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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Jawarneh, Malik
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Accurate prediction of chronic diseases using deep learning algorithms Cordova, Ronald S.; Maata, Rolou Lyn R.; Jawarneh, Malik; Alshar'e, Marwan I.; Agustin, Oliver C.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp570-583

Abstract

In this paper, the researchers studied the effects of different activation functions in hidden layers and how they impact the overfitting or underfitting of the model in the multiclass prediction of chronic diseases. This paper also evaluated the effects of varying the number of layers, and hyperparameters and its impact on the accuracy of the model and its generalization capabilities. It was found that exponential linear unit (ELU) does not have a significant advantage over rectified linear unit (ReLU) when used as an activation function in the hidden layer. Additionally, the performance of softmax function, when used in the output layer, is the same as a classic sigmoid output activation function. In terms of the ability of the model to generalize, the researchers achieved a classification accuracy of 100% when the trained model was used to predict unseen data. Through this research, the researchers should be able to assist medical professionals and practitioners in Oman in the validation and diagnosis of chronic diseases in clinics and hospitals.
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.
Co-Authors Agustin, Oliver C. Alshar'e, Marwan I. Awasthy, Mohan Bangare, Jyoti L. Bangare, Sunil L. Bhujanga Rao Madamanchi, Venkata Chikmurge, Diptee Cordova, Ronald S. F., Sammy Gupta, Gaurav J., Shreyas Kaliyaperumal, Karthikeyan Kasat, Kishori Khaleelahmed, Shaik Maata, Rolou Lyn R. Meenakshi, Meenakshi N. Phursule, Rajesh Naved, Mohd Omarov, Batyrkhan Pompapathi, Manasani Raghuvanshi, Abhishek Rane, Kantilal Pitambar S. Kharat, Reena S. Rakshe, Dhananjay Srinivas Kumar, Seepuram Veluri, Ravi Kishore