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Dr. SULIMAN Fati
Prince Sultan University

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0 Cyberbullying
0 Regression
0 Random forest
0 SVM Algorithm
0 machine learning

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Regression
machine learning
Random forest
Cyberbullying

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Journal article
Published: 27 July 2021 in Sustainability
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Big data is rapidly being seen as a new frontier for improving organizational performance. However, it is still in its early phases of implementation in developing countries’ healthcare organizations. As data-driven insights become critical competitive advantages, it is critical to ascertain which elements influence an organization’s decision to adopt big data. The aim of this study is to propose and empirically test a theoretical framework based on technology–organization–environment (TOE) factors to identify the level of readiness of big data adoption in developing countries’ healthcare organizations. The framework empirically tested 302 Malaysian healthcare employees. The structural equation modeling was used to analyze the collected data. The results of the study demonstrated that technology, organization, and environment factors can significantly contribute towards big data adoption in healthcare organizations. However, the complexity of technology factors has shown less support for the notion. For technology practitioners, this study showed how to enhance big data adoption in healthcare organizations through TOE factors.

ACS Style

Ebrahim Ghaleb; P. Dominic; Suliman Fati; Amgad Muneer; Rao Ali. The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees. Sustainability 2021, 13, 8379 .

AMA Style

Ebrahim Ghaleb, P. Dominic, Suliman Fati, Amgad Muneer, Rao Ali. The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees. Sustainability. 2021; 13 (15):8379.

Chicago/Turabian Style

Ebrahim Ghaleb; P. Dominic; Suliman Fati; Amgad Muneer; Rao Ali. 2021. "The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees." Sustainability 13, no. 15: 8379.

Journal article
Published: 21 June 2021 in IEEE Access
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Intelligent visual surveillance systems are attracting much attention from research and industry. The invention of smart surveillance cameras with greater processing power has now been the leading stakeholder, making it conceivable to design intelligent visual surveillance systems. It is possible to assure the safety of people in both homes and public places. This work aims to distinguish the suspicious activities for surveillance environments. For this, a 63 layers deep CNN model is suggested and named “L4-Branched-ActionNet”. The suggested CNN structure is centered on the alteration of AlexNet with added four blanched sub-structures. The developed framework is first transformed into a pre-trained framework by conducting its training on an object detection dataset called CIFAR-100 with the SoftMax function. The dataset for suspicious activity recognition is then forwarded to this pretrained model for feature acquisition. The acquired deep features are subjected to feature subset optimization. These extracted features are first coded by applying entropy and then an ant colony system (ACS) is utilized on the entropy-based coded features for optimization. The configured features are then fed into numerous SVM and KNN based classification models. The cubic SVM has the highest efficiency scores, with a performance of 0.9924 in order of accuracy. The proposed model is also validated on the Weizmann action dataset and attained an accuracy of 0.9796. The successful findings indicate the suggested work’s soundness.

ACS Style

Tanzila Saba; Amjad Rehman; Rabia Latif; Suliman Mohamed Fati; Mudassar Raza; Muhammad Sharif. Suspicious Activity Recognition Using Proposed Deep L4-Branched-ActionNet with Entropy Coded Ant Colony System Optimization. IEEE Access 2021, 9, 1 -1.

AMA Style

Tanzila Saba, Amjad Rehman, Rabia Latif, Suliman Mohamed Fati, Mudassar Raza, Muhammad Sharif. Suspicious Activity Recognition Using Proposed Deep L4-Branched-ActionNet with Entropy Coded Ant Colony System Optimization. IEEE Access. 2021; 9 ():1-1.

Chicago/Turabian Style

Tanzila Saba; Amjad Rehman; Rabia Latif; Suliman Mohamed Fati; Mudassar Raza; Muhammad Sharif. 2021. "Suspicious Activity Recognition Using Proposed Deep L4-Branched-ActionNet with Entropy Coded Ant Colony System Optimization." IEEE Access 9, no. : 1-1.

