Category Archives: Uncategorized

Authors: Anuradha Muttamwar, Devashri Ghotekar, Damini Mishra

Abstract: In the contemporary digital landscape, social media has become an integral part of daily communication for billions of users worldwide. While these platforms facilitate connectivity and self-expression, the growing trend of oversharing personal information has created unprecedented cybersecurity and privacy risks. Users increasingly disclose sensitive information such as location details, financial data, personal relationships, and health conditions, often without fully comprehending the potential consequences. This research presents a comprehensive study on oversharing culture, examining how habitual social media usage patterns intensify individual vulnerability to identity theft, social engineering attacks, data breaches, and psychological manipulation. The study integrates behavioral analysis, cybersecurity assessment frameworks, and vulnerability evaluation metrics to understand the mechanisms driving oversharing behavior and its security implications. Through survey-based analysis and comparative study of social media platforms, we examine the psychological motivations behind excessive self-disclosure, including the role of social validation through likes and comments, platform design strategies, and individual personality traits. The research demonstrates that approximately 93% of users who overshare personal information face significant privacy and security risks, making vulnerability assessment and user education critical priorities. The proposed framework employs data analysis techniques, behavioral pattern recognition, and machine learning algorithms to identify vulnerability indicators and predict susceptibility to cyber threats. The visualization layer presents findings through interactive dashboards and heat maps, enabling users and security professionals to understand oversharing risks and implement protective measures. Our findings indicate that comprehensive awareness programs, behavioral intervention strategies, and platform-level privacy controls can significantly reduce vulnerability when combined with individual digital literacy initiatives.

DOI: https://doi.org/10.5281/zenodo.20923714

Published by: vikaspatanker

Authors: Gawai Santosh Bhaskar, Shashikant B. Dhobale

Abstract: The process of developing a comprehensive project cost estimate is critical for a project to be adjudged successful on completion. Projects’ costing is one of the most critical and most widely used project management tools. The complex nature of Projects and the inherent uncertainty of the financial performance of construction projects, development funding, and the monitoring and controlling of costs and schedules make exact budget needs impossible to forecast accurately. This same characteristic also makes projects to deviate from plans. The main object of this paper is to identify the factors affecting the accuracy of project cost estimation, determine the various methods of carrying out project cost estimation in construction projects within INDIA. The study is motivated by the inability of most construction professionals to arrive at a tentative and reliable project cost estimate in project realization which has created obvious problems of project cost overrun and subsequent abandonment. The study sampled the opinion of fifty-three selected project professionals who had worked on related construction outfits in INDIA. An objective realization instrument developed using eighteen (18) factors identified in the literature as possible factors affecting the accuracy of project cost estimation were ranked based on a Likert four-point scale. The score of respondents to the factors were analyzed using descriptive and inferential statistics, mean score value and factor analytical approach as the major tool.

Published by: vikaspatanker

Authors: Rakesh Dondapati

Abstract: Corporate environmental, social, and governance (ESG) disclosures increasingly rely on persuasive sustainability narratives, yet investors, regulators, and civil society organizations often lack scalable tools to distinguish genuine environmental performance from rhetorical positioning. This study develops and validates a Greenwashing Intelligence System (GIS) that integrates six data modalities — ESG narrative text, verified emissions data, satellite and remote-sensing indicators, controversy and incident records, financial disclosures, and supply-chain risk signals — to construct two independent indices: a Narrative Ambition Score (NAS), derived from transformer-based analysis of sustainability disclosure text, and a Performance Index (PI), derived from verified and independently observable environmental performance data. The difference between these indices, the Greenwashing Gap Score (GGS = NAS – PI), is computed for a global panel of 4,642 public firms across five regions and six sectors over a 2019–2026 observation period. Firms are classified into four quadrants: Aligned Leaders (high NAS, high PI, 23.5% of sample), Greenwashing Risk (high NAS, low PI, 16.0%), Quiet Achievers (low NAS, high PI, 13.3%), and Disengaged (low NAS, low PI, 31.7%). Regression results show that GGS significantly predicts negative cumulative abnormal returns around disclosure events (β = –0.041, p < .001), elevated 24-month litigation risk (β = 0.0021, p < .001), and negative media sentiment shifts (β = –0.0089, p < .001), with these relationships substantially amplified when satellite-reported divergence (SRD) is high (GGS × SRD interaction significant across all outcomes, p < .001) — indicating that externally verifiable narrative-performance gaps carry the largest market and reputational consequences. Sector analysis reveals the largest gaps in Energy and Materials sectors, particularly for Scope 3 emissions claims. A validation study comparing GIS classifications against a 180-member expert panel shows substantial agreement (Cohen's κ = 0.65–0.78 across classification dimensions). A two-year disclosure-change pilot demonstrates that sharing GIS reports with firms reduces subsequent GGS, with the largest reductions (–9.7 points) among Greenwashing Risk firms receiving publicly benchmarked reports. The paper contributes the GIS architecture, the NAS/PI/GGS measurement framework, and a five-level ESG assurance maturity roadmap to ESG analytics, accounting information systems, and AI governance research, demonstrating that multimodal AI can operationalize sustainability assurance at scale.

