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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mukesh Kumar Tripathi
Vardhaman College of Engineering

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Identification of mango variety using near infrared spectroscopy Mukesh Kumar Tripathi; Praveen Kumar Reddy; M. Neelakantappa; Chetan Vikram Andhare; Shivendra Shivendra
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1776-1783

Abstract

The structure of the proposed framework is separated into three stages: i) foundation deduction, ii) component extraction, and iii) preparing and characterization. At first, K-implies grouping methods were carried out for foundation de- duction. The second step applies color, texture, and shape-based feature extraction methods. Finally, a “merging” fusion feature is analyzed with a C4.5, support vector machine (SVM), and K-nearest neighbors (KNN). Overall, the recognition system produces an adequate performance accuracy with 97.89, 94.60, and 90.25 percent values by utilizing C4.5, SVM, and KNN, respectively. The experimentation points out that the proposed fusion scheme can significantly support accurately recognizing various fruits and vegetables.
Pothole detection in bituminous road using convolutional neural network with transfer learning Mukesh Kumar Tripathi; Donagapure Baswaraj; Shyam Deshmukh; Kapil Misal; Nilesh P. Bhosle; Sunil Mahadev Sangve
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1950-1957

Abstract

The challenges of road maintenance, particularly in detecting potholes and cracks, and the proposed method using transfer learning and convolutional neural networks (CNNs) are significant advancements in this domain. Transfer learning is particularly beneficial, as it allows leverage pre-trained models to enhance the performance of the pothole detection system. CNNs, with their ability to capture spatial hierarchies in data, are well-suited for image-based tasks like pothole detection. The potential applications of the suggested method for intelligent transportation systems (ITS) services, such as alerting drivers about real-time potholes, demonstrate we research’s practical implications. This contributes to road safety and aligns with the broader goals of innovative city initiatives and infrastructure management. Achieving a 96% accuracy rate is a significant result, indicating the robustness of the proposed approach. Using this information to assess initial maintenance needs in a road management system is forward-thinking. Overall, we work is a valuable contribution to intelligent transportation and infrastructure management, showcasing the potential of advanced machine-learning techniques for addressing critical issues in road maintenance.
SWT-PCA-CNN: hyperspectral image classification with multi-stage feature extraction and parameter tuning Tilottama Goswami; Kandi Navya Shruthi; Sindhu Chokkarapu; Raghavendra Kune; Mukesh Kumar Tripathi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp59-68

Abstract

Hyperspectral imaging is an increasingly popular technique in remote sensing, offering a wealth of spectral information for a range of applications. This paper presents a comparative study of hyperspectral image classification techniques using three different datasets: Indian Pines, Salinas, and Pavia University. The study employs a combination of three methods, namely stationary wavelet transforms (SWT), principal component analysis (PCA), and convolutional neural network (CNN), to develop a model for hyperspectral image classification. The proposed approach combines SWT and PCA for spatial feature extraction and dimensionality reduction, followed by classification using CNN. Furthermore, the study performs parameter tuning by changing the optimizer, activation function, and filter size of the CNN model on the Indian Pines dataset. The results demonstrate that the proposed SWT-PCA-CNN approach outperforms the conventional DWT-PCA and PCA-KNN algorithms, achieving an overall classification accuracy of 98.2%, 99.86%, 99.80% on the Indian Pines, Salinas and Pavia University datasets respectively. The study highlights the effectiveness of the proposed approaches for hyperspectral image classification and their potential for applications in remote sensing and other fields.
SEM and TEM images’ dehazing using multiscale progressive feature fusion techniques Chellapilla V. K. N. S. N. Moorthy; Mukesh Kumar Tripathi; Suvarna Joshi; Ashwini Shinde; Tejaswini Kishor Zope; Vaibhavi Umesh Avachat
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp2007-2014

Abstract

We present a highly effective algorithm for image dehazing that leverages the valuable information within the hazy image to guide the haze removal process. Our proposed algorithm begins by employing a neural network that has been trained to establish a mapping between hazy images and their corresponding clear versions. This network learns to identify the shared structural elements and patterns between hazy and clear images through the training process. To enhance the utilization of guidance information from the generated reference image, we introduce a progressive feature fusion module that combines the features extracted from the hazy image and the reference image. Our proposed algorithm is an effective solution for image dehazing, as it capitalizes on the guidance information in the hazy appearance. By combining the strengths of deep learning, progressive feature fusion, and end-to-end training, we achieve impressive results in restoring clear images from hazy counterparts. The practical applicability of our algorithm is further validated by its success on benchmark data sets and real-world SEM and TEM images.
Implementing generative adversarial networks for increasing performance of transmission fault classification Tilottama Goswami; Uponika Barman Roy; Deepthi Kalavala; Mukesh Kumar Tripathi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1024-1032

Abstract

An electrical power system is a network that facilitates the sourcing, transfer, and distribution of electrical energy. In the traditional power system, there are eleven types of faults that can occur in the system. This paper focuses on the classification of these faults over a stretch of 100 kilometres. The dataset used is synthetic and generated from a simulated model using MATLAB/Simulink software. Data augmentation is carried out during training to improve the accuracy of the classification. An indirect training approach through generative adversarial network (GAN) is used to classify these overhead transmission line faults. The random forest (RF) classification is used as the base learning model on the original dataset and it achieves accuracy of 84%. However, the base learner RF when used on GAN model generated augmented faulty data, it performs exceptionally well achieving 99% accuracy. One of the recent state-of-art methods is compared with this approach.
Co-Authors Ashwini Shinde Chellapilla V. K. N. S. N. Moorthy Chetan Vikram Andhare Deepthi Kalavala Donagapure Baswaraj Kandi Navya Shruthi Kapil Misal M. Neelakantappa Nilesh P. Bhosle Praveen Kumar Reddy Raghavendra Kune Shivendra Shivendra Shyam Deshmukh Sindhu Chokkarapu Sunil Mahadev Sangve Suvarna Joshi Tejaswini Kishor Zope Tilottama Goswami Uponika Barman Roy Vaibhavi Umesh Avachat