Daily Archives: April 10, 2026

Authors: Jag Pratap Singh Yadav

Abstract: Classical diffusion models based on Fick’s law assume Brownian motion and local transport, leading to a mean squared displacement that grows linearly with time. However, many physical, biological, and engineering systems exhibit anomalous diffusion, where the mean squared displacement follows a power law in time rather than a linear relationship. Such behavior commonly arises in heterogeneous porous materials, crowded biological environments, polymeric systems, and disordered media, where long trapping times and memory effects invalidate standard integer-order diffusion equations. Despite significant progress in fractional modeling, there remains a need for mathematically consistent and computationally efficient formulations that clearly link the physical origin of anomalous transport to robust numerical implementation. In this paper, we develop a time-fractional diffusion model using the Caputo fractional derivative to represent memory-dependent transport induced by heavy-tailed waiting times. Starting from the conservation of mass and a constitutive relation with temporal memory, we derive a physically meaningful fractional diffusion equation. An analytical solution for a benchmark initial-boundary value problem is presented using Laplace and Fourier transforms, and a numerical approximation based on the L1 finite difference scheme is constructed. The stability and convergence properties of the numerical method are discussed. Numerical experiments demonstrate that the fractional order controls the transition from normal to subdiffusive transport and accurately reproduces power-law mean squared displacement behavior. The model captures anomalous transport with significantly fewer parameters than multi-scale classical alternatives. These results show that fractional differential equations provide an effective and parsimonious framework for describing memory-driven diffusion processes, with direct relevance to transport in porous media, biological tissues, and complex soft matter systems.

DOI: http://doi.org/10.5281/zenodo.70

 

 

Published by: vikaspatanker

Authors: Dr. Harsha R. Vyawahare1,, Sukhada Shripad Tare2,, Ashwini Nitin Shingane3,, Shreya Sunil Shinde4,, Bhavika Suraj Jain5

Abstract: This paper presents a practical and lightweight bidirectional communication system that translates between speech/text and Indian Sign Language (ISL) using machine learning and computer vision techniques. The system supports two modes: Speech-to-ISL and ISL-to-Text/Speech. In Speech Mode, spoken input is converted into text using speech recognition, then mapped to corresponding ISL alphabet images. In Camera Mode, hand gestures are captured using a webcam and classified using a Convolutional Neural Network (CNN) model to generate text and voice output. The system is implemented using Streamlit for the user interface, OpenCV for image processing, TensorFlow/Keras for gesture recognition, and pyttsx3 for speech synthesis. The proposed system provides a simple, real-time, and cost-effective solution to improve communication accessibility for the Deaf and Hard-of-Hearing (DHH) community

Published by: vikaspatanker

Authors: Assistant Professor Anshul Khandelwal, Abhishek Pakhariya, Associate Professor Brajesh Tripathi

Abstract: A comprehensive comparative computational fluid dynamics (CFD) investigation is presented, analyzing three representative wing configurations: the transonic ONERA M6 benchmark wing, a finite wing based on the symmetric NACA 0012 airfoil section, and a tapered finite wing evaluated at low subsonic speeds. The primary objective is to examine benchmark-oriented transonic flow prediction capabilities and evaluate low-speed finite-wing performance parameters within a unified aerodynamic framework. For the ONERA M6 configuration, the flow field is simulated under the standard validation conditions of a Mach number of 0.8395, an angle of attack of 3.06°, and a Reynolds number of $11.72 \times 10^6$ based on the mean aerodynamic chord. For the low-speed wings, integrated aerodynamic loads at a free-stream velocity of 50 m/s are utilized to determine the aerodynamic coefficients and efficiency trends across various angles of attack. The CFD solver successfully reproduces the expected transonic pressure redistribution, including the characteristic shock-dominated flow structure over the ONERA M6 wing. In the low-speed analysis, the rectangular NACA 0012 wing achieves its maximum aerodynamic efficiency near an 8° angle of attack, whereas the tapered wing exhibits superior aerodynamic efficiency at low angles but suffers a more rapid degradation at higher incidences due to accelerated drag growth. This study effectively consolidates benchmark computational validation, finite-wing aerodynamic theory, and comparative performance analysis.

Published by: vikaspatanker

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.

DOI:

 

 

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