Category Archives: Uncategorized

Authors: Dr. Pranav Mulaokar

Abstract: When interviewing the first year engineering students, it was observed that there is the urgent need to empower them with communication skills. This research paper focuses on the importance of communication, proficiency in English, concepts of LSRW, common mistakes, soft skills and global relevance. During the survey, the observations and recommendations were noted. English communication comes into light for international collaboration, technical documentation, workplace situations, etc. Real-life application of communication should be taught from the beginning of the learning process. The challenges which students face are discussed and solutions provided. The global scope of being a good communicator is noted in the paper.

Published by: vikaspatanker

Authors: Dr. Bangarappa L, Dr. Danappa G.T

Abstract: This present study has provided the fabrication of Mg/MWCNT nano composites, Mg/FA and Mg/MWCNT/FAhybrid nano composites with powder metallurgy processing techniques. The specimens prepared were characterized for mechanical properties like density of the materials, Vickers hardness, elastic modulus, and tensile properties. Nanocomposites are versatile material or multi-functional materials achieved by the unnatural mixture of verities of materials in turn to attain the characteristics in separate components by it that can’t be overcome. The extraordinary attention on carbon nano tubes were due to their unique structure and characteristics, they have a very tiny size of about 0.42nm and less than in diameter & the mechanical properties they exhibit. Carbon nanotubes have been expected to be one of the best reinforced materials to enhance the mechanical characterization as they possess good young’s modulus along with material strength and aspect ratios.

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Published by: vikaspatanker

Authors: Sukanya, G.Bharath Kumar, Karthik.D, Nikhil Reddy, ChannaKeshwa

Abstract: Crop diseases pose a major global threat to agricultural productivity, farmer income, and overall food security. Widespread disease outbreaks reduce crop quality, decrease yield, and contribute to economic instability—especially in regions dependent on agriculture for livelihood. The complexity of crop diseases arises from diverse environmental conditions, varying plant species, and the presence of multiple visually similar infections. Addressing these challenges requires a systematic, data-driven approach capable of identifying hidden patterns and supporting farmers and agricultural experts with timely, actionable insights. This project presents the design and development of an AI- Driven Crop Disease Prediction and Monitoring Dashboard, an interactive platform built using Streamlit. The dashboard enables visualization, prediction, and analysis of plant disease data using a trained Convolutional Neural Network (CNN). The system architecture is organized into three primary layers: the Presentation Layer, the Logic Layer, and the Data Layer. The Presentation Layer provides a user-friendly web interface developed in Streamlit, integrating dynamic components such as real-time prediction panels, probability bars, and comparative disease charts generated with Plotly Express. It also includes essential UI elements such as an image upload section and model output visualization to ensure smooth user interaction. The Logic Layer performs core analytical and computational tasks. It preprocesses leaf images, applies the CNN model for classification, generates confidence scores, and provides diseasespecific treatment recommendations. Pandas handles metadata processing, while session-state management ensures efficient handling of user inputs and outputs. The Data Layer consists of a structured plant disease dataset derived from sources such as PlantVillage, supplemented with augmented images to improve model robustness across lighting and environmental variations.

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Authors: Dr. Himanshu Sharma, Dr. Ankur Goel, Dr. Sapna Deshwal

Abstract: Present-day tourism research has increasingly focused on food tourism. Food is always together with human’s life and will always have a hope to grow in the tourism industry. Food experiences both inside and outside the country is always a part of food tourism. Varanasi is expanding in all directions, and as a result, its tourism industry is expanding as well. This will open up a lot of new opportunities for the locals of this tourist destination to improve their food experiences and share them with others. The fundamental ideas surrounding food tourism are identified as a major research concern in this paper, which focuses on tourism research. In addition, this study reveals that employment generation and the development of local culture are directly linked to the expansion of local food tourism. The researcher also finds food tourism research from a cultural perspective. Most people agree that eating local food is an important part of what tourists do. Food that is both original and one-of-a-kind to the area can be important as a tourist attraction in and of itself as well as in shaping a destination's image. Experiences with local food have the potential to significantly support agricultural diversification, maintain regional recognition, and contribute to sustainable development.

Published by: vikaspatanker

Authors: Adefila Adewale James, Onosemuode Christopher

Abstract: Effective environmental regulation is critical for minimizing the air quality impacts of energy infrastructure, particularly in regions with dense industrial activity. In Nigeria’s Niger Delta, gas-fired power plants form the backbone of electricity generation, yet concerns persist regarding their environmental compliance and regulatory oversight. This study evaluates the effectiveness of air quality regulation and environmental compliance mechanisms governing gas-fired power plants in selected Niger Delta states. Using a mixed-methods approach, the study integrates ambient air quality measurements, regulatory document review, institutional analysis, and stakeholder interviews to assess compliance with national air quality standards and the enforcement capacity of regulatory agencies. Findings reveal persistent exceedances of regulatory limits for particulate matter and nitrogen dioxide in host communities, alongside systemic gaps in monitoring, enforcement, and inter-agency coordination. While regulatory frameworks exist on paper, weak implementation, limited technical capacity, and poor data transparency undermine their effectiveness. The study provides policy-relevant insights and proposes actionable reforms to strengthen air quality governance and protect public health in Nigeria’s energy-producing regions.

