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Speech is a powerful medium of communication that always convey rich and useful information, such as gender, accent, and other unique characteristics of a speaker. These unique characteristics enable researchers to recognize human voice using artificial intelligence techniques that are important in the areas of forensic voice verification, security and surveillance, electronic voice eavesdropping, mobile banking and mobile shopping. Recent advancements in deep learning and other hardware techniques have gained attention of researchers working in the field of automatic speaker identification (SI). However, to the best of our knowledge, there is no in-depth survey is available that critically appraises and summarizes the existing techniques with their strengths and weaknesses for SI. Hence, this study identified and discussed various areas of SI, presented a comprehensive survey of existing studies, and also presented the future research challenges that require significant research efforts in the field of SI systems.
Rashid Jahangir; Ying Wah Teh; Henry Friday Nweke; Ghulam Mujtaba; Mohammed Ali Al-Garadi; Ihsan Ali. Speaker identification through artificial intelligence techniques: A comprehensive review and research challenges. Expert Systems with Applications 2021, 171, 114591 .
AMA StyleRashid Jahangir, Ying Wah Teh, Henry Friday Nweke, Ghulam Mujtaba, Mohammed Ali Al-Garadi, Ihsan Ali. Speaker identification through artificial intelligence techniques: A comprehensive review and research challenges. Expert Systems with Applications. 2021; 171 ():114591.
Chicago/Turabian StyleRashid Jahangir; Ying Wah Teh; Henry Friday Nweke; Ghulam Mujtaba; Mohammed Ali Al-Garadi; Ihsan Ali. 2021. "Speaker identification through artificial intelligence techniques: A comprehensive review and research challenges." Expert Systems with Applications 171, no. : 114591.
Breast cancer (BrC) is the leading cause of abnormal death in women. Mammograms and histopathology (Hp) biopsy images are generally recommended for early diagnosis of BrC because Hp image-based diagnosis enables doctors to make cancer diagnostic decisions more confidently than with mammograms. Several studies have used Hp images to classify BrC. However, the performance of classification models is compromised due to the higher misclassification rate. Therefore, this study aimed to develop a reliable, accurate, and computationally cost-effective ensembled BrC classification network (EBrC-Net) model with three misclassification algorithms to diagnose breast malignancy in early stages using Hp images. The proposed EBrC-Net model is based on the deep convolutional neural network approach. For experiments, the publicly available BreakHis dataset was used and split into training, validation, and testing sets. In addition, image augmentation was adopted for the training set only, and features were extracted through the well-trained EBrC-Net. Thereafter, the extracted features were further evaluated by six machine learning classifiers, of which two best performing classifiers (i.e., softmax and k-nearest neighbour [kNN]) were selected on the basis of five performance metric evaluation results. Furthermore, three misclassification reduction (McR) algorithms were developed and implemented in cascaded manner to reduce the false predictions of the softmax and kNN classifiers. After the implementation of the McR algorithms, experiments showed that the kNN results were much better and reliable than the softmax. The proposed BrC classification model achieved accuracy, specificity, and sensitivity rates of 97.74%, 100%, and 97.01%, respectively. Moreover, the performance of proposed BrC classification model was compared with that of state-of-the-art baseline models. Findings showed that the proposed EBrC-Net classification model, coupled with the proposed McR algorithms, achieved the best results in comparison with the baseline classification models. The proposed EBrC-Net model and the McR algorithms are a reliable source for doctors aiming for second opinion in making early diagnostic decisions for BrC using Hp images.
Ghulam Murtaza; Liyana Shuib; Ainuddin Wahid Abdul Wahab; Ghulam Mujtaba; Ghulam Raza. Ensembled deep convolution neural network-based breast cancer classification with misclassification reduction algorithms. Multimedia Tools and Applications 2020, 79, 18447 -18479.
AMA StyleGhulam Murtaza, Liyana Shuib, Ainuddin Wahid Abdul Wahab, Ghulam Mujtaba, Ghulam Raza. Ensembled deep convolution neural network-based breast cancer classification with misclassification reduction algorithms. Multimedia Tools and Applications. 2020; 79 (25-26):18447-18479.
Chicago/Turabian StyleGhulam Murtaza; Liyana Shuib; Ainuddin Wahid Abdul Wahab; Ghulam Mujtaba; Ghulam Raza. 2020. "Ensembled deep convolution neural network-based breast cancer classification with misclassification reduction algorithms." Multimedia Tools and Applications 79, no. 25-26: 18447-18479.
