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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Raghuvanshi, Abhishek
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Electrical & Electronics Engineering

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Gaussian filter and CNN based framework for accurate detection of brain tumor by analyzing MRI images Sivakumar, S; Chaudhari, Poonam; Thatavarti, Satish; Sucharitha, G.; Mahesh, Basuthkar; Raghuvanshi, Abhishek
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.6778

Abstract

The diagnosis of cancer can be challenging and time-consuming due to the complex characteristics of tumors and inherent noise in medical imaging. The significance of early detection and localization of tumors must be considered. Radiological imaging techniques can detect and potentially forecast the presence of neoplastic growths at various phases. The expeditiousness of the diagnosis process can be notably enhanced by amalgamating these images with algorithms designed for segmentation and relegation. Early detection of tumors and accurate localization of their position are critical factors. Medical scans, when used with segmentation and relegation procedures, enable the prompt and precise detection of cancerous tumor regions. The identification of malignant tumors enables this achievement. The present article introduces a framework for detecting brain tumors based on a convolutional neural network (CNN). The initial step in processing brain magnetic resonance imaging (MRI) images involves the application of a Gaussian filter to eliminate any noise present. Subsequently, CNN and long short-term memory (LSTM) deep learning methodologies are employed to classify images. CNN has demonstrated improved accuracy in the classification and detection of brain tumors. CNN has achieved an accuracy of 99.25% in cancer image classification. The sensitivity and specificity of CNN are also 98.75% and 99.25%, respectively.
Development of IoT based intelligent irrigation system using particle swarm optimization and XGBoost techniques Santosh, D. Teja; Anuradha, Nandula; Kolukuluri, Madhavi; Gupta, Gaurav; Pathak, Mrunal Kishor; Krishnan, V. Gokula; Raghuvanshi, Abhishek
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6332

Abstract

A crop needs regular watering throughout its life to grow well. Irrigation improves food growth. Machines irrigate plants. The dry Sahel, which gets a lot of rain during the summer season but is dry in winter, needs irrigation. When it doesn't rain enough, crops need watering. By constantly monitoring soil moisture, humidity, temperature, and pH, precision agriculture reduces water use and increases crop output. Precision gardening uses less water. In many wealthy nations, efficient farming requires the internet of things (IoT). Particle swarm optimization (PSO) and XGBoost are used in this IoT-based intelligent watering system. Humidity and moisture sensors gather soil data at grass roots. Sensors constantly gather this data. These data are useless for smart watering. PSOselects smart watering data. This reduces central cloud info storage. Then, machine learning methods are trained using soil humidity, moisture, crop, and weather data. These programs can calculate a crop's water requirements. IoT devices control irrigation system water flow and results in saving fresh water. XGBoost algorithm is saving water from 23% to 27% for different crops.
Effective crop categorization using wavelet transform based optimized long short-term memory technique Pompapathi, Manasani; Khaleelahmed, Shaik; Jawarneh, Malik; Naved, Mohd; Awasthy, Mohan; Srinivas Kumar, Seepuram; Omarov, Batyrkhan; Raghuvanshi, Abhishek
Bulletin of Electrical Engineering and Informatics 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/eei.v14i3.7748

Abstract

Effective crop categorization is important for keeping track of how crops grow and how much they produce in the future. Gathering crop data on categories, regions, and space distribution in a timely and accurate way could give a scientifically sound reason for changes to the way crops are organized. Polarimetric synthetic aperture radar dataset provides sufficient information for accurate crop categorization. It is essential to classify crops in order to successfully. This article presents wavelet transform (WT) based optimizedlong short-term memory (LSTM) deep learning (DL) for effective crop categorization. Image denoising is performed by WT. Denoising algorithms for images attempt to find a middle ground between totally removing all of the image’s noise and preserving essential, signal-free components of the picture in their original state. After denoising of images, crop image classification is achieved by LSTM and support vector machine (SVM) algorithm. LSTM has achieved 99.5% accuracy.
Chaotic grey wolf optimization based framework for efficient task scheduling in cloud fog computing J., Shreyas; S. Kharat, Reena; N. Phursule, Rajesh; Bhujanga Rao Madamanchi, Venkata; S. Rakshe, Dhananjay; Gupta, Gaurav; Jawarneh, Malik; F., Sammy; Raghuvanshi, Abhishek
Bulletin of Electrical Engineering and Informatics 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/eei.v14i3.8098

