Daily Archives: May 21, 2025

Future of Loan Approvals Using Explainable AI
Authors:-Kekkarla Madhu, V. Shirisha, Atla Sonya , L. Rahul Chandra

Abstract-:The advent of Artificial Intelligence (AI) and Ma- chine Learning (ML) is revolutionizing various industries by enhancing operational efficiency and facilitating innovation. In manufacturing, these technologies not only streamline processes but also contribute significantly to sustainability objectives. This paper explores how AI and ML are reshaping industrial practices, particularly focusing on sustainable manufacturing. It identifies current trends, the operational advantages, and future research areas for these technologies in the manufacturing domain.

DOI: 10.61137/ijsret.vol.11.issue3.112

Published by: Kajal Tripathi

Authors: Krishna Birla, Siddarth Patil, Prof. Vaibhav Srivastava

Abstract: Landslides are a serious natural hazard that cause major social, economic, and environmental damage around the world. To reduce their impact, it’s crucial to accurately predict where they might happen. In recent years, combining Geographic Information Systems (GIS) with Machine Learning (ML) has greatly improved landslide prediction and mapping. GIS helps organize and visualize complex spatial data, while ML can find hidden patterns between the factors that lead to landslides. This review looks at different ML models used for landslide prediction, including Logistic Regression, Support Vector Machines, Random Forest, as well as ensemble methods like Bagging, Boosting, and Stacking. It also explores newer Deep Learning approaches. We discuss common challenges such as limited data, difficulty in understanding models, and how to handle changing conditions. Finally, we highlight future directions like Explainable AI (XAI) and real-time monitoring. By bringing together findings from recent studies, this review provides insights into what’s working, what’s not, and how ML and GIS can help improve landslide risk management.

 

 

Published by: vikaspatanker

Authors: Krishna Birla, Siddarth Patil, Prof. Vaibhav Srivastava

 

Abstract: Landslides are a serious natural hazard that cause major social, economic, and environmental damage around the world. To reduce their impact, it’s crucial to accurately predict where they might happen. In recent years, combining Geographic Information Systems (GIS) with Machine Learning (ML) has greatly improved landslide prediction and mapping. GIS helps organize and visualize complex spatial data, while ML can find hidden patterns between the factors that lead to landslides. This review looks at different ML models used for landslide prediction, including Logistic Regression, Support Vector Machines, Random Forest, as well as ensemble methods like Bagging, Boosting, and Stacking. It also explores newer Deep Learning approaches. We discuss common challenges such as limited data, difficulty in understanding models, and how to handle changing conditions. Finally, we highlight future directions like Explainable AI (XAI) and real-time monitoring. By bringing together findings from recent studies, this review provides insights into what’s working, what’s not, and how ML and GIS can help improve landslide risk management.

DOI: http://doi.org/

 

Published by: vikaspatanker

Authors: Krishna Birla, Siddarth Patil, Prof. Vaibhav Srivastava

 

Abstract: Landslides are a serious natural hazard that cause major social, economic, and environmental damage around the world. To reduce their impact, it’s crucial to accurately predict where they might happen. In recent years, combining Geographic Information Systems (GIS) with Machine Learning (ML) has greatly improved landslide prediction and mapping. GIS helps organize and visualize complex spatial data, while ML can find hidden patterns between the factors that lead to landslides. This review looks at different ML models used for landslide prediction, including Logistic Regression, Support Vector Machines, Random Forest, as well as ensemble methods like Bagging, Boosting, and Stacking. It also explores newer Deep Learning approaches. We discuss common challenges such as limited data, difficulty in understanding models, and how to handle changing conditions. Finally, we highlight future directions like Explainable AI (XAI) and real-time monitoring. By bringing together findings from recent studies, this review provides insights into what’s working, what’s not, and how ML and GIS can help improve landslide risk management.

DOI: http://doi.org/

 

Published by: vikaspatanker

Authors: Mrs. R. BHUVANESHWARI, Ms. T. MISHA

 

 

Abstract: Predicting house prices is both vital and complex due to the ever-changing nature of the real estate market. Conventional statistical approaches often fall short in identifying intricate data trends, making machine learning a more suitable solution. This project adopts the Support Vector Machine (SVM) algorithm to forecast housing prices by analyzing historical data and key market influences. Known for its ability to manage high-dimensional datasets and model nonlinear relationships, SVM proves to be a dependable method for accurate price prediction. The system evaluates multiple factors including geographic location, property dimensions, prevailing market trends, and economic conditions to improve prediction precision. Through SVM’s capabilities in both classification and regression, the model delivers strong, data-informed insights that assist homebuyers, sellers, and investors in navigating the dynamic real estate environment effectively

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Akerele Olalekan Victor, Omojogberun Veronica Y, Abegunde-Abikoye O.S

 

 