Activity detection and classification using different sensor modalities have emerged as revolutionary technology for real-time and autonomous monitoring in behaviour analysis, ambient assisted living, activity of daily living (ADL), elderly care, rehabilitations, entertainments and surveillance in smart home environments. Wearable devices, smart-phones and ambient environments devices are equipped with variety of sensors such as accelerometers, gyroscopes, magnetometer, heart rate, pressure and wearable camera for activity detection and monitoring. These sensors are pre-processed and different feature sets such as time domain, frequency domain, wavelet transform are extracted and transform using machine learning algorithm for human activity classification and monitoring. Recently, deep learning algorithms for automatic feature representation have also been proposed to lessen the burden of reliance on handcrafted features and to increase performance accuracy. Initially, one set of sensor data, features or classifiers were used for activity recognition applications. However, there are new trends on the implementation of fusion strategies to combine sensors data, features and classifiers to provide diversity, offer higher generalization, and tackle challenging issues. For instances, combination of inertial sensors provide mechanism to differentiate activity of similar patterns and accurate posture identification while other multimodal sensor data are used for energy expenditure estimations, object localizations in smart homes and health status monitoring. Hence, the focus of this review is to provide in-depth and comprehensive analysis of data fusion and multiple classifier systems techniques for human activity recognition with emphasis on mobile and wearable devices. First, data fusion methods and modalities were presented and also feature fusion, including deep learning fusion for human activity recognition were critically analysed, and their applications, strengths and issues were identified. Furthermore, the review presents different multiple classifier system design and fusion methods that were recently proposed in literature. Finally, open research problems that require further research and improvements are identified and discussed.
Henry Friday Nweke; Ying Wah Teh; Ghulam Mujtaba; Mohammed Ali Al-Garadi. Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions. Information Fusion 2019, 46, 147 -170.
AMA StyleHenry Friday Nweke, Ying Wah Teh, Ghulam Mujtaba, Mohammed Ali Al-Garadi. Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions. Information Fusion. 2019; 46 ():147-170.
Chicago/Turabian StyleHenry Friday Nweke; Ying Wah Teh; Ghulam Mujtaba; Mohammed Ali Al-Garadi. 2019. "Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions." Information Fusion 46, no. : 147-170.
Norjihan Abdul Ghani; Ghulam Mujtaba; Nader Ale Ebrahim; Monther M. Elaish; Liyana Shuib. A bibliometric analysis of m-learning from topic inception to 2015. International Journal of Mobile Learning and Organisation 2019, 13, 91 .
AMA StyleNorjihan Abdul Ghani, Ghulam Mujtaba, Nader Ale Ebrahim, Monther M. Elaish, Liyana Shuib. A bibliometric analysis of m-learning from topic inception to 2015. International Journal of Mobile Learning and Organisation. 2019; 13 (1):91.
Chicago/Turabian StyleNorjihan Abdul Ghani; Ghulam Mujtaba; Nader Ale Ebrahim; Monther M. Elaish; Liyana Shuib. 2019. "A bibliometric analysis of m-learning from topic inception to 2015." International Journal of Mobile Learning and Organisation 13, no. 1: 91.
Mobile learning is a promising and widely adopted mode of education nowadays. However, to the best of our knowledge, there have been no studies that have investigated publications on the use of mobile learning in the Thomson Reuters Web of Science (WoS) through bibliometric analysis. This study aims to provide readers with statistical information to obtain a deep understanding of this domain. The results show that 3087 mobile learning publications have been published since 1982. As a result, the study has compared and determined the possible ways for researchers and authors to improve the citation rate of their publications on m-learning and to gain a deeper understanding of the field of mobile learning by studying the number of publications and country, the number of authors, the keywords, the number of references, the number of pages, the journal, the authors' publications, and the number of citations. Additionally, other bibliometric results are discussed.
Monther Elaish; Liyana Shuib; Norjihan Abdul Ghani; Ghulam Mujtaba; Nader Ale Ebrahim. A bibliometric analysis of m-learning from topic inception to 2015. International Journal of Mobile Learning and Organisation 2019, 13, 91 .
AMA StyleMonther Elaish, Liyana Shuib, Norjihan Abdul Ghani, Ghulam Mujtaba, Nader Ale Ebrahim. A bibliometric analysis of m-learning from topic inception to 2015. International Journal of Mobile Learning and Organisation. 2019; 13 (1):91.
Chicago/Turabian StyleMonther Elaish; Liyana Shuib; Norjihan Abdul Ghani; Ghulam Mujtaba; Nader Ale Ebrahim. 2019. "A bibliometric analysis of m-learning from topic inception to 2015." International Journal of Mobile Learning and Organisation 13, no. 1: 91.
