... S., Armand, M., Blumenstein, and V., Muthukkumarasamy, Off-Line Signature Verificatio based ... more ... S., Armand, M., Blumenstein, and V., Muthukkumarasamy, Off-Line Signature Verificatio based on the ... line Signature Verification and Recognition by Support Vector Machine, DissertationComputer Engineering Department ... [23] RC Eberhart and J. Kennedy, "A New Optimizer ...
With the availability of biological data and the power of sharing, it produces many opportunities... more With the availability of biological data and the power of sharing, it produces many opportunities for computer scientists to perform researches in bioinformatics. Generally the researches propose methods for different tasks, mainly to develop algorithms in diagnosing and identification of diseases. One of the primary studies that relevant to health and diseases is genome wide association studies (GWAS). Normally the studies are conducted in different populations to replicate the risk loci of specific disease and the number of groups are keep on progressing, including those from Asian country. Computer scientists should be involved in GWAS due to certain problems and the complexity of the processes involved. The problems and past studies related to GWAS are presented in this paper.
... Abdul-Rahman, Yap May Lin, Intelligent Systems SIG, Study Centre of Systems Science, Faculty ... more ... Abdul-Rahman, Yap May Lin, Intelligent Systems SIG, Study Centre of Systems Science, Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Shah Alam, Selangor, Malaysia {shuzlina, maylin}@fskm.uitm.edu.my Sofianita Mutalib, Azlinah Mohamed ...
Sentiment Analysis (SA) is opinion mining which often defines as the study of emotions, opinions,... more Sentiment Analysis (SA) is opinion mining which often defines as the study of emotions, opinions, or feedback that relates to the usage of computational linguistics, text analytics, and natural language processing. With the rise of social media posts, it is becoming more challenging to evaluate brief, casual, and non-structured texts to optimize consumer feedback and spot patterns. Meanwhile, social commerce involves social media for social interaction in assisting customers and merchants to do business transactions. From a social media perspective, the informal Malay Text is less explored by the researchers. Thus, it will directly yield difficulties in conducting and preparing the SA processes. Cross-Industry Standard Process for Data Mining (CRISP-DM) was adapted as a reference model for the methodology of this work with machine learning approaches in classifying the informal Malay textual data based on sentiment. The dataset was extracted from the Facebook platform of Pos Laju Malaysia pages. The comparison of the classification technique performances was analyzed in identifying the most accurate classifier for SA, within three different machine learning classifiers was experimented by using 1200 instances from an informal Malay textual dataset. The results of Decision Tree (J48), Support Vector Machine (SVM), and Naïve Bayes (NB) were analyzed and discussed. The result of the highest accuracy of Ten-Fold Cross-Validation is 69.7% and meanwhile, for the Percentage Split method, the highest accuracy result is 70.9%. It shows that Support Vector Machine (SVM) is the best classifier compared to other classifiers of text classification based on sentiment.
Communications in computer and information science, 2019
In most universities, the number of students who graduated on time reflect tremendously on their ... more In most universities, the number of students who graduated on time reflect tremendously on their operation costs. In such cases, the high number of graduate-on-time or GOT students achievement will indirectly reduce the university’s annual operation cost per student. Not as trivial as it seems, to ensure most of the students able to GOT is challenging. It may vary in the perspective of university practises, academic programmes, and students’ background. At the university’s level, students’ data can be used to identify the achievement and ability of students, interests, and weaknesses. To build an accurate predictive model, it requires an extensive study on significant factors that may contribute to students’ ability to graduate on time. Consequently, this study aims to construct a predictive model that can predict students’ graduation status. We applied five different machine learning algorithms (classifiers) namely Decision Tree, Random Forest, Naive Bayes, Support Vector Machine (PolyKernel), and Support Vector Machine (RBFKernel). These classifiers were evaluated with four different k folds of 5, 10, 15, and 20. The performance of these classifiers was compared based on different measurement subject to accuracy, precision, recall, and F-Score. The results indicated that Support Vector Machine (PolyKernel) outperformed other classifiers and the best numbers of k folds for this experiment are 5 and 20. This predictive model of GOT is hopefully will beneficial to university management and academicians to devise their strategies in helping and improving the weakness of students’ academic performance and to ensure they can graduate on time.
This paper attempts to predict the survival of patients using supervised machine learning techniq... more This paper attempts to predict the survival of patients using supervised machine learning techniques. To predict this task, the variables were identified and retrieved from the StatLib database. Both the artificial neural networks and linear regression models were used to perform the task. Experimental results, based on the classification accuracy were analysed from training and testing datasets. To increase the
Autonomous Mobile Robot (AMR) is widely used in a variety of applications. This paper describes a... more Autonomous Mobile Robot (AMR) is widely used in a variety of applications. This paper describes an early experiment towards modelling a low-cost and robust centimetre-level localization for mobile robots in crowded indoor and outdoor environments. While a wide range of methods have been developed and tested on high-end hardware in autonomous vehicles, the work utilizes multiple sensor information to achieve robustness with different types of mobile robots. The application can be used by any group or organization, especially the frontliners, in managing the COVID-19 pandemic. Different Simultaneous Localization and Mapping (SLAM) algorithms, such as GMapping, Google Cartographer and Hector SLAM, are used to achieve better localization. Sensor fusion strategy is applied for these SLAM packages using Real-Time Kinematic (RTK) positioning, a precise Global Navigation Satellite System (GNSS)-based sensor, by applying both Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) to estimate position, velocity and attitude (PVA). The performance of the proposed algorithm will be compared against the benchmark algorithm using different sets of data in crowded places in various settings.
