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Hanan Aljuaid
Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), Riyadh 11564, Saudi Arabia

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Journal article
Published: 19 May 2021 in Sustainability
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The term “mobile learning” (or “m-learning”) refers to using handheld phones to learn and wireless computing as a learning tool and connectivity technology. This paper presents and explores the latest mobile platform for teaching and studying programming basics. The M-Learning tool was created using a platform-independent approach to target the largest available number of learners while reducing development and maintenance time and effort. Since the code is completely shared across mobile devices (iOS, Android, and Windows Phone), students can use any smartphone to access the app. To make the programme responsive, scalable, and dynamic, and to provide students with personalised guidance, the core application is based on an analysis design development implementation and assessment (ADDIE) model implemented in the Xamarin framework. The application’s key features are depicted in a prototype. An experiment is carried out on BS students at a university to evaluate the efficacy of the generated application. A usefulness questionnaire is administered to an experimental community in order to determine students’ expectations of the developed mobile application’s usability. The findings of the experiment show that the application is considerably more successful than conventional learning in developing students’ online knowledge assessment abilities, with an impact size of 1.96. The findings add to the existing mobile learning literature by defining usability assessment features and offering a basis for designing platform-independent m-learning applications. The current findings are explored in terms of their implications for study and teaching practice.

ACS Style

Daniyal Alghazzawi; Syed Hasan; Ghadah Aldabbagh; Mohammed Alhaddad; Areej Malibari; Muhammad Asghar; Hanan Aljuaid. Development of Platform Independent Mobile Learning Tool in Saudi Universities. Sustainability 2021, 13, 5691 .

AMA Style

Daniyal Alghazzawi, Syed Hasan, Ghadah Aldabbagh, Mohammed Alhaddad, Areej Malibari, Muhammad Asghar, Hanan Aljuaid. Development of Platform Independent Mobile Learning Tool in Saudi Universities. Sustainability. 2021; 13 (10):5691.

Chicago/Turabian Style

Daniyal Alghazzawi; Syed Hasan; Ghadah Aldabbagh; Mohammed Alhaddad; Areej Malibari; Muhammad Asghar; Hanan Aljuaid. 2021. "Development of Platform Independent Mobile Learning Tool in Saudi Universities." Sustainability 13, no. 10: 5691.

Journal article
Published: 11 March 2021 in PeerJ Computer Science
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Keyword extraction is essential in determining influenced keywords from huge documents as the research repositories are becoming massive in volume day by day. The research community is drowning in data and starving for information. The keywords are the words that describe the theme of the whole document in a precise way by consisting of just a few words. Furthermore, many state-of-the-art approaches are available for keyword extraction from a huge collection of documents and are classified into three types, the statistical approaches, machine learning, and graph-based methods. The machine learning approaches require a large training dataset that needs to be developed manually by domain experts, which sometimes is difficult to produce while determining influenced keywords. However, this research focused on enhancing state-of-the-art graph-based methods to extract keywords when the training dataset is unavailable. This research first converted the handcrafted dataset, collected from impact factor journals into n-grams combinations, ranging from unigram to pentagram and also enhanced traditional graph-based approaches. The experiment was conducted on a handcrafted dataset, and all methods were applied on it. Domain experts performed the user study to evaluate the results. The results were observed from every method and were evaluated with the user study using precision, recall and f-measure as evaluation matrices. The results showed that the proposed method (FNG-IE) performed well and scored near the machine learning approaches score.

ACS Style

Noman Tahir; Muhammad Asif; Shahbaz Ahmad; Muhammad Sheraz Arshad Malik; Hanan Aljuaid; Muhammad Arif Butt; Mobashar Rehman. FNG-IE: an improved graph-based method for keyword extraction from scholarly big-data. PeerJ Computer Science 2021, 7, e389 .

AMA Style

Noman Tahir, Muhammad Asif, Shahbaz Ahmad, Muhammad Sheraz Arshad Malik, Hanan Aljuaid, Muhammad Arif Butt, Mobashar Rehman. FNG-IE: an improved graph-based method for keyword extraction from scholarly big-data. PeerJ Computer Science. 2021; 7 ():e389.

