores AI’s transformative role in reshaping diabetes care, making it more precise, predictive, and patient-centric.

DOI: 10.61137/ijsret.vol.11.issue2.379

Published by: vikaspatanker

AI-Powered Patient Flow Optimization in Emergency Rooms
Authors:-Kumar S

Abstract-Emergency Rooms (ERs) are high-pressure environments characterized by unpredictability, time-sensitive decisions, and often overcrowding. These conditions, when not optimally managed, can lead to prolonged wait times, increased medical errors, clinician burnout, and compromised patient outcomes. As healthcare systems strive to deliver efficient, equitable, and timely emergency care, Artificial Intelligence (AI) has emerged as a transformative force. AI-powered patient flow optimization employs machine learning, predictive analytics, and intelligent decision support systems to streamline triage, resource allocation, and care coordination. This paper explores how AI is revolutionizing emergency room operations by enhancing real-time decision-making, reducing bottlenecks, forecasting demand, and personalizing patient care pathways. It also examines the integration of AI tools into clinical workflows, the ethical and infrastructural challenges of implementation, and the future of AI-driven operational excellence in emergency healthcare settings.

DOI: 10.61137/ijsret.vol.11.issue2.378

Published by: vikaspatanker

Integrating AI into Pediatric Health Management

Authors:-Varsha

Abstract-The application of Artificial Intelligence (AI) in healthcare has shown tremendous potential in various domains, yet one of its most impactful and delicate arenas is pediatric health management. Children are not merely miniature adults; their physiological, psychological, and developmental needs are distinct and require tailored approaches in clinical care. Pediatric health management is particularly complex, involving routine checkups, vaccinations, developmental monitoring, chronic disease management, and acute care—all while ensuring minimal invasiveness and maximum safety. Integrating AI into this domain promises transformative improvements in diagnosis, treatment planning, patient monitoring, early detection of developmental disorders, and personalized health interventions. This paper explores the significant role of AI in pediatric healthcare, examining current applications, challenges, ethical considerations, and future possibilities. By analyzing the technological advancements and real-world implementations of AI in pediatrics, this research underscores the importance of intelligent systems in ensuring the long-term health and well-being of children.

DOI: 10.61137/ijsret.vol.11.issue2.377

Published by: vikaspatanker

AI-Enhanced Decision Support for Radiology Technicians
Authors:-Pavan T.K

Abstract-The exponential rise in diagnostic imaging demands has outpaced the capacity of radiologists and radiology technicians worldwide, creating a bottleneck in timely and accurate diagnosis. Artificial Intelligence (AI) has emerged as a revolutionary tool in the field of radiology, particularly as a decision support system for radiology technicians. While much of the AI research in medical imaging focuses on automating radiologist tasks, the integration of AI tools into radiology technician workflows presents a valuable, underexplored frontier. This paper investigates the role of AI in assisting radiology technicians by enhancing image acquisition quality, automating repetitive tasks, supporting error detection, and optimizing workflow management. It also discusses AI’s contribution to patient safety, data annotation, training, and real-time support during imaging procedures. As AI technology evolves, radiology technicians are increasingly becoming empowered with tools that boost accuracy, improve efficiency, and reduce burnout. The paper also examines ethical, technical, and operational considerations in deploying AI systems in radiological environments, concluding with insights into the future of collaborative human-AI integration in medical imaging.

DOI: 10.61137/ijsret.vol.11.issue2.376

Published by: vikaspatanker