AI for Predictive Analytics in Retail: Enhancing Inventory Management and Customer Engagement

Authors:-Krishna. M

Abstract- Artificial Intelligence (AI) is rapidly transforming various industries, and retail is no exception. Predictive analytics powered by AI is becoming a game-changer in the retail sector, especially when it comes to inventory management and customer engagement. By leveraging machine learning algorithms and big data, retailers can predict customer demand, optimize stock levels, reduce waste, and improve overall operational efficiency. This paper explores the role of AI-driven predictive analytics in retail, focusing on its impact on inventory management and customer engagement. It discusses the underlying technologies, such as machine learning and natural language processing, and highlights real-world applications where AI is revolutionizing retail operations. Additionally, the paper examines the challenges retailers face in implementing AI technologies and provides insights into the future potential of AI in shaping the retail landscape.

DOI: 10.61137/ijsret.vol.11.issue2.388

Published by: vikaspatanker

Machine Learning for Sustainable Agriculture: Enhancing Crop Yield Predictions and Resource Management

Authors:-Ashok.P

Abstract-The global population is expected to surpass 9 billion by 2050, placing unprecedented demand on agricultural systems to produce more food while minimizing environmental impact. Sustainable agriculture, which focuses on producing food while preserving environmental health, is vital for ensuring future food security. Machine learning (ML), a powerful subset of artificial intelligence (AI), holds significant potential for enhancing agricultural practices by improving crop yield predictions, optimizing resource management, and enabling precision farming techniques. This paper explores how ML algorithms are being applied to sustainable agriculture, from predictive analytics for crop yield forecasting to real-time monitoring of soil conditions and pest management. It examines key ML techniques such as supervised learning, unsupervised learning, and reinforcement learning and their role in enhancing agricultural sustainability. Furthermore, the paper highlights the challenges and ethical considerations involved in implementing ML in agriculture and discusses the future outlook for AI-driven innovations in the sector.

DOI: 10.61137/ijsret.vol.11.issue2.387

Published by: vikaspatanker

AI in Legal Tech: Revolutionizing Legal Research and Case Prediction Models

Authors:-Anand.P

Abstract-The legal industry is experiencing a transformative shift with the integration of Artificial Intelligence (AI), particularly in the fields of legal research and case prediction. Traditional legal processes, often characterized by time-consuming document reviews and complex case analyses, are being redefined by intelligent algorithms capable of processing massive volumes of legal data in seconds. This paper explores the role of AI in legal tech, emphasizing its impact on enhancing legal research efficiency, improving case prediction accuracy, and supporting data-driven decision-making. It analyzes how natural language processing, machine learning, and predictive analytics are revolutionizing the legal landscape, making legal services more accessible, efficient, and equitable. The paper also addresses the challenges associated with AI implementation in law, including ethical concerns, data bias, and regulatory hurdles. By examining current applications and future possibilities, the paper illustrates how AI is reshaping legal practice and offering unprecedented opportunities for innovation and reform in the justice system.

DOI: 10.61137/ijsret.vol.11.issue2.386

Published by: vikaspatanker

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