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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Haladappa, Manjula Sunkadakatte
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Reinforcement learning-empowered resource allocation with multi-head attention mechanism in V2X networks Khan, Irshad; Haladappa, Manjula Sunkadakatte
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5691-5700

Abstract

Intelligent transport systems (ITS) offer safe and autonomous service in vehicular applications. The vehicle to everything (V2X) network aids in performing communication between any vehicle to other entities such as networks, pedestrians or other objects. However, the allocation of power in the V2X network is still seen as a challenging task in recent resource allocation approaches. So, multi-head attention mechanism with reinforcement learning (MHAMRL) is utilized in resource allocation. This work considers real traffic scenes in highway traffic model and wireless transmission model. Specifically, in the mode 4 cellular V2X, every individual vehicle is considered as a resource which does not rely on the base station for resource allocation. Vehicle users are classified into V2I or V2V links based on the varied service requirements of V2X. The combination of multi-head attention mechanism sequences the signal with minimal noises which diminishes the energy consumption and improves channel gain. In the velocity range of 20-25 m/s, the proposed approach achieves a sum rate of 53 Mb/s, surpassing the 50 Mb/s achieved by the existing multi-agent deep reinforcement learning-based attention mechanism (AMARL) algorithm.
Comparative study of deep learning approaches for cucumber disease classification Shivaraj, Supreetha; Haladappa, Manjula Sunkadakatte
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp554-563

Abstract

Cucumber leaf diseases, such as downy mildew and leaf miner, pose significant challenges to crop yield and quality. Accurate and timely detection is essential to efficient management. The current research assesses seven convolutional neural network (CNN) models for the classification of diseases of cucumber leaves: DenseNet121, InceptionV3, ResNet50V2, VGG16, Xception, MobileNetV2, and NASNet. The dataset includes images from the cucumber disease recognition dataset (Mendeley) and 500 real-time images captured between December 2022 and February 2023 in Karnataka, covering varied lighting conditions. After augmentation, the dataset is divided into testing, validation, and training sets and includes 804 leaf miner, 807 downy mildew, and 804 healthy images. With an overall test accuracy of 99.37% and nearly flawless precision, recall, and F1-scores in every class, ResNet50V2 showed exceptional performance. InceptionV3 and MobileNetV2 also exhibited strong performance with accuracies of 97.29% and 97.70%, respectively. DenseNet121, VGG16, Xception, and NASNet performed well but were slightly outperformed by the top models. The findings indicate ResNet50V2 as the most reliable model for cucumber leaf disease classification, providing a robust foundation for developing automated disease detection systems. This work demonstrates how precise disease detection using deep learning models can improve agricultural management.
Optimizing sparse ternary compression with thresholds for communication-efficient federated learning Murthy Chittaiah, Nithyanianjan; Haladappa, Manjula Sunkadakatte
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4902-4912

Abstract

Federated learning (FL) enables decentralized model training while preserving client data privacy, yet suffers from significant communication overhead due to frequent parameter exchanges. This study investigates how varying sparse ternary compression (STC) thresholds impact communication efficiency and model accuracy across the CIFAR-10 and MedMNIST datasets. Experiments tested thresholds ranging from 1.0 to 1.9 and batch sizes of 10, 15, and 20. Results demonstrated that selecting thresholds between 1.2 and 1.5 reduced total communication costs by approximately 10–15%, while maintaining acceptable accuracy levels. These findings suggest that careful threshold tuning can achieve substantial communication savings with minimal compromise in model performance, offering practical guidance for improving the efficiency and scalability of FL systems.
Co-Authors Khan, Irshad Murthy Chittaiah, Nithyanianjan Shivaraj, Supreetha