Category Archives: Uncategorized

Authors: Muthulakshmi M, Saravanan P, Srihari M, Shunmugapandian P

Abstract: This paper presents Q-Track, a Smart QR Code and Geo-Fenced Attendance System designed to provide a secure, efficient, and automated solution for attendance management in educational institutions. Traditional attendance systems, including manual registers and biometric methods, suffer from limitations such as time consumption, proxy attendance, and lack of real-time monitoring. To overcome these challenges, the proposed system integrates dynamic QR code generation with geo-location verification.In this system, a unique and time-bound QR code is generated for each class session by the faculty. Students scan the QR code using their mobile devices to mark attendance. To ensure authenticity, the system incorporates geo-fencing technology, which validates the real-time location of the student. Attendance is recorded only when both QR authentication and location verification are successful, thereby eliminating proxy attendance and ensuring reliability.The system is implemented as a web-based application with a user-friendly interface accessible on both mobile and desktop devices. It includes modules for user authentication, QR code generation, attendance tracking, and report generation. Real-time data processing enables faculty to monitor attendance instantly and generate detailed reports for analysis.The proposed solution enhances accuracy, reduces manual workload, and improves transparency in attendance management. By combining QR technology with geo-location services, the system provides a scalable and cost- effective approach suitable for modern academic environments.

Published by: vikaspatanker

Authors: Mr. R.Rajesh, Bura Keerthi, K.Keerthan Reddy, A.Siddartha

Abstract: Smart Loan is an intelligent system designed to predict loan eligibility and assess risk using machine learning techniques. Traditional loan approval processes are time-consuming and prone to human bias. This system automates the evaluation process by analyzing applicant data such as income, credit history, employment status, and financial behavior. The model predicts whether a loan should be approved and categorizes applicants based on risk level (low, medium, high). The system ensures faster decision-making, reduces default risks, and improves efficiency for financial institutions.

Published by: vikaspatanker

Authors: Dr. CH. Kishore Kumar, Joruka Vigneshwar, SK Khaja Mohinuddin Pasha, Karingula Dileep Goud

Abstract: The “Intelligent Web-Based System for Automated Code Assessment and Learning”, designed to enhance programming education using Artificial Intelligence and Machine Learning techniques. The system allows users to submit programming code through a web interface, where it is automatically evaluated for syntax, correctness, logic, and efficiency. Unlike traditional manual evaluation methods, this system provides instant and meaningful feedback by analyzing errors, identifying logical mistakes, and suggesting improvements and optimized solutions. This enables learners to better understand their mistakes and improve their coding skills effectively. The web-based platform supports real-time code execution and evaluation, making it scalable and accessible to a large number of users. It also includes features such as performance analysis, scoring mechanisms, and personalized learning recommendations based on user performance. The system can be extended to support multiple programming languages and adaptive learning paths. Overall, this project focuses on developing a smart and efficient solution that bridges the gap between theoretical learning and practical coding skills, offering benefits such as reduced instructor workload, faster evaluation, improved learning outcomes, and enhanced user engagement through intelligent feedback.

Published by: vikaspatanker

Authors: Saurabh.D. Kamble, Dr. V. P. Varghese, Prof. M.N. Umare

Abstract: The high consumption rate of raw materials by the construction sector, results in chronic shortage of building material and the associated environmental damage. In the last decade, many research on the utilization of waste products in concrete in order to reduce the utilization of natural available resource have been undertaken. Thus, in this paper we have tried to review the use of waste products which are replaced as cement partially by ground granulated blast furnace slag (GGBFS) and coarse aggregate by rubber pallets. The aim is to study literature review & determine how GGBFS & Rubber pallets would affect the compressive strength of concrete when used in different proportions of GGBFS 20%, 30% & 50%, & Rubber pallets 5%, 10%, 15%. This literature review investigation supports the potential use of GGBFS and rubber pallets as partial replacement in concrete production, contributing to eco-friendly and resource efficient construction practice.