DOI: http://doi.org/10.5281/zenodo.20920915

Published by: vikaspatanker

Authors: Associate Professor Dr.Revanesh M, Punyashree B s, Punyashree T, Srujana H P, Yogitha A

Abstract: The rapid growth of Unmanned Aerial Vehicles (UAVs) in applications such as surveillance, delivery, public safety, and remote sensing demands highly reliable, low-latency wireless communication. Fifth-generation (5G) New Radio (NR) technology, with its support for Massive MIMO, millimeter-wave bands, and intelligent beamforming, offers a promising framework for enabling robust, high-throughput aerial connectivity. 5G Toolbox. The study includes modeling UAV mobility profiles, implementing an A2G channel model with Doppler effects, and designing an adaptive beamforming strategy to track the UAV in real time. Key performance metrics such as Signal-to-Noise Ratio (SNR), Reference Signal Received Power (RSRP), Bit Error Rate (BER), and throughput are evaluated under varying mobility and altitude conditions. The results demonstrate how beamforming significantly improves link stability and signal strength in high-mobility UAV communication scenarios.

Published by: vikaspatanker

Authors: Sushma P S Assistant Professor, Srujan H S, Vybhav Gowda S, Sumukh Kashyap S, Ajay R Shetty

Abstract: Security and access control are important requirements in modern homes, offices, and institutions. This project presents an RFID and Fingerprint-Based Door Lock System using Arduino Uno. The design employs RFID technology and biometric fingerprint authentication to provide dual-layer security against unauthorized access. The system verifies both the RFID tag and fingerprint before activating a servo motor to unlock the door. An LCD display and buzzer provide real-time status messages and alerts during operation. The proposed system provides a secure, reliable, and user-friendly solution for access control applications in residential, commercial, and institutional environments.

Published by: vikaspatanker

Authors: Bala Sundara Rao Kimmoju, Y.Jagadeesh Kumar, P. Pradeep

Abstract: Climate change has significantly increased the occurrence of extreme weather events such as floods, cyclones, droughts, heatwaves, and heavy rainfall, creating a strong need for accurate and reliable forecasting systems. Traditional climate prediction methods often fail to effectively capture the complex spatial and temporal relationships present in large-scale climate data and generally lack interpretability. This project proposes an Explainable Hybrid Deep Learning Framework for Climate Trend Analysis and Extreme Weather Prediction that integrates Convolutional Neural Networks (CNN) for spatial feature extraction, Long Short-Term Memory (LSTM) networks for temporal sequence learning, and an Attention Mechanism for identifying important climatic features. To enhance transparency and trustworthiness, Explainable Artificial Intelligence (XAI) techniques such as SHAP and Grad-CAM are incorporated into the framework. The system utilizes climate parameters including temperature, humidity, rainfall, wind speed, atmospheric pressure, cloud cover, and satellite imagery collected from multiple sources. Data preprocessing techniques such as normalization, missing value handling, and feature engineering are applied to improve data quality and model performance. The hybrid CNN-LSTM architecture effectively learns spatiotemporal climate patterns, enabling accurate climate trend analysis and extreme weather forecasting. Experimental results demonstrate improved prediction accuracy, reduced false alarm rates, and better interpretability compared to traditional forecasting approaches. The proposed framework supports real-time climate monitoring and provides reliable, transparent, and efficient forecasting solutions for disaster management, agriculture, environmental monitoring, and public safety applications.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Simhachalam Patnana, S.Sudeer Kumar, Y. Jagadesh Kumar

Abstract: Automatic image recognition has become a fundamental component of modern intelligent systems, finding applications in areas such as food recognition, healthcare imaging, smart surveillance, object detection, and visual analytics. However, traditional image classification techniques often face challenges due to image noise, class imbalance, varying lighting conditions, complex backgrounds, and diverse visual patterns, which reduce classification accuracy and prediction reliability. To address these challenges, this project proposes a Smart Vision Fusion Architecture for Multi-Class Image Recognition (SVFA-MCIR), an intelligent hybrid framework that combines deep learning and machine learning techniques for efficient multi-class image classification.The proposed framework incorporates image preprocessing, enhancement, augmentation, and feature optimization techniques to improve dataset quality and model performance. Existing image recognition models such as CNN, EfficientNet + XGBoost, and DenseNet + XGBoost are initially evaluated to analyze their classification capabilities. To further enhance recognition accuracy and classification stability, the proposed system integrates ResNet50 and XGBoost into a unified hybrid architecture. ResNet50 is utilized to extract high-level visual features and complex image representations, while XGBoost performs optimized multi-class classification using the extracted deep feature vectors.Experimental results demonstrate that the proposed SVFA-MCIR framework achieves superior performance in terms of recognition accuracy, prediction robustness, feature learning capability, and computational efficiency when compared with existing approaches. The framework provides a scalable, adaptive, and intelligent solution for modern image recognition applications and contributes to the advancement of smart vision systems through accurate and reliable multi-class image classification.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Mrs. Vidyashree B.P Assistant Professor, Manjunath A J, Pradeep Nagavath, Shreyank S D, Poorna Chandra Thejaswi M D

