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211 results on '"Arbaiy, Nureize"'

1. Portfolio Optimization with Percentage Error-Based Fuzzy Random Data for Industrial Production

Author

Othman, Mohammad Haris Haikal, Arbaiy, Nureize, Che Lah, Muhammad Shukri, Lin, Pei-Chun, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ghazali, Rozaida, editor, Nawi, Nazri Mohd, editor, Deris, Mustafa Mat, editor, Abawajy, Jemal H., editor, and Arbaiy, Nureize, editor

Published
2024
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2. Enhanced Air Quality Index Prediction Using a Hybrid Convolutional Network

Author

Lin, Pei-Chun, Arbaiy, Nureize, Yu, Chen-Yu, Salikon, Mohd Zaki Mohd, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ghazali, Rozaida, editor, Nawi, Nazri Mohd, editor, Deris, Mustafa Mat, editor, Abawajy, Jemal H., editor, and Arbaiy, Nureize, editor

Published
2024
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3. An In-Depth Strategy using Deep Generative Adversarial Networks for Addressing the Cold Start in Movie Recommendation Systems

Author

Shahab, Muhammad, Hassim, Yana Mazwin Mohmad, Ghazali, Rozaida, Javid, Irfan, Arbaiy, Nureize, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ghazali, Rozaida, editor, Nawi, Nazri Mohd, editor, Deris, Mustafa Mat, editor, Abawajy, Jemal H., editor, and Arbaiy, Nureize, editor

Published
2024
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4. Improved Rough-Multiple Regression for Unemployment Rate Model in Indonesia

Author

Efendi, Riswan, Rejab, Mazidah Mat, Arbaiy, Nureize, Yofi, Widya T., Widyawati, Sri R., Rahmi, Izzati, Yozza, Hazmira, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ghazali, Rozaida, editor, Nawi, Nazri Mohd, editor, Deris, Mustafa Mat, editor, Abawajy, Jemal H., editor, and Arbaiy, Nureize, editor

Published
2024
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5. Fuzzy-Autoregressive Integrated Moving Average (F-ARIMA) Model to Improve Temperature Forecast

Author

Lah, Muhammad Shukri Che, Arbaiy, Nureize, Hassim, Yana Mazwin Mohmad, Lin, Pei-Chun, Yaakob, Shamshul Bahar, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ghazali, Rozaida, editor, Mohd Nawi, Nazri, editor, Deris, Mustafa Mat, editor, Abawajy, Jemal H., editor, and Arbaiy, Nureize, editor

Published
2022
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6. Effects of the Number of Network’s Order Used in a Higher Order Neural Network on Time Series Prediction

Author

Husaini, Noor Aida, Ghazali, Rozaida, Arbaiy, Nureize, Lasisi, Ayodele, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Mahyuddin, Nor Muzlifah, editor, Mat Noor, Nor Rizuan, editor, and Mat Sakim, Harsa Amylia, editor

Published
2022
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7. Firefly Algorithm for Functional Link Neural Network Learning

Author

Hassim, Yana Mazwin Mohmad, Ghazali, Rozaida, Hassan, Norlida, Arbaiy, Nureize, Mustapha, Aida, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Ab. Nasir, Ahmad Fakhri, editor, Ibrahim, Ahmad Najmuddin, editor, Ishak, Ismayuzri, editor, Mat Yahya, Nafrizuan, editor, Zakaria, Muhammad Aizzat, editor, and P. P. Abdul Majeed, Anwar, editor

Published
2022
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11. Fuzzy Coordination System for Traffic Light

Author

Shyang, Tang Ren, Pang, Adrian Loh Sheng, Arbaiy, Nureize, Haikal Othman, Mohammad Haris, Mohd Salikon, Mohd Zaki, Lin, Pei Chun, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Mohd Razman, Mohd Azraai, editor, Mat Jizat, Jessnor Arif, editor, Mat Yahya, Nafrizuan, editor, Myung, Hyun, editor, Zainal Abidin, Amar Faiz, editor, and Abdul Karim, Mohamad Shaiful, editor

