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Your search keyword '"Jaafar, Jafreezal"' showing total 169 results
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169 results on '"Jaafar, Jafreezal"'

3. Question Guru: An Automated Multiple-Choice Question Generation System

Author

Gilal, Abdul Rehman, Waqas, Ahmad, Talpur, Bandeh Ali, Abro, Rizwan Ali, Jaafar, Jafreezal, Amur, Zaira Hassan, 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, Al-Sharafi, Mohammed A., editor, Al-Emran, Mostafa, editor, Al-Kabi, Mohammed Naji, editor, and Shaalan, Khaled, editor

Published
2023
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4. Do We Use the Right Elements for Assurance Case Development?

Author

Gilal, Abdul Rehman, Palli, Abdul Sattar, Jaafar, Jafreezal, Talpur, Bandeh Ali, Waqas, Ahmad, Gilal, Ruqaya, 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, Al-Sharafi, Mohammed A., editor, Al-Emran, Mostafa, editor, Al-Kabi, Mohammed Naji, editor, and Shaalan, Khaled, editor

Published
2023
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6. Machine Learning in Healthcare: Current Trends and the Future

Author

Usmani, Usman Ahmad, Jaafar, Jafreezal, 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, Ibrahim, Rosdiazli, editor, K. Porkumaran, editor, Kannan, Ramani, editor, Mohd Nor, Nursyarizal, editor, and S. Prabakar, editor

Published
2022
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7. Enhanced Reinforcement Learning Model for Extraction of Objects in Complex Imaging

Author

Usmani, Usman Ahmad, Roy, Arunava, Watada, Junzo, Jaafar, Jafreezal, Aziz, Izzatdin Abdul, 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, and Arai, Kohei, editor

Published
2022
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11. Finding an Effective Classification Technique to Develop a Software Team Composition Model

Author

Gilal, Abdul Rehman, Jaafar, Jafreezal, Capretz, Luiz Fernando, Omar, Mazni, and Aziz, Izzatdin Abdul

Subjects
Computer Science - Software Engineering
Abstract

Ineffective software team composition has become recognized as a prominent aspect of software project failures. Reports from results extracted from different theoretical personality models have produced contradicting fits, validity challenges, and missing guidance during software development personnel selection. It is also believed that the technique/s used while developing a model can impact the overall results. Thus, this study aims to: 1) discover an effective classification technique to solve the problem, and 2) develop a model for composition of the software development team. The model developed was composed of three predictors: team role, personality types, and gender variables; it also contained one outcome: team performance variable. The techniques used for model development were logistic regression, decision tree, and Rough Sets Theory (RST). Higher prediction accuracy and reduced pattern complexity were the two parameters for selecting the effective technique. Based on the results, the Johnson Algorithm (JA) of RST appeared to be an effective technique for a team composition model. The study has proposed a set of 24 decision rules for finding effective team members. These rules involve gender classification to highlight the appropriate personality profile for software developers. In the end, this study concludes that selecting an appropriate classification technique is one of the most important factors in developing effective models., Comment: 12 pages

Published
2017
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18. Online Machine Learning from Non-stationary Data Streams in the Presence of Concept Drift and Class Imbalance: A Systematic Review

Author

Palli, Abdul Sattar, Jaafar, Jafreezal, Gilal, Abdul Rehman, Alsughayyir, Aeshah, Gomes, Heitor Murilo, Alshanqiti, Abdullah, Omar, Mazni, Palli, Abdul Sattar, Jaafar, Jafreezal, Gilal, Abdul Rehman, Alsughayyir, Aeshah, Gomes, Heitor Murilo, Alshanqiti, Abdullah, and Omar, Mazni

Abstract

In IoT environment applications generate continuous non-stationary data streams with in-built problems of concept drift and class imbalance which cause classifier performance degradation. The imbalanced data affects the classifier during concept detection and concept adaptation. In general, for concept detection, a separate mechanism is added in parallel with the classifier to detect the concept drift called a drift detector. For concept adaptation, the classifier updates itself or trains a new classifier to replace the older one. In case, the data stream faces a class imbalance issue, the classifier may not properly adapt to the latest concept. In this survey, we study how the existing work addresses the issues of class imbalance and concept drift while learning from nonstationary data streams. We further highlight the limitation of existing work and challenges caused by other factors of class imbalance along with concept drift in data stream classification. Results of our survey found that, out of 1110 studies, by using our inclusion and exclusion criteria, we were able to narrow the pool of articles down to 35 that directly addressed our study objectives. The study found that issues such as multiple concept drift types, dynamic class imbalance ratio, and multi-class imbalance in presence of concept drift are still open for further research. We also observed that, while major research efforts have been dedicated to resolving concept drift and class imbalance, not much attention has been given to with-in-class imbalance, rear examples, and borderline instances when they exist with concept drift in multi-class data. This paper concludes with some suggested future directions.

