arXiv subjects

Shadeeb Hossain

Publications and source records attributed to Shadeeb Hossain.

8 recordsLinked to original sources

arXiv2026

GTI-mSEMP Framework : A Proposed Framework to Simulate Malware Propagation with Inclusion of Attacker-Defender Strategy

The rapid proliferation of automated, multi-vector malware threats poses a significant risk to heterogeneous, resource constrained cyber-physical networks. Conventional epidemiological models often treat security defenses as static parameters, failing to capture the strategic, asymmetric maneuvers between an attacker and a defender. To address the gap, this paper proposes a Game-Theory-Integrated Modified Multi- Wireless Sensor Epidemic Malware Propagation (GTI-mSEMP) framework. This paper analyzed and compared the operational trajectories of Susceptible (S) and Recovered (R) node populations across three different operational regimes: Balanced Matchup, Exploit Surge and Hardened Defense. Numerical simulation results capture the real-time transient dynamics of the network state variables, demonstrating how the epidemic curve shifts when either the defensive or offensive scaling vectors hold an efficiency advantage. The proposed mathematical and numerical framework provides a rigorous foundation that can be deployed in highly adversarial network environments to evaluate dynamic malware propagation and predict localized node population states.

cs.CR↗

arXiv2026

Extended Reality (XR): The Next Frontier in Education

Extended Reality (XR), encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), is revolutionizing education by creating immersive, interactive learning environments. This article explores the potential of XR to enhance student engagement, experiential learning, and skill development while addressing the challenges of widespread adoption. High implementation costs, technical complexities, and ethical concerns especially regarding student privacy and biometric data protection still possess significant barriers to integration. The discussion also highlights regulatory compliance with GDPR and FERPA and the importance of cybersecurity frameworks to safeguard sensitive learner data. Ultimately, the article provides insights into balancing innovation with accessibility and ethics in the evolution of XR based education

cs.CY↗

arXiv2026

Prediction of Cellular Malignancy Using Electrical Impedance Signatures and Supervised Machine Learning

Bioelectrical properties of cells such as relative permittivity, conductivity, and characteristic time constants vary significantly between healthy and malignant cells across different frequencies. These distinctions provide a promising foundation for diagnostic and classification applications. This study systematically reviewed 20 scholarly articles to compile 535 datasets of quantitative bioelectric parameters in the kHz-MHz frequency range and evaluated their utility in predictive modeling. Three supervised machine learning algorithms- Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) were implemented and tuned using key hyperparameters to assess classification performance. In the second stage, a physics informed framework was incorporated to derive additional dielectric descriptors such as imaginary permittivity, loss tangent and charge relaxation time from the measured parameters. Random Forest based feature importance analysis was employed to identify the most discriminative dielectric parameters influencing the classification process. The results indicate that dielectric loss related parameters, particularly imaginary permittivity and conductivity, contribute significantly to the classification of cellular states. While the incorporation of physics-derived features improves model interpretability and reduces overfitting tendencies, the overall classification accuracy remains comparable to models trained using primary dielectric descriptors. The proposed approach highlights the potential of physics-informed machine learning for improving the analysis of dielectric spectroscopy data in the biomedical diagnostics.

eess.SP↗

arXiv2025

Exploring the Integration of Extended Reality and Artificial Intelligence (AI) for Remote STEM Education and Assessment

This paper presents a dynamic gamification architecture for an Extended Reality Artificial Intelligence virtual training environment designed to enhance STEM education through immersive adaptive, and kinesthetic learning. The proposed system can be introduced in four phases: Introduction Phase, Component Development Phase, Fault Introduction and Correction Phase and Generative AI XR scenarios Phase. Security and privacy are discussed via a defense-in-depth approach spanning client, middleware, and backend layers, incorporating AES 256 encryption, multi-factor authentication, role-based access control and GDPR or FERPA compliance. Risks such as sensor exploitation, perceptual manipulation, and virtual physical harm are identified, with mitigation strategies embedded at the design stage. Potential barriers to large scale adoption-including technical complexity, cost of deployment, and need for cybersecurity expertise are discussed.

cs.HC↗

arXiv2025

Using Artificial Intelligence to Improve Classroom Learning Experience

This paper explores advancements in Artificial Intelligence technologies to enhance classroom learning, highlighting contributions from companies like IBM, Microsoft, Google, and ChatGPT, as well as the potential of brain signal analysis. The focus is on improving students learning experiences by using Machine Learning algorithms to : identify a student preferred learning style and predict academic dropout risk. A Logistic Regression algorithm is applied for binary classification using six predictor variables, such as assessment scores, lesson duration, and preferred learning style, to accurately identify learning preferences. A case study, with 76,519 candidates and 35 predictor variables, assesses academic dropout risk using Logistic Regression, achieving a test accuracy of 87.39%. In comparison, the Stochastic Gradient Descent classifier achieved an accuracy of 83.1% on the same dataset.

cs.CY↗

arXiv2024

Using LLM such as ChatGPT for Designing and Implementing a RISC Processor: Execution,Challenges and Limitations

This paper discusses the feasibility of using Large Language Models LLM for code generation with a particular application in designing an RISC. The paper also reviews the associated steps such as parsing, tokenization, encoding, attention mechanism, sampling the tokens and iterations during code generation. The generated code for the RISC components is verified through testbenches and hardware implementation on a FPGA board. Four metric parameters Correct output on the first iteration, Number of errors embedded in the code, Number of trials required to achieve the code and Failure to generate the code after three iterations, are used to compare the efficiency of using LLM in programming. In all the cases, the generated code had significant errors and human intervention was always required to fix the bugs. LLM can therefore be used to complement a programmer code design.

cs.LG↗

arXiv2023

Low-Cost Architecture for an Advanced Smart Shower System Using Internet of Things Platform

Wastage of water is a critical issue amongst the various global crises. This paper proposes an architecture model for a low-cost, energy efficient SMART Shower system that is ideal for efficient water management and be able to predict reliably any accidental fall in the shower space. The sensors in this prototype can document the surrounding temperature and humidity in real time and thereby circulate the ideal temperature of water for its patron, rather than its reliance on predictive values . Three different scenarios are discussed that can allow reliably predicting any accidental fall in the shower vicinity. Motion sensors, sound sensors and gesture sensors can be used to compliment prediction of possible injuries in the shower. The integration with the Internet of Things (IoT) platform will allow caretakers to monitor the activities in the shower space especially in the case of elderly individuals as there have been reported cases of casualties in the slippery shower space. The proposed proof-of-concept prototype is cost effective and can be incorporated into an existing system for the added precedence of safety and convenience. The intelligent system is conserving water by optimizing its flow temperature and the IoT platform allows real time monitoring for safety.

cs.HC↗

arXiv2023

Evaluation of Induced Transmembrane potential on Membrane Poration through Molecular Dynamics Simulation and Analytical Calculation

A molecular dynamics (MD) simulation is used to quantitatively analyze the induced membrane potential for an applied external field varied between 0.4 V/nm to 2.0 V/nm. The change in the electrostatic potential in the DPPC is directly correlated to the membrane permeability. The effect of the decrease in external conductivity on the DPPC is also evaluated and the analytical results are compared with the simulation. The correlation between the electrostatic potential of the DPPC and the total dipole are compared, and a positive correlation is identified until saturation. This is because the membrane permeability factor plays a dominant role to control reversible and irreversible electroporation. The obtained dipole parameters through simulation for various electric field allows for an accurate determination of the quantitative changes in the membrane and external conductivity during the process of electroporation.

physics.bio-ph↗