Chicago/Turabian Style

Noman Tahir; Muhammad Asif; Shahbaz Ahmad; Muhammad Sheraz Arshad Malik; Hanan Aljuaid; Muhammad Arif Butt; Mobashar Rehman. 2021. "FNG-IE: an improved graph-based method for keyword extraction from scholarly big-data." PeerJ Computer Science 7, no. : e389.

Journal article
Published: 04 January 2021 in Sensors
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Health 4.0 is an extension of the Industry standard 4.0 which is aimed at the virtualization of health-care services. It employs core technologies and services for integrated management of electronic health records (EHRs), captured through various sensors. The EHR is processed and transmitted to distant experts for better diagnosis and improved healthcare delivery. However, for the successful implementation of Heath 4.0 many challenges do exist. One of the critical issues that needs attention is the security of EHRs in smart health systems. In this work, we have developed a new interpolation scheme capable of providing better quality cover media and supporting reversible EHR embedding. The scheme provides a double layer of security to the EHR by firstly using hyperchaos to encrypt the EHR. The encrypted EHR is reversibly embedded in the cover images produced by the proposed interpolation scheme. The proposed interpolation module has been found to provide better quality interpolated images. The proposed system provides an average peak signal to noise ratio (PSNR) of 52.38 dB for a high payload of 0.75 bits per pixel. In addition to embedding EHR, a fragile watermark (WM) is also encrypted using the hyperchaos embedded into the cover image for tamper detection and authentication of the received EHR. Experimental investigations reveal that our scheme provides improved performance for high contrast medical images (MI) when compared to various techniques for evaluation parameters like imperceptibility, reversibility, payload, and computational complexity. Given the attributes of the scheme, it can be used for enhancing the security of EHR in health 4.0.

ACS Style

Hanan Aljuaid; Shabir A. Parah. Secure Patient Data Transfer Using Information Embedding and Hyperchaos. Sensors 2021, 21, 282 .

AMA Style

Hanan Aljuaid, Shabir A. Parah. Secure Patient Data Transfer Using Information Embedding and Hyperchaos. Sensors. 2021; 21 (1):282.

Chicago/Turabian Style

Hanan Aljuaid; Shabir A. Parah. 2021. "Secure Patient Data Transfer Using Information Embedding and Hyperchaos." Sensors 21, no. 1: 282.

Journal article
Published: 21 July 2020 in IEEE Access
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The management and estimation of agile projects are challenging tasks for software companies due to their high failure rates. This paper emphasizes how to improve management and estimation challenges in the context of scrum, which is an agile process widely used for the development of small to medium size software projects. The scrum emphasis on code results in spending inadequate time on the estimation process. Mostly, the scrum master, along with the scrum team, estimates the upcoming software projects based on experience or historical data. Many issues can arise in a case where expert judgment is not available or historical data are not properly organized. In this paper, an Intelligent Recommender and Decision Support System (IRDSS) is proposed that can help the scrum master to better estimate an upcoming software project in terms of cost, time, and recommendations of human resources. Formal specification of IRDSS is also performed using the formalism known as Z language. Furthermore, an experiment on fifteen web projects was performed to validate the proposed approach and compared it with Delphi and Planning Poker estimation methods. The overall results indicate that the proposed system can produce better estimation than Planning Poker and Delphi methods by applying MMRE and PRED evaluation. This research opens new directions for the scrum community for the development of software projects within the allocated time and cost.

ACS Style

Muhammad Hamid; Furkh Zeshan; Adnan Ahmad; Farooq Ahmad; Muhammad Ali Hamza; Zuhaib Ashfaq Khan; Saima Munawar; Hanan Aljuaid. An Intelligent Recommender and Decision Support System (IRDSS) for Effective Management of Software Projects. IEEE Access 2020, 8, 140752 -140766.

AMA Style

Muhammad Hamid, Furkh Zeshan, Adnan Ahmad, Farooq Ahmad, Muhammad Ali Hamza, Zuhaib Ashfaq Khan, Saima Munawar, Hanan Aljuaid. An Intelligent Recommender and Decision Support System (IRDSS) for Effective Management of Software Projects. IEEE Access. 2020; 8 (99):140752-140766.

