Category Archives: Uncategorized

Authors: Dr. A. Saravanakumar

Abstract: The advent of new technologies has streamlined business transactions, enhancing the buying experience for both companies and customers. Digital marketing, in particular, has enabled mutual fund companies to expand their investor base while providing potential investors with convenient access to information. In this context, the primary objective of this study is to examine the impact of digital marketing on investors' decisions to invest in mutual funds, with a focus on identifying key demographic factors influencing online investments.

 

 

Published by: vikaspatanker

Authors: Assistant Professor Mrs. Priyanka Bamne, Nimesh Agrawal

Abstract: The increasing volume of products on online platforms has made product recommendation systems (PRS) essential for enhancing user experience and driving sales. This survey paper provides a comprehensive review of PRS, focusing on their necessity, implementation methods, and relevance in e-commerce and digital marketplaces. We explore the motivation behind recommendation systems, emphasizing their role in improving customer satisfaction, personalization, and business profitability. Various implementation techniques, including collaborative filtering, content-based filtering, hybrid filtering, and deep learning methods, are analyzed with a discussion on their advantages and limitations. Furthermore, we examine real-world applications, challenges such as cold start and scalability, and emerging trends in AI-driven recommendations. To establish the relevance of these concepts, we review key research papers, industry applications, and case studies from platforms like Amazon, Netflix, and Spotify. Finally, we highlight future directions, including explainable AI, privacy-aware recommendations, and real-time personalization, offering insights for researchers and practitioners aiming to enhance recommender systems.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Assistant Professor Mrs. Priyanka Bamne, Nimesh Agrawal

Abstract: The increasing volume of products on online platforms has made product recommendation systems (PRS) essential for enhancing user experience and driving sales. This survey paper provides a comprehensive review of PRS, focusing on their necessity, implementation methods, and relevance in e-commerce and digital marketplaces. We explore the motivation behind recommendation systems, emphasizing their role in improving customer satisfaction, personalization, and business profitability. Various implementation techniques, including collaborative filtering, content-based filtering, hybrid filtering, and deep learning methods, are analyzed with a discussion on their advantages and limitations. Furthermore, we examine real-world applications, challenges such as cold start and scalability, and emerging trends in AI-driven recommendations. To establish the relevance of these concepts, we review key research papers, industry applications, and case studies from platforms like Amazon, Netflix, and Spotify. Finally, we highlight future directions, including explainable AI, privacy-aware recommendations, and real-time personalization, offering insights for researchers and practitioners aiming to enhance recommender systems.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Assistant Professor Mrs. Priyanka Bamne, Nimesh Agrawal

Abstract: The increasing volume of products on online platforms has made product recommendation systems (PRS) essential for enhancing user experience and driving sales. This survey paper provides a comprehensive review of PRS, focusing on their necessity, implementation methods, and relevance in e-commerce and digital marketplaces. We explore the motivation behind recommendation systems, emphasizing their role in improving customer satisfaction, personalization, and business profitability. Various implementation techniques, including collaborative filtering, content-based filtering, hybrid filtering, and deep learning methods, are analyzed with a discussion on their advantages and limitations. Furthermore, we examine real-world applications, challenges such as cold start and scalability, and emerging trends in AI-driven recommendations. To establish the relevance of these concepts, we review key research papers, industry applications, and case studies from platforms like Amazon, Netflix, and Spotify. Finally, we highlight future directions, including explainable AI, privacy-aware recommendations, and real-time personalization, offering insights for researchers and practitioners aiming to enhance recommender systems.

Published by: vikaspatanker

Authors: Assistant Professor Mrs. Priyanka Bamne, Nimesh Agrawal

Abstract: The increasing volume of products on online platforms has made product recommendation systems (PRS) essential for enhancing user experience and driving sales. This survey paper provides a comprehensive review of PRS, focusing on their necessity, implementation methods, and relevance in e-commerce and digital marketplaces. We explore the motivation behind recommendation systems, emphasizing their role in improving customer satisfaction, personalization, and business profitability. Various implementation techniques, including collaborative filtering, content-based filtering, hybrid filtering, and deep learning methods, are analyzed with a discussion on their advantages and limitations. Furthermore, we examine real-world applications, challenges such as cold start and scalability, and emerging trends in AI-driven recommendations. To establish the relevance of these concepts, we review key research papers, industry applications, and case studies from platforms like Amazon, Netflix, and Spotify. Finally, we highlight future directions, including explainable AI, privacy-aware recommendations, and real-time personalization, offering insights for researchers and practitioners aiming to enhance recommender systems.

 

Published by: vikaspatanker

Authors: Assistant Professor Mrs. Priyanka Bamne, Nimesh Agrawal

Abstract: The increasing volume of products on online platforms has made product recommendation systems (PRS) essential for enhancing user experience and driving sales. This survey paper provides a comprehensive review of PRS, focusing on their necessity, implementation methods, and relevance in e-commerce and digital marketplaces. We explore the motivation behind recommendation systems, emphasizing their role in improving customer satisfaction, personalization, and business profitability. Various implementation techniques, including collaborative filtering, content-based filtering, hybrid filtering, and deep learning methods, are analyzed with a discussion on their advantages and limitations. Furthermore, we examine real-world applications, challenges such as cold start and scalability, and emerging trends in AI-driven recommendations. To establish the relevance of these concepts, we review key research papers, industry applications, and case studies from platforms like Amazon, Netflix, and Spotify. Finally, we highlight future directions, including explainable AI, privacy-aware recommendations, and real-time personalization, offering insights for researchers and practitioners aiming to enhance recommender systems.

