Muhaini Othman | Universiti Tun Hussein Onn Malaysia (original) (raw)
Papers by Muhaini Othman
Journal of Advanced Research in Dynamic and Control Systems, 2019
Sports analytics has played a crucial role for assisting in decision making for players drafting,... more Sports analytics has played a crucial role for assisting in decision making for players drafting, trading, training, development, coaching and tactical system in association football (British English) or soccer (American English). Sports analytics in Malaysian football especially on main national football league, Malaysia Super League (MSL) has become a popular topic to be discussed among researchers in the past few years. This paper is set to review and study all available research articles with specific keyword of query in SCOPUS database. The main focus of this paper is to analyze and discuss the application of sports analytics in MSL. This review is hoping to benefit future research in Malaysia football field so that it may help national team as well as football association club in Malaysia league increase their ranking and make Malaysia football more attractive locally and globally in term of social community, economy and prestige
International Journal of Advanced Trends in Computer Science and Engineering, 2019
Research on the detection of social media content, especially Twitter, has been done. Twitter con... more Research on the detection of social media content, especially Twitter, has been done. Twitter content detection is based on classifying content or words made by users (tweets) into two groups, namely positive and negative. Research to detect negative content or harsh words in Indonesian tweets is still rare. There are several studies that have been conducted, the detection of negative words is only limited to certain categories, such as pornography, hate speech, and others, so that if the negative word only includes one category, then if there are other negative words that do not belong to that category, this word will not be detected. This is a challenge for researchers to classify texts in Indonesian. In some research, to be able to detect and separate negative words and positive words in Indonesian, the Naive Bayes (NB) and Support Vector Machine (SVM) proved to produce better performance among other algorithms. Therefore, this paper aims to analyze the comparison of the results that have been achieved about detecting negative content on Indonesian twitter using NB and SVM. First, this paper will briefly explain the NB and SVM, then proceed with an explanation of the general research framework that has been carried out. In the results and discussion section, a comparison of the results achieved by existing researchers is explained. And based on these results, another approach will be proposed to detect negative content on Indonesian twitter.
Advances in Intelligent Systems and Computing, 2018
This research paper is focused on the framework design of temporal data by using personalised mod... more This research paper is focused on the framework design of temporal data by using personalised modelling approach in order to cluster the temporal data. Real world problem on flood occurrences is used as a case study focusing only in Malaysia region. The data are designed according to the criteria needed for temporal data clustering, tested with three clustering techniques including K-means, X-means, and K-medoids. Rapid Miner is used for conducting the clustering processes. Finally, the result from each clustering method is compared to conclude and justify the best clustering approach for clustering temporal data.
Advances in Intelligent Systems and Computing, 2019
Inspired by the brain working mechanism, the spiking neural networks has proven the capability of... more Inspired by the brain working mechanism, the spiking neural networks has proven the capability of revealing significant association between different variables spike behavior during an event. The combination of the capability of SNN to produce personalised model has allowed high-precision for data classification. The exiting accuracy of weighted k-nearest neighbors classifier being used in the spiking neural networks architecture, noticeably can be further improved by implementing fuzzy-weights on the features, therefore allowing data to be classified more precisely to the high-impacting features. Simulation has been done by using three classifiers—Multi-layer Perceptron, weighted k-nearest neighbors, and Fuzzy-weighted k-nearest neighbors (FwkNN) using a real-world flood case study dataset and two benchmark dataset. Based on the result using the Kuala Krai Rainfall Dataset, FwkNN classifier has improved accuracy by 3.48% and 3.57% for 3-days earlier and 1-day earlier classification respectively. As compared to, FwkNN classifier has proven the capability to reduce misclassification and increase the accuracy of dataset classification.