Journal article
Published: 13 May 2021 in IEEE Access
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In biological systems, Nitration is a crucial post-translational modification which occurs on various amino acids. Nitration of Tyrosine is regarded as nitorsative stress biomarker resulting in the formation of peroxynitrite and other reactive and harmful nitrogen species. NitroTyrosine is closely related to Carcinogenesis, tumor growth progression and other major pathological conditions including systemic autoimmune diseases, inflammation, neurodegeneration and cardiovascular disorders. Additionally, the alteration in Nitrotyrosine profile occurs well before appearance of any symptoms of aforementioned diseases making nitrotyrosine a biomarker and potential target for early prognosis of aforementioned diseases. The wet lab identification of potential nitrotyrosine sites is laborious, time-taking and costly due to challenges of in vitro, ex vivo and in vivo identification processes. To supplement wet lab identification of nitrotyrosine, we proposed, implemented and evaluated a different approach to develop tyrosine nitration site predictors using pseudo amino acid compositions (PseAAC) and deep neural networks (DNNs). Proposed approach does not require any feature extraction and uses DNNs for learning a feature representation of peptide sequences and classification thereof. Validation of proposed approach is done using well-known model evaluation measures. Among different deep neural networks, convolutional neural network-based predictor achieved best scores on independent dataset with accuracy of 87.2%, matthew’s correlation coefficient score of 0.74 and AuC score of 0.91 which outperforms the previous reported scores of Nitrotyrosine predictors.

ACS Style

Sheraz Naseer; Rao Faizan Ali; Suliman Mohamed Fati; Amgad Muneer. iNitroY-Deep: Computational Identification of Nitrotyrosine Sites to Supplement Carcinogenesis Studies Using Deep Learning. IEEE Access 2021, 9, 73624 -73640.

AMA Style

Sheraz Naseer, Rao Faizan Ali, Suliman Mohamed Fati, Amgad Muneer. iNitroY-Deep: Computational Identification of Nitrotyrosine Sites to Supplement Carcinogenesis Studies Using Deep Learning. IEEE Access. 2021; 9 (99):73624-73640.

Chicago/Turabian Style

Sheraz Naseer; Rao Faizan Ali; Suliman Mohamed Fati; Amgad Muneer. 2021. "iNitroY-Deep: Computational Identification of Nitrotyrosine Sites to Supplement Carcinogenesis Studies Using Deep Learning." IEEE Access 9, no. 99: 73624-73640.

Journal article
Published: 04 May 2021 in IEEE Access
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Regression techniques are generally used to predict a response variable using one or more predictor variables. In many fields of study, the regressors can be highly intercorrelated, which leads to the problem of multicollinearity. Consequently, the ordinary least squares estimates become inconsistent and lead to wrong inferences. To handle the problem, machine learning techniques particularly, the ridge regression approach, are commonly used. In this paper, we revisit the problem of estimating the ridge parameter “ ${k}$ ” by proposing some new estimators using the Jackknife method and compare them with some existing estimators. The performance of the proposed estimators compared to the existing ones is evaluated using extensive Monte Carlo simulations as well as two real data sets. The results suggested that the proposed estimators outperform the existing estimators.

ACS Style

Ismail Shah; Faiza Sajid; Sajid Ali; Amjad Rehman; Saeed Ali Bahaj; Suliman Mohamed Fati. On the Performance of Jackknife Based Estimators for Ridge Regression. IEEE Access 2021, 9, 68044 -68053.

AMA Style

Ismail Shah, Faiza Sajid, Sajid Ali, Amjad Rehman, Saeed Ali Bahaj, Suliman Mohamed Fati. On the Performance of Jackknife Based Estimators for Ridge Regression. IEEE Access. 2021; 9 ():68044-68053.

Chicago/Turabian Style

Ismail Shah; Faiza Sajid; Sajid Ali; Amjad Rehman; Saeed Ali Bahaj; Suliman Mohamed Fati. 2021. "On the Performance of Jackknife Based Estimators for Ridge Regression." IEEE Access 9, no. : 68044-68053.

Journal article
Published: 15 April 2021 in Symmetry
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High blood pressure (BP) may lead to further health complications if not monitored and controlled, especially for critically ill patients. Particularly, there are two types of blood pressure monitoring, invasive measurement, whereby a central line is inserted into the patient’s body, which is associated with infection risks. The second measurement is cuff-based that monitors BP by detecting the blood volume change at the skin surface using a pulse oximeter or wearable devices such as a smartwatch. This paper aims to estimate the blood pressure using machine learning from photoplethysmogram (PPG) signals, which is obtained from cuff-based monitoring. To avoid the issues associated with machine learning such as improperly choosing the classifiers and/or not selecting the best features, this paper utilized the tree-based pipeline optimization tool (TPOT) to automate the machine learning pipeline to select the best regression models for estimating both systolic BP (SBP) and diastolic BP (DBP) separately. As a pre-processing stage, notch filter, band-pass filter, and zero phase filtering were applied by TPOT to eliminate any potential noise inherent in the signal. Then, the automated feature selection was performed to select the best features to estimate the BP, including SBP and DBP features, which are extracted using random forest (RF) and k-nearest neighbors (KNN), respectively. To train and test the model, the PhysioNet global dataset was used, which contains 32.061 million samples for 1000 subjects. Finally, the proposed approach was evaluated and validated using the mean absolute error (MAE). The results obtained were 6.52 mmHg for SBS and 4.19 mmHg for DBP, which show the superiority of the proposed model over the related works.