... S., Armand, M., Blumenstein, and V., Muthukkumarasamy, Off-Line Signature Verificatio based ... more ... S., Armand, M., Blumenstein, and V., Muthukkumarasamy, Off-Line Signature Verificatio based on the ... line Signature Verification and Recognition by Support Vector Machine, DissertationComputer Engineering Department ... [23] RC Eberhart and J. Kennedy, "A New Optimizer ...
With the availability of biological data and the power of sharing, it produces many opportunities... more With the availability of biological data and the power of sharing, it produces many opportunities for computer scientists to perform researches in bioinformatics. Generally the researches propose methods for different tasks, mainly to develop algorithms in diagnosing and identification of diseases. One of the primary studies that relevant to health and diseases is genome wide association studies (GWAS). Normally the studies are conducted in different populations to replicate the risk loci of specific disease and the number of groups are keep on progressing, including those from Asian country. Computer scientists should be involved in GWAS due to certain problems and the complexity of the processes involved. The problems and past studies related to GWAS are presented in this paper.
... Abdul-Rahman, Yap May Lin, Intelligent Systems SIG, Study Centre of Systems Science, Faculty ... more ... Abdul-Rahman, Yap May Lin, Intelligent Systems SIG, Study Centre of Systems Science, Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Shah Alam, Selangor, Malaysia {shuzlina, maylin}@fskm.uitm.edu.my Sofianita Mutalib, Azlinah Mohamed ...
Sentiment Analysis (SA) is opinion mining which often defines as the study of emotions, opinions,... more Sentiment Analysis (SA) is opinion mining which often defines as the study of emotions, opinions, or feedback that relates to the usage of computational linguistics, text analytics, and natural language processing. With the rise of social media posts, it is becoming more challenging to evaluate brief, casual, and non-structured texts to optimize consumer feedback and spot patterns. Meanwhile, social commerce involves social media for social interaction in assisting customers and merchants to do business transactions. From a social media perspective, the informal Malay Text is less explored by the researchers. Thus, it will directly yield difficulties in conducting and preparing the SA processes. Cross-Industry Standard Process for Data Mining (CRISP-DM) was adapted as a reference model for the methodology of this work with machine learning approaches in classifying the informal Malay textual data based on sentiment. The dataset was extracted from the Facebook platform of Pos Laju Malaysia pages. The comparison of the classification technique performances was analyzed in identifying the most accurate classifier for SA, within three different machine learning classifiers was experimented by using 1200 instances from an informal Malay textual dataset. The results of Decision Tree (J48), Support Vector Machine (SVM), and Naïve Bayes (NB) were analyzed and discussed. The result of the highest accuracy of Ten-Fold Cross-Validation is 69.7% and meanwhile, for the Percentage Split method, the highest accuracy result is 70.9%. It shows that Support Vector Machine (SVM) is the best classifier compared to other classifiers of text classification based on sentiment.
Communications in computer and information science, 2019
In most universities, the number of students who graduated on time reflect tremendously on their ... more In most universities, the number of students who graduated on time reflect tremendously on their operation costs. In such cases, the high number of graduate-on-time or GOT students achievement will indirectly reduce the university’s annual operation cost per student. Not as trivial as it seems, to ensure most of the students able to GOT is challenging. It may vary in the perspective of university practises, academic programmes, and students’ background. At the university’s level, students’ data can be used to identify the achievement and ability of students, interests, and weaknesses. To build an accurate predictive model, it requires an extensive study on significant factors that may contribute to students’ ability to graduate on time. Consequently, this study aims to construct a predictive model that can predict students’ graduation status. We applied five different machine learning algorithms (classifiers) namely Decision Tree, Random Forest, Naive Bayes, Support Vector Machine (PolyKernel), and Support Vector Machine (RBFKernel). These classifiers were evaluated with four different k folds of 5, 10, 15, and 20. The performance of these classifiers was compared based on different measurement subject to accuracy, precision, recall, and F-Score. The results indicated that Support Vector Machine (PolyKernel) outperformed other classifiers and the best numbers of k folds for this experiment are 5 and 20. This predictive model of GOT is hopefully will beneficial to university management and academicians to devise their strategies in helping and improving the weakness of students’ academic performance and to ensure they can graduate on time.
This paper attempts to predict the survival of patients using supervised machine learning techniq... more This paper attempts to predict the survival of patients using supervised machine learning techniques. To predict this task, the variables were identified and retrieved from the StatLib database. Both the artificial neural networks and linear regression models were used to perform the task. Experimental results, based on the classification accuracy were analysed from training and testing datasets. To increase the
Autonomous Mobile Robot (AMR) is widely used in a variety of applications. This paper describes a... more Autonomous Mobile Robot (AMR) is widely used in a variety of applications. This paper describes an early experiment towards modelling a low-cost and robust centimetre-level localization for mobile robots in crowded indoor and outdoor environments. While a wide range of methods have been developed and tested on high-end hardware in autonomous vehicles, the work utilizes multiple sensor information to achieve robustness with different types of mobile robots. The application can be used by any group or organization, especially the frontliners, in managing the COVID-19 pandemic. Different Simultaneous Localization and Mapping (SLAM) algorithms, such as GMapping, Google Cartographer and Hector SLAM, are used to achieve better localization. Sensor fusion strategy is applied for these SLAM packages using Real-Time Kinematic (RTK) positioning, a precise Global Navigation Satellite System (GNSS)-based sensor, by applying both Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) to estimate position, velocity and attitude (PVA). The performance of the proposed algorithm will be compared against the benchmark algorithm using different sets of data in crowded places in various settings.
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