This paper provides a comprehensive review and analysis of the detection of suspicious terrorist electronic mails (emails) using various phases and methods of text classification. We explored, analyzed, and compared different datasets, features, feature extraction techniques, feature representation techniques, feature selection schemes, text classification techniques, and performance measurement metrics used in the detection of suspicious terrorist e-mails. 30 articles were retrieved from 6 well-known academic databases after rigorous selection. From the study, we found that researchers often generate their own e-mails dataset since there is no public dataset is available in the research area of detecting suspicious terrorist e-mails. In most of the studies, researchers used content and context-based features to detect terrorist e-mails. Our findings also show that the most commonly used feature extraction techniques are the bag of words and n-gram, the most typically applied feature representation schemes are binary representation and term frequency, the most usually adopted feature selection method is information gain,, the most common and most accurate text classification algorithms are naïve bayes, decision trees, and support vector machines, and the widely employed performance measurement metrics are accuracy, precision, and recall. Open research challenges and research issues that involve significant research efforts are also summarized in this review for future researchers in the area of suspicious terrorist e-mail detection using text classification techniques where the critical analysis presented in this paper also provides valuable insights to guide these researchers. Finally, the indicated issues and challenges presented in this paper can be used as future research directions in this area.
Ghulam Mujtaba; Liyana Shuib; Ram Gopal Raj; Roshan Gunalan. DETECTION OF SUSPICIOUS TERRORIST EMAILS USING TEXT CLASSIFICATION: A REVIEW. Malaysian Journal of Computer Science 2018, 31, 271 -299.
AMA StyleGhulam Mujtaba, Liyana Shuib, Ram Gopal Raj, Roshan Gunalan. DETECTION OF SUSPICIOUS TERRORIST EMAILS USING TEXT CLASSIFICATION: A REVIEW. Malaysian Journal of Computer Science. 2018; 31 (4):271-299.
Chicago/Turabian StyleGhulam Mujtaba; Liyana Shuib; Ram Gopal Raj; Roshan Gunalan. 2018. "DETECTION OF SUSPICIOUS TERRORIST EMAILS USING TEXT CLASSIFICATION: A REVIEW." Malaysian Journal of Computer Science 31, no. 4: 271-299.
The pervasive use of electronic health databases has increased the accessibility of free-text clinical reports for supplementary use. Several text classification approaches, such as supervised machine learning (SML) or rule-based approaches, have been utilized to obtain beneficial information from free-text clinical reports. In recent years, many researchers have worked in the clinical text classification field and published their results in academic journals. However, to the best of our knowledge, no comprehensive systematic literature review (SLR) has recapitulated the existing primary studies on clinical text classification in the last five years. Thus, the current study aims to present SLR of academic articles on clinical text classification published from January 2013 to January 2018. Accordingly, we intend to maximize the procedural decision analysis in six aspects, namely, types of clinical reports, data sets and their characteristics, pre-processing and sampling techniques, feature engineering, machine learning algorithms, and performance metrics. To achieve our objective, 72 primary studies from 8 bibliographic databases were systematically selected and rigorously reviewed from the perspective of the six aspects. This review identified nine types of clinical reports, four types of data sets (i.e., homogeneous–homogenous, homogenous–heterogeneous, heterogeneous–homogenous, and heterogeneous–heterogeneous), two sampling techniques (i.e., over-sampling and under-sampling), and nine pre-processing techniques. Moreover, this review determined bag of words, bag of phrases, and bag of concepts features when represented by either term frequency or term frequency with inverse document frequency, thereby showing improved classification results. SML-based or rule-based approaches were generally employed to classify the clinical reports. To measure the performance of these classification approaches, we used precision, recall, F-measure, accuracy, AUC, and specificity in binary class problems. In multi-class problems, we primarily used micro or macro-averaging precision, recall, or F-measure. Lastly, open research issues and challenges are presented for future scholars who are interested in clinical text classification. This SLR will definitely be a beneficial resource for researchers engaged in clinical text classification.
Ghulam Mujtaba; Nor Liyana Mohd Shuib; Norisma Idris; Wai Lam Hoo; Ram Gopal Raj; Kamran Khowaja; Khairunisa Shaikh; Henry Friday Nweke. Clinical text classification research trends: Systematic literature review and open issues. Expert Systems with Applications 2018, 116, 494 -520.
AMA StyleGhulam Mujtaba, Nor Liyana Mohd Shuib, Norisma Idris, Wai Lam Hoo, Ram Gopal Raj, Kamran Khowaja, Khairunisa Shaikh, Henry Friday Nweke. Clinical text classification research trends: Systematic literature review and open issues. Expert Systems with Applications. 2018; 116 ():494-520.
Chicago/Turabian StyleGhulam Mujtaba; Nor Liyana Mohd Shuib; Norisma Idris; Wai Lam Hoo; Ram Gopal Raj; Kamran Khowaja; Khairunisa Shaikh; Henry Friday Nweke. 2018. "Clinical text classification research trends: Systematic literature review and open issues." Expert Systems with Applications 116, no. : 494-520.
