Authors: Dr Meghna Sharma, Dr Namita Yash

Abstract: This research paper makes an effort to identify the different reasons and methods adopting the downsizing activities by the management in their organizations. The objective is fulfilled with the help of analysing the responses received against the questionnaire. The descriptive analysis has been done to work out the reasons to downsizing in a systematic manner. It’s the employee’s perspective by which the research can be concluded by providing the rational reasons to the layoffs. The reasons can be stated as cost factor, company policy format, performance criteria, company reorganization.

DOI: https://doi.org/10.5281/zenodo.17986288

Published by: vikaspatanker

Authors: Aryan Pate, Mohit Patil, Yadnesh Dalvi, Kanchan Birari, Ashutosh Kale

Abstract: Education changes dramatically today, with personal and adaptive learning experience needs at an all-time high. Amidst this dynamism, acampus.ai stands in the helm of revolutionary change with a product using artificial intelligence to fundamentally change the way people learn. The cutting-edge platform brings into every classroom personalized AI tutors with a customized educational experience tailored for each learner's specific needs. ACampus.ai is nothing less than an AI tutor system. So, it's much more than just a classroom approach. Instead of customized courses and real-time assistance on doubts, the teaching methodology adjusts dynamically to the learner's pace and style. It analyzes user behavior and learning patterns by finding out what needs to be done in relevance and alignment to the goal of the learner. It's either deep and comprehensive understanding about the subject or short answers to doubts-there is empowerment through a holistic and individualized experience provided by acampus.ai.

DOI: https://doi.org/10.5281/zenodo.17985744

Published by: vikaspatanker

Authors: Sali Radha, Sali Radha, Bacchav Jayesh, Patil Harshal, Boraste Siddesh

Abstract: In today’s educational landscape, institutions increasingly recognize the value of student feedback for enhancing learning experiences. However, traditional methods like manual reviews and basic statistics often fail to capture the complex and complicated patterns within this feedback. Our project proposes a novel approach using Long Short-Term Memory (LSTM) algorithms to analyse student feedback and predict sentiment more effectively. LSTM’s strength in handling sequential data enables us to uncover deeper insights into student experiences and trends. This innovative method aims to transform feedback analysis into a comprehensive, data-driven evaluation tool, ultimately improving educational practices. Additionally, we implement a Generative Pre-trained Transformer (GPT) model to provide dynamic, tailored suggestions for student growth. By combining advanced machine learning techniques, our system not only analyses feedback but also offers actionable recommendations, fostering a more supportive and effective learning environment. This holistic approach aims to enhance both student outcomes and institutional practices.

DOI: https://doi.org/10.5281/zenodo.17985712

Published by: vikaspatanker

Authors: Aaditya Said, Vivek Khalate, Shubham Sanap, Aayush Nahire, Deepali Suryawanshi

Abstract: Modern agriculture faces challenges such as soil degradation and suboptimal crop selection, leading to reduced productivity. The “Smart Farming-IOT based” project focuses on three key components: crop recommendation, fertilizer recommendation, and climate-based predictions. Using historical data, climate conditions, and soil characteristics, a machine learning model predicts the most suitable crops for a given area, ensuring optimal land use and increased yield. The project empowers farmers with real time insights to enhance productivity and support sustainable agriculture.

DOI: https://doi.org/10.5281/zenodo.17985636

Published by: vikaspatanker

Authors: Chaitanya Madhav Mate, Sanil Nivrutti Shinde, Aakansha Ganesh Tambe, Niranjan Deepak Lahane, Kirti Patil

Abstract: This paper describes a "Dynamic Job Market Analysis Platform" that allows capturing real-time analytics and prediction of the employment fluctuations with the goal of benefiting universities students and employer. By utilizing machine learning models to forecast trends accurately, the platform fills this gap between student skill sets and the leading demands of the industry. It provides students with actionable insights to help them ensure their career paths are in line with market requirements, and it helps employers better understand trends in the workforce. The results highlight an accuracy of over 97% in prediction of the employment patterns, showcasing the ability of the platform to fuel data-based decision making. This project helps to improve employability and provides a better alignment between academia and market requirements by addressing issues, such as data imbalance and dynamic changes in the market. Future directions include expanding the scope of real-time data integration and refining prediction models for broader applicability.

DOI: https://doi.org/10.5281/zenodo.17985571

Published by: vikaspatanker

Authors: Bhavesh Avinash Gadekar, Neha Sanjay Gaikwad, Swayam Vijay Bhosale, Prasad Ambadas Bidgar, Ganesh K Gaikwad

Abstract: Dementia diagnosis is a critical challenge in neurology, often relying on time-intensive and subjective manual analysis of MRI scans. This research proposes a novel hybrid AI-based system for early and accurate dementia detection, combining traditional neuroimaging techniques with advanced deep learning models. The system employs a hybrid 2D-3D pipeline that integrates slice-based 2D convolutional models with volumetric 3D CNN architectures, ensuring a balance between computational efficiency and spatial pattern recognition. The 2D models focus on extracting detailed features from individual MRI slices, while the 3D models capture spatial relationships across the entire brain volume. Additionally, clinical metrics such as cognitive scores are integrated with the MRI data to enhance diagnostic accuracy. Attention mechanisms and Grad-CAM visualizations improve model interpretability by highlighting critical brain regions, addressing the need for transparent AI-driven clinical tools. This hybrid approach significantly improves diagnostic accuracy, generalizability, and explainability compared to conventional methods. The system classifies scans into Strongly Demented, Mildly emented, or Non-Demented categories, providing actionable insights for clinicians. By bridging AI with neuroimaging and multimodal data integration, the proposed system aims to revolutionize dementia detection, enabling earlier intervention and improved patient outcomes.

