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All Journal International Journal of Artificial Intelligence Journal of Computer Science and Engineering (JCSE) Asian Journal of Science, Technology, Engineering, and Art
Gital, Abdulsalam Ya’u
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Arts Computer Science & IT Decision Sciences, Operations Research & Management Engineering Social Sciences

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Big Data Indexing: Taxonomy, Performance Evaluation, Challenges and Research Opportunities Othman, Abubakar Usman; Moses, Timothy; Aisha, Umar Yahaya; Gital, Abdulsalam Ya’u; Souley, Boukari; Adeleke, Badmos Tajudeen
Journal of Computer Science and Engineering (JCSE) Vol 3, No 2: August (2022)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In order to efficiently retrieve information from highly huge and complicated datasets with dispersed storage in cloud computing, indexing methods are continually used on big data. Big data has grown quickly due to the accessibility of internet connection, mobile devices like smartphones and tablets, body-sensor devices, and cloud applications. Big data indexing has a variety of problems as a result of the expansion of big data, which is seen in the healthcare industry, manufacturing, sciences, commerce, social networks, and agriculture. Due to their high storage and processing requirements, current indexing approaches fall short of meeting the needs of large data in cloud computing. To fulfil the indexing requirements for large data, an effective index strategy is necessary. This paper presents the state-of-the-art indexing techniques for big data currently being proposed, identifies the problems these techniques and big data are currently facing, and outlines some future directions for research on big data indexing in cloud computing. It also compares the performance taxonomy of these techniques based on mean average precision and precision-recall rate.
A Proposed Multilayer Perceptron Model and Kernel Principal Component Analysis for the Prediction of Chronic Kidney Disease Iliyas, Iliyas Ibrahim; Boukari, Souley; Gital, Abdulsalam Ya’u
International Journal of Artificial Intelligence Vol 11 No 2: December 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01102.783

Abstract

unfortunately, this stage is mostly detected at a late stage, leading to dialysis or transplantation. Early detection is important for the effective management of CKD. ML has shown success in the early prediction of CKD by using an algorithm that learns and predicts without being programmed. ML requires appropriate datasets for this process, and one of the aspects is dimensionality reduction, which addresses the challenges of unnecessary tests, high-cost tests and the use of redundant tests. Principal Component Analysis (PCA) is a widely used method for dimensionality reduction; however, it relies on linear transformation to identify relationships within features. Medical datasets such as CKD exhibit complex nonlinear features, which is important for exploring alternative dimensionality reduction methods that can rely on nonlinear transformation. This study aims to propose an ML approach that utilises kernel PCA to reduce dimensionality based on nonlinearity structures and enhance the prediction of CKD. We evaluated seven ML models on the different kernel functions of PCA. The ML models included random forest (RF), decision tree (DT), multilayer perceptron (MLP), support vector machine (SVM), extreme gradient boosting (XgBoost), adaptive boosting (AdaBoost), logistic regression (LR), and gradient boosting. The kernel functions used for dimensionality reduction are cosine principal component analysis (CPCA), polynomial principal component analysis (PPCA), radial basis principal component analysis (RPCA), sigmoid principal component analysis (SPCA) and linear principal component analysis (LPCA). The results of the study revealed that the MLP with RPCA, SPCA and CPCA achieved good performance in predicting CKD, with an accuracy score of 99% on DB1, and that the MLP with RPCA and SPCA achieved good performance in predicting CKD, with an accuracy score of 100% on DB2. The study showed how kernel PCA, which effectively reduces high dimensionality-based nonlinearity relationships, can positively affect the performance of predictive models and the power of dimensionality reduction toward disease prediction.
Modified Cardiac Arrhythmia Classification from Electrocardiography Signals Using a Convolutional Neural Network Model Abdulhafiz, Sabo; Gital, Abdulsalam Ya’u; Mohammed, Sani Sabo; Nazif, D. M.
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 4 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i4.5905

Abstract

Manual classification of cardiac arrhythmias from electrocardiogram (ECG) signals is a labor-intensive and error-prone process due to the complex and variable nature of cardiac waveforms. Convolutional Neural Networks (ConvNets), widely recognized for their success in image classification, offer a promising solution for automating this task. This study proposes an enhanced ConvNet-based approach for the classification of cardiac arrhythmias, leveraging AlexNet as a feature extractor. The features obtained from the convolutional layers are input into a backpropagation neural network for final classification. The proposed model was evaluated on four distinct arrhythmia conditions using ECG waveforms from the MIT-BIH Arrhythmia Database. Comparative analysis against traditional models revealed the superior performance of the proposed ConvNet architecture, achieving high scores across multiple evaluation metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. The feature extractor demonstrated robust performance, with classification accuracies of 1.00 and 0.99 on training and testing datasets, respectively. These findings underscore the potential of ConvNet-based models to serve as efficient, accurate, and fully automated tools for arrhythmia diagnosis, contributing significantly to advancements in cardiovascular disease detection and clinical decision support systems.
Co-Authors Abdulhafiz, Sabo Adeleke, Badmos Tajudeen Aisha, Umar Yahaya Boukari, Souley Iliyas, Iliyas Ibrahim Mohammed, Sani Sabo Moses, Timothy Nazif, D. M. Othman, Abubakar Usman Souley, Boukari