Published by: vikaspatanker

Authors: Vishva Shedge, Shubham Lashkar, Swaraj Pawar, Aaditya Shinde, Prof. A. N. Dubey

Abstract: This paper presents the design and implementation of a Mobile Phone Detection System intended for deployment in restricted environments such as examination halls, secure meeting rooms, and classified zones. The proposed system integrates an ESP32 microcontroller with an NMC5883L digital compass module (HMC5883L-compatible) to detect the electromagnetic and magnetic field signatures associated with active mobile devices. Upon detection, the system triggers an audible alarm via a buzzer and displays status information on a 16×2 LCD screen. Wireless data transmission using the NRF24L01 module enables communication between multiple sensor nodes and a central monitoring unit. The system is designed to be low-cost, energy-efficient, and scalable for multi-zone surveillance. Experimental results confirm reliable detection of active mobile phones within a defined proximity range, demonstrating the practical viability of the proposed approach.

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Published by: vikaspatanker

Authors: Andi Pratama

Abstract: The unprecedented escalation in global data traffic, driven by 5G expansion, hyperscale cloud computing, and the Internet of Things (IoT), has fundamentally altered the threat landscape for high-speed networks. Traditional Network Intrusion Detection Systems (NIDS) that rely on manual signature matching or basic statistical thresholds are increasingly incapable of processing traffic at terabit-per-second scales, leading to significant visibility gaps. This review examines the paradigm shift toward Machine Learning (ML)-based anomaly detection as a solution to the "data deluge" in high-speed environments. By focusing on flow-level metadata and statistical behavioral patterns rather than computationally expensive deep packet inspection (DPI), ML models can identify malicious intent within microseconds. We categorize current methodologies, ranging from unsupervised clustering for zero-day discovery to deep learning architectures like Convolutional Neural Networks (CNNs) for spatial traffic analysis and Long Short-Term Memory (LSTM) networks for temporal sequence modeling. This article explores how these models mitigate "alert fatigue" by providing high-precision filtering of benign noise while identifying subtle "low and slow" adversarial tactics. Furthermore, the review addresses the critical challenges of real-time inference at the network edge, the necessity for model quantization to fit within limited hardware buffers, and the emerging risk of adversarial machine learning. By synthesizing recent academic breakthroughs and industrial implementations, this paper provides a strategic roadmap for building "Cognitive Defense" systems. The findings suggest that ML-integrated anomaly detection is the only viable mechanism for maintaining network resilience and integrity in an increasingly automated and high-velocity digital ecosystem.

DOI: https://doi.org/10.5281/zenodo.19492447

Published by: vikaspatanker

Authors: Dmitry Kuznetsov

Abstract: The escalating complexity of modern network infrastructures, characterized by the convergence of multi-cloud environments, microservices, and massive IoT deployments, has pushed traditional network monitoring beyond its structural limits. Traditional monitoring, which relies on static thresholds and reactive alerting, fails to provide the deep "internal state" visibility required for modern digital resilience. This review examines the paradigm shift toward AI-powered network observability systems. Unlike traditional monitoring, observability leverages high-cardinality telemetry data—including logs, metrics, and traces—to enable the "Unknown-Unknown" discovery of system behaviors. By integrating Artificial Intelligence (AI) and Machine Learning (ML), these systems transition from simple data aggregation to "Cognitive Insight" engines. We categorize the core methodologies of AI-driven observability, including the use of unsupervised learning for real-time anomaly detection, Graph Neural Networks (GNNs) for mapping relational topologies, and Natural Language Processing (NLP) for parsing unstructured log telemetry. This article explores how these systems automate Root Cause Analysis (RCA) and enable "Self-Healing" network architectures. Furthermore, the review addresses critical challenges, such as the "Data Silo" problem, the computational overhead of real-time inference at the network edge, and the necessity for Explainable AI (XAI) to foster operator trust. By synthesizing recent breakthroughs in Deep Learning and AIOps, this paper provides a strategic roadmap for building "Autonomous Observability" frameworks. The findings suggest that AI-powered observability is the foundational technology required to manage the invisible complexity of the 6G and hyper-connected era, ensuring that network operations move from reactive troubleshooting to proactive, foresight-driven optimization.

