Authors: Okai Tettey-Antie Samuel

Abstract: Modern Quantum Key Distribution (QKD) often fails in highly dynamic mobile environments due to rigid post-processing architectures. This paper introduces a pioneering self-adaptive privacy amplification (SAPA) framework that replaces traditional static compression with a closed-loop controller. By integrating twelve distinct quantum noise models—including Non-Markovian and Gaussian Bosonic channels—we demonstrate that real-time entropy estimation can reclaim up to 25% of secure key material previously lost in mobile-induced fluctuations. Our results establish a new paradigm for "living" security in future 6G ecosystems.

DOI: https://doi.org/10.5281/zenodo.18429991

 

Published by: vikaspatanker

Authors: Nishant Kadam, Swarup Chaudhari, Rushikesh Patil, Hrishikesh Kakade

Abstract: Automated vehicle monitoring is a cornerstone of modern security infrastructure, essential for maintaining safety and operational efficiency in high-traffic environments such as industrial complexes, gated communities, and public facilities. Traditional manual surveillance methods are frequently plagued by human error, significant labor costs, and operational bottlenecks that compromise the integrity of security protocols. This paper presents a robust framework for an automated Vehicle Entry Monitoring System (VEMS) utilizing the state-of-the-art You Only Look Once (YOLO) object detection architecture. The proposed system integrates real-time video stream processing with advanced deep learning models to achieve high-speed detection and classification of various vehicle types, including cars, trucks, and motorcycles. A critical component of the methodology involves the integration of Optical Character Recognition (OCR) and tracking algorithms, such as DeepSORT, to automatically extract alphanumeric license plate data and maintain unique vehicle identities across consecutive frames. This integration enables the creation of a comprehensive, searchable database that cross-references detected plates with authorized whitelists for proactive access control. Experimental results demonstrate that the system ensures near 100% operational uptime by automating the data trail for security auditing and regulatory compliance. The framework provides a scalable solution for intelligent transportation management, significantly reducing manpower dependency while enhancing the reliability of entry logs. By combining real-time detection overlays with a centralized monitoring dashboard, this research offers a sophisticated, data-driven approach to facility security, fostering safer and more efficient urban mobility environments.

Published by: vikaspatanker

Authors: Aher Pratiksha Mahendra, Auti Samiksha Ankush, Adak Dnyaneshwari Santosh, Adinath shankar satpute

Abstract: This paper presents the design and implementation of an Alcohol Detection with Engine Locking system for vehicles using the MQ-3 alcohol sensor, HC-SR04 ultrasonic sensor, and Arduino UNO as the Master Control Unit (MCU). The system continuously monitors alcohol concentration in the vehicle cabin and automatically locks the engine if the alcohol concentration exceeds the predefined threshold level. The proposed system also incorporates a SIM900A GSM module to send alert messages regarding the vehicle's whereabouts to designated authorities. Additionally, the ultrasonic sensor measures the distance between vehicles and activates warning indicators when the safe following distance is compromised. Experimental results demonstrate that the system provides an efficient and reliable solution to control accidents caused by drunk driving.

DOI: https://doi.org/10.5281/zenodo.18429030

 

Published by: vikaspatanker

Authors: Hachimenum N. Amadi, Sopakiriba Maxwell West, Richeal Chinaeche Ijeoma

Abstract: The incessant national grid collapse has become a global embarrassment; from 2015 to May 2024 the Transmission Company of Nigeria (TCN) has recorded 105 cases of grid collapse. Phasor Measurement Units (PMUs) are necessary for the extensive use and efficient running of international power networks, the present Supervisory Control and Data Acquisition (SCADA) system used in Nigeria does not provide a robust and dependable solution that improves the power grid’s real time monitoring and control capabilities. PMU will reduce the frequency of power grid breakdowns and also resolve fault location troubleshooting safely and timely. The optimal placement of Phasor Measurement Units (PMUs) is an important requirement in power systems research, particularly for the localization of transmission line faults. This research has proposed a Modified Sparsity Genetic Algorithm Optimizer (MS-GAO) for optimal placement of Phasor Measurement Units (PMUs) in Power Systems over the standard Genetic Algorithm (GA) approach used in various related studies. To further validate the performance, the time complexity studies were performed to determine the better technique considering enhanced PMU placement. The proposed approach has been applied to two IEEE power system networks – the IEEE 6-Bus and 14-bus power networks. The simulations were performed using the MATLAB software tool and results compared with the standard Genetic Algorithm (sGA) on the basis of the percentage Classification Efficiency (CE) and the number of trial iteration runs (iters) used per simulation. The results showed that the proposed MS-GAO gave comparable CEs when compared to sGA with 100% CE for 100iters. However, it was found that reducing the iterations to about 50iters resulted in a degradation of CE. Thus, a compromise should be made between the number of iterations required and the level of CE needed in the problem solution. In addition, computational run-time complexity results considering the 6-bus power network revealed that the MS-GAO will give better run-times when compared to the sGA with an average run-time reduction of about 0.5s. Thus, it is recommended that the MS-GAO be employed for a higher power bus networks as the computational demands will obviously be higher using a sGA.