Published
2020
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12. Fuzzy Random Based Mean Variance Model for Agricultural Production Planning

Author

Othman, Mohammad Haris Haikal, Arbaiy, Nureize, Lah, Muhammad Shukri Che, Lin, Pei-Chun, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Ghazali, Rozaida, editor, Nawi, Nazri Mohd, editor, Deris, Mustafa Mat, editor, and Abawajy, Jemal H., editor

Published
2020
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13. An Enhanced GRU model with Optimized Update Gate: A Novel Approach in Municipal Solid Waste Prediction.

Author

Batool, Tuba, Ghazali, Rozaida, Arbaiy, Nureize Binti, Ismail, Lokman Hakim, and Javid, Irfan

Abstract

The management of Municipal Solid Waste (MSW) establishes a decisive facet in the development of sustainable and vigorous societies that requires an adequate framework in order to prevent a risk of environmental pollution. Therefore, it's imperative to establish a robust framework to handle the generated MSW. In this regard, early predictions of waste generation with high accuracy emerges as a pivotal factor in serving the municipal authorities to formulate an effective MSW management system. Various researchers have significantly contributed to the field of early waste predictions by developing different deep learning models to attain the highly accurate predicted results. However, it is important to note that each model has its strengths and limitations. This study has predominantly focused on the enhancement of the Gated Recurrent Unit (GRU) model. The primary limitation in the standard GRU model lies in both of its gates (reset and update) processing the identical data. This redundancy has contributed to a prolonged training time and a diminished convergence rate. Therefore, this study has proposed an enhanced GRU model with optimized update gate (EGRU-OU) to address the redundancy issue that lies between the two gates. The EGRU-OU model will provide the filtered data specifically to the update gate which is instrumental in significantly reducing the redundant information between the two gates. Moreover, this study has employed two different datasets to conduct a comprehensive analysis of the results. One dataset has been collected for 16 different developed countries, whereas, the other dataset has been collected for Multan City, Pakistan. These datasets have been segmented into three subdivisions: training data, constituting 70% of the dataset for model training; testing data, comprising 15% for model evaluation; and validation data, representing the remaining 15% for additional model validation. In addition, The EGRU-OU model has been compared with other benchmark models including standard GRU model, Long Short Term Memory (LSTM) and Artificial Neural Network (ANN) model based on three distinct error metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The outcomes have clearly demonstrated a superior performance of the EGRU-OU approach as compared to other models with the least error matrices values of MAE being 0.036 and RMSE being 0.0684. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Stock Market Forecasting Model Based on AR(1) with Adjusted Triangular Fuzzy Number Using Standard Deviation Approach for ASEAN Countries

Author

Lah, Muhammad Shukri Che, Arbaiy, Nureize, Efendi, Riswan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Piuri, Vincenzo, editor, Balas, Valentina Emilia, editor, Borah, Samarjeet, editor, and Syed Ahmad, Sharifah Sakinah, editor

Published
2019
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19. Fuzzy Evaluation Scheme for KDF Based on Stream Ciphers

Author

Mohd. Rahman, Hamijah, Arbaiy, Nureize, Wen, Chuah Chai, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Ghazali, Rozaida, editor, Deris, Mustafa Mat, editor, Nawi, Nazri Mohd, editor, and Abawajy, Jemal H., editor

Published
2018
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20. An Algorithm Design of Kansei Recommender System

Author

Lin, Pei-Chun, Arbaiy, Nureize, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Ghazali, Rozaida, editor, Deris, Mustafa Mat, editor, Nawi, Nazri Mohd, editor, and Abawajy, Jemal H., editor

Published
2018
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21. Fuzzy Coordination System for Traffic Light

Author

Shyang, Tang Ren, primary, Pang, Adrian Loh Sheng, additional, Arbaiy, Nureize, additional, Haikal Othman, Mohammad Haris, additional, Mohd Salikon, Mohd Zaki, additional, and Lin, Pei Chun, additional