DOI: https://doi.org/10.5281/zenodo.19492433

Published by: vikaspatanker

Authors: Dr. M. S. Yadhav, Mrs. S. V. Zanjad, Abhishek Prakash Lohar, Malhar Ravindra Kale, Vivek Surendra Gadekar, Avinash Mariba Paikrao.D

Abstract: The Carbon Purification System is designed to improve the quality of gas produced during the decomposition of organic waste. Biogas generated from kitchen waste or other biodegradable materials contains useful methane gas along with unwanted impurities such as hydrogen sulfide, carbon dioxide, and bad odor. These impurities reduce the efficiency and usability of the gas. Therefore, purification of biogas is necessary before it can be used for practical applications. This project focuses on developing a simple and cost-effective carbon purification system that uses activated carbon as the main filtering material. Activated carbon has a very large surface area with many tiny pores that can absorb harmful gases and impurities through the process of adsorption. In this system, the raw gas produced from the digester passes through different filter layers such as a pre-filter, activated carbon layer, and cotton layer, which help remove dust particles, toxic gases, and unpleasant smell. The purification chamber is designed using simple materials so that it can be easily implemented in small-scale applications such as homes, laboratories, and small biogas plants. As the gas passes through the filter layers, harmful substances are trapped and the output gas becomes cleaner and safer to use.

DOI: https://doi.org/10.5281/zenodo.19492379

 

Published by: vikaspatanker

Authors: Siti Rahmawati

 

 

Abstract: The rapid proliferation of cloud-native applications, hybrid work models, and bandwidth-intensive services has fundamentally challenged the static nature of traditional Wide Area Networks (WAN). Software-Defined WAN (SD-WAN) introduced a centralized control plane to decouple network software from hardware, yet the manual definition of steering policies often fails to account for the highly volatile nature of internet transport circuits. This review examines the paradigm shift toward Intelligent SD-WAN Management powered by Artificial Intelligence (AI) and Machine Learning (ML). By leveraging deep learning architectures and reinforcement learning agents, SD-WAN controllers can now transition from reactive, threshold-based switching to proactive, intent-driven optimization. This article explores the core methodologies of AI-integrated management, focusing on predictive traffic engineering, automated root cause analysis, and self-healing infrastructure. We analyze how AI models optimize Quality of Experience (QoE) for mission-critical applications—such as VoIP and real-time video—by analyzing multi-dimensional telemetry including jitter, latency, and packet loss in real-time. Furthermore, the review addresses the critical challenges of model interpretability in network operations, the "cold start" problem in new deployments, and the necessity for federated learning to ensure data privacy across multi-tenant SD-WAN environments. By synthesizing recent academic breakthroughs and industrial implementations, this paper provides a strategic roadmap for building "Self-Driving WANs." The findings suggest that AI-integrated management not only reduces operational expenditure by automating complex routing decisions but also provides the cognitive intelligence required to manage the unpredictable performance of commodity internet underlays in a global digital economy.

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Published by: vikaspatanker

Authors: Farah Syazwani

Abstract: Explainable Artificial Intelligence (XAI) has emerged as a critical paradigm in enhancing trust, transparency, and accountability in cybersecurity systems. As cyber threats become increasingly sophisticated, traditional black-box machine learning models often fail to provide interpretable insights into their decision-making processes, thereby limiting their adoption in high-stakes environments. This review explores the integration of explainable AI techniques within cybersecurity frameworks, focusing on how interpretability improves threat detection, incident response, and risk assessment. The article highlights key methodologies such as feature attribution, model-agnostic explanations, and rule-based learning that enable analysts to understand and validate model outputs. Additionally, the role of XAI in regulatory compliance and ethical AI deployment is examined, emphasizing the need for transparency in automated decision systems. Challenges such as trade-offs between accuracy and interpretability, adversarial manipulation of explanations, and scalability issues are also discussed. Emerging trends, including hybrid explainability approaches and human-in-the-loop systems, are presented as promising directions for future research. By bridging the gap between complex machine learning models and human understanding, XAI holds significant potential to transform cybersecurity decision-making into a more reliable and interpretable process. This review provides a comprehensive overview of current advancements and outlines future pathways for integrating explainable intelligence into cybersecurity infrastructures.

DOI: https://doi.org/10.5281/zenodo.19492116

Published by: vikaspatanker