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All Journal International Journal of Artificial Intelligence The Indonesian Journal of Computer Science
Boukari, Souley
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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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.
An Encryption Approach Based on Formal Method for Securing Distributed Big Data Storage in Cloud Environment Hassan, Anah Bijik; Boukari, Souley; Yau Gital, Abdulsalam; Abdulhamid, Mohammed
The Indonesian Journal of Computer Science Vol. 12 No. 1 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i1.3157

Abstract

Cloud computing is the technology used to store massive amounts of data (Big data) on Servers which locations are unknown to the user of the service. One of the major source of concern in security and privacy is the fact that cloud operators have chances to reach the sensitive data. This makes Cloud computing adoption by organizations with users’ sensitive details including the banking sector and government agencies marred by resentment. Therefore, a cryptography approach is proposed, named Secured Efficient Distributed Storage SecDcloud) model that is designed to obtain an efficient mass distributed service, and is supported by the Modified Alternative Data Distribution (MAD2) Algorithm Modified Secured Data distribution (MSED2) Algorithm as well as Improved Data Conflation (IDCon) Algorithm based on formal method. Our proposed mechanism aims to split, encrypt sensitive data and store the data to the different cloud servers without causing big overheads using formal method which prevents cloud service providers from directly accessing the user’s data. Our experiments, evaluated on security and efficiency of our technique, and is compared with state of the art Advanced Encryption Standard(AES) Algorithm and the results show that it is capable of effectively defending against the most common cloud-based threats while still maintaining a reasonable amount of processing time.
Co-Authors Abdulhamid, Mohammed Gital, Abdulsalam Ya’u Hassan, Anah Bijik Iliyas, Iliyas Ibrahim Yau Gital, Abdulsalam