Published
2020
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23. Extended Local Mean-Based Nonparametric Classifier for Cervical Cancer Screening

Author

Samsudin, Noor Azah, Mustapha, Aida, Arbaiy, Nureize, Hamid, Isredza Rahmi A., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Herawan, Tutut, editor, Ghazali, Rozaida, editor, Nawi, Nazri Mohd, editor, and Deris, Mustafa Mat, editor

Published
2017
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24. Dynamic Trackback Strategy for Email-Born Phishing Using Maximum Dependency Algorithm (MDA)

Author

Hamid, Isredza Rahmi A., Samsudin, Noor Azah, Mustapha, Aida, Arbaiy, Nureize, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Herawan, Tutut, editor, Ghazali, Rozaida, editor, Nawi, Nazri Mohd, editor, and Deris, Mustafa Mat, editor

Published
2017
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25. Non-linear Based Fuzzy Random Regression for Independent Variable Selection

Author

Salikon, Mohd Zaki Mohd, Arbaiy, Nureize, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Herawan, Tutut, editor, Ghazali, Rozaida, editor, Nawi, Nazri Mohd, editor, and Deris, Mustafa Mat, editor

Published
2017
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26. One-Way ANOVA Model with Fuzzy Data for Consumer Demand

Author

Lin, Pei Chun, Arbaiy, Nureize, Hamid, Isredza Rahmi Abd., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Herawan, Tutut, editor, Ghazali, Rozaida, editor, Nawi, Nazri Mohd, editor, and Deris, Mustafa Mat, editor

Published
2017
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29. Gated Recurrent Unit for Landfill Area Estimation Based on Municipal Solid Waste Prediction.

Author

Batool, Tuba, Ghazali, Rozaida, Javid, Irfan, and Arbaiy, Nureize Binti

Subjects
SOLID waste, LANDFILLS, WASTE management, POLLUTION, INCINERATION, SOLID waste management
Abstract

Solid waste management is an imperative aspect of a hygienic environment. Lack of attention towards solid waste management leads to environmental pollution which causes catastrophic health issues and reduces the quality of life. Therefore, it's required to develop environmentally safe and adequate protocols to manage solid waste for the fortification of human health and the environment. The study presents a master plan to facilitate the local municipal authorities in developing a better waste management framework. The main aim of the research is to predict municipal solid waste (MSW) collection by using the GRU model in Multan city, Pakistan. The GRU model has utilized a dataset collected through a weighbridge at Habiba Sial landfill site (HSLS) which is the most reliable source for the data. The dataset consists of monthly data for the past five years with a total of 180 records; 60 records for months 60 records for vehicle trip numbers and 60 for collected MSW. The number of months and monthly vehicle trip numbers have been used as the input data and monthly collected MSW has been used as output data. The window of time for collected data is June 2017 through June 2022. The dataset has been divided into 3 subparts where 70% of the data has been utilized for model training, 15% of the data has been used for model testing and 15% of the data has been utilized for validation purposes. The GRU model's performance has been evaluated by using the performance metrics; mean square error (MSE), and Regression values for training, testing, and validation data. According to the results, 2-5-2-1 topology with 5 hidden neurons gives the best results with the least value of MSE being 0.0388 and the maximum value of regression being 0.94. The study also proposed a master plan for waste management, considering options like landfilling, recycling, incineration, and composting. As the landfilling method is the most common practice to dispose of waste, therefore the study estimated the required landfill area based on the predicted solid waste generation rate and its collection rate by the year 2031 for Multan city, Pakistan. The master plan can be utilized alternatively, especially for the cities with the same type of demographics which may help the municipal authorities for the development of a better solid waste management framework. [ABSTRACT FROM AUTHOR]

Published
2023
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45. Forecasting of ARIMA air pollution with improved fuzzy data preparation.

