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All Journal Bulletin of Electrical Engineering and Informatics CommIT (Communication & Information Technology) JOIV : International Journal on Informatics Visualization
Su-Cheng Haw
Faculty of Information Technology, Multimedia University, Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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Analysis of multi-criteria recommendation system based on fuzzy algorithm Anaam, Elham Abdulwahab; Haw, Su-Cheng; Ng, Kok-Why; Naveen, Palanichamy; Tong, Gee-Kok
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7801

Abstract

There is a gap in defining the multi-criteria decision-making issues and with recommendation techniques and theories that can help develop the modulation coefficient recommenders. The main objective of this research is to identify an in-depth examination of the category of multiple variables recommendation systems. The methodology that is used in the current study is fuzzy multi-critical decision-making to enhance the precision and appropriateness of the recommendations provided to users, and make recommendations by representing an individual's performance for the product as an ordered collection of rankings in addition to different parameters. The techniques used to make forecasts and produce recommendations using multi-criteria rankings are reviewed. In addition, we propose the multiple-criteria ranking algorithms. Experimental evaluations demonstrated that our proposed algorithms can solve the multi-criteria issues. Furthermore, the research considers unresolved problems and upcoming difficulties for the category of recommendations for multiple variables ratings.
Chronic disease prediction chatbot using deep learning and machine learning algorithms Sia, Mandy; Ng, Kok-Why; Haw, Su-Cheng; Jayaram, Jayapradha
Bulletin of Electrical Engineering and Informatics 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/eei.v14i1.8462

Abstract

Ever since the rise of human civilization, more and more diseases have been discovered with the rapid growth of medical knowledge. This sheer volume of information makes it hard for humans to memorize or even utilize it efficiently. Thus, machine learning emerged as a powerful tool for complex calculations by offering a solution to this challenge. This paper intends to use deep learning and machine learning algorithms to develop a predictive model that can recognize potential diseases based on symptoms. The model is then seamlessly integrated into a text-based disease prediction assistant chatbot that serves as a communication platform between the users and the system. The algorithms researched for the disease prediction models are k-nearest neighbours (KNN), support vector machines (SVM), random forest, and neural networks. After that, a chatbot application is created by integrating long short-term memory (LSTM), natural language toolkit (NLTK) libraries, and Telegram. As a result, the SVM models demonstrated excellent performance by achieving an accuracy of 92.24%, closely followed by random forest with 92.23%, KNN with 91.57%, and artificial neural network (ANN) with 91.52% accuracy. In short, this paper presents a potential solution for a more accurate disease prediction tool by implementing the best disease prediction model with the chatbot models together.
Co-Authors Abdulwahab Anaam, Elham Anaam, Elham Abdulwahab Arshad, Mohammad Syafwan Azri, Muhammad Haziq Fikri Bin Baharum, Zirawani Hairulnizam Mahdin, Hairulnizam Hassan, Azman Heru Nurwarsito Horn Boe, Chang Jayaram, Jayapradha Kamri, Khairol Anuar Kesavan, Revathi Lucia D. Krisnawati Mahastama, Aditya Mahastama, Aditya W. Muhammad Syafiq Chelvam, Nor Liyana Natasha Naveen, Palanichamy Ng, Kok-Why Palanichamy, Naveen Saad, Mohamad Firdaus Mat Sia, Mandy Tong, Gee-Kok