2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)
Penggunaan sistem aplikasi berasaskan web kini semakin mendapat perhatian, seiring dengan kemajua... more Penggunaan sistem aplikasi berasaskan web kini semakin mendapat perhatian, seiring dengan kemajuan teknologi di Malaysia. Sistem Aplikasi Bantuan Kerosakan Kereta merupakan sistem aplikasi yang membantu pengguna kereta untuk mencari bengkel berdekatan dalam menyelesaikan masalah kerosakan kereta yang dihadapi. Kebarangkalian untuk kereta mengalami kerosakan secara tiba-tiba adalah tinggi walaupun telah diselenggara mengikut jadual yang telah ditetapkan. Ini menjadikan bengkel sebuah tempat yang penting untuk menyelesaikan masalah berkaitan kerosakan kereta. Namun begitu ramai pengguna kereta tidak mengetahui lokasi bengkel yang berdekatan dengan lokasi kerosakan kereta mereka. Sistem aplikasi ini menyediakan platform kepada pengguna kereta untuk mencari bengkel berdekatan ketika mengalami kerosakan kereta. Pekerja bengkel akan mendapat notifikasi yang memaklumkan bahawa seseorang memerlukan khidmat bantuan untuk membaiki kerosakan dan segera ke lokasi yang dinyatakan. Metodologi Ori...
information technology and computer science, Dec 8, 2020
The number of individuals who suffer from chronic disease continues to increase worldwide (WHO, 2... more The number of individuals who suffer from chronic disease continues to increase worldwide (WHO, 2015). Health awareness together with the improvement in living conditions and treatment has increased the life expectancy of people suffers from chronic disease; nevertheless without efficient health management and monitoring, the quality of life is decreased (Whitehead, Seaton, 2016). Progressive growth in computer-mediated technologies such as social networking, smartphones and medical applications provide a useful platform for self-health management and awareness. Towards empowering people in practicing self-health management, individuals who suffers from chronic disease need to have access to timely information, advice, assessment and treatment from medical practitioners in order for them to manage their long-term illnesses conditions systematically (Zoffmann et al. 2016). Medical practitioners play an important role in empowering self-health management by giving guidance, monitoring...
Inspired by the brain working mechanism, the spiking neural networks has proven the capability of... more Inspired by the brain working mechanism, the spiking neural networks has proven the capability of revealing significant association between different variables spike behavior during an event. The combination of the capability of SNN to produce personalised model has allowed high-precision for data classification. The exiting accuracy of weighted k-nearest neighbors classifier being used in the spiking neural networks architecture, noticeably can be further improved by implementing fuzzy-weights on the features, therefore allowing data to be classified more precisely to the high-impacting features. Simulation has been done by using three classifiers—Multi-layer Perceptron, weighted k-nearest neighbors, and Fuzzy-weighted k-nearest neighbors (FwkNN) using a real-world flood case study dataset and two benchmark dataset. Based on the result using the Kuala Krai Rainfall Dataset, FwkNN classifier has improved accuracy by 3.48% and 3.57% for 3-days earlier and 1-day earlier classification...
Introduction: Capturing the nature of spatio/spectro-temporal data (SSTD) is not an easy task nor... more Introduction: Capturing the nature of spatio/spectro-temporal data (SSTD) is not an easy task nor is understanding the relationships between the different data dimensions such as between temporal and spatial, temporal and static, and between temporal variables themselves. In the past it has been normal to separate the SSTD dimensions and only take one dimension of the data and convert it into a static representation and model from there. While other dimensions are either ignored or modelled separately. Although this practice has had significant outcomes, the relationships between data dimensions and the meaning of that relationship defined be the data is lost and can result in inaccurate solutions. Any relationship between the static and dynamic or temporal data has been under analysed, if analysed at all, dependent upon the field of study. Purpose of the research: The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relations...
The increment of spatial-temporal data (STD) collected in many domain areas, including bioinforma... more The increment of spatial-temporal data (STD) collected in many domain areas, including bioinformatics, engineering, medicine, environment, telecommunication, computer vision and many more has leads towards the emergent of information technology initiatives that facilitate the knowledge acquisition, organization and dissemination among research community. The initiatives include but not exhaust to spatial-temporal representation in ontology and databases, spatial-temporal modeling and spatial-temporal reasoning that fall under data mining research. Both the temporal and spatial dimensions add substantial complexity to data mining tasks [1]. Spatiotemporal data mining here refers to the extraction of implicit knowledge, spatial and temporal relationships or other patterns not explicitly stored in spatio-temporal databases [2].