Author

Lah, Muhammad Shukri Che, Arbaiy, Nureize, and Lin, Pei-Chun

Subjects
*AIR pollution, *AIR quality management, *TIME series analysis, *FUZZY numbers, *MEASUREMENT errors, *FORECASTING
Abstract

An important way to protect public health is to forecast air quality by giving early warnings about air pollution. Management of air quality is highly dependent on data from time series collected at air monitoring stations. However, data input must be processed first in time series observations for several reasons, such as measurement errors, to prevent misleading results during forecast analysis. Data obtained from different measurements brings a degree of uncertainty that contributes to uncertainty in the data. Therefore, by constructing the Fuzzy Symmetry Triangular Fuzzy number data using the standard deviation and percentage error methods, the procedure for preparing data containing uncertain information is presented in this paper. ARIMA then uses the pre-processed data to create a predictive model. The findings of this study suggest that smaller prediction errors have resulted in the use of the proposed method to create fuzzy numbers for ARIMA. This implies that, with fewer prediction errors, the accuracy of the prediction model is higher. [ABSTRACT FROM AUTHOR]

Published
2022
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46. An improved ARIMA fitting procedure.

Author

Lah, Muhammad Shukri Che, Arbaiy, Nureize, and Lin, Pei-Chun

Subjects
*TIME series analysis, *FORECASTING
Abstract

The basic goal of time series modeling is to collect and analyze historical time-series observations to build a model that accurately describes series trends. More attention is required while fitting this newly developed forecasting model to a timely data set. The fitting of the model is very important because it allows the model to capture complex interactions between its parameters, which allows for better prediction results. Hence, this research provides a systematic ARIMA model fitting procedure. The proposed model fitting procedure is very important during the data preparation phase as it can eliminate unreliable models. The result shows that the fitting in ARIMA model buildings has a significant impact. When the experimental results and performance were compared with the benchmark results, it was found that the proposed model showed better performance in terms of accuracy. As a result, the predictive accuracy of ARIMA fitting techniques can be improved. [ABSTRACT FROM AUTHOR]

Published
2022
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47. Deep learning for football outcomes prediction based on football rating system.

Author

Razali, Nazim, Mustapha, Aida, Arbaiy, Nureize, and Lin, Pei-Chun

Subjects
SOCCER tournaments, DEEP learning, SPORTS forecasting, SOCCER, ARTIFICIAL neural networks, EDUCATIONAL outcomes, K-nearest neighbor classification
Abstract

The prediction models of association football can be categorized into three natural clusters, which are the statistical models, the machine learning and probabilistic graphical models and rating systems. The prediction may focus on matches outcomes prediction (win, draw and lose) or the number of goals scored obtained by home and away team. Match result prediction is very important as a benchmark for the management team to assess the chances of winning, drawing and losing for a particular team before the match starts. In the 2017 soccer challenge, the conventional machine learning and ensemble methods, as well as probabilistic graphical models such as k-Nearest Neighbor (k-NN), Gradient Boosting Tree (GBT) and Bayesian Networks (BN) have been a choice of researchers that participate the challenge and successfully dominate the challenge. Even so, there are some well-known techniques such as Neural Networks (NN) and Deep Neural Networks (DNN) that are left in this challenge although this technique has advantages in producing good results in many fields including solving real world problems which the results are comparable and sometimes superior to expert knowledge in some cases. In this paper, we propose a football match outcome prediction based on pi-rating system using TabNet, a DNN architecture for tabular data. The experiment cover two parts namely: (1) generates pi-rating system from 216,743 instances of raw football dataset and (2) predicts 206 football match outcomes using TabNet. As a result, the proposed prediction model based on the pi-rating system using TabNet successfully overcomes the existing model for football match outcomes for both scoring rules, in terms of accuracy and Rank Probability Score (RPS). The findings from this study are important because they can be used for future researchers in developing new football match outcome prediction models that incorporate several new features along with other features. This is important to improve the predictive performance of football prediction models using other advanced techniques. [ABSTRACT FROM AUTHOR]

Published
2022
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