Abstract: This report outlines the design and implementation of an automated DMV (Department of Motor Vehicles) system in the United States, capable of issuing driver's licenses using a machine akin to an ATM. The system integrates hardware, AI-driven software, secure data transmission protocols, and database management to ensure fast, reliable, and secure processing.

Keywords: DMV (Department of Motor Vehicles), driver's licenses, AI-driven software.

Title: AI-Driven DMV License Issuance System in U.S.A.

Author: Rohan Desai

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 12, Issue 1, January 2025 - April 2025

Page No: 30-36

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 18-January-2025

Abstract: The paper identifies the current trends and development in applying AI to increase accuracy and efficiency in the assignment of ICD code within U.S. healthcare. Researchers have used deep learning, natural language processing, and machine learning models to improve automation in this important process. This review outlines state-of-the-art methods, assesses their effectiveness using various metrics, and discusses future research directions based on the experimental results with publicly available online clinical data. Extended tables and references are provided for clarity, along with comparative analyses. Diagrams are included to explain the methodologies visually.

Keywords: AI in Healthcare, ICD Code Assignment, Deep Learning, NLP, Reinforcement Learning, Attention Mechanisms, Meta-Learning.

Title: Advancing ICD Code Assignment with AI in U.S. Healthcare: Extended Analysis and Benchmarking

Author: Rohan Desai

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 12, Issue 1, January 2025 - April 2025

Page No: 24-29

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 17-January-2025

Abstract: Cybersickness, a significant challenge in virtual reality (VR) environments, impedes the seamless adoption of immersive technologies. This study evaluates the effectiveness of various mitigation techniques, including Field of View (FoV) Adjustment, Frame Rate Optimization, Motion Smoothing, and Cognitive Behavioral Therapy (CBT), on reducing Simulator Sickness Questionnaire (SSQ) scores among participants. Data were collected from 50 individuals divided into age and gender groups to examine baseline cybersickness levels and post-intervention outcomes. Combined strategies, particularly CBT with FoV Adjustment, achieved the highest reduction of SSQ scores (49.9%). Male participants exhibited a higher baseline SSQ but responded better to mitigation, whereas females showed consistent improvement across interventions. Satisfaction rates exceeded 75% for most techniques, reflecting their practical applicability. Comparative analysis with prior studies highlighted the unique efficacy of multi-strategy approaches. Limitations included sample diversity and exposure duration, suggesting avenues for future research. These findings emphasize the critical role of tailored interventions in enhancing VR user experience and reducing cybersickness risks.

Keywords: Cybersickness, Virtual Reality, Mitigation Techniques, Cognitive Behavioral Therapy, Field of View Adjustment, Simulator Sickness Questionnaire.

Title: Mitigating Cybersickness in Virtual Reality: Challenges and Solutions

Author: Md Badrul Islam Palash, Md Fahim Islam, Foysal Ahamed

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 12, Issue 1, January 2025 - April 2025

Page No: 12-23

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 07-January-2025

Abstract: Bi-modal biometric systems integrate modalities such as palm-vein and face by fusion techniques to enhance biometric based security systems. Several techniques (especially evolutionary algorithms/swarm intelligence) have been developed and improvised as fusion techniques to reduce false positive rate and increase accuracies of biometric based recognition systems. However, these new techniques have not been adequately analyzed and compared with the conventional techniques like Weighted Sum rule. This study evaluates the performance of Chameleon Swarm Optimization (swarm intelligence algorithm) and Weighted Sum Rule as feature level fusion technique in a bi-modal recognition system. One thousand faces and palm-veins samples were collected from a university environment. The acquired images were pre-processed to remove noisy areas and Local Binary Pattern was employed to extract features. The two outputs from face and palm-vein features were fused by the selected techniques. The fused features were subjected to classification by Support Vector Machine and the performance of these techniques was evaluated and compared. The results of the evaluation at an threshold of 0.85 showed that the Chameleon Swarm Optimization achieved a false positive rate (FPR) of 5.00% and accuracy of 95.67% and at a recognition time of 169.01µs while the Weighted Sum Rule achieved a FPR of 10.00%, and an accuracy of 92.33% at a recognition time of 116.35µs.

Keywords: Chameleon Swarm Optimization, Support Vector Machine, Bi-modal biometric systems, Weighted Sum Rule, Fusion.

Title: Comparative Analysis of Chameleon Swarm Optimization and Weighted Sum Fusion Techniques in Bi-Modal Recognition System

Author: Elegbede M. Rahman, Oladapo Oladejo, Ismaila W. Oladimeji, Ismaila Folasade M.

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 12, Issue 1, January 2025 - April 2025

Page No: 1-11

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 06-January-2025