Category Archives: Uncategorized

Authors: Ms. Gude Kalyani

Abstract: Mental health challenges are rising worldwide, making early detection and monitoring increasingly important. With millions of people actively sharing thoughts and emotions on platforms like Facebook, Twitter, and Reddit, social media has become a valuable resource for understanding mental well-being. Earlier studies relied mainly on traditional machine learning (ML) techniques such as logistic regression, support vector machines, random forests, and ensemble models. These methods achieved only moderate results and often struggled with the complexity of natural language and diverse forms of data, limiting their effectiveness in real-world use. This work introduces a Mental Health Diagnostics framework that combines both social media data and personal details—such as age, family history, medical leave, and workplace challenges—to predict mental health conditions. The system applies a wide range of ML and deep learning (DL) approaches, with particular focus on a hybrid model that blends Bidirectional Long Short-Term Memory (BLSTM) with Convolutional Neural Networks (CNN). This design captures both sequential patterns and key contextual features, offering stronger predictive performance. Together with advanced models like RoBERTa and other ensemble methods, the proposed system achieves 99.6% accuracy. The findings demonstrate how integrating structured inputs with social media insights can create a reliable, scalable, and practical tool for mental health prediction, supporting early interventions and improved digital healthcare solutions.

DOI: https://doi.org/10.5281/zenodo.17062374

Published by: vikaspatanker

Authors: Miss . Chintapalli Lakshmi, Mr. kunjam Nageshwar Rao, P.Mohan rao 3

Abstract: India, as an agro-based economy, continues to have a substantial share of its population dependent on agriculture as the primary source of livelihood. However, productivity is often constrained by challenges such as limited access to timely information, difficulty in diagnosing plant diseases, and inadequate awareness of government schemes and market dynamics. Traditional reliance on manual methods or intermediaries frequently results in delays and misinformation, further hindering agricultural efficiency. To address these limitations, this paper presents an AI-powered Chatbot for Farmers, designed to deliver real-time, accurate, and accessible assistance. The system integrates Natural Language Processing (NLP) for query understanding, Convolutional Neural Networks (CNNs) with fine-tuned VGG-16 for plant disease detection, and machine learning models for crop recommendation and decision support. Furthermore, the chatbot incorporates multilingual support via translation APIs, enabling seamless interaction in regional languages and ensuring inclusivity across diverse farming communities. The proposed chatbot provides a wide range of services, including query resolution, crop suggestion, disease diagnosis from leaf images, and dissemination of critical updates on weather, market prices, and government policies. Experimental results demonstrate an accuracy of nearly 96% in disease classification and high precision in intent recognition, establishing the reliability and robustness of the system. By functioning as a virtual agricultural assistant, the solution empowers farmers with expert-level, user-friendly guidance, thereby enhancing decision-making, reducing losses, and ultimately improving agricultural productivity.

DOI: https://doi.org/10.5281/zenodo.17062303

 

Published by: vikaspatanker

Authors: Abhilasha Pokharna, Dr. Dinesh Shrimali

Abstract: As we all know, India is the world's second most populous country, and agriculture employs the vast majority of its people. Farmers continue to plant the same crops without testing new varieties, and they apply fertilizers in haphazard amounts without comprehending the inadequate composition and quantity. As a result, agricultural output suffers while the soil becomes acidic and the player is damaged. So we created a solution to help farmers using machine learning techniques. Based on soil content and climatic conditions, our technology will choose the optimum crop for a certain piece of land. In addition, the system offers information on the necessary fertilizer content and quantity, as well as the seeds for growth.

Published by: vikaspatanker

Authors: Ranjitha.S, Tamilaracy.K, Mary Jenifer.G, Dhilipan.M, D.Jeevanantham,B.E.

Abstract: Water quality plays a critical role in ensuring human health and well-being. This study compares the physico-chemical quality of water collected from Puliyampatti pond (near P.A. Educational Institution) and the treated drinking water supplied within the college campus. The parameters examined include pH, Total Dissolved Solids (TDS), chlorine content, and hardness. Results revealed that pond water exhibited higher TDS (489 ppm), hardness (3.6 mg/L), and lower chlorine (0.173 mg/L) compared to college drinking water, which showed a lower TDS (37 ppm), lower hardness (0.45 mg/L), but higher chlorine (1.3 mg/L). Both samples maintained pH within acceptable limits. The findings indicate that untreated pond water is unsuitable for direct consumption without treatment, while the treated college water meets desirable drinking water standards.

