Daily Archives: March 6, 2026

Authors: Sagar Kumar

Abstract: The rapid digitalization of commerce has significantly transformed how local businesses connect with customers. Small and medium enterprises (SMEs), particularly in developing economies like India, face challenges related to visibility, discoverability, and digital presence. Local business directories have emerged as cost-effective digital tools that bridge the gap between consumers and businesses. This research examines the role of online local business directories in improving market accessibility and digital inclusion, with a case study of IndiaBusinessTree (IBT), a free business listing and local directory platform in India. The study evaluates how structured business listings, search optimization, and location-based categorization enhance business exposure and customer engagement. Using qualitative analysis and platform-based observations, the paper highlights the impact of digital directories on customer acquisition, search engine visibility, and trust-building. The findings suggest that local directories significantly contribute to MSME growth by enabling affordable digital marketing, improving local search rankings, and fostering regional economic development. The study concludes that digital business directories are critical components of the modern digital ecosystem, especially in emerging markets.

 

 

Published by: vikaspatanker

Authors: Surbhi Sahu

Abstract: In modern digital environments, sensitive information is constantly transmitted across networks and stored in distributed systems such as databases, cloud infrastructures, and storage devices. The increasing number of cyber threats such as data breaches, interception attacks, and unauthorized access has made data security a major concern for organizations and individuals. This research paper examines techniques used to secure data during transmission and storage, including encryption algorithms, secure communication protocols, and access control mechanisms. Symmetric and asymmetric cryptographic methods such as AES, DES, RSA, and ECC are analyzed to understand their effectiveness in protecting data confidentiality and integrity. Additionally, modern security approaches such as homomorphic encryption, blockchain-based storage, and quantum‑resistant cryptography are discussed. The paper concludes that a combination of encryption techniques, secure protocols, and strong authentication systems is essential for protecting sensitive information in modern computing systems.

DOI: https://doi.org/10.5281/zenodo.18888623

Published by: vikaspatanker

Authors: Deepak Tomar, Kismat Chhillar, Sanchit Agarwal

Abstract: This paper addresses the critical challenge of energy efficiency in distributed Internet of Things (IoT) networks through the application of federated learning-based energy management techniques tailored for green computing. With the exponential growth of connected devices, traditional centralized processing poses significant privacy, communication and energy consumption issues. Federated learning offers a decentralized paradigm that preserves user privacy while enabling collective model training across heterogeneous IoT nodes. This work proposes novel energy-aware federated learning algorithms that optimize communication and computation costs by leveraging techniques such as adaptive model updates, quantization, and device participation scheduling. The proposed framework integrates trust mechanisms to ensure secure and reliable cooperation among devices, thereby enhancing sustainability and network longevity. Experimental evaluations demonstrate significant reductions in energy consumption without compromising learning accuracy, highlighting the potential for real-world implementation in diverse IoT environments. The findings underscore the importance of leveraging collaborative intelligence for sustainable, green computing infrastructures, paving the way for future research in scalable, energy-efficient federated learning applications within IoT networks.

 

 

Published by: vikaspatanker

Authors: Aaditya Duche, Raviraj Deore, Sanskar Dalvi, Swapnil Paik, Prof. R. B. Shinde

Abstract: The Smart Classroom System using IoT modernizes the conventional education environment by integrating automation, sensing, and communication technologies. The system implements automatic student attendance using face recognition, smart control of lighting and fans, environmental monitoring, and remote data access through cloud platforms. A Raspberry Pi 3B single board computer and Pi camera module continuously monitor the classroom. During enrollment, facial images of each student are captured and stored in a database. During lecture hours, the system detects faces from live video frames and compares them with the stored dataset using OpenCV and the face_recognition library. When a match is confirmed, attendance is recorded with date and time. A 16×2 LCD display shows confirmation and a buzzer provides audio indication. The system eliminates proxy attendance, reduces manual effort, and improves accuracy, demonstrating the practical value of embedded systems and computer vision in smart educational infrastructure

Published by: vikaspatanker

Authors: Siddhi Ekawade, Apurva Jate, Arya Kamble, Sharvari Kate, Prof.Pradnya Satpute

Abstract: Artificial Intelligence has made it easy to create realistic images, videos, and texts. These technologies have been misused to create deepfakes and online scams, which can lead to the spread of misinformation, financial scams, and cybersecurity attacks. It is hard to detect such content by human beings, as it is time-consuming. Hence, there is a need to develop an automated detection system for AI-generated content. The proposed project aims to develop a multimodal AI-generated content detection system that can analyze images, videos, and texts to detect potentially fake or scam content. The system can detect deepfakes in images and videos using a Convolutional Neural Network (CNN) model, and it can also analyze the text messages sent by the user to detect scams using a machine learning-based approach. The application has been developed as a web application using the Flask framework in Python. This processed media is analyzed, and the important features are identified, providing a probability score on whether the media is real or fake. The output is given in percentage probability, making it easier for the user to interpret the results. All analysis results are stored in a SQLite database, which is used for monitoring and administrative purposes. This proposed system has shown how deep learning and machine learning can be combined into a single framework to detect AI-generated content. This type of system can be used to enhance digital security, helping users identify fake media and possibly scam messages.