MATEC Web of Conferences, 2018
The advancement in mobile technology has led towards a new frontier of medical intervention that ... more The advancement in mobile technology has led towards a new frontier of medical intervention that never been thought possible before. Through the development of MedsBox Reminder (MBR) application for Android as a pilot project of M-Health, health care information system for patient selfmanagement is made possible. The application acts as an assistant to remind users for their timely medicine intake by notifying them through their mobile phone. MedsBox Reminder application aims to facilitate in the self-management of patient's health where they can monitor and schedule their own medicine intake more efficiently. Development of the application is performed using Android Studio 1.4, Android SDK, MySQL database, SQLite, Java language and Netbeans IDE 8.1. Object-Oriented System Development (OOSD) methodology has been adapted to facilitate the development of the application.
Indonesian Journal of Electrical Engineering and Computer Science, 2019
The evolution of Artificial Neural Network recently gives researchers an interest to explore deep... more The evolution of Artificial Neural Network recently gives researchers an interest to explore deep learning evolved by Spiking Neural Network clustering methods. Spiking Neural Network (SNN) models captured neuronal behaviour more precisely than a traditional neural network as it contains the theory of time into their functioning model [1]. The aim of this paper is to reviewed studies that are related to clustering problems employing Spiking Neural Networks models. Even though there are many algorithms used to solve clustering problems, most of the methods are only suitable for static data and fixed windows of time series. Hence, there is a need to analyse complex data type, the potential for improvement is encouraged. Therefore, this paper summarized the significant result obtains by implying SNN models in different clustering approach. Thus, the findings of this paper could demonstrate the purpose of clustering method using SNN for the fellow researchers from various disciplines to...
JOIV : International Journal on Informatics Visualization, 2017
In the fast-paced changing world, the Internet keeps people connected to each other. Online shopp... more In the fast-paced changing world, the Internet keeps people connected to each other. Online shopping has changed the way people buy things, and so does how people book flight tickets and movie passes. Cakelicious Web App is another interesting story of how we revolutionize the way people book wedding cakes the way they love it. The system is designed to replace the current manual booking methods used by Dr. Munie’s Kitchen for managing cakes order, thus is more efficient and effective, as well as meets the user requirements. Prototyping methodology approach has been used to develop and test the system in a systematic manner, which includes the development phases of planning, design, and testing and implementation. This system is developed using the PHP programming language, MySQL database, and runs on an Apache web server.
IEEE Access, 2021
In the last few years, deep learning-based models have made significant inroads into the field of... more In the last few years, deep learning-based models have made significant inroads into the field of handwriting recognition. However, deep learning requires the availability of massive labelled data and considerable computation for training or automatic feature extraction. The role of handcrafted features and their significance is still crucial for a specific language type because it is a unique way of writing the characters. These are primitive segments that describe the letter horizontally or vertically distinguish an Arabic letter. This article develops a new type of feature for handwriting using Segments Interpolation (SI) to find the best fitting line in each of the windows and build a model for finding the best operating point window size for SI features. The experimental design was done on two subsets of the Institute for Communications Technology/Ecole Nationale d'Ingénieurs de Tunis (IFN/ENIT) database. The first one contains 10 classes (C10), and the second one has 22 classes (C22). The extracted features were trained with Support Vector Machine (SVM) and Extreme Learning Machine (ELM) with different kernels and activation functions. The evaluation metrics from a classification perspective (Accuracy, Precision, Recall and F-measure) were applied. As a result, SI shows significant results with SVM 90.10% accuracy for C10 and 88.53% accuracy for C22. INDEX TERMS Arabic handwriting word recognition, classification, ELM, feature extraction, segments interpolation and SVM. I. BACKGROUND Handwriting recognition is a dynamic model and simulation environment that is considered a part of pattern recognition. It can contribute an essential benefit to our real life [1]. The diversity of handwriting recognition comes with extensive usage of a massive number of costly computational aspects. Currently, the technology provides an exceptionally smooth technique and, at the same time, hides the bright side of handwriting text. Several applications where handwriting recognition is necessary, such as bank cheques [2], postal addresses [3], and handwritten form processing [4]. Numerous studies on handwriting recognition, especially for the Latin script [2], [3], have been conducted over the last few decades. There are quite good results for machine The associate editor coordinating the review of this manuscript and approving it for publication was Zahid Akhtar .