DOI: https://doi.org/10.5281/zenodo.17985501

Published by: vikaspatanker

Authors: Mayuri Mahajan, P. D. Jadhav, Mehraj Khan

Abstract: The metaverse, an immersive a virtual world where people can live, work, shop, learn, and interact, is anticipated to revolutionize how we engage with technology and each other. By merging physical and digital realities through technologies like virtual reality (VR), augmented reality (AR), blockchain, and artificial intelligence (AI), the metaverse offers unprecedented opportunities for connection, communication, and collaboration across boundaries. This paper investigates the potential effects of the metaverse in India, focusing on socio-economic impacts, technological advancements, and regulatory challenges. It explores how the metaverse could reshape critical sectors such as education, healthcare, business, and entertainment. By analyzing current trends and projecting future developments, the study highlights both the opportunities and challenges in India's journey toward embracing this transformative technology.

DOI: https://doi.org/10.5281/zenodo.17985389

Published by: vikaspatanker

Authors: Sai Manish M S, Karunakar Reddy D, Prajwal H, Varun Tej R, Mrs. Trisha V S

Abstract: Women’s safety remains a significant concern, particularly during emergency situations where victims may be unable to manually operate their mobile phones. Many existing safety applications fail to provide timely assistance under such circumstances. To address this limitation, this paper presents an IoT-based real-time women safety and alert system that enables automatic emergency detection and communication. The system integrates GPS, GSM, and a microcontroller to continuously monitor the user’s condition through a heartbeat sensor and a panic button for manual activation. Once a distress situation is detected, the system sends an SOS message containing the user’s live location to pre-registered contacts and updates the IoT dashboard for real-time monitoring. The proposed system enhances emergency response efficiency and provides a practical, affordable, and reliable solution for improving women’s safety.

Published by: vikaspatanker

Authors: Saisha Hiray, Manisha Shinde, Priti Choudhari, Samarth Palve, Rushi Bagul

Abstract: In this era of rapid globalization and technological advancement, the ability to communicate in multiple languages has become a crucial skill. Traditional language learning methods often lack the interactivity and personalization needed to fully engage learners. This project addresses this challenge by developing a multilingual language learning chatbot robot that combines both software and hardware to offer an innovative, interactive language learning experience. The system is built around a Raspberry Pi, connected to a microphone and speaker, enabling real-time voice interactions with the chatbot. Leveraging Natural Language Processing (NLP), the chatbot can understand and respond in multiple languages, making it suitable for learners at various levels. Speech recognition allows the system to accurately interpret user input, while speech synthesis enables the chatbot to respond naturally, creating a conversational environment that mimics real-world language use. As learners interact with the chatbot, they engage in simulated dialogues that enhance their ability to speak and understand the language in realistic contexts. The chatbot offers a dynamic learning experience by adapting to different languages, providing users with the flexibility to switch between languages and practice multiple linguistic skills within the same session. Powered by the Raspberry Pi platform, the system is portable, affordable, and easy to deploy in various educational settings, from classrooms to home use. This project represents an innovative approach to language learning, combining AI-driven software with accessible hardware to provide a scalable, interactive tool for multilingual education.

DOI: https://doi.org/10.5281/zenodo.17985349

Published by: vikaspatanker

Authors: Suhani Ranjay Sinha, Vedika Sanjay, Sushant Ganesh Vidhate, Manisha Shinde

Abstract: The increasing human-leopard conflict in agricultural areas necessitates innovative solutions to prevent leopard intrusions and protect farms. This project proposes an intelligent wildlife intrusion detection system utilizing Raspberry Pi, GSM technology, and audio warning systems to deter leopards from entering farms. The system consists of two primary modules: leopard detection and deterrent mechanisms. Camera traps capture images of approaching animals, which are then processed using Convolutional Neural Networks (CNNs) to detect leopard presence. Upon detection, the system triggers a GSM alert to farmers and simultaneously activates speakers emitting loud, leopard-deterrent sounds. The audio warning system, designed to mimic natural threats, effectively scares leopards away from the farm perimeter. Integration of CNNs enables accurate leopard detection, while the GSM module ensures timely alerts to farmers. This cost-effective, IoT-based solution contributes to: 1. Enhanced farm security, 2. Reduced human-leopard conflict, 3. Decreased crop damage, 4. Increased farmer safety By leveraging AI-powered detection and audio deterrents, this system offers a promising solution for mitigating leopard intrusions, promoting coexistence between humans and wildlife, and ensuring sustainable agricultural practices.

DOI: https://doi.org/10.5281/zenodo.17985293

Published by: vikaspatanker