Abstract

Task scheduling is an essential component of any cloud computing architecture that seeks to cater to the requirements of its users in the most effective manner possible. It is essential in the process of assigning resources to new jobs while simultaneously optimising performance. Effective job scheduling is the only method by which it is possible to achieve the essential goals of any cloud computing architecture, including high performance, high profit, high utilisation, scalability, provision efficiency, and economy. This article gives a framework based on chaotic grey wolf optimization (CGWO) for efficiently scheduling tasks in cloud fog computing. Task scheduling is done with CGWO, ant colony optimization (ACO), and min-max algorithms. CloudSim is used to implement task scheduling algorithms. Makespan time required by CGWO algorithm for 500 tasks is 73.27 seconds. CGWO is taking minimum resources to accomplish the tasks in comparison to ACO and min-max methods. Response time of CGWO is also 3745.2 seconds. CGWO is performing better in terms of Makespan time, response time and resource utilization among the methods used in the experimental work.
Design of face recognition based effective automated smart attendance system Bangare, Jyoti L.; Chikmurge, Diptee; Kaliyaperumal, Karthikeyan; Meenakshi, Meenakshi; Bangare, Sunil L.; Kasat, Kishori; Rane, Kantilal Pitambar; Veluri, Ravi Kishore; Omarov, Batyrkhan; Jawarneh, Malik; Raghuvanshi, Abhishek
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp2020-2030

Abstract

The issue of automatic attendance marking has been successfully resolved in recent years through the utilization of standard biometric approaches. Although this strategy is automated and forward-thinking, its use is hindered by time constraints. Acquiring a thumb impression requires the individual to form a line, which might lead to inconvenience. The innovative visual system utilizes a computer and camera to detect and record students’ attendance based on their facial features. This article presents a face recognition based automatic attendance system. This system includes- image acquisition, image enhancement using histogram equalization, image segmentation by fuzzy C means clustering technique, building classification model using K-nearest neighbour (KNN), support vector machine (SVM) and AdaBoost technique. For experimental work, 500 images of students of a class are selected at random. Accuracy of KNN algorithm in proposed framework is 98.75%. It is higher than the accuracy of SVM (96.25%) and AdaBoost (86.50%). KNN is also performing better on parameters likesensitivity, specificity, precision and F_measure.
Enhanced deep auto encoder technique for brain tumor classification and detection Badashah, Syed Jahangir; Moholkar, Kavita; Bangare, Sunil L.; Gupta, Gaurav; T., Devi; Francis, Sammy; Hariram, Venkatesan; Omarov, Batyrkhan; Rane, Kantilal Pitambar; Raghuvanshi, Abhishek
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp2031-2040

Abstract

A brain tumor can develop due to uncontrolled proliferation of aberrant cells in brain tissue. Malignant tumor can influence the nearby brain tissues, potentially resulting in the person's death. Early diagnosis of a brain tumor is crucial for ensuring the survival of patients. This article introduces an improved method using a deep auto encoder for the classification and detection of brain tumor. Magnetic resonance imaging (MRI) images are obtained from the BraTS data sets. The images undergo preprocessing using an adaptive Wiener filter. Image preprocessing is essential for eliminating noise from the input MRI pictures, hence enhancing the accuracy of MRI image classification. The fuzzy C-means technique is used to accomplish image segmentation. The classification model comprises deep auto encoder, convolution neural network (CNN), and K-nearest neighbor techniques. The classification model is developed and evaluated using MRI image slices from the BraTS dataset. Accuracy of deep auto encoder is 98.81%. Accuracy of CNN is 95.50 and accuracy of K-nearest neighbor (KNN) technique is 91.30%.
CLAHE-AlexNet optimized deep learning model for accurate detection of diabetic retinopathy G., Swetha; Gupta, Gaurav; Rane, Kantilal Pitambar; Ghag, Omkar M.; Korde, Sachin K.; Lalar, Sachin; Omarov, Batyrkhan; Raghuvanshi, Abhishek
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.7854