Advances in Intelligent Systems and Computing, 2019
The field of Arabic handwriting recognition and translation is currently experiencing rapid growt... more The field of Arabic handwriting recognition and translation is currently experiencing rapid growth in terms of research, which is evident in the coverage of major conferences and journals that specialise in the area of handwriting recognition. Against this backdrop, a significant increase has been observed in the classification and features techniques used, as compared to some years back. Researchers have put in more efforts geared towards building a variety of databases for Arabic handwriting recognition. This article aims to provide a comprehensive survey of advances in Arabic offline handwriting recognition. We have been provided details of availability Arabic databases with limitation. Further, we focus on techniques of feature extraction and different variety of classification approaches such as ANN, HMM, SVM that used in Arabic handwriting recognition.
Studies in Systems, Decision and Control
Advances in Intelligent Systems and Computing
Indonesian Journal of Electrical Engineering and Computer Science
This paper presents Bat Algorithm and K-Means techniques for classification performance improveme... more This paper presents Bat Algorithm and K-Means techniques for classification performance improvement. The objective of this study is to investigate efficiency of Bat Algorithm in discrete dataset and to find the optimum feature in discrete dataset. In this study, one technique that comprise the discretization technique and feature selection technique have been proposed. Our contribution is in two process of classification: pre-processing and feature selection process. First, to proposed discretization techniques called as BkMD, where we hybrid Bat Algorithm technique and K-Means classifier. Second, to proposed BkMDFS as feature selection technique where Bat Algorithm is embed into BkMD. In order to evaluate our proposed techniques, 14 continuous dataset from various applications are used in experiment. From the experiment, results show that BkMDFS outperforms in most performance measures. Hence it shows that, Bat Algorithm have potential to be one of the discretization technique and ...
Journal of Advanced Research in Dynamic and Control Systems, 2019
Sports analytics has played a crucial role for assisting in decision making for players drafting,... more Sports analytics has played a crucial role for assisting in decision making for players drafting, trading, training, development, coaching and tactical system in association football (British English) or soccer (American English). Sports analytics in Malaysian football especially on main national football league, Malaysia Super League (MSL) has become a popular topic to be discussed among researchers in the past few years. This paper is set to review and study all available research articles with specific keyword of query in SCOPUS database. The main focus of this paper is to analyze and discuss the application of sports analytics in MSL. This review is hoping to benefit future research in Malaysia football field so that it may help national team as well as football association club in Malaysia league increase their ranking and make Malaysia football more attractive locally and globally in term of social community, economy and prestige
International Journal of Advanced Trends in Computer Science and Engineering, 2019
Research on the detection of social media content, especially Twitter, has been done. Twitter con... more Research on the detection of social media content, especially Twitter, has been done. Twitter content detection is based on classifying content or words made by users (tweets) into two groups, namely positive and negative. Research to detect negative content or harsh words in Indonesian tweets is still rare. There are several studies that have been conducted, the detection of negative words is only limited to certain categories, such as pornography, hate speech, and others, so that if the negative word only includes one category, then if there are other negative words that do not belong to that category, this word will not be detected. This is a challenge for researchers to classify texts in Indonesian. In some research, to be able to detect and separate negative words and positive words in Indonesian, the Naive Bayes (NB) and Support Vector Machine (SVM) proved to produce better performance among other algorithms. Therefore, this paper aims to analyze the comparison of the results that have been achieved about detecting negative content on Indonesian twitter using NB and SVM. First, this paper will briefly explain the NB and SVM, then proceed with an explanation of the general research framework that has been carried out. In the results and discussion section, a comparison of the results achieved by existing researchers is explained. And based on these results, another approach will be proposed to detect negative content on Indonesian twitter.