Abstract: Many buildings available today are built without considering whether they are energy efficient or not. This gives rise to either over-estimation or under-estimation of energy (electricity) to be used by the building. Hence, a way of estimating the total energy consumption of a building is to properly account for the variables that demand energy usage from a building and then calculate the resultant energy used using a suitable computer application. The energy performance of two two-bedroom bungalows was estimated using a developed computer application. The computer application allowed input of various building parameters such as geometry (height, breadth, and width), roof type, building orientation, window shading, cooling load, and other electrical appliances. The estimation was done during the peak hour of the day (Cooling Load Temperature Difference between 11 am and 3 pm) for one hour with the building facing due west to efficiently ascertain how energy efficient the building would perform under peak load. The results from computed data show that the building required more energy to keep it cool due to excessive sunlight incident on the building. Also, the roofing material and window shading contributed to the poor energy performance of the building. With an estimated value of 16kW, it can be concluded that the energy performance of the building was below average as a result of the poor selection of building materials and building orientation.

 

 

Published by: vikaspatanker

Authors: Kuraganti Syam Kumar, Palineti Karthik, Thodindala Siva Teja, Shaik Anwar Mohiddeen, Syed Mohammad Waseem

 

Abstract: Electricity theft remains a major challenge for power distribution systems, leading to significant financial losses and reduced supply reliability. This paper presents a smart and automated solution for detecting unauthorized electricity usage and enabling remote load control through the Internet of Things (IoT). The proposed system continuously monitors electrical parameters such as current and voltage using embedded sensors. Anomalies indicative of theft such as elevated current without corresponding voltage change trigger an automatic disconnection of the power supply via a relay module. Simultaneously, a GSM module transmits an alert message containing GPS coordinates to the concerned authorities, enabling quick response and location-based intervention. The system also supports cloud integration for real-time monitoring, data logging, and consumption analysis. Leveraging the ESP32 microcontroller, this approach offers a cost- effective, scalable, and energy-efficient framework applicable to residential, commercial, and industrial environments. The integration of automated theft detection, instant notification, and IoT-based control enhances grid transparency, reduces human intervention, and ensures equitable power distribution.

DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.117

 

Published by: vikaspatanker

Development and Enhancement of a Scalable React Platform with Front- end Development, AI/ML Integration and API-Driven Architecture
Authors:-Mansi Hatwar, Associate Professor Dr. Pavithra G

Abstract-:This paper presents the architectural evolution, development methodology, and QA practices involved in building and enhancing a scalable React-based web platform. The platform underwent a systematic migration from legacy frameworks to React, incorporating modern development practices and component-driven architecture. Furthermore, the platform was augmented with AI/ML capabilities for predictive analytics and integrated with RESTful APIs for seamless interoperability. Emphasis is placed on quality assurance (QA) strategies, automation in testing, design systems using Material UI, and real-world challenges encountered during migration and integration. The development process included collaborative UI/UX prototyping in Figma, effective use of Git and GitHub for version control, and performance- focused HTML/CSS design. The approach reflects modern trends in web-based intelligent applications and offers insights into the operational benefits of a decoupled architecture.

DOI: 10.61137/ijsret.vol.11.issue3.111

Published by: Kajal Tripathi

Authors: Assistant Professor C.K.Sukanya, V.Priyadharshini

 

 

Abstract: As the world becomes increasingly digital, the concept of the traditional bookstore is evolving rapidly. This paper explores how emerging technologies can transform the bookstore experience in the near future. From augmented reality (AR) and artificial intelligence (AI) to smart shelves and personalized recommendation systems, technology is set to redefine how readers discover, interact with, and purchase books. Future bookstores may become hybrid spaces—part library, part community hub, part digital experience center—offering immersive storytelling through AR, voice-guided book previews, and AI-powered reading assistants. This presentation highlights key innovations and envisions a future where bookstores blend physical charm with digital convenience, enhancing accessibility, engagement, and reader satisfaction.

 

 

Published by: vikaspatanker

Nanotechnology in Agriculture and Food Safety
Authors:-Dr. Anwar Qureshi

Abstract:- Nanotechnology is rapidly emerging as a powerful tool to address critical challenges in agriculture and food safety. By leveraging engineered nanomaterials with unique properties, it enables enhanced crop production, precision nutrient delivery, targeted pest control, and real-time monitoring of soil and food quality. Nano-enabled livestock health solutions and advanced food processing techniques improve productivity and extend shelf life. Additionally, nanosensors and smart packaging technologies offer sensitive, rapid detection of contaminants, enhancing food safety throughout the supply chain. Despite these benefits, concerns over nanoparticle toxicity, environmental persistence, and regulatory gaps remain. This paper provides a comprehensive review of nanotechnology applications in agriculture and food safety, discusses safety and policy considerations, and highlights emerging trends such as green nanotechnology and digital integration. Responsible innovation, supported by harmonized regulations and stakeholder engagement, is imperative to maximize the positive impact of nanotechnology on sustainable food systems.

DOI: 10.61137/ijsret.vol.5.issue6.581

Published by: Kajal Tripathi