 

Published by: vikaspatanker

Authors: Assistant Professor Mrs. Priyanka Bamne, Nimesh Agrawal

Abstract: The increasing volume of products on online platforms has made product recommendation systems (PRS) essential for enhancing user experience and driving sales. This survey paper provides a comprehensive review of PRS, focusing on their necessity, implementation methods, and relevance in e-commerce and digital marketplaces. We explore the motivation behind recommendation systems, emphasizing their role in improving customer satisfaction, personalization, and business profitability. Various implementation techniques, including collaborative filtering, content-based filtering, hybrid filtering, and deep learning methods, are analyzed with a discussion on their advantages and limitations. Furthermore, we examine real-world applications, challenges such as cold start and scalability, and emerging trends in AI-driven recommendations. To establish the relevance of these concepts, we review key research papers, industry applications, and case studies from platforms like Amazon, Netflix, and Spotify. Finally, we highlight future directions, including explainable AI, privacy-aware recommendations, and real-time personalization, offering insights for researchers and practitioners aiming to enhance recommender systems.

 

Published by: vikaspatanker

Authors: Dr. Nitin Kumar Patel

Abstract: Cloud computing's extensive adoption has made Virtual Machine (VM) placement a critical aspect of resource management. Beyond performance and cost optimization, securing VM placement is paramount to mitigating several threats, including co-residency attacks, data breaches, and denial- of-service attacks. This article offers a comprehensive analysis of techniques designed to enhance VM placement security in cloud environments. I explore a variety of security considerations, encompassing physical security, logical isolation, and data protection, and examine how they influence VM placement strategies. Specifically, we delve into techniques like anti-collocation policies, affinity and anti-affinity rules, trust-based VM placement, security-aware scheduling algorithms, and dynamic VM migration strategies. Furthermore, I analyse the trade-offs between security, performance, and cost associated with each technique. By evaluating the strengths and weaknesses of existing approaches, this paper identifies research gaps and highlights promising directions for future research in securing VM placement. I accomplish this by advocating for a holistic, multi-layered approach to VM placement security that integrates diverse techniques and adapts dynamically to evolving threat landscapes in cloud computing environments. This research purposes to provide valuable insights for cloud providers and consumers seeking to enhance the security posture of their cloud infrastructure through optimized VM placement strategies.

 

 

Published by: vikaspatanker

Authors: Mohammed Moiz1, Mohd Habban Ahmed, Mohammad Shanawaz

Abstract: This academic project showcases the architectural modeling and visualization of a stilt-plus-four-story residential building using Revit Architecture and AutoCAD. The study aims to create a digitally simulated residential structure that balances functional efficiency with modern urban housing requirements. The building's design, situated on a 40×60 feet plot with a southeast orientation, prioritizes climate responsiveness and natural daylight optimization for improved ventilation and thermal comfort. The project workflow begins with conceptual planning in AutoCAD, transitioning to detailed 3D modeling in Revit Architecture. This process integrates architectural elements, including walls, doors, windows, and roofing systems, while incorporating features like lighting, ventilation, and staircase design. The stilt floor accommodates parking, reflecting urban planning demands and space optimization. By leveraging Revit's Building Information Modeling (BIM) capabilities, the project achieves high design coordination, visualization, and parametric control. The digital model enables efficient generation of construction documentation, elevations, and sections. Sustainability aspects are incorporated through passive design elements, reducing potential design conflicts and material wastage. This project highlights the benefits of advanced architectural software tools in residential building design, enhancing precision, creativity, and efficiency in the field.

 

 

 

Published by: vikaspatanker

Authors: Assistant Professor Sourabh Jain, Prachi Gupta, Manish Yadav, Pooja Srivastava

Abstract: In this day and age when we need to manage resources prudently, accurately predicting how much energy one would require is an extremely important task. In this abstract, we showcase how the application of advanced technologies – machine learning, data mining, and artificial intelligence techniques can be blended with energy management systems to enhance the efficiency of forecasting energy consumption rates. The sample we use in this research contains a wide array of data, including casual and seasonal weather data, time, building occupancy figures, as well as the figures attained for energy consumption during the various time slices. Some of the various approaches to solve the problem we are working on that we analyze include: linear regression, decision tree regression, random forest regression, and artificial neural networks. It is vital to accurately predict future power consumption considering factors like resource optimization and sustainable energy management. This work describes an approach that uses advanced methodologies in machine learning techniques for precise forecasting based on historical data alongside a variety of descriptive features to predict energy consumption within the foreseeable future. In this research, we use an extensive dataset containing weather data, timestamps, occupancy statistics, and previous energy consumption data. We apply many algorithms that include linear regression, decision trees, random forests, and neural networks to energy consumption prediction and analyze which model best performs the prediction task.

Published by: vikaspatanker