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All Journal International Journal of Advances in Intelligent Informatics
Nadzurah Zainal Abidin
Department of Computer Science, International Islamic University Malaysia
Computer Science & IT

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A particle swarm optimization levy flight algorithm for imputation of missing creatinine dataset Amelia Ritahani Ismail; Normaziah Abdul Aziz; Azrina Md Ralib; Nadzurah Zainal Abidin; Samar Salem Bashath
International Journal of Advances in Intelligent Informatics Vol 7, No 2 (2021): July 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i2.677

Abstract

Clinicians could intervene during what may be a crucial stage for preventing permanent kidney injury if patients with incipient Acute Kidney Injury (AKI) and those at high risk of developing AKI could be identified. This paper proposes an improved mechanism to machine learning imputation algorithms by introducing the Particle Swarm Levy Flight algorithm. We improve the algorithms by modifying the Particle Swarm Optimization Algorithm (PSO), by enhancing the algorithm with levy flight (PSOLF). The creatinine dataset that we collected, including AKI diagnosis and staging, mortality at hospital discharge, and renal recovery, are tested and compared with other machine learning algorithms such as Genetic Algorithm and traditional PSO. The proposed algorithms' performances are validated with a statistical significance test. The results show that SVMPSOLF has better performance than the other method. This research could be useful as an important tool of prognostic capabilities for determining which patients are likely to suffer from AKI, potentially allowing clinicians to intervene before kidney damage manifests.
An improved K-Nearest neighbour with grasshopper optimization algorithm for imputation of missing data Nadzurah Zainal Abidin; Amelia Ritahani Ismail
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.696

Abstract

K-nearest neighbors (KNN) has been extensively used as imputation algorithm to substitute missing data with plausible values. One of the successes of KNN imputation is the ability to measure the missing data simulated from its nearest neighbors robustly. However, despite the favorable points, KNN still imposes undesirable circumstances. KNN suffers from high time complexity, choosing the right k, and different functions. Thus, this paper proposes a novel method for imputation of missing data, named KNNGOA, which optimized the KNN imputation technique based on the grasshopper optimization algorithm. Our GOA is designed to find the best value of k and optimize the imputed value from KNN that maximizes the imputation accuracy. Experimental evaluation for different types of datasets collected from UCI, with various rates of missing values ranging from 10%, 30%, and 50%. Our proposed algorithm has achieved promising results from the experiment conducted, which outperformed other methods, especially in terms of accuracy.
Optimized COCOMO parameters using hybrid particle swarm optimization Noor Azura Zakaria; Amelia Ritahani Ismail; Nadzurah Zainal Abidin; Nur Hidayah Mohd Khalid; Afrujaan Yakath Ali
International Journal of Advances in Intelligent Informatics Vol 7, No 2 (2021): July 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i2.583

Abstract

Software effort and cost estimation are crucial parts of software project development. It determines the budget, time, and resources needed to develop a software project. The success of a software project development depends mainly on the accuracy of software effort and cost estimation. A poor estimation will impact the result, which worsens the project management. Various software effort estimation model has been introduced to resolve this problem. COnstructive COst MOdel (COCOMO) is a well-established software project estimation model; however, it lacks accuracy in effort and cost estimation, especially for current projects. Inaccuracy and complexity in the estimated effort have made it difficult to efficiently and effectively develop software, affecting the schedule, cost, and uncertain estimation directly. In this paper, Particle Swarm Optimization (PSO) is proposed as a metaheuristics optimization method to hybrid with three traditional state-of-art techniques such as Support Vector Machine (SVM), Linear Regression (LR), and Random Forest (RF) for optimizing the parameters of COCOMO models. The proposed approach is applied to the NASA software project dataset downloaded from the promise repository. Comparing the proposed approach has been made with the three traditional algorithms; however, the obtained results confirm low accuracy before hybrid with PSO. Overall, the results showed that PSOSVM on the NASA software project dataset could improve effort estimation accuracy and outperform other models.
Co-Authors Afrujaan Yakath Ali Amelia Ritahani Ismail Azrina Md Ralib Noor Azura Zakaria Normaziah Abdul Aziz Nur Hidayah Mohd Khalid Samar Salem Bashath