•Compare various ATC techniques to determine CoD from forensic autopsy reports.•Unigram feature extraction scheme outperformed bigram, trigram and hybrid schemes.•For feature ranking chi-square outperformed information gain and Pearson Correlation.•SVM outperformed kNN, J48, RF, NB and ensemble voted classifiers.•Our findings could stimulate researchers to propose new techniques in this domain. AbstractObjectivesAutomatic text classification techniques are useful for classifying plaintext medical documents. This study aims to automatically predict the cause of death from free text forensic autopsy reports by comparing various schemes for feature extraction, term weighing or feature value representation, text classification, and feature reduction.MethodsFor experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. The six different text classification techniques were applied on these 43 master feature vectors to construct a classification model that can predict the cause of death. Finally, classification model performance was evaluated using four performance measures i.e. overall accuracy, macro precision, macro-F-measure, and macro recall.ResultsFrom experiments, it was found that that unigram features obtained the highest performance compared to bigram, trigram, and hybrid-gram features. Furthermore, in feature representation schemes, term frequency, and term frequency with inverse document frequency obtained similar and better results when compared with binary frequency, and normalized term frequency with inverse document frequency. Furthermore, the chi-square feature reduction approach outperformed Pearson correlation, and information gain approaches. Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier.ConclusionOur results and comparisons hold practical importance and serve as references for future works. Moreover, the comparison outputs will act as state-of-art techniques to compare future proposals with existing automated text classification techniques. ObjectivesAutomatic text classification techniques are useful for classifying plaintext medical documents. This study aims to automatically predict the cause of death from free text forensic autopsy reports by comparing various schemes for feature extraction, term weighing or feature value representation, text classification, and feature reduction. Automatic text classification techniques are useful for classifying plaintext medical documents. This study aims to automatically predict the cause of death from free text forensic autopsy reports by comparing various schemes for feature extraction, term weighing or feature value representation, text classification, and feature reduction. MethodsFor experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. The six different text classification techniques were applied on these 43 master feature vectors to construct a classification model that can predict the cause of death. Finally, classification model performance was evaluated using four performance measures i.e. overall accuracy, macro precision, macro-F-measure, and macro recall. For experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. The six different text classification techniques were applied on these 43 master feature vectors to construct a classification model that can predict the cause of death. Finally, classification model performance was evaluated using four performance measures i.e. overall accuracy, macro precision, macro-F-measure, and macro recall. ResultsFrom experiments, it was found that that unigram features obtained the highest performance compared to bigram, trigram, and hybrid-gram features. Furthermore, in feature representation schemes, term frequency, and term frequency with inverse document frequency obtained similar and better results when compared with binary frequency, and normalized term frequency with inverse document frequency. Furthermore, the chi-square feature reduction approach outperformed Pearson correlation, and information gain approaches. Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier. From experiments, it was found that that unigram features obtained the highest performance compared to bigram, trigram, and hybrid-gram features. Furthermore, in feature representation schemes, term frequency, and term frequency with inverse document frequency obtained similar and better results when compared with binary frequency, and normalized term frequency with inverse document frequency. Furthermore, the chi-square feature reduction approach outperformed Pearson correlation, and information gain approaches. Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier. ConclusionOur results and comparisons hold practical importance and serve as references for future works. Moreover, the comparison outputs will act as state-of-art techniques to compare future proposals with existing automated text classification techniques. Our results and comparisons hold practical importance and serve as references for future works. Moreover, the comparison outputs will act as state-of-art techniques to compare future proposals with existing automated text classification techniques.
Ghulam Mujtaba; Liyana Shuib; Ram Gopal Raj; Retnagowri Rajandram; Khairunisa Shaikh. Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study. Journal of Forensic and Legal Medicine 2018, 57, 41 -50.
AMA StyleGhulam Mujtaba, Liyana Shuib, Ram Gopal Raj, Retnagowri Rajandram, Khairunisa Shaikh. Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study. Journal of Forensic and Legal Medicine. 2018; 57 ():41-50.
Chicago/Turabian StyleGhulam Mujtaba; Liyana Shuib; Ram Gopal Raj; Retnagowri Rajandram; Khairunisa Shaikh. 2018. "Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study." Journal of Forensic and Legal Medicine 57, no. : 41-50.
Text categorization has been used extensively in recent years to classify plain-text clinical reports. This study employs text categorization techniques for the classification of open narrative forensic autopsy reports. One of the key steps in text classification is document representation. In document representation, a clinical report is transformed into a format that is suitable for classification. The traditional document representation technique for text categorization is the bag-of-words (BoW) technique. In this study, the traditional BoW technique is ineffective in classifying forensic autopsy reports because it merely extracts frequent but discriminative features from clinical reports. Moreover, this technique fails to capture word inversion, as well as word-level synonymy and polysemy, when classifying autopsy reports. Hence, the BoW technique suffers from low accuracy and low robustness unless it is improved with contextual and application-specific information. To overcome the aforementioned limitations of the BoW technique, this research aims to develop an effective conceptual graph-based document representation (CGDR) technique to classify 1500 forensic autopsy reports from four (4) manners of death (MoD) and sixteen (16) causes of death (CoD). Term-based and Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT) based conceptual features were extracted and represented through graphs. These features were then used to train a two-level text classifier. The first level classifier was responsible for predicting MoD. In addition, the second level classifier was responsible for predicting CoD using the proposed conceptual graph-based document representation technique. To demonstrate the significance of the proposed technique, its results were compared with those of six (6) state-of-the-art document representation techniques. Lastly, this study compared the effects of one-level classification and two-level classification on the experimental results. The experimental results indicated that the CGDR technique achieved 12% to 15% improvement in accuracy compared with fully automated document representation baseline techniques. Moreover, two-level classification obtained better results compared with one-level classification. The promising results of the proposed conceptual graph-based document representation technique suggest that pathologists can adopt the proposed system as their basis for second opinion, thereby supporting them in effectively determining CoD.