Chicago/Turabian Style

Muhammad Hamid; Furkh Zeshan; Adnan Ahmad; Farooq Ahmad; Muhammad Ali Hamza; Zuhaib Ashfaq Khan; Saima Munawar; Hanan Aljuaid. 2020. "An Intelligent Recommender and Decision Support System (IRDSS) for Effective Management of Software Projects." IEEE Access 8, no. 99: 140752-140766.

Journal article
Published: 01 July 2020 in Computers & Electrical Engineering
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Forecasting electricity consumption can help policymakers to properly plan for economic development. This is possible through energy conservation by avoiding excessive consumption of electricity through enhanced operational strategy. Power utilization and financial improvement are in long term relationship with all member nations of the Organization of Petroleum Exporting Countries (OPEC). In order to improve electricity consumption forecasting performance, this paper proposes an alternate machine learning method for forecasting OPEC electricity consumption with improved performance. The modeling of the OPEC electricity utilization forecast depends on the Cuckoo Search Algorithm by means of Lévy flights. The proposed method is found to be efficient, operative, consistent, and robust compared to the electricity consumption forecasting methods that have already been discussed by researchers in the literature. In turn, energy conservation can be motivated in the twelve OPEC member countries.

ACS Style

Abdullah Khan; Haruna Chiroma; Muhammad Imran; Asfandyar Khan; Javed Iqbal Bangash; Muhammad Asim; Mukhtar F. Hamza; Hanan Aljuaid. Forecasting electricity consumption based on machine learning to improve performance: A case study for the organization of petroleum exporting countries (OPEC). Computers & Electrical Engineering 2020, 86, 106737 .

AMA Style

Abdullah Khan, Haruna Chiroma, Muhammad Imran, Asfandyar Khan, Javed Iqbal Bangash, Muhammad Asim, Mukhtar F. Hamza, Hanan Aljuaid. Forecasting electricity consumption based on machine learning to improve performance: A case study for the organization of petroleum exporting countries (OPEC). Computers & Electrical Engineering. 2020; 86 ():106737.

Chicago/Turabian Style

Abdullah Khan; Haruna Chiroma; Muhammad Imran; Asfandyar Khan; Javed Iqbal Bangash; Muhammad Asim; Mukhtar F. Hamza; Hanan Aljuaid. 2020. "Forecasting electricity consumption based on machine learning to improve performance: A case study for the organization of petroleum exporting countries (OPEC)." Computers & Electrical Engineering 86, no. : 106737.

Journal article
Published: 17 April 2020 in Journal of King Saud University - Computer and Information Sciences
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The rapid growth in the digital era initiates the need to inculcate and preserve the academic originality of translated texts. Cross-lingual semantic similarity is concerned with identifying the degree of similarity of textual pairs written in two different languages and determining whether they are plagiarized. Unlike existing approaches, which exploit lexical and syntax features for mono-lingual similarity, this work proposed rich semantic features extracted from cross-language textual pairs, including topic similarity, semantic role labeling, spatial role labeling, named entities recognition, bag-of-stop words, bag-of-meanings for all terms, n-most frequent terms, n-least frequent terms, and different sets of their combinations. Knowledge-based semantic networks such as BabelNet and WordNet were used for computing semantic relatedness across different languages. This paper attempts to investigate two tasks, namely, cross-lingual semantic text similarity (CL-STS) and plagiarism detection and judgement (PD) using deep neural networks, which, to the best of our knowledge, have not been implemented before for STS and PD in cross-lingual setting, and using such combination of features. For this purpose, we proposed different neural network architectures to solve the PD task as either binary classification (plagiarism/independently written), or even deeper classification (literally translated/paraphrased/summarized/independently written). Deep neural networks were also used as regressors to predict semantic connotations for CL-STS tasks. Experimental results were performed on a large number of handmade data taken from multiple sources consisting of 71,910 Arabic-English pairs. Overall, experimental results showed that using deep neural networks with rich semantic features achieves encouraging results in comparison to the baselines. The proposed classifiers and regressors tend to show comparable performances when using different architectures of neural networks, but both the binary and multi-class classifiers outperform the regressors. Finally, the evaluation and analysis of using different sets of features reflected the supremacy of deeper semantic features on the classification results.