Advances in Intelligent Systems and Computing, 2018
This research paper is focused on the framework design of temporal data by using personalised mod... more This research paper is focused on the framework design of temporal data by using personalised modelling approach in order to cluster the temporal data. Real world problem on flood occurrences is used as a case study focusing only in Malaysia region. The data are designed according to the criteria needed for temporal data clustering, tested with three clustering techniques including K-means, X-means, and K-medoids. Rapid Miner is used for conducting the clustering processes. Finally, the result from each clustering method is compared to conclude and justify the best clustering approach for clustering temporal data.
Advances in Intelligent Systems and Computing, 2019
Inspired by the brain working mechanism, the spiking neural networks has proven the capability of... more Inspired by the brain working mechanism, the spiking neural networks has proven the capability of revealing significant association between different variables spike behavior during an event. The combination of the capability of SNN to produce personalised model has allowed high-precision for data classification. The exiting accuracy of weighted k-nearest neighbors classifier being used in the spiking neural networks architecture, noticeably can be further improved by implementing fuzzy-weights on the features, therefore allowing data to be classified more precisely to the high-impacting features. Simulation has been done by using three classifiers—Multi-layer Perceptron, weighted k-nearest neighbors, and Fuzzy-weighted k-nearest neighbors (FwkNN) using a real-world flood case study dataset and two benchmark dataset. Based on the result using the Kuala Krai Rainfall Dataset, FwkNN classifier has improved accuracy by 3.48% and 3.57% for 3-days earlier and 1-day earlier classification respectively. As compared to, FwkNN classifier has proven the capability to reduce misclassification and increase the accuracy of dataset classification.
2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)
Penggunaan sistem aplikasi berasaskan web kini semakin mendapat perhatian, seiring dengan kemajua... more Penggunaan sistem aplikasi berasaskan web kini semakin mendapat perhatian, seiring dengan kemajuan teknologi di Malaysia. Sistem Aplikasi Bantuan Kerosakan Kereta merupakan sistem aplikasi yang membantu pengguna kereta untuk mencari bengkel berdekatan dalam menyelesaikan masalah kerosakan kereta yang dihadapi. Kebarangkalian untuk kereta mengalami kerosakan secara tiba-tiba adalah tinggi walaupun telah diselenggara mengikut jadual yang telah ditetapkan. Ini menjadikan bengkel sebuah tempat yang penting untuk menyelesaikan masalah berkaitan kerosakan kereta. Namun begitu ramai pengguna kereta tidak mengetahui lokasi bengkel yang berdekatan dengan lokasi kerosakan kereta mereka. Sistem aplikasi ini menyediakan platform kepada pengguna kereta untuk mencari bengkel berdekatan ketika mengalami kerosakan kereta. Pekerja bengkel akan mendapat notifikasi yang memaklumkan bahawa seseorang memerlukan khidmat bantuan untuk membaiki kerosakan dan segera ke lokasi yang dinyatakan. Metodologi Ori...
information technology and computer science, Dec 8, 2020
The number of individuals who suffer from chronic disease continues to increase worldwide (WHO, 2... more The number of individuals who suffer from chronic disease continues to increase worldwide (WHO, 2015). Health awareness together with the improvement in living conditions and treatment has increased the life expectancy of people suffers from chronic disease; nevertheless without efficient health management and monitoring, the quality of life is decreased (Whitehead, Seaton, 2016). Progressive growth in computer-mediated technologies such as social networking, smartphones and medical applications provide a useful platform for self-health management and awareness. Towards empowering people in practicing self-health management, individuals who suffers from chronic disease need to have access to timely information, advice, assessment and treatment from medical practitioners in order for them to manage their long-term illnesses conditions systematically (Zoffmann et al. 2016). Medical practitioners play an important role in empowering self-health management by giving guidance, monitoring...