Abstract

Diabetic retinopathy (DR) is a disease that affects the blood vessels that are located in the retina. Loss of vision due to diabetes is a common consequence of the illness and a key factor in the progression of vision loss and blindness. Both ophthalmology and diabetes research have become more dependent on computer vision and image processing techniques in recent years. Fundus photography, also known as a fundus image, is a method that may be used to capture an image of the back of a person's eye. This article presents optimized deep learning model for diagnostic marking in retinal fundus images towards accurate detection of retinopathy. For experimental work, 500 images were selected from available open source Kaggle data set. 400 images were used to train deep learning model and remaining 100 images were used to validate the model. Images were enhanced using the contrast limited adaptive histogram equalization (CLAHE) algorithm. Pre trained convolutional neural network (CNN) models-AlexNet, VGG16, GoogleNet, and ResNet are used for classification and prediction of images. Accuracy, specificity, precision and F1-score of AlexNet is better than VGG16, ResNet-50, and GoogleNet. Sensitivity of ResNet-50 is higher than other pre trained CNN models.
Task scheduling algorithm using grey wolf optimization technique in cloud computing environment Khaleelahmed, Shaik; Selvaraj, Sivakumar; Mohite, Rajendra B.; Bangare, Manoj L.; Bangare, Pushpa M.; Kulkarni, Shriram S.; Ajibade, Samuel-Soma M.; Raghuvanshi, Abhishek
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.7695

Abstract

Scheduling refers to the process of allocating cloud resources to several users according to a schedule that has been established in advance. It is not possible to get acceptable performance in settings that are distributed without proper planning for simultaneous processes. When developing productive schedules in the cloud, it is necessary for work scheduling to take a variety of constraints and goals into consideration.When dealing with activities that have performance optimization limits, resource allocation is a very important aspect to consider. When it comes to cloud computing, the only way to achieve great performance, high profits, high scalability, efficient provisioning, and cost savings is with an exceptional task scheduling system. This article presents a grey wolf optimization (GWO) based framework for efficient task scheduling in cloud computing environment. The proposed algorithm is compared with particle swarm optimization (PSO) and flower pollination algorithm (FPA) and GWO is performing task scheduling in less execution time and cost in comparison with PSO and FPA techniques. Execution time taken by GWO to finish 200 task in 120.2 ms. It is less than the time taken by PSO and FPA algorithm to finish same number of tasks.
An efficient course recommendation system for higher education students using machine learning techniques M. Arcinas, Myla; Meenakshi, Meenakshi; S. Bahalkar, Pranjali; Bhaturkar, Deepali; Lalar, Sachin; Pitambar Rane, Kantilal; Garg, Shaifali; Omarov, Batyrkhan; Raghuvanshi, Abhishek
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.7711

Abstract

Education institutions and teachers are in desperate need of automated, non-intrusive means of getting student feedback that would allow them to better understand the learning cycle and assess the success of course design. Students would benefit from a framework that intelligently guides their actions and provides exercises or resources to support and enhance their learning. The recommender system framework is a software agent that learns the user's preferences through a variety of channels and then utilizes that knowledge to provide product suggestions. A recommendation engine considers all potential user interests as background information, uses that knowledge to produce convincing recommendations, and then returns those ideas to the user. This article presents a feature selection and machine learning based course recommendation system for higher education students. principal component analysis (PCA) algorithm is used for feature selection. AdaBoost, k nearest neighbour (KNN), and Naïve Bayes algorithms are used to classify and predict student data. It is found that the AdaBoost algorithm is having better accuracy and F1 score for course recommendation to students. PCA AdaBoost is achieving an accuracy of 99.5%.
Co-Authors Ajibade, Samuel-Soma M. Anuradha, Nandula Awasthy, Mohan Badashah, Syed Jahangir Bangare, Jyoti L. Bangare, Manoj L. Bangare, Pushpa M. Bangare, Sunil L. Bhaturkar, Deepali Bhujanga Rao Madamanchi, Venkata Chaudhari, Poonam Chikmurge, Diptee F., Sammy Francis, Sammy G., Swetha Garg, Shaifali Ghag, Omkar M. Gupta, Gaurav Hariram, Venkatesan J., Shreyas Jawarneh, Malik Kaliyaperumal, Karthikeyan Kasat, Kishori Khaleelahmed, Shaik Kolukuluri, Madhavi Korde, Sachin K. Krishnan, V. Gokula Kulkarni, Shriram S. Lalar, Sachin M. Arcinas, Myla Mahesh, Basuthkar Meenakshi, Meenakshi Mohite, Rajendra B. Moholkar, Kavita N. Phursule, Rajesh Naved, Mohd Omarov, Batyrkhan Pathak, Mrunal Kishor Pitambar Rane, Kantilal Pompapathi, Manasani Rane, Kantilal Pitambar S. Bahalkar, Pranjali S. Kharat, Reena S. Rakshe, Dhananjay Santosh, D. Teja Selvaraj, Sivakumar Sivakumar, S Srinivas Kumar, Seepuram Sucharitha, G. T., Devi Thatavarti, Satish Veluri, Ravi Kishore