ACS Style

Salha Alzahrani; Hanan Aljuaid. Identifying cross-lingual plagiarism using rich semantic features and deep neural networks: A study on Arabic-English plagiarism cases. Journal of King Saud University - Computer and Information Sciences 2020, 1 .

AMA Style

Salha Alzahrani, Hanan Aljuaid. Identifying cross-lingual plagiarism using rich semantic features and deep neural networks: A study on Arabic-English plagiarism cases. Journal of King Saud University - Computer and Information Sciences. 2020; ():1.

Chicago/Turabian Style

Salha Alzahrani; Hanan Aljuaid. 2020. "Identifying cross-lingual plagiarism using rich semantic features and deep neural networks: A study on Arabic-English plagiarism cases." Journal of King Saud University - Computer and Information Sciences , no. : 1.

Research article
Published: 05 March 2020 in PLOS ONE
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A citation is deemed as a potential parameter to determine linkage between research articles. The parameter has extensively been employed to form multifarious academic aspects like calculating the impact factor of journals, h-Index of researchers, allocate different research grants, find the latest research trends, etc. The current state-of-the-art contends that all citations are not of equal importance. Based on this argument, the current trend in citation classification community categorizes citations into important and non-important reasons. The community has proposed different approaches to extract important citations such as citation count, context-based, metadata, and textual based approaches. The contemporary state-of-the-art in citation classification community ignores significantly potential features that can play a vital role in citation classification. This research presents a novel approach for binary citation classification by exploiting section-wise in-text citation frequencies, similarity score, and overall citation count-based features. The study also introduces machine learning algorithms based novel approach for assigning appropriate weights to the logical sections of research papers. The weights are allocated to the citations with respect to their sections. To perform the classification, we used three classification techniques, Support Vector Machine, Kernel Linear Regression, and Random Forest. The experiment was performed on two annotated benchmark datasets that contain 465 and 311 citation pairs of research articles respectively. The results revealed that the proposed approach attained an improved value of precision (i.e., 0.84 vs 0.72) from contemporary state-of-the-art approach.

ACS Style

Shahzad Nazir; Muhammad Asif; Shahbaz Ahmad; Faisal Bukhari; Muhammad Tanvir Afzal; Hanan Aljuaid. Important citation identification by exploiting content and section-wise in-text citation count. PLOS ONE 2020, 15, e0228885 .

AMA Style

Shahzad Nazir, Muhammad Asif, Shahbaz Ahmad, Faisal Bukhari, Muhammad Tanvir Afzal, Hanan Aljuaid. Important citation identification by exploiting content and section-wise in-text citation count. PLOS ONE. 2020; 15 (3):e0228885.

Chicago/Turabian Style

Shahzad Nazir; Muhammad Asif; Shahbaz Ahmad; Faisal Bukhari; Muhammad Tanvir Afzal; Hanan Aljuaid. 2020. "Important citation identification by exploiting content and section-wise in-text citation count." PLOS ONE 15, no. 3: e0228885.

Journal article
Published: 15 January 2020 in Telematics and Informatics
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Uncertainty in political, religious, and social issues causes extremism among people that are depicted by their sentiments on social media. Although, English is the most common language used to share views on social media, however, other vicinity based languages are also used by locals. Thus, it is also required to incorporate the views in such languages along with widely used languages for revealing better insights from data. This research focuses on the sentimental analysis of social media multilingual textual data to discover the intensity of the sentiments of extremism. Our study classifies the incorporated textual views into any of four categories, including high extreme, low extreme, moderate, and neutral, based on their level of extremism. Initially, a multilingual lexicon with the intensity weights is created. This lexicon is validated from domain experts and it attains 88% accuracy for validation. Subsequently, Multinomial Naïve Bayes and Linear Support Vector Classifier algorithms are employed for classification purposes. Overall, on the underlying multilingual dataset, Linear Support Vector Classifier out-performs with an accuracy of 82%.