Inspired by the brain working mechanism, the spiking neural networks has proven the capability of... more Inspired by the brain working mechanism, the spiking neural networks has proven the capability of revealing significant association between different variables spike behavior during an event. The combination of the capability of SNN to produce personalised model has allowed high-precision for data classification. The exiting accuracy of weighted k-nearest neighbors classifier being used in the spiking neural networks architecture, noticeably can be further improved by implementing fuzzy-weights on the features, therefore allowing data to be classified more precisely to the high-impacting features. Simulation has been done by using three classifiers—Multi-layer Perceptron, weighted k-nearest neighbors, and Fuzzy-weighted k-nearest neighbors (FwkNN) using a real-world flood case study dataset and two benchmark dataset. Based on the result using the Kuala Krai Rainfall Dataset, FwkNN classifier has improved accuracy by 3.48% and 3.57% for 3-days earlier and 1-day earlier classification...
Introduction: Capturing the nature of spatio/spectro-temporal data (SSTD) is not an easy task nor... more Introduction: Capturing the nature of spatio/spectro-temporal data (SSTD) is not an easy task nor is understanding the relationships between the different data dimensions such as between temporal and spatial, temporal and static, and between temporal variables themselves. In the past it has been normal to separate the SSTD dimensions and only take one dimension of the data and convert it into a static representation and model from there. While other dimensions are either ignored or modelled separately. Although this practice has had significant outcomes, the relationships between data dimensions and the meaning of that relationship defined be the data is lost and can result in inaccurate solutions. Any relationship between the static and dynamic or temporal data has been under analysed, if analysed at all, dependent upon the field of study. Purpose of the research: The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relations...
The increment of spatial-temporal data (STD) collected in many domain areas, including bioinforma... more The increment of spatial-temporal data (STD) collected in many domain areas, including bioinformatics, engineering, medicine, environment, telecommunication, computer vision and many more has leads towards the emergent of information technology initiatives that facilitate the knowledge acquisition, organization and dissemination among research community. The initiatives include but not exhaust to spatial-temporal representation in ontology and databases, spatial-temporal modeling and spatial-temporal reasoning that fall under data mining research. Both the temporal and spatial dimensions add substantial complexity to data mining tasks [1]. Spatiotemporal data mining here refers to the extraction of implicit knowledge, spatial and temporal relationships or other patterns not explicitly stored in spatio-temporal databases [2].
MATEC Web of Conferences, 2018
The advancement in mobile technology has led towards a new frontier of medical intervention that ... more The advancement in mobile technology has led towards a new frontier of medical intervention that never been thought possible before. Through the development of MedsBox Reminder (MBR) application for Android as a pilot project of M-Health, health care information system for patient selfmanagement is made possible. The application acts as an assistant to remind users for their timely medicine intake by notifying them through their mobile phone. MedsBox Reminder application aims to facilitate in the self-management of patient's health where they can monitor and schedule their own medicine intake more efficiently. Development of the application is performed using Android Studio 1.4, Android SDK, MySQL database, SQLite, Java language and Netbeans IDE 8.1. Object-Oriented System Development (OOSD) methodology has been adapted to facilitate the development of the application.
Indonesian Journal of Electrical Engineering and Computer Science, 2019
The evolution of Artificial Neural Network recently gives researchers an interest to explore deep... more The evolution of Artificial Neural Network recently gives researchers an interest to explore deep learning evolved by Spiking Neural Network clustering methods. Spiking Neural Network (SNN) models captured neuronal behaviour more precisely than a traditional neural network as it contains the theory of time into their functioning model [1]. The aim of this paper is to reviewed studies that are related to clustering problems employing Spiking Neural Networks models. Even though there are many algorithms used to solve clustering problems, most of the methods are only suitable for static data and fixed windows of time series. Hence, there is a need to analyse complex data type, the potential for improvement is encouraged. Therefore, this paper summarized the significant result obtains by implying SNN models in different clustering approach. Thus, the findings of this paper could demonstrate the purpose of clustering method using SNN for the fellow researchers from various disciplines to...