Abstract: Vehicle theft remains a significant concern worldwide, especially in urban areas where vehicle density is high and traditional security systems are often insufficient. This project presents a cost-effective and intelligent Vehicle Theft Protection System that enhances vehicle security through biometric authentication and real-time user intervention using GSM communication. The core of the system is built around the Arduino UNO microcontroller, interfaced with a fingerprint sensor module (R305S), a GSM module (SIM800L), and a relay module to control the ignition system. Authorized users register their fingerprints in the system memory. Upon an unauthorized access attempt, the system sends an SMS alert to the vehicle owner, who can remotely allow or deny engine start. The proposed system provides a high-speed, reliable, and cost-effective solution for automotive embedded security applications.

Published by: vikaspatanker

Authors: Pilla Rushitha, Puppala Pradeep, Yerrapatruni Jagadeesh Kumar

Abstract: Natural disasters such as floods, earthquakes, cyclones, droughts, landslides, and wildfires continue to pose significant threats to human life, infrastructure, and environmental sustainability. The growing complexity of climate patterns and environmental changes has increased the need for intelligent disaster prediction systems capable of providing accurate and timely forecasts. This project presents a Neural-XGBoost Hybrid Framework for Disaster Prediction and Management that integrates deep learning-based feature extraction with the robust classification capability of Extreme Gradient Boosting (XGBoost). The proposed approach utilizes disaster-related environmental and meteorological data, including rainfall, temperature, humidity, wind speed, and atmospheric conditions, to identify potential disaster events. Data preprocessing techniques such as cleaning, normalization, and feature selection are employed to enhance data quality and model performance. The neural network component automatically learns complex patterns and hidden relationships within the dataset, while XGBoost performs efficient multi-class disaster classification. Experimental evaluation demonstrates that the hybrid framework achieves superior prediction accuracy, improved generalization capability, and reduced overfitting when compared with conventional machine learning approaches. The system supports disaster preparedness, risk assessment, resource planning, and early warning mechanisms, enabling authorities to make informed decisions and minimize disaster-related losses. The proposed framework offers a scalable, reliable, and data-driven solution for modern disaster management applications.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Balla Revathi, Dhavala Shilpa, Yerrapatruni Jagadeesh Kumar

Abstract: Accurate product pricing has become a critical requirement for modern e-commerce platforms due to rapidly changing market conditions, customer preferences, competitor strategies, and fluctuating product demand. Traditional pricing methods often rely on static rules and historical analysis, making them ineffective in responding to real-time market dynamics. To address these challenges, this project proposes an intelligent framework called Adaptive Commerce Intelligence Framework for Real-Time Product Value Forecasting Using Hybrid Predictive Learning Models, which integrates machine learning techniques with business intelligence analytics to support intelligent pricing decisions and real-time product value forecasting.The proposed system collects and analyzes various pricing-related parameters, including product base cost, competitor pricing, sales volume, stock availability, customer ratings, reviews, and market trends. Individual machine learning algorithms such as Linear Regression, Random Forest, Support Vector Machine (SVM), and XGBoost are initially trained and evaluated independently to assess their forecasting capabilities. These models are then combined into a Hybrid Predictive Learning Model that leverages the strengths of each algorithm to improve prediction accuracy, forecasting stability, and pricing adaptability.Random Forest and XGBoost effectively identify complex market patterns and pricing trends, while SVM captures non-linear relationships among pricing factors. Linear Regression contributes to understanding pricing dependencies and improving model consistency. The framework also incorporates real-time analytics, competitor monitoring, historical prediction tracking, interactive dashboards, and MySQL-based data management to enhance business intelligence and decision-making capabilities.Experimental analysis demonstrates that the proposed hybrid framework provides more accurate and reliable pricing forecasts compared to standalone machine learning approaches. By integrating predictive learning with adaptive commerce analytics, the system enables dynamic pricing optimization, improves market responsiveness, supports revenue growth, and enhances competitiveness in modern digital commerce environments.

DOI: http://doi.org/

Published by: vikaspatanker