ACS Style

Muhammad Asif; Atiab Ishtiaq; Haseeb Ahmad; Hanan Aljuaid; Jalal Shah. Sentiment analysis of extremism in social media from textual information. Telematics and Informatics 2020, 48, 101345 .

AMA Style

Muhammad Asif, Atiab Ishtiaq, Haseeb Ahmad, Hanan Aljuaid, Jalal Shah. Sentiment analysis of extremism in social media from textual information. Telematics and Informatics. 2020; 48 ():101345.

Chicago/Turabian Style

Muhammad Asif; Atiab Ishtiaq; Haseeb Ahmad; Hanan Aljuaid; Jalal Shah. 2020. "Sentiment analysis of extremism in social media from textual information." Telematics and Informatics 48, no. : 101345.

Journal article
Published: 14 January 2020 in Sustainable Cities and Society
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Blockchain technology has gained considerable attention for different types of stakeholders due to its stable implementation in the field of digital currency like Bitcoin. Some users use Bitcoin for payment exchanges against any business while others use the Bitcoin network for earning Bitcoins itself, and there is also another type of user who called hackers those flood different types of attacks to illegally earn some Bitcoins or collapsing overall network. There are also numerous uses of blockchain technology, e.g. health, automation industry, energy sector, security and authentication in smart grids. In this study, we have elaborated on different critical aspects of Blockchain technology like its style of working mechanism, possible improvement suggestions by using Proof-of-Stake, and other custom variations, attempting seven types of challenges by different novel techniques. Moreover, we have also explained the current state-of-the-artwork in blockchain’s non-financial applications like Healthcare in which contribution of four-layered custom blockchain models related to precision medicine and the clinical trial was notable. Moreover, a mobile app model called HDG for the automation of medical records without compromising privacy was also a prominent contribution.

ACS Style

Fakhri Alam Khan; Muhammad Asif; Awais Ahmad; Mafawez Alharbi; Hanan Aljuaid. Blockchain technology, improvement suggestions, security challenges on smart grid and its application in healthcare for sustainable development. Sustainable Cities and Society 2020, 55, 102018 .

AMA Style

Fakhri Alam Khan, Muhammad Asif, Awais Ahmad, Mafawez Alharbi, Hanan Aljuaid. Blockchain technology, improvement suggestions, security challenges on smart grid and its application in healthcare for sustainable development. Sustainable Cities and Society. 2020; 55 ():102018.

Chicago/Turabian Style

Fakhri Alam Khan; Muhammad Asif; Awais Ahmad; Mafawez Alharbi; Hanan Aljuaid. 2020. "Blockchain technology, improvement suggestions, security challenges on smart grid and its application in healthcare for sustainable development." Sustainable Cities and Society 55, no. : 102018.

Journal article
Published: 01 January 2020 in International Journal of Advanced Computer Science and Applications
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The purpose of cross-language textual similarity detection is to approximate the similarity of two textual units in different languages. This paper embeds the distributed representation of words in cross-language textual similarity detection using word embedding and IDF. The paper introduces a novel cross-language plagiarism detection approach constructed with the distributed representation of words in sentences. To improve the textual similarity of the approach, a novel method is used called CL-CTS-CBOW. Consequently, adding the syntax feature to the approach is improved by a novel method called CL-WES. Afterward, the approach is improved by the IDF weighting method. The corpora used in this study are four Arabic-English corpora, specifically books, Wikipedia, EAPCOUNT, and MultiUN, which have more than 10,017,106 sentences and uses with supported parallel and comparable assemblages. The proposed method in this paper combines different methods to confirm their complementarity. In the experiment, the proposed system obtains 88% English-Arabic similarity detection at the word level and 82.75% at the sentence level with various corpora.

ACS Style

Hanan Aljuaid. Cross-Language Plagiarism Detection using Word Embedding and Inverse Document Frequency (IDF). International Journal of Advanced Computer Science and Applications 2020, 11, 1 .

AMA Style

Hanan Aljuaid. Cross-Language Plagiarism Detection using Word Embedding and Inverse Document Frequency (IDF). International Journal of Advanced Computer Science and Applications. 2020; 11 (2):1.

