ICSEMT December 2025 Proceeding

Optimal Total Coloring Techniques For Enhancing Honeycomb Network Performance

Authors: Rupam Shrivastava, Dr. Satish Agnihotri

Abstract: Honeycomb networks—also referred to as honey graphs, owing to their hexagonal lattice representation in graph theory are among the most influential topological structures used in computational and engineering systems. Their geometric regularity, scalability, and symmetry make them fundamental in domains such as parallel processing, VLSI layout design, chemical molecular modeling, wireless communication, and distributed computing. In these systems, efficient resource allocation and conflict-free scheduling are critical for performance optimization. Total coloring, which assigns colors to both vertices and edges such that no adjacent or incident elements share a color, provides a robust mathematical tool for enforcing these operational constraints. Equitable total coloring further requires that color class sizes differ by at most one, a property essential for fairness in load-balanced systems. Although extensive work exists on total coloring, equitable total coloring of honey graphs remains mostly unexplored, particularly in relation to optimal bounds and constructive algorithms. This study determines the equitable total chromatic number of honey graphs of varying orders and develops constructive algorithms that ensure balanced color classes. Results show that the equitable total chromatic number conforms to Δ + 1 or Δ + 2, consistent with the Total Coloring Conjecture, and equitability does not increase the chromatic requirement. These findings confirm that honey graphs inherently support balanced total colorings suitable for conflict-free and fairness-driven scheduling in complex networks.

DOI: http://doi.org/10.5281/zenodo.17896965

LifeMemory: An AI-Assisted Personal Memory System For Enhanced Everyday Recall And Cognitive Support

Authors: K. Dhakshina, Jaya Nandhini S, Karthika B., Mr.P.T Prithivirajan

Abstract: Human memory is under increasing strain due to constant digital interaction, pervasive multitasking, and informa-tion overload. While conventional note-taking and productivity tools exist, they often necessitate significant active effort and structured input, which limits their practicality for everyday users and individuals facing attention-related challenges, such as Attention-Deficit/Hyperactivity Disorder (ADHD). This paper introduces LifeMemory, an AI-assisted personal memory sys-tem engineered to passively capture, intelligently organize, and contextually retrieve user memories through natural language interaction. The system facilitates the storage of thoughts, tasks, conversations, and decisions with minimal friction, enabling their recall precisely when needed. By leveraging advanced large lan-guage models and semantic memory structuring, LifeMemory functions as a cognitive extension rather than a conventional static note-taking application. The core design principles pri-oritize accessibility, emotional awareness, and cognitive load reduction. A functional web-based prototype was developed and rigorously evaluated for usability and recall effectiveness. Prelim-inary results demonstrate significant improvements in memory retrieval speed and a noticeable reduction in mental effort for both neurotypical users and those experiencing attention difficulties, highlighting its potential as a robust cognitive support tool.

DOI: http://doi.org/10.5281/zenodo.18052885

GEN IAI I- IiIMAGE

Authors: Alok Singh Chauhan, Piyush Kumar, Harshit Singh, Mohit Joshi

Abstract: This model is proposed to induce images that's given textbook. This can induce imaginary filmland to realistic one. For the conversion, we need DALL- E. It'll be delightful creating the artisitic, realistic images from the description. This is an Android operation design developed using Kotlin and Java.Here, we used Natural language description advisement for our project.It creates images through prompts. This will be useful for enforcing our different ideas, studies into diagrammatic donation. DALLE can be used for marketable purposes like advertising, printing, dealing etc. It'll display the images of our choice making anthropomorphic filmland and collaboration of unconnected generalities. It's doable and generates presumptive objects.Using this Android operation design, We can enhance our imaginative ideas into a realistic bone. It's a friendly app where we will not face any issues in filmland. And We can not find this imaginary picture creator in any hunt engine.This Android operation design will give you a picture with whatever size you want. And it will not reduce the quality of a picture. The quality size of a picture will be 256 × 256, 512 × 512 and 1024 × 1024. We can choose a quality size grounded on our network quality.Before using this operation, we've to make sure the network installations.

Facial Recognition Technology in India: Socio-Legal Debates on Privacy and Human Rights

Authors: Ms. Mohita Yadav, Dr. Arti Sharma

Abstract: Facial Recognition Technology (FRT) has emerged as a powerful surveillance tool in modern governance, enabling the identification and tracking of individuals through biometric facial data. In India, the rapid deployment of FRT by law enforcement agencies, airports, and public authorities—most notably through initiatives such as the National Automated Facial Recognition System (NAFRS)—has occurred in the absence of a comprehensive legislative framework. This development raises significant constitutional and human rights concerns, particularly in light of the recognition of the right to privacy as a fundamental right under Article 21 of the Constitution of India. This paper critically examines the socio-legal implications of facial recognition technology in India, focusing on its impact on privacy, personal liberty, freedom of expression, and the right to dissent. Anchored in constitutional jurisprudence and human rights principles, the study evaluates whether the use of FRT satisfies the proportionality standard laid down by the Supreme Court in Justice K.S. Puttaswamy v. Union of India. Through doctrinal and comparative analysis, the paper highlights regulatory gaps, risks of mass surveillance, algorithmic bias, and the chilling effect on democratic freedoms. It argues for the urgent need for a rights-centric statutory framework governing facial recognition technology in India, incorporating transparency, accountability, and judicial oversight.