Ghulam Mujtaba; Liyana Shuib; Ram Gopal Raj; Retnagowri Rajandram; Khairunisa Shaikh; Mohammed Ali Al-Garadi. Classification of forensic autopsy reports through conceptual graph-based document representation model. Journal of Biomedical Informatics 2018, 82, 88 -105.
AMA StyleGhulam Mujtaba, Liyana Shuib, Ram Gopal Raj, Retnagowri Rajandram, Khairunisa Shaikh, Mohammed Ali Al-Garadi. Classification of forensic autopsy reports through conceptual graph-based document representation model. Journal of Biomedical Informatics. 2018; 82 ():88-105.
Chicago/Turabian StyleGhulam Mujtaba; Liyana Shuib; Ram Gopal Raj; Retnagowri Rajandram; Khairunisa Shaikh; Mohammed Ali Al-Garadi. 2018. "Classification of forensic autopsy reports through conceptual graph-based document representation model." Journal of Biomedical Informatics 82, no. : 88-105.
Online social networks (OSNs) are structures that help users to interact, exchange, and propagate new ideas. The identification of the influential users in OSNs is a significant process for accelerating the propagation of information that includes marketing applications or hindering the dissemination of unwanted contents, such as viruses, negative online behaviors, and rumors. This article presents a detailed survey of influential users’ identification algorithms and their performance evaluation approaches in OSNs. The survey covers recent techniques, applications, and open research issues on analysis of OSN connections for identification of influential users.
Mohammed Ali Al-Garadi; Kasturi Dewi Varathan; Sri Devi Ravana; Ejaz Ahmed; Ghulam Mujtaba; Muhammad Usman Shahid Khan; Samee U. Khan. Analysis of Online Social Network Connections for Identification of Influential Users. ACM Computing Surveys 2018, 51, 1 -37.
AMA StyleMohammed Ali Al-Garadi, Kasturi Dewi Varathan, Sri Devi Ravana, Ejaz Ahmed, Ghulam Mujtaba, Muhammad Usman Shahid Khan, Samee U. Khan. Analysis of Online Social Network Connections for Identification of Influential Users. ACM Computing Surveys. 2018; 51 (1):1-37.
Chicago/Turabian StyleMohammed Ali Al-Garadi; Kasturi Dewi Varathan; Sri Devi Ravana; Ejaz Ahmed; Ghulam Mujtaba; Muhammad Usman Shahid Khan; Samee U. Khan. 2018. "Analysis of Online Social Network Connections for Identification of Influential Users." ACM Computing Surveys 51, no. 1: 1-37.
Soft set theory is used as an effective mathematical tool for handling different operations with vague data including decision making. Decision making in soft sets is almost fully dependent on accuracy of input values. Obtaining incorrect values as input from various sources through different methods can badly effect the decision making and resulting in huge financial loss to organizations and individuals due to wrong decisions. Multi-soft set is one of those sources for providing input data for decision, but there is no existing proper technique for converting multi-soft set categorical values to numerical values. In this paper we present a technique for obtaining precise numerical data from multi-soft set. We categorize multi-soft set attributes to two types and present the method for representation of both types in a combined information system. We also present the concept of forward and reverse order in attribute values and discuss its effect on decision making.
Muhammad Sadiq Khan; Ghulam Mujtaba; Mohammed Ali Al-Garadi; Nweke Henry Friday; Ahmad Waqas; Fahad Raheem Qasmi. Multi-soft sets-based decision making using rank and fix valued attributes. 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) 2018, 1 -11.
AMA StyleMuhammad Sadiq Khan, Ghulam Mujtaba, Mohammed Ali Al-Garadi, Nweke Henry Friday, Ahmad Waqas, Fahad Raheem Qasmi. Multi-soft sets-based decision making using rank and fix valued attributes. 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). 2018; ():1-11.
Chicago/Turabian StyleMuhammad Sadiq Khan; Ghulam Mujtaba; Mohammed Ali Al-Garadi; Nweke Henry Friday; Ahmad Waqas; Fahad Raheem Qasmi. 2018. "Multi-soft sets-based decision making using rank and fix valued attributes." 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) , no. : 1-11.