Published
2024

19. A Set of Rules for Constructing Gender-Based Personality Types’ Composition for Software Programmer

Author

Gilal, Abdul Rehman, Jaafar, Jafreezal, Omar, Mazni, Basri, Shuib, Aziz, Izzat Din Abdul, 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, Martin, 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, Abawajy, Jemal H., editor, Othman, Mohamed, editor, Ghazali, Rozaida, editor, Deris, Mustafa Mat, editor, Mahdin, Hairulnizam, editor, and Herawan, Tutut, editor

Published
2019
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20. A Survey on Signal Processing Methods in Fiber Optic Sensor for Oxidized Carbon Steel

Author

Jaafar, Nur Syakirah Mohd, Aziz, Izzatdin Abdul, Jaafar, Jafreezal, Mahmood, Ahmad Kamil, Gilal, Abdul Rehman, 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, and Silhavy, Radek, editor

Published
2019
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21. An Enhance Approach of Filtering to Select Adaptive IMFs of EEMD in Fiber Optic Sensor for Oxidized Carbon Steel

Author

Mohd Jaafar, Nur Syakirah, Aziz, Izzatdin Abdul, Jaafar, Jafreezal, Mahmood, Ahmad Kamil, Gilal, Abdul Rehman, 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, and Silhavy, Radek, editor

Published
2019
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22. Evaluation of Machine Learning Algorithms in Predicting Internal Corrosion in Oil and Gas Pipelines

Author

Mohammad Zubir, Wan Mohammad Aflah, Abdul Aziz, Izzatdin, Jaafar, Jafreezal, 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, Silhavy, Radek, editor, Silhavy, Petr, editor, and Prokopova, Zdenka, editor

Published
2019
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23. An Approach of Filtering to Select IMFs of EEMD in Acoustic Emission [AE] Sensors for Oxidized Carbon Steel

Author

Jaafar, Nur Syakirah Mohd, Aziz, Izzatdin Abdul, Jaafar, Jafreezal, Mahmood, Ahmad Kamil, 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, Silhavy, Radek, editor, Silhavy, Petr, editor, and Prokopova, Zdenka, editor

Published
2019
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24. An Approach of Filtering to Select IMFs of EEMD in Signal Processing for Acoustic Emission [AE] Sensors

Author

Mohd Jaafar, Nur Syakirah, Aziz, Izzatdin Abdul, Jaafar, Jafreezal, Mahmood, Ahmad Kamil, 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, Silhavy, Radek, editor, Silhavy, Petr, editor, and Prokopova, Zdenka, editor

Published
2019
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25. Inference Algorithms in Latent Dirichlet Allocation for Semantic Classification

Author

Mohammad Zubir, Wan Mohammad Aflah, Abdul Aziz, Izzatdin, Jaafar, Jafreezal, Hasan, Mohd Hilmi, 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, Silhavy, Radek, editor, Silhavy, Petr, editor, and Prokopova, Zdenka, editor

Published
2018
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26. A Rough-Fuzzy Inference System for Selecting Team Leader for Software Development Teams

Author

Jaafar, Jafreezal, Gilal, Abdul Rehman, Omar, Mazni, Basri, Shuib, Aziz, Izzatdin Abdul, Hasan, Mohd Hilmi, 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, Silhavy, Radek, editor, Silhavy, Petr, editor, and Prokopova, Zdenka, editor

Published
2018
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28. Common Spatial Pattern with Feature Scaling (FSc-CSP) for Motor Imagery Classification

Author

Prathama, Yohanes de Britto Hertyasta, Shapiai, Mohd Ibrahim, Aris, Siti Armiza Mohd, Ibrahim, Zuwairie, Jaafar, Jafreezal, Fauzi, Hilman, Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Mohamed Ali, Mohamed Sultan, editor, Wahid, Herman, editor, Mohd Subha, Nurul Adilla, editor, Sahlan, Shafishuhaza, editor, Md. Yunus, Mohd Amri, editor, and Wahap, Ahmad Ridhwan, editor

Published
2017
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48. Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced Data: Improving Machine Failure Prediction.

Author

Yap Bee Wah, Ismail, Azlan, Naslina Azid, Nur Niswah, Jaafar, Jafreezal, Aziz, Izzatdin Abdul, Hasan, Mohd Hilmi, and Zain, Jasni Mohamad

Subjects
MACHINE learning, RADIAL basis functions, SUPPORT vector machines, MACHINERY, LOGISTIC regression analysis
Abstract

Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure rate. The common approach to handle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling, random oversampling, or Synthetic Minority Oversampling Technique (SMOTE) algorithms. This paper compared the classification performance of three popular classifiers (Logistic Regression, Gaussian Naïve Bayes, and Support Vector Machine) in predicting machine failure in the Oil and Gas industry. The original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945 (97%) ‘non-failure’ and 528 (3%) ‘failure data’. The three independent variables to predict machine failure were pressure indicator, flow indicator, and level indicator. The accuracy of the classifiers is very high and close to 100%, but the sensitivity of all classifiers using the original dataset was close to zero. The performance of the three classifiers was then evaluated for data with different imbalance rates (10% to 50%) generated from the original data using SMOTE, SMOTE-Support Vector Machine (SMOTE-SVM) and SMOTEEdited Nearest Neighbour (SMOTE-ENN). The classifiers were evaluated based on improvement in sensitivity and F-measure. Results showed that the sensitivity of all classifiers increases as the imbalance rate increases. SVM with radial basis function (RBF) kernel has the highest sensitivity when data is balanced (50:50) using SMOTE (Sensitivitytest = 0.5686, Ftest = 0.6927) compared to Naïve Bayes (Sensitivitytest = 0.4033, Ftest = 0.6218) and Logistic Regression (Sensitivitytest = 0.4194, Ftest = 0.621). Overall, the Gaussian Naïve Bayes model consistently improves sensitivity and F-measure as the imbalance ratio increases, but the sensitivity is below 50%. The classifiers performed better when data was balanced using SMOTE-SVM compared to SMOTE and SMOTE-ENN. [ABSTRACT FROM AUTHOR]

Published
2023
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