JOIV : International Journal on Informatics Visualization, 2017
In the fast-paced changing world, the Internet keeps people connected to each other. Online shopp... more In the fast-paced changing world, the Internet keeps people connected to each other. Online shopping has changed the way people buy things, and so does how people book flight tickets and movie passes. Cakelicious Web App is another interesting story of how we revolutionize the way people book wedding cakes the way they love it. The system is designed to replace the current manual booking methods used by Dr. Munie’s Kitchen for managing cakes order, thus is more efficient and effective, as well as meets the user requirements. Prototyping methodology approach has been used to develop and test the system in a systematic manner, which includes the development phases of planning, design, and testing and implementation. This system is developed using the PHP programming language, MySQL database, and runs on an Apache web server.
IEEE Access, 2021
In the last few years, deep learning-based models have made significant inroads into the field of... more In the last few years, deep learning-based models have made significant inroads into the field of handwriting recognition. However, deep learning requires the availability of massive labelled data and considerable computation for training or automatic feature extraction. The role of handcrafted features and their significance is still crucial for a specific language type because it is a unique way of writing the characters. These are primitive segments that describe the letter horizontally or vertically distinguish an Arabic letter. This article develops a new type of feature for handwriting using Segments Interpolation (SI) to find the best fitting line in each of the windows and build a model for finding the best operating point window size for SI features. The experimental design was done on two subsets of the Institute for Communications Technology/Ecole Nationale d'Ingénieurs de Tunis (IFN/ENIT) database. The first one contains 10 classes (C10), and the second one has 22 classes (C22). The extracted features were trained with Support Vector Machine (SVM) and Extreme Learning Machine (ELM) with different kernels and activation functions. The evaluation metrics from a classification perspective (Accuracy, Precision, Recall and F-measure) were applied. As a result, SI shows significant results with SVM 90.10% accuracy for C10 and 88.53% accuracy for C22. INDEX TERMS Arabic handwriting word recognition, classification, ELM, feature extraction, segments interpolation and SVM. I. BACKGROUND Handwriting recognition is a dynamic model and simulation environment that is considered a part of pattern recognition. It can contribute an essential benefit to our real life [1]. The diversity of handwriting recognition comes with extensive usage of a massive number of costly computational aspects. Currently, the technology provides an exceptionally smooth technique and, at the same time, hides the bright side of handwriting text. Several applications where handwriting recognition is necessary, such as bank cheques [2], postal addresses [3], and handwritten form processing [4]. Numerous studies on handwriting recognition, especially for the Latin script [2], [3], have been conducted over the last few decades. There are quite good results for machine The associate editor coordinating the review of this manuscript and approving it for publication was Zahid Akhtar .
Advances in Intelligent Systems and Computing, 2019
The field of Arabic handwriting recognition and translation is currently experiencing rapid growt... more The field of Arabic handwriting recognition and translation is currently experiencing rapid growth in terms of research, which is evident in the coverage of major conferences and journals that specialise in the area of handwriting recognition. Against this backdrop, a significant increase has been observed in the classification and features techniques used, as compared to some years back. Researchers have put in more efforts geared towards building a variety of databases for Arabic handwriting recognition. This article aims to provide a comprehensive survey of advances in Arabic offline handwriting recognition. We have been provided details of availability Arabic databases with limitation. Further, we focus on techniques of feature extraction and different variety of classification approaches such as ANN, HMM, SVM that used in Arabic handwriting recognition.
Studies in Systems, Decision and Control
Advances in Intelligent Systems and Computing
Indonesian Journal of Electrical Engineering and Computer Science
This paper presents Bat Algorithm and K-Means techniques for classification performance improveme... more This paper presents Bat Algorithm and K-Means techniques for classification performance improvement. The objective of this study is to investigate efficiency of Bat Algorithm in discrete dataset and to find the optimum feature in discrete dataset. In this study, one technique that comprise the discretization technique and feature selection technique have been proposed. Our contribution is in two process of classification: pre-processing and feature selection process. First, to proposed discretization techniques called as BkMD, where we hybrid Bat Algorithm technique and K-Means classifier. Second, to proposed BkMDFS as feature selection technique where Bat Algorithm is embed into BkMD. In order to evaluate our proposed techniques, 14 continuous dataset from various applications are used in experiment. From the experiment, results show that BkMDFS outperforms in most performance measures. Hence it shows that, Bat Algorithm have potential to be one of the discretization technique and ...