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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Noor Hidayah Zakaria
Universiti Teknologi Malaysia
Computer Science & IT Engineering

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Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification Nur Azieta Mohamad Aseri; Mohd Arfian Ismail; Abdul Sahli Fakharudin; Ashraf Osman Ibrahim; Shahreen Kasim; Noor Hidayah Zakaria; Tole Sutikno
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp50-64

Abstract

The world health organization (WHO) proclaimed the COVID-19, commonly known as the coronavirus disease 2019, was a pandemic in March 2020. When people are in close proximity to one another, the virus spreads mostly through the air. It causes some symptoms in the affected person. COVID-19 symptoms are quite variable, ranging from none to severe sickness. As a result, the fuzzy method is seen favourably as a tool for determining the severity of a person’s COVID-19 sickness. However, when applied to a large situation, manually generating a fuzzy parameter is challenging. This could be because of the identification of a large number of fuzzy parameters. A mechanism, such as an automatic procedure, is consequently required to identify the right fuzzy parameters. The metaheuristic algorithm is regarded as a viable strategy. Five meta-heuristic algorithms were analyzed and utilized in this article to classify the severity of COVID-19 sickness data. The performance of the five meta-heuristic algorithms was evaluated using the COVID-19 symptoms dataset. The COVID-19 symptom dataset was created in accordance with WHO and the Indian ministry of health and family welfare criteria. The findings provide the average classification accuracy for each approach.
A systematic literature review of machine learning methods in predicting court decisions Nur Aqilah Khadijah Rosili; Noor Hidayah Zakaria; Rohayanti Hassan; Shahreen Kasim; Farid Zamani Che Rose; Tole Sutikno
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp1091-1102

Abstract

Envisaging legal cases’ outcomes can assist the judicial decision-making process. Prediction is possible in various cases, such as predicting the outcome of construction litigation, crime-related cases, parental rights, worker types, divorces, and tax law. The machine learning methods can function as support decision tools in the legal system with artificial intelligence’s advancement. This study aimed to impart a systematic literature review (SLR) of studies concerning the prediction of court decisions via machine learning methods. The review determines and analyses the machine learning methods used in predicting court decisions. This review utilised RepOrting Standards for Systematic Evidence Syntheses (ROSES) publication standard. Subsequently, 22 relevant studies that most commonly predicted the judgement results involving binary classification were chosen from significant databases: Scopus and Web of Sciences. According to the SLR’s outcomes, various machine learning methods can be used in predicting court decisions. Additionally, the performance is acceptable since most methods achieved more than 70% accuracy. Nevertheless, improvements can be made on the types of judicial decisions predicted using the existing machine learning methods.
Video steganography using 3D distance calculator based on YCbCr color components Esraa Jaffar Baker; Adil Abbas Majeed; Sundos Abdulameer Alazawi; Shahreen Kasim; Rohayanti Hassan; Noor Hidayah Zakaria; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp831-842

Abstract

Steganography techniques have taken a major role in the development in the field of transferring multimedia contents and communications. Therefore, field of steganography become interested as the need for security increased significantly. Steganography is a technique to hide information within cover media so that this media does not change significantly. Steganography process in a video is to hide the information from the intruder and prevent him access to that hidden information. This paper presents the algorithm of steganography in the video frames. The proposed algorithm selected the best frames to hide the message in video using 3D distance equation to increasing difficulty onto the intruder to detect and guess the location of the message in the video frames. As well as selected the best frames in this algorithm increased the difficulty and give us the best stego-video quality using structural similarity (SSIM). Also, the hash function was used to generate random positions to hide the message in the lines of video frames. The proposed algorithm evaluated with mean squared error (MSE), peak signalto-noise ratio (PSNR) and SSIM measurement. The results were acceptable and shows that is the difficulty of distinguishing the hidden message in stego-video with the human eye.
Co-Authors Abdul Sahli Fakharudin Adil Abbas Majeed Ashraf Osman Ibrahim Esraa Jaffar Baker Farid Zamani Che Rose Mohd Arfian Ismail Nur Aqilah Khadijah Rosili Nur Azieta Mohamad Aseri Rohayanti Hassan Shahreen Kasim Sundos Abdulameer Alazawi Tole Sutikno