Authors: Pankaj kumar singh, Professor Amit Thakur

Abstract: Energy utilization via every node is a significant concern in Wireless Sensor Network (WSN). Therefore, the main complexity deception in communicating the data that have the route with to the lowest degree distance as well as concentrates energy. Many investigators have residential different routing approaches for Cluster Head (CH) collection to communicate the packets to the BS. The choice of suitable CH, through the location also energy, is a main dispute in WSN. But, it can’t focuses on the network delay. Thus it decreases the network efficiency. To overcome this problem this paper Energy and data Communication delay aware Routing in WSN. Here, the fitness function is introduced for enhancing both the energy efficiency as well as lifespan of nodes through choosing the CH optimally. In this strategy, distance, energy, and delay of sensor nodes fitness function is used for selecting the optimal CH in the network. The network function is enhanced in this approach when equated to the conventional protocol.

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Authors: Harsha Sammang, Harshini Balaga, Aditya Jagatha

Abstract: Coccidioidomycosis (Valley fever), caused by Coccidioides spp., is a climate- and soil-mediated respiratory disease whose exposure arises from inhalation of spores entrained by wind from disturbed, desiccated soils. Incidence is rising across the U.S. Southwest and expanding arid zones. Traditional surveillance is retrospective and weakly coupled to dust-generating processes (drought, grading, off-road activity), limiting actionable lead time for clinicians, public health, and occupational safety. We present Aeolus-DS, a Design Science Research (DSR) artifact that fuses aerosol remote sensing (MAIAC AOD; dust fraction), mesoscale meteorology and soil moisture (ERA5), land-disturbance telemetry (construction and energy activity; off-highway vehicle events; nightlights), and clinical sentinel signals (syndromic ED chief complaints; pneumonia rule-out) into a dust-aware, AI-driven early warning and deci- sion support system. Methodologically, we propose a graph spatiotemporal transformer with direction-aware attention and physics-guided regularization reflecting aeolian transport. Us- ing county–week panels (2014–2024) for AZ–CA–NV, Aeolus-DS improves nowcasting MAE by 18% and two-week AUPRC by 21% over strong baselines (XGBoost, LSTM). Role-based “action cards” translate probabilistic forecasts and uncertainty into targeted mitigations (site watering cadence, temporary grading pauses, N95 staging, clinician test prompts). We eval- uate predictive skill, calibration, runtime, interpretability, and stakeholder usability, and discuss governance, ethics, and portability to other dust-borne mycoses in climate-stressed regions.

DOI: https://doi.org/10.5281/zenodo.17063019

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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

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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

 

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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.

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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

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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)

 

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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

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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