ACS Style

Suliman Fati; Amgad Muneer; Nur Akbar; Shakirah Taib. A Continuous Cuffless Blood Pressure Estimation Using Tree-Based Pipeline Optimization Tool. Symmetry 2021, 13, 686 .

AMA Style

Suliman Fati, Amgad Muneer, Nur Akbar, Shakirah Taib. A Continuous Cuffless Blood Pressure Estimation Using Tree-Based Pipeline Optimization Tool. Symmetry. 2021; 13 (4):686.

Chicago/Turabian Style

Suliman Fati; Amgad Muneer; Nur Akbar; Shakirah Taib. 2021. "A Continuous Cuffless Blood Pressure Estimation Using Tree-Based Pipeline Optimization Tool." Symmetry 13, no. 4: 686.

Journal article
Published: 29 March 2021 in Symmetry
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Amidation is an important post translational modification where a peptide ends with an amide group (–NH2) rather than carboxyl group (–COOH). These amidated peptides are less sensitive to proteolytic degradation with extended half-life in the bloodstream. Amides are used in different industries like pharmaceuticals, natural products, and biologically active compounds. The in-vivo, ex-vivo, and in-vitro identification of amidation sites is a costly and time-consuming but important task to study the physiochemical properties of amidated peptides. A less costly and efficient alternative is to supplement wet lab experiments with accurate computational models. Hence, an urgent need exists for efficient and accurate computational models to easily identify amidated sites in peptides. In this study, we present a new predictor, based on deep neural networks (DNN) and Pseudo Amino Acid Compositions (PseAAC), to learn efficient, task-specific, and effective representations for valine amidation site identification. Well-known DNN architectures are used in this contribution to learn peptide sequence representations and classify peptide chains. Of all the different DNN based predictors developed in this study, Convolutional neural network-based model showed the best performance surpassing all other DNN based models and reported literature contributions. The proposed model will supplement in-vivo methods and help scientists to determine valine amidation very efficiently and accurately, which in turn will enhance understanding of the valine amidation in different biological processes.

ACS Style

Sheraz Naseer; Rao Ali; Amgad Muneer; Suliman Fati. iAmideV-Deep: Valine Amidation Site Prediction in Proteins Using Deep Learning and Pseudo Amino Acid Compositions. Symmetry 2021, 13, 560 .

AMA Style

Sheraz Naseer, Rao Ali, Amgad Muneer, Suliman Fati. iAmideV-Deep: Valine Amidation Site Prediction in Proteins Using Deep Learning and Pseudo Amino Acid Compositions. Symmetry. 2021; 13 (4):560.

Chicago/Turabian Style

Sheraz Naseer; Rao Ali; Amgad Muneer; Suliman Fati. 2021. "iAmideV-Deep: Valine Amidation Site Prediction in Proteins Using Deep Learning and Pseudo Amino Acid Compositions." Symmetry 13, no. 4: 560.

Journal article
Published: 29 October 2020 in Future Internet
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The advent of social media, particularly Twitter, raises many issues due to a misunderstanding regarding the concept of freedom of speech. One of these issues is cyberbullying, which is a critical global issue that affects both individual victims and societies. Many attempts have been introduced in the literature to intervene in, prevent, or mitigate cyberbullying; however, because these attempts rely on the victims’ interactions, they are not practical. Therefore, detection of cyberbullying without the involvement of the victims is necessary. In this study, we attempted to explore this issue by compiling a global dataset of 37,373 unique tweets from Twitter. Moreover, seven machine learning classifiers were used, namely, Logistic Regression (LR), Light Gradient Boosting Machine (LGBM), Stochastic Gradient Descent (SGD), Random Forest (RF), AdaBoost (ADB), Naive Bayes (NB), and Support Vector Machine (SVM). Each of these algorithms was evaluated using accuracy, precision, recall, and F1 score as the performance metrics to determine the classifiers’ recognition rates applied to the global dataset. The experimental results show the superiority of LR, which achieved a median accuracy of around 90.57%. Among the classifiers, logistic regression achieved the best F1 score (0.928), SGD achieved the best precision (0.968), and SVM achieved the best recall (1.00).