DOI: https://doi.org/10.5281/zenodo.18093569

Enhancing Cyber Security With A Hybrid Machine Learning Framework: The SART Model

Authors: Ms.Monika Saini, Dr. Gaurav Aggarwal

Abstract: This research presents a novel hybrid machine learning (ML) framework, SART (Supervised-unsupervised Anomaly Recognition Threat detection), for enhancing cyber security by integrating supervised, unsupervised, and deep learning techniques. The study addresses the limitations of traditional cyber security methods by leveraging advanced ML algorithms to improve threat detection, anomaly identification, and predictive defense mechanisms. Using benchmark datasets such as CICIDS2017, UNSW-NB15, KDDCup99, CSE-CIC-IDS2018, and NSL-KDD, the proposed framework combines Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Auto encoders to achieve high detection accuracy and robustness against evolving cyber threats. The framework integrates security mechanisms including AES encryption, RSA asymmetric encryption, and SHA hashing for confidentiality, authentication, and integrity. The experimental results demonstrate significant improvements in security, accuracy, and performance compared to conventional approaches, highlighting the effectiveness of the hybrid ML-based model in real-world cyber security applications. The proposed model achieved detection accuracy exceeding 97% on benchmark datasets while maintaining computational efficiency suitable for real-time deployment.

DOI: http://doi.org/10.5281/zenodo.18093494

A Secure And Privacy-Preserving Architecture For Scalable Virtual Classroom Environments

Authors: Ms. Swati Mishra, Dr.Kavita Mittal

Abstract: The rapid expansion of online education has led to increased reliance on virtual classroom platforms, raising critical concerns related to data security, user privacy, and system scalability. Existing e-learning systems often struggle to protect sensitive academic and personal data while supporting a large number of concurrent users. This paper proposes a secure and privacy-preserving virtual classroom architecture designed to support scalable online learning environments. The proposed architecture integrates robust authentication mechanisms, role-based access control, end-to-end encrypted communication, and secure data storage to safeguard user information. Privacy-preserving techniques such as data minimization, anonymization, and consent-based access are incorporated to ensure compliance with modern data protection standards. Additionally, cloud-based scalability and load-balancing strategies are employed to maintain system performance during peak usage. The proposed model enhances trust, resilience, and efficiency in virtual learning platforms, making it suitable for educational institutions and large-scale e-learning providers seeking secure and privacy-focused digital education solutions. This research aims to bridge this gap by proposing a secure and privacy-preserving virtual classroom architecture designed for scalable online learning. The proposed architecture integrates Zero-Trust principles to eliminate implicit trust, AI-driven threat detection for proactive defence against emerging cyber threats, privacy-preserving data analytics to protect learner identities, and cloud-independent modular components to ensure flexible deployment. By embedding regulatory compliance and scalability at the architectural level, this work seeks to provide a robust foundation for next-generation virtual learning environments. The contributions of this research are intended to support both academic inquiry and practical implementation, offering educational institutions a secure, trustworthy, and scalable solution for delivering high-quality online education in an increasingly digital world.

DOI: http://doi.org/10.5281/zenodo.18094112

Architectural Integration of Cross-Cutting Security Concerns in Web Applications: A Model-Driven Perspective

Authors: Anil Kumar, Dr. Gaurav Aggarwal

Abstract: The proliferation of web applications and the increasing sophistication of cyber threats necessitate robust security measures. However, integrating security effectively often leads to tangled and scattered code, making applications difficult to develop, maintain, and evolve. This paper proposes a novel Model-Driven Aspect-Oriented Framework (MDAOF) for the systematic integration of cross-cutting security concerns in web applications. By combining the high-level abstraction capabilities of Model-Driven Engineering (MDE) with the modularity and separation-of-concerns offered by Aspect-Oriented Programming (AOP), the framework aims to reduce complexity, enhance reusability, and improve the overall security posture of web applications. We delineate the architectural components of the MDAOF, illustrating how security policies are modeled, transformed into aspect-oriented code, and woven into the application at various stages of the development lifecycle. This approach promises a more efficient and less error-prone way to build secure web applications, moving security from an afterthought to an integral part of the design process.

DOI: https://doi.org/10.5281/zenodo.18107769