Over the last few years, online communication has moved toward user-driven technologies, such as online social networks (OSNs), blogs, online virtual communities, and online sharing platforms. These social technologies have ushered in a revolution in user-generated data, online global communities, and rich human behavior-related content. Human-generated data and human mobility patterns have become important steps toward developing smart applications in many areas. Understanding human preferences is important to the development of smart applications and services to enable such applications to understand the thoughts and emotions of humans, and then act smartly based on learning from social media data. This paper discusses the role of social media data in comprehending online human data and in consequently different real applications of SM data for smart services are executed.
Mohammed Ali Al-Garadi; Ghulam Mujtaba; Muhammad Sadiq Khan; Nweke Henry Friday; Ahmad Waqas; Ghulam Murtaza. Applications of big social media data analysis: An overview. 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) 2018, 1 -5.
AMA StyleMohammed Ali Al-Garadi, Ghulam Mujtaba, Muhammad Sadiq Khan, Nweke Henry Friday, Ahmad Waqas, Ghulam Murtaza. Applications of big social media data analysis: An overview. 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). 2018; ():1-5.
Chicago/Turabian StyleMohammed Ali Al-Garadi; Ghulam Mujtaba; Muhammad Sadiq Khan; Nweke Henry Friday; Ahmad Waqas; Ghulam Murtaza. 2018. "Applications of big social media data analysis: An overview." 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) , no. : 1-5.
In a computer vision system, handwritten digits recognition is a complex task that is central to a variety of emerging applications. It has been widely used by machine learning and computer vision researchers for implementing practical applications like computerized bank check numbers reading. In this study, we implemented a multi-layer fully connected neural network with one hidden layer for handwritten digits recognition. The testing has been conducted from publicly available MNIST handwritten database. From the MNIST database, we extracted 28,000 digits images for training and 14,000 digits images for performing the test. Our multi-layer artificial neural network has an accuracy of 99.60% with test performance.
Kh Tohidul Islam; Ghulam Mujtaba; Ram Gopal Raj; Henry Friday Nweke. Handwritten digits recognition with artificial neural network. 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T) 2017, 1 -4.
AMA StyleKh Tohidul Islam, Ghulam Mujtaba, Ram Gopal Raj, Henry Friday Nweke. Handwritten digits recognition with artificial neural network. 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T). 2017; ():1-4.
Chicago/Turabian StyleKh Tohidul Islam; Ghulam Mujtaba; Ram Gopal Raj; Henry Friday Nweke. 2017. "Handwritten digits recognition with artificial neural network." 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T) , no. : 1-4.
To successfully move a robot into the building, the elevator button and elevator floor number detection and recognition can play an important role. It can help a robot move in the building, just as it also can help a visually impaired person who wants to move another floor in the building. Due to vision-based approach, the difference in lighting condition and the complex background are the main obstacles in this research. A hybrid image classification model is presented in this research to overcome all these difficulties. This hybrid model is the combination of histogram of oriented gradients and bag of words models, which later reduces the dimension of image features by using the feature selection algorithm. An artificial neural network has been implemented to get the experimental result by training and testing. In order to get training performance, 1000 training image samples have been used and additional 1000 image samples also been used to get the testing performance. The experimental results of this research indicate that this proposed framework is important for real-time implementation to implement the elevator button and elevator floor number recognition framework.
Kh Tohidul Islam; Ghulam Mujtaba; Ram Gopal Raj; Henry Friday Nweke. Elevator button and floor number recognition through hybrid image classification approach for navigation of service robot in buildings. 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T) 2017, 1 -4.
AMA StyleKh Tohidul Islam, Ghulam Mujtaba, Ram Gopal Raj, Henry Friday Nweke. Elevator button and floor number recognition through hybrid image classification approach for navigation of service robot in buildings. 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T). 2017; ():1-4.
Chicago/Turabian StyleKh Tohidul Islam; Ghulam Mujtaba; Ram Gopal Raj; Henry Friday Nweke. 2017. "Elevator button and floor number recognition through hybrid image classification approach for navigation of service robot in buildings." 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T) , no. : 1-4.
The traffic sign recognition system is a support system that can be useful to give notification and warning to drivers. It may be effective for traffic conditions on the current road traffic system. A robust artificial intelligence based traffic sign recognition system can support the driver and significantly reduce driving risk and injury. It performs by recognizing and interpreting various traffic sign using vision-based information. This study aims to recognize the well-maintained, un-maintained, standard, and non-standard traffic signs using the Bag-of-Words and the Artificial Neural Network techniques. This research work employs a Bag-of-Words model on the Speeded Up Robust Features descriptors of the road traffic signs. A robust classifier Artificial Neural Network has been employed to recognize the traffic sign in its respective class. The proposed system has been trained and tested to determine the suitable neural network architecture. The experimental results showed high accuracy of classification of traffic signs including complex background images. The proposed traffic sign detection and recognition system obtained 99.00% classification accuracy with a 1.00% false positive rate. For real-time implementation and deployment, this marginal false positive rate may increase reliability and stability of the proposed system.
