Category Archives: Uncategorized

Authors: Aashika .K, Assistant Professor Dr.M.kathiresan

Abstract: With the rapid expansion of digital infrastructure, energy consumption by communication networks has become a critical concern. This paper presents an AI-enabled framework for energy-efficient routing and traffic management in next-generation networks. It utilizes machine learning to predict network demand and optimize energy use dynamically, reducing the carbon footprint of data transmission. The system incorporates renewable energy tracking, load balancing, and carbon-aware routing to achieve green networking. Our simulation results show a significant reduction in energy usage without compromising performance, aligning network operations with global sustainability goals.

 

 

Published by: vikaspatanker

Authors: Pratichi Dhar

Abstract: This study explores the effect of AI-powered technologies on productivity, cost reduction, and decision-making within the “Supply Chain Management (SCM)” of “Fast-Moving Consumer Goods (FMCG)”. It aims to explore the ways in which AI enhances operational performance and sustainability. Academic research identifies inadequate infrastructure, particularly in underprivileged regions, high implementation costs, and data privacy concerns as significant challenges. This study reached conclusions by utilizing both primary and secondary data through a combination of research methods. AI solutions enhance logistics, inventory management, and resource allocation, minimizing waste and errors while boosting cost efficiency. The use of AI in predictive analytics and real-time decision-making enhances strategic planning and improves supply chain agility. The advantages of AI surpass its disadvantages, including integration with legacy systems and significant upfront expenses. The results show that AI enhances the resilience and sustainability of FMCG supply chains. There is a need for research on data security, implementation methods, and scalability to fully realize its potential. AI has the ability to revolutionize supply chains entirely, making it crucial for organizations to stay competitive in the ever-evolving global market.

 

 

Published by: vikaspatanker

Authors: Yashas D R, Vinutha H N, Merlin B, Soundarya R, Chethan V

Abstract: Identification of an existent's blood group is pivotal in exigency situations, for identity authentication, and in population analysis. It would else involve drawing a blood sample and assaying it in a laboratory, which is painful, tedious and requires trained labor force and installations. Herein, we suggest a way to prognosticate blood groups without blood through the use of point images and a Convolutional Neural Network (CNN). Since fingerprints are distinct in each existent, we suppose they could have patterns associated with natural characteristics similar as blood type. We gathered point images with eight colorful blood groups marked and used them to train a CNN model to classify them. We estimated the performance of the trained model using criteria similar as delicacy, perfection, recall, and F1- score upon testing. Our findings were encouraging, indicating that fingerprints may be potentially employed to cast blood groups using deep literacy. In the future, we will expand our dataset with fresh samples, try out bettered CNN models, and work on securing individualities' data. This system has the implicit to offer an invasive-free, hastily, and easier system for blood group vaticination, particularly in locales with no lab setup.

 

 

Published by: vikaspatanker

Authors: Professor Smita Chunamari, Sahil Tejam, Bhavesh Sonawane, Yash Daund, Janhvi Pawar

Abstract: Paraphrase detection is a crucial task in Natural Language Processing (NLP) that helps systems understand when two sentences mean the same thing, even if they’re phrased differently. While this has been explored extensively in English and a few other global languages, regional languages—rich in diversity and nuance—remain significantly underrepresented. In this study, we explore the challenges and opportunities of building paraphrase detection systems for regional languages, focusing on the unique linguistic features such as dialect variations, code- mixing, and syntactic differences. We develop a multilingual model trained on both parallel and non-parallel regional datasets, enhanced with data augmentation techniques and semantic similarity measures. We also introduce a small but diverse paraphrase corpus for select Indian languages as a benchmark. Our results show that transformer-based models fine-tuned on language-specific data outperform traditional ap- proaches, highlighting the importance of contextual embeddings in low-resource settings. This work not only advances the field of NLP in regional languages but also opens the door for more inclusive and accessible language technologies, ranging from intelligent search systems to educational tools that truly understand the linguistic richness of everyday users.

Published by: vikaspatanker

Authors: Professor Sheetal N. Mindolkar, Mr. Naveen Gamanagatti, Mr. Pratap R Goudar, Mr. Sammed Belavi

Abstract: Controlling a robot arm can be made simple and intuitive using basic electronic components like potentiometers and an Arduino microcontroller. By directly linking each potentiometer’s rotation to a specific joint on the robotic arm, users experience a tangible and immediate connection between their input and the arm’s movement. This straightforward setup offers an accessible introduction to robotics, ideal for beginners exploring mechatronics, sensor interfacing, and basic control principles. The affordability and ease of the Arduino platform further enhance its educational value, allowing hands-on learning without complex equipment. Building and operating the system reveals the essential control loop of robotics: the robot "senses" user input via electrical signals from potentiometers, the Arduino processes this data, and servo motors execute the movements. While this open-loop system lacks advanced accuracy and autonomy, it provides a clear, practical understanding of how robots respond to control signals, laying the foundation for more sophisticated robotics concepts in the future./

Published by: vikaspatanker

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