ACS Style

Amgad Muneer; Suliman Mohamed Fati. A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter. Future Internet 2020, 12, 187 .

AMA Style

Amgad Muneer, Suliman Mohamed Fati. A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter. Future Internet. 2020; 12 (11):187.

Chicago/Turabian Style

Amgad Muneer; Suliman Mohamed Fati. 2020. "A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter." Future Internet 12, no. 11: 187.

Journal article
Published: 26 October 2020 in IEEE Access
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Recognizing the desired herb among thousands of herbs is an exhausting and time-consuming practice. Hence, herbs identification via a vision system is beneficial since the pharmacist and botanic need not to collect them through traditional ways. Thus, this paper proposed an efficient and automatic classification system to recognize Malaysian herbs that would be used in medical or cooking areas. As per the authors’ knowledge, there is no evidence for similar studies on medical herbs in Malaysia. In the proposed system, we have investigated different classifiers to build an efficient classifier; then, the classifier was integrated with a mobile app to ease the real-time classification. The proposed system employed two classifiers, namely Support Vector Machine (SVM) and Deep Learning Neural Network (DLNN). The two models have been tested on our own dataset, which contains 1000 leaves. The experimental results showed that SVM achieved 74.63% recognition accuracy and DLNN achieved 93% recognition accuracy for both of the experimental model and the developed mobile app. Furthermore, the processing time was 4 seconds for SVM and 5 seconds for DLNN classifier, while the processing time using the mobile app was 2 seconds only.

ACS Style

Amgad Muneer; Suliman Mohamed Fati. Efficient and Automated Herbs Classification Approach Based on Shape and Texture Features using Deep Learning. IEEE Access 2020, 8, 1 -18.

AMA Style

Amgad Muneer, Suliman Mohamed Fati. Efficient and Automated Herbs Classification Approach Based on Shape and Texture Features using Deep Learning. IEEE Access. 2020; 8 ():1-18.

Chicago/Turabian Style

Amgad Muneer; Suliman Mohamed Fati. 2020. "Efficient and Automated Herbs Classification Approach Based on Shape and Texture Features using Deep Learning." IEEE Access 8, no. : 1-18.

Journal article
Published: 01 October 2020 in Journal of Information Technology Research
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Cloud computing, as a trend technology, has stemmed from the concept of virtualization. Virtualization makes the resources available to the public to use without any concern for ownership or maintenance cost. In addition, the hosted applications in cloud computing platforms are highly interactive and require intensive resources. The new trend is to duplicate these applications in multiple virtual machines based on demand. Such a scheme needs an efficient resource provisioning to manage the resource assignment to multiple virtual machines properly. One of the issues in the current resource provisioning technique is assigning the resources proactively without predicting the workload of hosted applications, which cause load imbalance and resource wasting. Thus, this paper proposes a new model to predict the application workload. The experimental results show the ability of the proposed model to allocate more virtual machines and to balance the workload among the physical machines.

ACS Style

Suliman Mohamed Fati; Ayman Kamel Jaradat; Ibrahim Abunadi; Ahmed Sameh Mohammed. Modelling Virtual Machine Workload in Heterogeneous Cloud Computing Platforms. Journal of Information Technology Research 2020, 13, 156 -170.

AMA Style

Suliman Mohamed Fati, Ayman Kamel Jaradat, Ibrahim Abunadi, Ahmed Sameh Mohammed. Modelling Virtual Machine Workload in Heterogeneous Cloud Computing Platforms. Journal of Information Technology Research. 2020; 13 (4):156-170.

Chicago/Turabian Style

Suliman Mohamed Fati; Ayman Kamel Jaradat; Ibrahim Abunadi; Ahmed Sameh Mohammed. 2020. "Modelling Virtual Machine Workload in Heterogeneous Cloud Computing Platforms." Journal of Information Technology Research 13, no. 4: 156-170.