Kh Tohidul Islam; Ram Gopal Raj; Ghulam Mujtaba. Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network. Symmetry 2017, 9, 138 .
AMA StyleKh Tohidul Islam, Ram Gopal Raj, Ghulam Mujtaba. Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network. Symmetry. 2017; 9 (8):138.
Chicago/Turabian StyleKh Tohidul Islam; Ram Gopal Raj; Ghulam Mujtaba. 2017. "Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network." Symmetry 9, no. 8: 138.
Personal and business users prefer to use e-mail as one of the crucial sources of communication. The usage and importance of e-mails continuously grow despite the prevalence of alternative means, such as electronic messages, mobile applications, and social networks. As the volume of business-critical e-mails continues to grow, the need to automate the management of e-mails increases for several reasons, such as spam e-mail classification, phishing e-mail classification, and multi-folder categorization, among others. This paper comprehensively reviews articles on e-mail classification published in 2006-2016 by exploiting the methodological decision analysis in five aspects, namely, e-mail classification application areas, data sets used in each application area, feature space utilized in each application area, e-mail classification techniques, and the use of performance measures. A total of 98 articles (56 articles from Web of Science core collection databases and 42 articles from Scopus database) are selected. To achieve the objective of the study, a comprehensive review and analysis is conducted to explore the various areas where e-mail classification was applied. Moreover, various public data sets, features sets, classification techniques, and performance measures are examined and used in each identified application area. This review identifies five application areas of e-mail classification. The most widely used data sets, features sets, classification techniques, and performance measures are found in the identified application areas. The extensive use of these popular data sets, features sets, classification techniques, and performance measures is discussed and justified. The research directions, research challenges, and open issues in the field of e-mail classification are also presented for future researchers.
Ghulam Mujtaba; Liyana Shuib; Ram Gopal Raj; Nahdia Majeed; Mohammed Ali Al-Garadi. Email Classification Research Trends: Review and Open Issues. IEEE Access 2017, 5, 9044 -9064.
AMA StyleGhulam Mujtaba, Liyana Shuib, Ram Gopal Raj, Nahdia Majeed, Mohammed Ali Al-Garadi. Email Classification Research Trends: Review and Open Issues. IEEE Access. 2017; 5 ():9044-9064.
Chicago/Turabian StyleGhulam Mujtaba; Liyana Shuib; Ram Gopal Raj; Nahdia Majeed; Mohammed Ali Al-Garadi. 2017. "Email Classification Research Trends: Review and Open Issues." IEEE Access 5, no. : 9044-9064.
Background: An accurate and automatic computer-aided multi-class decision support system to classify the magnetic resonance imaging (MRI) scans of the human brain as normal, Alzheimer, AIDS, cerebral calcinosis, glioma, or metastatic, which helps the radiologists to diagnose the disease in brain MRIs is created. Methods: The performance of the proposed system is validated by using benchmark MRI datasets (OASIS and Harvard) of 310 patients. Master features of the images are extracted using a fast discrete wavelet transform (DWT), then these discriminative features are further analysed by principal component analysis (PCA). Different subset sizes of principal feature vectors are provided to five different decision models. The classification models include the J48 decision tree, k-nearest neighbour (kNN), random forest (RF), and least-squares support vector machine (LS-SVM) with polynomial and radial basis kernels. Results: The RF-based classifier outperformed among all compared decision models and achieved an average accuracy of 96% with 4% standard deviation, and an area under the receiver operating characteristic (ROC) curve of 99%. LS-SVM (RBF) also shows promising results (i.e., 89% accuracy) when the least number of principal features was used. Furthermore, the performance of each classifier on different subset sizes of principal features was (80%–96%) for most performance metrics. Conclusion: The presented medical decision support system demonstrates the potential proof for accurate multi-class classification of brain abnormalities; therefore, it has a potential to use as a diagnostic tool for the medical practitioners.
Muhammad Faisal Siddiqui; Ghulam Mujtaba; Ahmed Wasif Reza; Liyana Shuib. Multi-Class Disease Classification in Brain MRIs Using a Computer-Aided Diagnostic System. Symmetry 2017, 9, 37 .
AMA StyleMuhammad Faisal Siddiqui, Ghulam Mujtaba, Ahmed Wasif Reza, Liyana Shuib. Multi-Class Disease Classification in Brain MRIs Using a Computer-Aided Diagnostic System. Symmetry. 2017; 9 (3):37.
Chicago/Turabian StyleMuhammad Faisal Siddiqui; Ghulam Mujtaba; Ahmed Wasif Reza; Liyana Shuib. 2017. "Multi-Class Disease Classification in Brain MRIs Using a Computer-Aided Diagnostic System." Symmetry 9, no. 3: 37.
Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.
Ghulam Mujtaba; Liyana Shuib; Ram Gopal Raj; Retnagowri Rajandram; Khairunisa Shaikh; Mohammed Ali Al-Garadi. Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection. PLOS ONE 2017, 12, e0170242 .
