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

Interpretable AI for Intelligent Event Detection and Anomaly Classification in Healthcare Monitoring Systems
Authors:-Assistant Professor Mrs.K.S.R.Manjusha, D.Ashok Kumar, M.Harish, M.Hari Sathvik, M.Vinsy, A.Sri Sai Keerthi.

Abstract-Artificial intelligence (AI) is transforming healthcare by automating the detection and classification of events and anomalies, enhancing patient monitoring and intervention. In this context, events refer to abnormalities caused by medical conditions such as seizures or falls, while anomalies are erroneous data resulting from sensor faults or malicious attacks. AI-based event and anomaly detection (EAD) enables early identification of critical issues, reducing false alarms and improving patient outcomes. The advancement of Medical Internet of Things (MIoT) devices has further facilitated real-time data collection, AI-driven processing, and transmission, enabling remote monitoring and personalized healthcare. However, ensuring the transparency and explainability of AI systems is crucial in medical applications to foster trust and understanding among healthcare professionals. This work presents an online EAD approach utilizing a lightweight autoencoder (AE) on MIoT devices to detect abnormalities in real time. The detected abnormalities are then explained using Kernel SHAP, a technique from explainable AI (XAI), and subsequently classified as either events or anomalies using an artificial neural network (ANN). Extensive simulations conducted on the Medical Information Mart for Intensive Care (MIMIC) dataset demonstrate the robustness of the proposed approach in accurately detecting and classifying events, regardless of the proportion of anomalies present.

DOI: 10.61137/ijsret.vol.11.issue2.292

Published by: vikaspatanker

DeepSpineNet: Advanced Deep Learning for Multi-Class Spine X-Ray Condition Classification
Authors:-Assistant Professor Mrs.K.S.R.Manjusha, D.Ashok Kumar, M.Harish, M.Hari Sathvik, M.Vinsy, A.Sri Sai Keerthi.

Abstract-Addressing the complexchallenges of automated spine X-rayanalysis, our research introduces Deep Spine, a deep learning model designed for the multi-class classification of diverse spine conditions. Utilizing Convolutional Neural Networks (CNNs), Deep Spine demonstrates exceptional proficiency in identifying a range of spinal abnormalities, including Scoliosis, Osteochondrosis, Osteoporosis, Spondylolisthesis, Vertebral Compression Fractures (VCFs),Disability, Other, and Healthy cases. Trained on a Kaggle dataset, Deep Spine achieves high accuracy and robustness, ensuring reliable performance in classifying spinal conditions. The incorporation of transfer learning techniques further enhances its generalization capability, enabling the model to adapt effectively across different datasets. This approach not only strengthens its diagnostic accuracy but also highlights its potential for automated diagnosis and decision support in musculoskeletal radiology. This research contributes to the evolving intersection of artificial intelligence and medical imaging, demonstrating the transformative potential of deep learning in spine X-ray analysis. By leveraging AI-driven advancements, Deep Spine offers a promising step toward enhancing clinical outcomes, improving diagnostic precision, and revolutionizing spinal healthcare.

DOI: 10.61137/ijsret.vol.11.issue2.291

Published by: vikaspatanker

AI-Powered Ransomware Defence: Cutting-Edge Machine Learning Techniques for Threat Detection
Authors:-Assistant Professor Mrs.P.Satyavathi, M.Naga Sai Ganesh, N.V.Gowtham Kumar, V.S.V.Satya Yaswanth, G.Satya Nandini, V.Giri Sathvika.

Abstract-The increasing frequency and sophistication of ransomware attacks, there is a growing need for dynamic and effective detection and mitigation strategies. Traditional signature-based approaches often fall short in identifying new and evolving ransomware variants. This paper explores the application of machine learning techniques for ransomware detection, aiming to enhance the accuracy and adaptability of detection mechanisms. It provides a comprehensive analysis of various machine learning methods and algorithms, evaluating their effectiveness in identifying ransomware patterns. The findings offer valuable insights into the advancement of cybersecurity solutions, emphasizing resilience and proactive defense against the ever-evolving ransomware threat landscape.

DOI: 10.61137/ijsret.vol.11.issue2.290

Published by: vikaspatanker

Rapid Depression Detection Using Extreme Learning Machine: An AI-Driven Approach
Authors:-Assistant Professor Mrs.G.V.Rajeswari, Ch.Harikiran, K.L.Rishitha, K.H.Venkat Ganesh, B.Raj Kumar, V.L.Apoorva.

Abstract-Depression is one of the most prevalent psychological and mental health disorders, affecting a significant number of people worldwide. In recent years, Extreme Learning Machine (ELM) techniques have gained preference for addressing various health-related disease detection and prediction challenges. ELM is a single hidden layer feed-forward neural network (SLFN) that offers significantly faster convergence compared to traditional machine learning (ML) methods while delivering promising results. Although numerous studies have explored the application of ML models for depression detection, limited research has focused on utilizing ELM for this purpose. This study implements Extreme Learning Machine (ELM) alongside other ML techniques for depression detection, comparing their performance. The results demonstrate that ELM outperforms other methods, achieving the highest accuracy of 91.73%.

DOI: 10.61137/ijsret.vol.11.issue2.289

Published by: vikaspatanker