Journal article
Published: 06 August 2020 in Energies
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In recent times, the field of wireless sensor networks (WSNs) has attained a growing popularity in observing the environment due to its dynamic factors. Sensor data are gathered and forwarded to the base station (BS) through a wireless transmission medium. The data from the BS is further distributed to end-users using the Internet for their post analysis and operations. However, all sensors except the BS have limited constraints in terms of memory, energy and computational resources that degrade the network performance concerning the network lifetime and trustworthy routing. Therefore, improving energy efficiency with reliable and secure transmissions is a valuable debate among researchers for critical applications based on low-powered sensor nodes. In addition, security plays a significant cause to achieve responsible communications among sensors due to their unfixed and variable infrastructures. Keeping in view the above-mentioned issues, this paper presents an energy-aware graph clustering and intelligent routing (EGCIR) using a supervised system for WSNs to balance the energy consumption and load distribution. Moreover, a secure and efficient key distribution in a hierarchy-based mechanism is adopted by the proposed solution to improve the network efficacy in terms of routes and links integrity. The experimental results demonstrated that the EGCIR protocol enhances the network throughput by an average of 14%, packet drop ratio by an average of 50%, energy consumption by an average of 13%, data latency by an average of 30.2% and data breaches by an average of 37.5% than other state-of-the-art protocols.

ACS Style

Tanzila Saba; Khalid Haseeb; Ikram Ud Din; Ahmad Almogren; Ayman Altameem; Suliman Mohamed Fati. EGCIR: Energy-Aware Graph Clustering and Intelligent Routing Using Supervised System in Wireless Sensor Networks. Energies 2020, 13, 4072 .

AMA Style

Tanzila Saba, Khalid Haseeb, Ikram Ud Din, Ahmad Almogren, Ayman Altameem, Suliman Mohamed Fati. EGCIR: Energy-Aware Graph Clustering and Intelligent Routing Using Supervised System in Wireless Sensor Networks. Energies. 2020; 13 (16):4072.

Chicago/Turabian Style

Tanzila Saba; Khalid Haseeb; Ikram Ud Din; Ahmad Almogren; Ayman Altameem; Suliman Mohamed Fati. 2020. "EGCIR: Energy-Aware Graph Clustering and Intelligent Routing Using Supervised System in Wireless Sensor Networks." Energies 13, no. 16: 4072.

Journal article
Published: 14 March 2020 in Computer Standards & Interfaces
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Replica selection in data grids aims to select the best replica location based on the quality-of-service parameters preferred by the user. This choice is important because of the limited number of available data resources in comparison with the large number of users. Typically, user requests are fulfilled in a first-in, first-out manner. This may satisfy the users at the beginning of the queue more than those at the end. Better results can be achieved by considering the requests of all users simultaneously, thereby leading to a higher level of overall satisfaction; however, this is a difficult task because it requires a vast order of magnitude to search through a huge set of users. Therefore, in this study, the proposed combination of hybrid of the genetic algorithm and user-preference algorithm is used to overcome this problem. The results overwhelmingly verify that the proposed hybrid approach outperformed previously known used methods significantly.

ACS Style

Ayman Jaradat; Hitham Alhussian; Ahmed Patel; Suliman Mohamed Fati. Multiple users replica selection in data grids for fair user satisfaction: A hybrid approach. Computer Standards & Interfaces 2020, 71, 103432 .

AMA Style

Ayman Jaradat, Hitham Alhussian, Ahmed Patel, Suliman Mohamed Fati. Multiple users replica selection in data grids for fair user satisfaction: A hybrid approach. Computer Standards & Interfaces. 2020; 71 ():103432.

Chicago/Turabian Style

Ayman Jaradat; Hitham Alhussian; Ahmed Patel; Suliman Mohamed Fati. 2020. "Multiple users replica selection in data grids for fair user satisfaction: A hybrid approach." Computer Standards & Interfaces 71, no. : 103432.

Journal article
Published: 01 July 2019 in Journal of Information Technology Research
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Coma or unconsciousness is a state wherein the patient cannot respond to any internal or external stimulus. In this situation, the patient has no physical control over his entire body. Such cases require a serious attention and continuous monitoring to save patient's life. Currently, monitoring coma patients critically is very expensive and needs more manpower. Besides, such continuous intensive care by a paramedical assistant are error-prone, which may lead to further complications. Thus, the need for automated healthcare systems still exist. These automated systems help in continuously monitoring and recording all the vital information of a particular subject by maintaining all the comatose records. In this article, a health monitoring system for the coma patient based on the global system for mobile (GSM) and the Internet of Things (IoT) is proposed. IoT as a new technology which facilitates the process of extracting, analyzing and sending data with high efficiency. In this proposed system, four health parameters, temperature, heartbeat, accelerometer and eye blinks are monitored. By integrating these four parameters with live monitoring module and/or a GSM module, the need for clinical staff and accompanying persons will be less as the systems allows relatives and staff to monitor the coma patient online via mobile phones or receive notification based on the patient's status changes. The results achieved by the system shows real time reading of body temperature and the heartbeat. Finally, the results obtained by the MPU-6050 gyroscope and the eye blink sensor were very satisfactory.