Chicago/Turabian Style

Hanan Aljuaid. 2020. "Cross-Language Plagiarism Detection using Word Embedding and Inverse Document Frequency (IDF)." International Journal of Advanced Computer Science and Applications 11, no. 2: 1.

Conference paper
Published: 19 December 2019 in Lecture Notes in Electrical Engineering
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The purpose of cross-language plagiarism detection is to detect similar fragments of text among different languages. There are a limited number of free Arabic-English parallel corpora. This paper reports on the creation of Arabic-English parallel corpora for the estimation of Arabic-English cross-language document similarity detection. The paper discusses available Arabic-English corpora and their limitations. The paper then explains the building of our dataset. The proposed dataset is parallel Arabic-English and involves alignment for different granularities. It is constructed on comparable and parallel corpora and includes human and machine translated text. In addition, the collected texts were written by authors of varying ability. Cross-language plagiarism detection methods are used on the dataset. These methods are then assessed.

ACS Style

Hanan Aljuaid. Arabic-English Corpus for Cross-Language Textual Similarity Detection. Lecture Notes in Electrical Engineering 2019, 527 -536.

AMA Style

Hanan Aljuaid. Arabic-English Corpus for Cross-Language Textual Similarity Detection. Lecture Notes in Electrical Engineering. 2019; ():527-536.

Chicago/Turabian Style

Hanan Aljuaid. 2019. "Arabic-English Corpus for Cross-Language Textual Similarity Detection." Lecture Notes in Electrical Engineering , no. : 527-536.

Journal article
Published: 20 November 2019 in IEEE Access
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Social network site usage has grown to be a worldwide trend. These sites facilitate online contact between individuals having similar concerns. The speedy and extensive usage of such sites has affirmed their perception and positioning in the minds of users. This research adds to our consideration by empirically exploring determinants affecting users’ acceptance of such sites. A HUMP model has been introduced to evaluate the effect of hedonic (perceived playfulness), utilitarian (perceived usefulness) e-mavenism, polychronicity, and perceived ease of use on the intention to use and thus actual use of social networking sites. The causal modeling technique is used to study the outline of associations amid the projected model constructs and to analytically evaluate the research propositions. All the conjectured factors possess a considerable unswerving sway on the intention to use, with e-mavenism and polychronicity, the robust indicators.

ACS Style

Muhammad Awais; Tanzila Samin; Muhammad Awais Gulzar; Hanan Aljuaid; Muhammad Ahmad; Manuel Mazzara. User Acceptance of HUMP-Model: The Role of E-Mavenism and Polychronicity. IEEE Access 2019, 7, 174972 -174985.

AMA Style

Muhammad Awais, Tanzila Samin, Muhammad Awais Gulzar, Hanan Aljuaid, Muhammad Ahmad, Manuel Mazzara. User Acceptance of HUMP-Model: The Role of E-Mavenism and Polychronicity. IEEE Access. 2019; 7 (99):174972-174985.

Chicago/Turabian Style

Muhammad Awais; Tanzila Samin; Muhammad Awais Gulzar; Hanan Aljuaid; Muhammad Ahmad; Manuel Mazzara. 2019. "User Acceptance of HUMP-Model: The Role of E-Mavenism and Polychronicity." IEEE Access 7, no. 99: 174972-174985.

Journal article
Published: 11 October 2019 in IEEE Access
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ACS Style

Mohamed Jaward Bah; Hongzhi Wang; Mohamed Hammad; Furkh Zeshan; Hanan Aljuaid. An Effective Minimal Probing Approach With Micro-Cluster for Distance-Based Outlier Detection in Data Streams. IEEE Access 2019, 7, 154922 -154934.

AMA Style

Mohamed Jaward Bah, Hongzhi Wang, Mohamed Hammad, Furkh Zeshan, Hanan Aljuaid. An Effective Minimal Probing Approach With Micro-Cluster for Distance-Based Outlier Detection in Data Streams. IEEE Access. 2019; 7 ():154922-154934.