 

 

Published by: vikaspatanker

Authors: Megha Garud, Lalit Gaikwad, Prakash Mane, Ranjit Misal, Amey Phatak, Premraj Takawale

Abstract: The increasing adoption of Electric Vehicles (EVs) has brought significant attention to the availability and efficiency of charging infrastructure. Although governments and private organizations are actively deploying public charging stations, their limited number and uneven distribution continue to pose challenges for EV users. In many cases, users experience long waiting times or difficulty locating nearby charging facilities. This survey paper presents an EV Charger Sharing Platform that encourages the utilization of privately owned EV chargers through a web-based system. The platform enables charger owners to list their chargers and EV users to search, view, and book available charging slots based on location and availability. Developed using standard web technologies such as HTML, CSS, and JavaScript, the system aims to improve charger accessibility, reduce waiting time, and promote sustainable transportation. By adopting a sharing-economy approach, the proposed solution offers a cost-effective and scalable alternative to traditional public charging infrastructure.

Published by: vikaspatanker

Authors: Krishna Prisad Bajgai, Dr. Saroj Khanal, Dr. Bhoj Raj Ghimire

Abstract: Type 2 Diabetes Mellitus (T2DM) remains a major global health burden and a leading contributor to cardiovascular, renal, and hepatic mortality. Traditional risk assessment models rely on limited clinical parameters and fail to capture complex nonlinear interactions among multi-organ biomarkers. This study proposes a comprehensive machine learning (ML) and deep learning (DL)-based survival modeling framework to predict mortality risk in T2DM patients using multi-organ biomarkers, including fasting blood glucose, HbA1c, serum creatinine, triglycerides (TG), total cholesterol, LDL, HDL, liver enzymes (ALT, AST), and fatty liver indicators. Using the National Health and Nutrition Examination Survey (NHANES) linked mortality dataset, we compare Cox Proportional Hazards, Random Survival Forest (RSF), Gradient Boosting Survival (GBM), DeepSurv, and Long Short-Term Memory (LSTM) models. Performance was evaluated using Concordance Index (C-index), time-dependent Area Under Curve (AUC), Hazard Ratio (HR), and Brier score. Results show DeepSurv achieved the highest C-index (0.82), followed by RSF (0.79), outperforming traditional Cox regression (0.72). SHAP-based feature importance revealed HbA1c, creatinine, triglycerides, and ALT as dominant mortality predictors. Risk stratification analysis demonstrated clear separation between low-, medium-, and high-risk groups (log-rank p < 0.001). The findings highlight the superiority of nonlinear survival models for mortality prediction in T2DM and provide clinically interpretable insights for personalized risk management.

DOI: https://doi.org/10.5281/zenodo.18886211

Published by: vikaspatanker

Authors: Deepak Tomar, Kismat Chhillar, Dhruv Srivastava

Abstract: This paper examines the potential of blockchain technology in strengthening of academic data integrity within institutions of higher education by addressing persistent challenges of credential fraud, limited traceability, record falsification and fragmented oversight in traditional centralized systems. Grounded in contemporary research on decentralized architectures and verifiable credentials, the study analyzes weaknesses in existing platforms of student management and proposes a conceptual model that is integrating key blockchain principles of distributed consensus, immutability and smart contracts, with requirements of integrity such as auditability, verifiability, non-repudiation and selective disclosure. The proposed model provides an outline of a consortium-based platform for management of transcripts, qualifications, assessments and co-curricular records, designed to interoperate with existing databases of institutions while complying with regulations of data protection and sovereignty regulations such as FERPA and GDPR. Scenario-based evaluations suggest improvements in verification efficiency, inter-institutional trust and provenance tracking, along with reductions in administrative overhead and faster dissemination of academic records to external stakeholders. The study also critically considers practical challenges that are related to jurisdictional interoperability, scalability, institutional resistance and governance.

 

 

Published by: vikaspatanker

Authors: Dr. L. N. Malviya

Abstract: To improve the main shortcomings of insufficient nutrients, high salinity and low productivity of soils, soil testing and analysis are crucial processes. Understanding the composition, quality, and characteristics of soil is essential for making informed decisions related to land use, crop management, building foundation design, and environmental remediation. Soil testing and analysis is very important in agriculture, construction, environmental science, and various other fields.

 

 

Published by: vikaspatanker

Authors: Akhilash Pennam

Abstract: This paper proposes an Artificial Intelligence (AI)–enabled cloud-based CRM architecture developed on the Salesforce platform to modernize gas station operations through intelligent automation and predictive analytics. The system integrates fuel booking, inventory management, supplier coordination, and customer interaction into a unified digital platform. AI techniques including time-series forecasting, anomaly detection, and customer behavior analytics are embedded to transform operational data into predictive insights. Machine learning models analyze historical transactions to forecast fuel demand, optimize inventory levels, and detect abnormal operational patterns. Salesforce automation tools such as Flows and Apex triggers enforce business rules, while AI-driven dashboards provide real-time predictive decision support. Experimental evaluation demonstrates improved forecasting accuracy, reduced operational errors, faster transaction processing, and enhanced managerial decision-making. The proposed architecture demonstrates how AI can elevate traditional CRM systems into intelligent, scalable, and proactive operational platforms suitable for multi-branch fuel retail environments.

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Published by: vikaspatanker