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Authors: Palwinder Kaur Sandhu

Abstract: The design and management of sensor networks, which enable smooth communication between a variety of devices, from home appliances to specialized monitoring equipment, are critical components of the Internet of Things (IoT) ecosystem. An effective sensor network's design is greatly influenced by the topology chosen, such as mesh or star configurations, each of which is suitable for a specific application. As IoT adoption grows, the challenges of big data—volume, velocity, variety, and veracity—become more apparent. Since sensor data is inexpensive to generate but costly to transmit, store, and process, early-stage edge processing is essential for system efficiency. Modern, affordable, low-power aggregation devices reduce unnecessary data load by enabling local data processing, filtering, and transmission.Additionally, by providing remote configuration, real-time monitoring, and integrated data visualization, cloud-based sensor network management tools increase scalability and user-friendliness. Combining these technologies maximizes dependability, performance, and cost-effectiveness while satisfying the evolving requirements of Internet of Things applications.

DOI: http://doi.org/10.5281/zenodo.17075400

Published by: vikaspatanker

 

Prasanthi Vallurupalli
Affiliation	Cybersecurity Software Engineer J.B. Hunt Transport Inc, AR
Email-Id:	prasanthiv@ieee.org
Education Qualification: Master of Science in Software Engineering (2015) University OF Houston Clear Lake. PVP Siddhartha Institute of technology. Bachelor of Technology in Electronics and Communications Engineering (2013)

 

Published by: vikaspatanker

Authors: Dr. Yashika Kewalramani, Arjun Singh Parihar

Abstract: Artificial Intelligence (AI) and its incorporation into dental imaging, particularly in the interpretation of radiographs known as Orthopantomograms, has led to many promising advancements. However, its clinical utility and diagnostic consistency remain subjects of investigation when compared to the judgment of trained oral radiologists. This study evaluates the diagnostic precision and variability between experienced oral radiologists and a widely accessible AI model “ChatGPT”, in analyzing different confirmed Jaw Pathologies through Orthopantomograms. By using systematic assessment methods, the study aims to ensure a balanced and objective examination of the potential incorporation of AI in oral radiodiagnosis.

DOI: https://doi.org/10.5281/zenodo.17052221

Published by: vikaspatanker

Authors: Gaddam UshaKiran, Dr.B.Srinivasa Rao(Professor)

Abstract: This project enables secure video processing, encryption, and watermark embedding, focusing on user authentication, video encryption, and decryption capabilities. Users can register, log in, and upload videos along with watermarks for processing. Using the cryptography library, each uploaded video is encrypted, and its encryption key is split using Shamir's Secret Sharing, ensuring secure key distribution and storage. The encrypted frames are stored separately for later retrieval and decryption. Decryption occurs through reassembling key shares, allowing the original video to be reconstructed, with the watermark extracted from the first frame. The application further provides options to download the decrypted video, view split frames, and explore contact and performance information pages. Employing OpenCV for video processing and secure file handling techniques, this system ensures data confidentiality and integrity through a user-friendly interface and robust back-end encryption mechanisms. The application uses secure upload and storage mechanisms for sensitive data, like key shares and encrypted frames, storing them in predefined folders. Key shares are stored separately, further protecting the decryption process from unauthorized access.

 

 

Published by: vikaspatanker

Authors: Omon ENI, Arun K Menon

Abstract: The COVID-19 pandemic fundamentally transformed the operational landscape of U.S. Software-as-a-Service (SaaS) companies, forcing rapid adoption of remote-first product management practices. This study examines the impact of distributed product teams on software delivery timelines through a comprehensive analysis of 127 U.S.-based SaaS companies that transitioned to remote operations between March 2020 and December 2021. Using mixed-methods research combining quantitative performance metrics and qualitative interviews with product managers, this investigation reveals significant variations in delivery performance based on organizational adaptation strategies, communication frameworks, and asynchronous workflow implementations. Key findings indicate that companies implementing structured asynchronous decision-making processes experienced 23% faster feature delivery times, while organizations lacking formal remote collaboration frameworks saw 31% longer development cycles. These results contribute to the growing body of literature on distributed software development and provide actionable insights for product management practitioners navigating the post-pandemic digital workplace.

DOI: http://doi.org/10.5281/zenodo.17044243

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Authors: Sylvester Worlanyo Gbadrive, Ifeoma Lynda Okpala

Abstract: This study examines the critical role of workforce capacity building in advancing sustainable accounting practices within U.S. public organizations. Through an analysis of professional development programs and training interventions, this research investigates how public sector finance personnel acquire competencies in sustainability accounting and international financial standards, including IPSAS (International Public Sector Accounting Standards) and GAAP (Generally Accepted Accounting Principles) . The findings reveal significant gaps in current training frameworks and highlight the transformative potential of comprehensive capacity-building initiatives. This research contributes to the growing literature on sustainability accounting in the public sector by providing empirical evidence of effective training methodologies and their impact on organizational performance and accountability

DOI: http://doi.org/10.5281/zenodo.17043410

Published by: vikaspatanker