Chicago/Turabian Style

Mohamed Jaward Bah; Hongzhi Wang; Mohamed Hammad; Furkh Zeshan; Hanan Aljuaid. 2019. "An Effective Minimal Probing Approach With Micro-Cluster for Distance-Based Outlier Detection in Data Streams." IEEE Access 7, no. : 154922-154934.

Journal article
Published: 01 February 2014 in Journal of Computer Science
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CHILD VIDEO DATASET TOOL TO DEVELOP OBJECT TRACKING SIMULATES BABYSITTER VISION ROBOT

ACS Style

Hanan Aljuaid. CHILD VIDEO DATASET TOOL TO DEVELOP OBJECT TRACKING SIMULATES BABYSITTER VISION ROBOT. Journal of Computer Science 2014, 10, 296 -304.

AMA Style

Hanan Aljuaid. CHILD VIDEO DATASET TOOL TO DEVELOP OBJECT TRACKING SIMULATES BABYSITTER VISION ROBOT. Journal of Computer Science. 2014; 10 (2):296-304.

Chicago/Turabian Style

Hanan Aljuaid. 2014. "CHILD VIDEO DATASET TOOL TO DEVELOP OBJECT TRACKING SIMULATES BABYSITTER VISION ROBOT." Journal of Computer Science 10, no. 2: 296-304.

Conference paper
Published: 01 December 2013 in 2013 International Conference on IT Convergence and Security (ICITCS)
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The aim of this paper is to explore novel algorithms to track a child-object in an indoor and outdoor background video. It focuses on tracking a whole child-object while simultaneously tracking the body parts of that object to produce a positive system. This effort suggests an approach for labeling three body sections, i.e., the head, upper, and lower sections, and then for detecting a specific area within the three sections, and tracking this section using a Gaussian mixture model (GMM) algorithm according to the labeling technique. The system is applied in three situations: child-object walking, crawling, and seated moving. During system experimentation, walking object tracking provided the best performance, achieving 91.932% for body-part tracking and 96.235% for whole-object tracking. Crawling object tracking achieved 90.832% for body-part tracking and 96.231% for whole- object tracking. Finally, seated-moving-object tracking achieved 89.7% for body-part tracking and 93.4% for whole-object tracking.

ACS Style

Hanan Aljuaid; Dzulkifli Mohamad. Child's Body Part Tracking Simulates Babysitter Vision Robot. 2013 International Conference on IT Convergence and Security (ICITCS) 2013, 1 -4.

AMA Style

Hanan Aljuaid, Dzulkifli Mohamad. Child's Body Part Tracking Simulates Babysitter Vision Robot. 2013 International Conference on IT Convergence and Security (ICITCS). 2013; ():1-4.

Chicago/Turabian Style

Hanan Aljuaid; Dzulkifli Mohamad. 2013. "Child's Body Part Tracking Simulates Babysitter Vision Robot." 2013 International Conference on IT Convergence and Security (ICITCS) , no. : 1-4.

Conference paper
Published: 01 December 2013 in 2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)
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Numerous image-processing technologies are essential in order to recognize an object. Object detection depends on the time-sequence of the video frames. Furthermore, manifold object tracking should be done in the line of the computer's vision. To simulate a babysitter's vision, our application was developed to track objects in a scene with the main goal of creating a reliable and operative moving child-object detection system. The aim of this paper is to explore novel algorithms to track a child-object in an indoor and outdoor background video. It focuses on tracking a whole child-object while simultaneously tracking the body parts of that object to produce a positive system. This effort suggests an approach for labeling three body sections, i.e., the head, upper, and lower sections, and then for detecting a specific area within the three sections, and tracking this section using a Gaussian mixture model (GMM) algorithm according to the labeling technique. The system is applied in three situations: child-object walking, crawling, and seated moving. During system experimentation, walking object tracking provided the best performance, achieving 91.932% for body-part tracking and 96.235% for whole-object tracking. Crawling object tracking achieved 90.832% for body-part tracking and 96.231% for whole-object tracking. Finally, seated-moving-object tracking achieved 89.7% for body-part tracking and 93.4% for whole-object tracking.

