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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Sumit Gupta
Xsys Software Technologies
Computer Science & IT Engineering

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Traffic flow optimization using reinforcement and thread-based V2X communication for smart traffic management Parul Arora; Ritika Wason; Devansh Arora; M. N. Hoda; Sumit Gupta
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4061-4070

Abstract

This research proposes an advanced framework that integrates reinforcement learning techniques with thread-based vehicle-to-everything (V2X) communication protocol to address urban traffic jams, which lead to increased delay, inefficient fuel consumption, and deteriorating air quality, posing a significant challenge to sustainable living. This framework empowers cars to “talk” directly to each other as well as with traffic signals, adapting to the real-time flow on the roads. By placing the decision-making process closer to where it’s needed, right at the network’s edge, the system is able to respond quickly to changes in traffic without relying on the usual pre-set signal cycles. Unlike many traditional traffic management solutions that are slow to adjust, the proposed framework allows vehicles and intersections to coordinate in real time. This reduces unnecessary waiting, smooths out traffic flow, and helps cut down on emissions. Through detailed simulations using the simulation of urban mobility (SUMO) traffic model, it was found that this system led to significant improvements: average waiting time was reduced by over 40%, queue lengths were cut by more than half, and vehicle throughput increased by nearly 30%. Additionally, fuel consumption dropped by approximately 24%, and carbon dioxide (CO2) emissions were reduced by more than 25% compared to existing systems.
Synergistic deep learning ensemble of DenseNet, ResNet, and Xception for accurate brain tumor diagnosis Jayashree Shedbalkar Inchal; Sumit Gupta; Annapurna V. K.; Siddesha Kempaningaiah; Anoop Ganadalu Lingaraju; Ramya Shivamadegowda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4459-4469

Abstract

Brain tumor classification plays a pivotal role in medical diagnostics, treatment planning, and patient care. Currently, the biomedical domain has experienced a surge in demand of automated computer-aided diagnosis (CAD) systems for efficient diagnosis. Recently, deep learning (DL) architectures have experienced tremendous growth as prevailing tools for automated classification tasks. This study explores the synergistic potential of combining three state-of-the-art DL architectures i.e. densely connected convolutional network (DenseNet), residual network (ResNet), and extreme Inception (Xception) for brain tumor classification. Each architecture brings unique strengths, including feature reuse, skip connections, and depth-wise separable convolutions, respectively. This study proposes an ensemble methodology that capitalizes on the distinctive strengths of each architecture. DenseNet excels in feature reuse, ResNet employs skip connections to mitigate gradient issues, Inception captures multi-scale features, and Xception employs depth-wise separable convolutions for efficiency. By integrating predictions from these diverse models, our ensemble approach aims to achieve enhanced classification accuracy and robustness. After feature extraction, an improved feature selection method is introduced and finally, an ensemble classifier is introduced which uses majority voting method to achieve the classification task. The proposed approach has reported the average score as 0.99, 0.98, 0.99, 0.98, and 0.98 for precision, recall, specificity, sensitivity, and accuracy, respectively.
Co-Authors Annapurna V. K. Anoop Ganadalu Lingaraju Devansh Arora Jayashree Shedbalkar Inchal M. N. Hoda Parul Arora Ramya Shivamadegowda Ritika Wason Siddesha Kempaningaiah