DOI: https://doi.org/10.5281/zenodo.18427464

Published by: vikaspatanker

Authors: Mr. Rushikesh Kalhale, Mr. Venkatesh Bansode, Mr. Utkarsh Maske, Prof.Deepa Shivshimpi

Abstract: Mathematics is at the base of all Artificial Intelligence (AI) systems. Throughout the AI lifecycle, mathematics is the pillar for representing data at the start, learning, reasoning on behalf of the human user and adapting in the mid-section, and finally optimizing any algorithm or data driven model at the end. This paper will discuss how the main mathematics will start to emerge as critical constructs for AI – linear algebra, calculus, probability and statistics, and optimization. We will demonstrate the pertinence of mathematical models as a pathway for the development of neural networks, machine learning algorithms, and data driven decision systems. In demonstrating examples of how mathematics has evolved as part of the responsive development of Artificial Intelligence, we can clearly delineate the ongoing, sometimes inescapable, role mathematics will have in defining intelligent systems in the future.

DOI: https://doi.org/10.5281/zenodo.18427209

Published by: vikaspatanker

Authors: Hachimenum Nyebuchi Amadi, Ogadinma Agha Onya,, Richeal Chinaeche Ijeoma

Abstract: The reliability of 33kV distribution networks is crucial to the stability of Nigeria's electricity supply. Serving as the interface between the transmission grid and 11kV feeders, these networks directly affect service delivery, customer satisfaction, and operational efficiency. This paper examines the operational challenges and reliability issues of the 33kV feeders within the Port Harcourt Electricity Distribution Company (PHEDC) network, with a focus on performance assessment using standard indices such as SAIDI, SAIFI, and CAIDI. Preventive maintenance, feeder automation, and improved operational practices are identified as key measures for enhancing reliability. Results reveal major network challenges such as overloaded feeders, poor voltage profiles, high technical losses, and frequent interruptions. Reliability indices, including SAIFI, SAIDI, CAIDI, and ENS, were significantly above IEEE and NERC thresholds, indicating poor service continuity. To address these deficiencies, the study proposes targeted improvement strategies such as feeder reconfiguration, installation of automated reclosers and sectionalizers, preventive maintenance, and upgrading of aging conductors and transformers. The study concludes that targeted investment in maintenance, automation, and workforce training can significantly reduce outages and improve service continuity.

DOI: https://doi.org/10.5281/zenodo.18427035

 

Published by: vikaspatanker

Authors: Mr. Danish Ishfaq, Ms. Aasifa Jan

Abstract: Artificial Intelligence (AI) and robotic surgical systems represent transformative technologies in modern healthcare, with profound potential for enhancing surgical precision, reducing operative risk, and improving patient outcomes. In the Indian context, research and clinical practice are increasingly exploring this convergence, encompassing both academic inquiry and real-world deployments. This paper synthesizes recent literature on AI integration with robotic surgery, highlights Indian research efforts, examines clinical case developments, identifies technical and ethical challenges, and discusses future directions for advancing AI-enabled surgical robotics within India’s healthcare ecosystem.

DOI: https://doi.org/10.5281/zenodo.18426331

Published by: vikaspatanker

Authors: Jasmine A, Dr. G. Arul Selvi

Abstract: Social media has become an inseparable part of young people’s everyday life, particularly among college students. This article examines how social media engagement influences value orientation among college students in Tamil Nadu, with specific reference to empathy, morality and civic engagement. Drawing on empirical observations among undergraduate students from rural and urban backgrounds, the study shows that excessive and unregulated social media use is associated with weakened empathy, reduced family bonding and diminished moral responsibility. At the same time, responsible and reflective engagement with social media platforms enhances prosocial values, civic awareness and social sensitivity. The article emphasises the need for value-based digital literacy in higher education to ensure ethical and socially responsible digital citizenship.

Published by: vikaspatanker

Authors: Mohd saquib siddiqui, ashar ahmed

Abstract: Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

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Published by: vikaspatanker

Authors: Mustakim Khan, Ashok Yadav

Abstract: We present a multi-layer email threat detection system that integrates header authentication analysis, URL/attachment reputation checks via threat intelligence, and machine learning classification. The system parses incoming emails, verifies SPF/DKIM/DMARC results, extracts URLs and attachment hashes, and queries VirusTotal for each indicator. It then applies a trained ML model (TF-IDF + Logistic Regression) to classify the email as phishing or benign. Finally, a scoring engine correlates all signals into a composite risk score. In testing, the system successfully identified simulated phishing emails: for example, a malicious email with known bad links and spoofed headers was flagged as Phishing with high confidence, while benign messages were rated low-risk. The GUI (Figures 1–2) displays the analysis report, including header results, VirusTotal findings, ML verdict, and final threat score. Our multi-layer method leverages complementary techniques to improve detection accuracy and reduce false negatives compared to single- method approaches.

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Published by: vikaspatanker