ACS Style

Hanan Aljuaid; Dzulkifli Mohamad. Object tracking simulates babysitter vision robot using GMM. 2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) 2013, 60 -65.

AMA Style

Hanan Aljuaid, Dzulkifli Mohamad. Object tracking simulates babysitter vision robot using GMM. 2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR). 2013; ():60-65.

Chicago/Turabian Style

Hanan Aljuaid; Dzulkifli Mohamad. 2013. "Object tracking simulates babysitter vision robot using GMM." 2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) , no. : 60-65.

Chapter
Published: 01 January 2013 in Intelligent Computer Vision and Image Processing
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This paper proposes and contributes towards designing a complete system for off-line Arabic character recognition. The proposed system is specifically meant for Arabic handwriting recognition, but it equally works for the typed character recognition. It has various phases including preprocessing and segmentation. It also includes thinning phase and finds vertical and horizontal projection profiles. The recognition phase is managed by genetic algorithm. The genetic algorithm stands on feature extraction algorithm that defines six features for each segment. The algorithm, for Arabic handwriting recognition, obtained 90.46 recognition rate. The proposed system has been compared with other systems in the literature. It has achieved the second best recognition rate.

ACS Style

Hanan Aljuaid; Dzulkifli Mohamad; Muhammad Sarfraz. Evaluation Approach of Arabic Character Recognition. Intelligent Computer Vision and Image Processing 2013, 128 -145.

AMA Style

Hanan Aljuaid, Dzulkifli Mohamad, Muhammad Sarfraz. Evaluation Approach of Arabic Character Recognition. Intelligent Computer Vision and Image Processing. 2013; ():128-145.

Chicago/Turabian Style

Hanan Aljuaid; Dzulkifli Mohamad; Muhammad Sarfraz. 2013. "Evaluation Approach of Arabic Character Recognition." Intelligent Computer Vision and Image Processing , no. : 128-145.

Journal article
Published: 01 June 2010 in Journal of Computer Science
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A Tool to Develop Arabic Handwriting Recognition System Using Genetic Approach

ACS Style

Hanan Aljuaid; Zulkifli Muhammad; Muhammad Sarfraz. A Tool to Develop Arabic Handwriting Recognition System Using Genetic Approach. Journal of Computer Science 2010, 6, 619 -624.

AMA Style

Hanan Aljuaid, Zulkifli Muhammad, Muhammad Sarfraz. A Tool to Develop Arabic Handwriting Recognition System Using Genetic Approach. Journal of Computer Science. 2010; 6 (6):619-624.

Chicago/Turabian Style

Hanan Aljuaid; Zulkifli Muhammad; Muhammad Sarfraz. 2010. "A Tool to Develop Arabic Handwriting Recognition System Using Genetic Approach." Journal of Computer Science 6, no. 6: 619-624.

Conference paper
Published: 01 November 2009 in 2009 Fifth International Conference on Signal Image Technology and Internet Based Systems
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This paper presents a complete system to recognize off-line Arabic handwriting. The proposed system starts from preprocessing and segmentation phases. It also includes thinning phase and finds vertical and horizontal projection profiles. The recognition phase is managed by genetic algorithm. The genetic algorithm stands on feature extraction algorithm that defines six features for each segment.

ACS Style

Hanan Aljuaid; Dzulkifli Mohamad; Muhammad Sarfraz. Arabic Handwriting Recognition Using Projection Profile and Genetic Approach. 2009 Fifth International Conference on Signal Image Technology and Internet Based Systems 2009, 118 -125.

AMA Style

Hanan Aljuaid, Dzulkifli Mohamad, Muhammad Sarfraz. Arabic Handwriting Recognition Using Projection Profile and Genetic Approach. 2009 Fifth International Conference on Signal Image Technology and Internet Based Systems. 2009; ():118-125.

Chicago/Turabian Style

Hanan Aljuaid; Dzulkifli Mohamad; Muhammad Sarfraz. 2009. "Arabic Handwriting Recognition Using Projection Profile and Genetic Approach." 2009 Fifth International Conference on Signal Image Technology and Internet Based Systems , no. : 118-125.