AMA StyleGhulam Mujtaba, Liyana Shuib, Ram Gopal Raj, Retnagowri Rajandram, Khairunisa Shaikh, Mohammed Ali Al-Garadi. Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection. PLOS ONE. 2017; 12 (2):e0170242.
Chicago/Turabian StyleGhulam Mujtaba; Liyana Shuib; Ram Gopal Raj; Retnagowri Rajandram; Khairunisa Shaikh; Mohammed Ali Al-Garadi. 2017. "Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection." PLOS ONE 12, no. 2: e0170242.
Soft set is a mathematical tool for dealing with vague and imprecise data. It is used in many applications and decision-making after representing the uncertain data in the Boolean-valued information system (BIS). BISs become incomplete because of various reasons, such as security, viral attack, and errors. Several soft set-based approaches exist to handle incomplete BISs for decision-making. These approaches are categorized into two categories: preprocessed (PP) and unprocessed (UP). UP approaches cannot be used for the recalculation of overall missing values. Meanwhile, PP approaches can be extended to calculate the entire missing values. This paper presents the basic concept of actual technique and initially applies it to the PP incomplete soft set. This novel concept will open a new chapter for researchers in the development of applications in the fields of mathematics, especially in Boolean data, discrete mathematics, and computer science.
Muhammad Sadiq Khan; Tutut Herawan; Ainuddin Wahid Abdul Wahab; Ghulam Mujtaba; Mohammed Ali Al-Garadi. Concept of Entire Boolean Values Recalculation From Aggregates in the Preprocessed Category of Incomplete Soft Sets. IEEE Access 2016, 5, 11444 -11454.
AMA StyleMuhammad Sadiq Khan, Tutut Herawan, Ainuddin Wahid Abdul Wahab, Ghulam Mujtaba, Mohammed Ali Al-Garadi. Concept of Entire Boolean Values Recalculation From Aggregates in the Preprocessed Category of Incomplete Soft Sets. IEEE Access. 2016; 5 ():11444-11454.
Chicago/Turabian StyleMuhammad Sadiq Khan; Tutut Herawan; Ainuddin Wahid Abdul Wahab; Ghulam Mujtaba; Mohammed Ali Al-Garadi. 2016. "Concept of Entire Boolean Values Recalculation From Aggregates in the Preprocessed Category of Incomplete Soft Sets." IEEE Access 5, no. : 11444-11454.
Existing studies have contributed immensely to link prediction by identifying different types of network communities. In this paper, a new type of network community in online social networks (OSNs) is identified using the association between network nodes. This new network community is called “virtual community.” Virtual communities are based on either the real/ physical relationships of users that are connected to their constituency, social, and professional activities or their virtual interactions associated with their cognitive levels, choice selection, and ideology. Users belonging to the same virtual community exhibit similar behavior in linking to nodes of common interest. These nodes, which reflect the common interest of a community, are called “prime nodes.” Prime nodes are linked to the prediction problem in OSN completion and are generally recommended for OSN growth. Recent studies on ranking algorithms have shown that the incompleteness of OSNs contributes to the low accuracy of ranking algorithms in identifying top spreaders. Thus, in this paper, we propose an OSN completion method based on link prediction through association between prime nodes. An experiment on predicting new links in two real big data sets of two global OSNs, namely, Facebook and Twitter, is conducted. The effectiveness of the proposed method is also validated by applying prominent ranking algorithms to the newly predicted and original networks. Results show that the accuracy rates of the ranking algorithms are improved, thereby validating the importance of the proposed method in predicting vital links.
Muhammad Sadiq Khan; Ainuddin Wahid Abdul Wahab; Tutut Herawan; Ghulam Mujtaba; Sani Danjuma; Mohammed Ali Al-Garadi; Wahid Abdul Wahab. Virtual Community Detection Through the Association between Prime Nodes in Online Social Networks and Its Application to Ranking Algorithms. IEEE Access 2016, 4, 9614 -9624.
AMA StyleMuhammad Sadiq Khan, Ainuddin Wahid Abdul Wahab, Tutut Herawan, Ghulam Mujtaba, Sani Danjuma, Mohammed Ali Al-Garadi, Wahid Abdul Wahab. Virtual Community Detection Through the Association between Prime Nodes in Online Social Networks and Its Application to Ranking Algorithms. IEEE Access. 2016; 4 ():9614-9624.
Chicago/Turabian StyleMuhammad Sadiq Khan; Ainuddin Wahid Abdul Wahab; Tutut Herawan; Ghulam Mujtaba; Sani Danjuma; Mohammed Ali Al-Garadi; Wahid Abdul Wahab. 2016. "Virtual Community Detection Through the Association between Prime Nodes in Online Social Networks and Its Application to Ranking Algorithms." IEEE Access 4, no. : 9614-9624.