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

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

Authors: Abdul Kalam A, Mrs.J.Gokulapriya

 

 

Abstract: Digital forensics has become a cornerstone of modern crime investigation, particularly in a world where cyber threats, digital fraud, and electronic evidence are increasing rapidly. Artificial Intelligence (AI) is reshaping this field by automating evidence collection, accelerating analysis, and uncovering hidden patterns across massive datasets. This paper explores how AI enhances digital forensic processes such as image analysis, malware detection, data reconstruction, and behavioral profiling. It also highlights the risks, such as algorithmic bias and evidentiary admissibility, and the need for explainable AI in forensic contexts. As technology evolves, AI will not just assist but transform digital forensic science into a faster, smarter, and more reliable discipline.

 

 

Published by: vikaspatanker

Authors: Vaishnavi K,, Dr.Uthiramoorthy

 

Abstract: The Flower Shop: Elegant Efflorescence is a modern, user-centric mobile and web-based application designed to enhance the floral shopping experience. This platform bridges the gap between florists and customers by offering a seamless interface for browsing, customizing, and purchasing floral arrangements. With features such as real-time inventory tracking, personalized recommendations, occasion-based filtering, and efficient delivery tracking, the application emphasizes elegance, convenience, and customer satisfaction. It also supports local flower vendors by providing them with a digital storefront, analytics, and order management tools. The project combines aesthetics with functionality, aiming to transform the traditional flower buying process into a sophisticated digital experience.

 

 

Published by: vikaspatanker

Authors: Sparsh Silhan, Assistant Professor Dr. Afsa Parveen

 

Abstract: Understanding customer experience and the customer journey is critical for firms. Customers now interact with firms through myriad touch points in multiple channels and media, and customer experiences are more social in nature. These changes require firms to integrate multiple business functions, and even external partners, in creating and delivering positive customer experiences. In this article, the authors aim to develop a stronger understanding of customer experience and the customer journey in this era of increasingly complex customer behavior. To achieve this goal, they examine existing definitions and conceptualizations of customer experience as a construct and provide a historical perspective of the roots of customer experience within marketing. Next, they attempt to bring together what is currently known about customer experience, customer journeys, and customer experience management. Finally, they identify critical areas for future research on this important topic. The study emphasizes that the increasing complexity of customer behavior has made it crucial for companies to prioritize and thoroughly understand customer experience. Customers engage with companies through a variety of channels and media, and these interactions are increasingly social, requiring companies to integrate various business functions and collaborate with external partners to ensure positive customer experiences. The authors explore the evolution of customer experience within marketing, tracing its origins and development. They analyze how customer experience has been defined and conceptualized, and they consolidate current knowledge on customer journeys and customer experience management. The article also identifies significant gaps in existing research, highlighting areas that require further investigation. The research underscores the importance of a holistic approach to customer experience, noting that it encompasses a customer's cognitive, emotional, behavioral, sensory, and social responses throughout their entire purchase journey. This comprehensive understanding enables businesses to design more effective marketing strategies, enhance customer satisfaction, and build long-term loyalty. The study concludes by proposing a future research agenda aimed at deepening the understanding of customer experience and its impact on business outcomes.

DOI: http://doi.org/

 

Published by: vikaspatanker

Book Store

Authors: 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

Understanding Streaming Success Through Spotify Analytics

Authors: Ure Sanath Kumar, Borkar Srikanth, Erukonda Mohan Manoj, Dr. Diana Moses

 

 

Abstract: The Top Spotify Songs in 73 Countries dataset provides a dynamic overview of global music trends by tracking the most popular songs across 73 countries daily. It includes key attributes such as song rank, track name, artist name, country, stream count, and ranking date, allowing for in-depth analysis of song popularity. Additionally, it contains song characteristics like danceability, energy, loudness, and valence, offering insights into musical trends and listener preferences. This dataset is particularly useful for music analysts, data scientists, and researchers aiming to explore streaming patterns, predict music trends, and analyze regional differences in music consumption. It can be leveraged for data visualization, trend analysis, machine learning models, and recommendation systems. Furthermore, businesses in the music industry can utilize this dataset to gain insights into audience preferences and optimize marketing strategies.

 

 

 

Published by: vikaspatanker