DOI: http://doi.org/10.5281/zenodo.18139392

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Authors: K. Nagarathna, Darshan C D, Chetan Shanawad, Channu Anand Honnammanavar, Vijay Musaguri

Abstract: This project presents an IoT-based system for real-time vehicle tracking, fuel monitoring, and theft detection. The system integrates an ESP32 microcontroller, GPSmodule, GSMmodule, andfuel -levelsensorstomonitor vehicle conditions and transmit data to the cloud. A comprehensive alert mechanism notifies the user during unauthorizedvehiclemovement, fueltheft, orcaptampering. The system is designed to be cost-effective, accurate, and reliable, making it suitable for fleet management and personal vehicle security.

DOI: http://doi.org/10.5281/zenodo.18137733

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Authors: Ashok Kumar Kanagala

Abstract: The proliferation of AI-native architectures has introduced autonomous, model-driven systems with unprecedented capabilities and complex security challenges. These systems, often deployed across multi-agent pipelines and edge environments, expand the attack surface and exhibit dynamic, unpredictable behaviors that traditional security frameworks fail to address. Despite emerging research on AI robustness and alignment, comprehensive strategies for proactively securing agentic AI remain underdeveloped. This paper investigates the operationalization of Zero Trust principles in AI-native architectures, aiming to provide a forward-looking framework for resilient and accountable systems. The proposed approach integrates continuous model verification, alignment assurance with transparency tooling, lifecycle-integrated security validation, and autonomous red-teaming to proactively identify and mitigate vulnerabilities. Key findings indicate that embedding self-assessing mechanisms, standardizing behavioral benchmarks, and applying cross-layer defenses significantly enhance system resilience and reduce dependency on reactive interventions. This research contributes a structured methodology for securing autonomous AI, advancing both practical and theoretical understanding of AI-native security in complex, adaptive environments.

DOI: http://doi.org/10.5281/zenodo.18137101

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Authors: Mrs.S. Dhivya,, Kanika N, Kavipriya A, Madhumitha S, Preethi S

Abstract: This This report presents the project titled “Donar Hub,” a web-based platform developed to connect individuals in need with donors and volunteers through a structured and accessible system. The platform enables the sharing of essential resources such as food, clothes, books, and medical assistance. Users can create Request Help and Offer Help posts, which are categorized and searchable to ensure efficient matching of needs and available support. Donar Hub aims to reduce resource wastage while promoting social responsibility and community collaboration. To ensure authenticity and prevent misuse, the system incorporates Aadhaar-based identity verification for users. Requests related to medical assistance require verification of medical reports or hospital-issued documents before approval. This validation mechanism enhances trust, transparency, and security within the platform. The application is designed with intuitive forms and a user-friendly interface, making it accessible to users with varying levels of digital literacy.

Published by: vikaspatanker

Authors: Shaun Paul Moses, Vignesh. S

Abstract: Another emerging danger in the world of cybersecurity is the term steganography, which means concealing secret data in digital form, because concealed messages can be easily transferred to a different information exchange format. Other modalities such as audio steganography possess unique features that make it difficult to detect such signals, such as the temporal-frequency properties and audio signals are high dimensional. This project offers a DLDA, Deep Learning Based Detection System Stegware in Audio Files, that will inform whether the audio sample is a real cover or it is a stegware, i.e. it has embedded data in it. The system employs improved methods of feature extraction like Spectrogram Analysis and Mel-Frequency Cepstral Coefficients (MFCCs) to identify requisite frequency, amplitude and temporal indications to identify stegmodifications. The CNNs and LSTMs process subsequently learn a discriminative feature (CNNs) and temporal patterns (LSTM) that occurs between normal and manipulated audio. Training and testing are done using a dataset of clean audiofiles and audiofiles with various modifications done using steganography. The performance is measured by the accuracy, precision, recall and F1-score and the system has been found to be very reliable with accuracy of 97.8 and very few false detections. In the experimental results, it is seen that the model works fairly well when noise and compression is introduced, indicating its strength in the real world. Overall, the framework that is created due to the research effectively applies deep learning to offer a scalable, automated and accurate method of audio steganalysis, which is an outstanding achievement that can provide cybersecurity, digital forensics and secure communications as the number of illegal data transmission via audio channels decreases.

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Authors: K. Nagarathna, Mallikarjun Aralimard

Abstract: This paper presents the design and implementation of a simple Convolutional Neural Network (CNN) using Tensor Flow for binary image classification. The proposed model classifies 5×5 pixel images into two categories: images containing the pattern of an 'X' and images that do not. The study demonstrates dataset generation, model architecture, training, and evaluation, highlighting the effectiveness of CNNs for pattern recognition tasks.

DOI: http://doi.org/10.5281/zenodo.18129745

Published by: vikaspatanker