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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Gaddadevara Matt, Siddesh
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Computer Science & IT Electrical & Electronics Engineering Engineering

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A novel pairwise based convolutional neural network for image preprocessing enhancement Ravi, Chaitra; Gaddadevara Matt, Siddesh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4095-4105

Abstract

Wildfires are untamable and devastating forces that impact both urban and rural regions. While predicting wildfires is challenging, efforts are made to mitigate the damage they inflict. The previous researches have limitations such as not being able to find a small region of fire in the dataset. In this research, pairwise region-based convolutional neural network (PR-CNN) is proposed for wildfire detection. The dataset used for wildfire detection is the fire luminosity airborne-based machine learning evaluation (FLAME) dataset that is pre-processed through normalization and hue, saturation, and lightness (HSV) color space to improve the image quality. Pre-processed images are taken as input to region-based convolutional neural network (R-CNN) for detection, the R-CNN has a region proposal layer that is enhanced by pairwise region and named PR-CNN. These wrapped images are fed into CNN architecture to extract and features to detect wildfire. Additionally, post processing technique like soft-non-maximum suppression (NMS) is utilized to eliminate the duplicate detection from PR-CNN for enhancing the detection accuracy. The proposed method achieves a higher accuracy of 97.44%, a precision of 97.32%, recall of 97.31%, and f1-score of 96.67%, which is comparatively superior to the existing algorithms like recurrent neural network (RNN), and R-CNN.
IC-CGAN: Imbalanced class-conditional generative adversarial network with weighted loss function Ravi, Chaitra; Gaddadevara Matt, Siddesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1632-1646

Abstract

This research proposes an advanced deep learning model that deals with the over-distribution of plant leaf disease classes by using an imbalanced class-conditional generative adversarial network (IC-CGAN) that is coupled with a weighted loss function. IC-CGAN model provides a solution to class imbalance through the synthesis of tomato leaf disease images and adding them to the dataset which as a consequence, improves the accuracy of disease detection. The weighted loss function essentially does a crucial job of solving the problem of imbalance in class during the training stage. Mixing of these models leads to the generation of realistic leaf disease synthetic images and balancing class distribution in the dataset, hence improving of tomato disease detection model’s accuracy. This study is another step toward the development of effective disease detection systems for agricultural purposes by addressing the concern of class imbalance with IC-CGAN through the vector-weighted loss function. The proposed IC-CGAN has a high chance of enhancing the disease detection at its early stage with a much higher level of accuracy (99.95%), precision (99.98%), recall (99.98%) and F1-score (99.98%) in tomato plant leaf disease detection.
Effective task allocation in fog computing environments using fractional selectivity model Kannughatta Ranganna, Prasanna Kumar; Gaddadevara Matt, Siddesh; Babu Jayachandra, Ananda; Kumara Mahadevachar, Vasantha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2444-2458

Abstract

In recent scenario, fog computing is a new technology deployed between cloud computing systems and internet of things (IoT) devices to filter out important information from a massive amount of collected IoT data. Cloud computing offers several advantages, but also has the disadvantages of high latency and network congestion, when processing a vast amount of data collected from various devices and sources. For overcoming these problems in fog computing environments, an efficient model is proposed in this article for precise load balancing (LB). The proposed fractional selectivity model significantly handles LB in fog computing by reducing network bandwidth consumption, latency, task-waiting time, and also enhances the quality of experience. The proposed model allocates the required resources by eliminating sleepy, unreferenced, and long-time inactive services. The fractional selectivity model’s performance is investigated on three application scenarios, namely virtual reality (VR) game, electroencephalogram (EEG) healthcare, and toy game. The efficiency of the introduced model is analyzed on the basis of makespan, average resource utilization (ARU), load balancing level (LBL), total cost, delay, and energy consumption. Specifically, in comparison to the traditional task allocation models, the proposed model reduces almost 5 to 15% of the total cost and makespan time.
Co-Authors Babu Jayachandra, Ananda Kannughatta Ranganna, Prasanna Kumar Kumara Mahadevachar, Vasantha Ravi, Chaitra