ACS Style

Amgad Muneer; Suliman Fati. Automated Health Monitoring System Using Advanced Technology. Journal of Information Technology Research 2019, 12, 104 -132.

AMA Style

Amgad Muneer, Suliman Fati. Automated Health Monitoring System Using Advanced Technology. Journal of Information Technology Research. 2019; 12 (3):104-132.

Chicago/Turabian Style

Amgad Muneer; Suliman Fati. 2019. "Automated Health Monitoring System Using Advanced Technology." Journal of Information Technology Research 12, no. 3: 104-132.

Article
Published: 02 January 2019 in Multimedia Tools and Applications
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Nowadays, with the evolution of digital video broadcasting, as well as, the advent of high speed broadband networks, a new era of TV services has emerged known as IPTV. IPTV is a system that exploits the high speed broadband networks to deliver TV services to the subscribers. From the service provider viewpoint, the challenge in IPTV systems is how to build delivery networks that exploits the resources efficiently and reduces the service cost, as well. However, designing such delivery networks are affected by many factors including choosing the suitable network architecture, load balancing, resources waste, and cost reduction. Furthermore, IPTV contents characteristics; particularly size, popularity, and interactivity play an important role in balancing the load and avoiding the resources waste for delivery networks. Ignoring the content status in solving delivery networks issues particularly replica placement, request distribution, and resource allocation problems leads to load imbalance, which in turn, leads to performance degradation in IPTV system. In this survey paper, we introduce IPTV delivery networks terminology and taxonomy. Upon that, we investigate the challenges related to the contents’ awareness in those delivery networks. At the end of the paper, we propose a content-awareness in ITV delivery networks and CDN as the future direction and discuss its importance in different aspects as request redirection, resource allocation, and replica placement.

ACS Style

Suliman Mohamed Fati; Putra Sumari. A survey on content awareness challenges in IPTV delivery networks. Multimedia Tools and Applications 2019, 78, 16817 -16842.

AMA Style

Suliman Mohamed Fati, Putra Sumari. A survey on content awareness challenges in IPTV delivery networks. Multimedia Tools and Applications. 2019; 78 (12):16817-16842.

Chicago/Turabian Style

Suliman Mohamed Fati; Putra Sumari. 2019. "A survey on content awareness challenges in IPTV delivery networks." Multimedia Tools and Applications 78, no. 12: 16817-16842.

Book chapter
Published: 10 April 2018 in IPTV Delivery Networks
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This chapter explores the challenges that hinder Internet Protocol Television (IPTV) delivery networks and the current research directions that are looking at resolving these challenges in light of content characteristics. To make IPTV a popular and a standard platform worldwide, standardisations are conducted. IPTV standardisation aims to achieve the following purposes: interoperability, investment confidence and cost reduction. The general IPTV architecture explains the different shareholders of IPTV and the relationships among them. This chapter depicts the IPTV architecture and the involved domains. The IPTV delivery network has been witnessing several developments starting from centralised architecture, which delivers content using a single main server. The chapter further focuses to formulate and evaluate the load status of IPTV contents aiming at building content‐aware IPTV delivery networks. Modelling the status of content according to the characteristics of IPTV content is very important in designing IPTV delivery networks.

ACS Style

Suliman Mohamed Fati; Putra Sumari. IPTV: Delivering TV Services over IP Networks. IPTV Delivery Networks 2018, 1 -23.

AMA Style

Suliman Mohamed Fati, Putra Sumari. IPTV: Delivering TV Services over IP Networks. IPTV Delivery Networks. 2018; ():1-23.

Chicago/Turabian Style

Suliman Mohamed Fati; Putra Sumari. 2018. "IPTV: Delivering TV Services over IP Networks." IPTV Delivery Networks , no. : 1-23.

Book chapter
Published: 10 April 2018 in IPTV Delivery Networks
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The authors model the content status in an IPTV environment. Cost reduction is an area of concern to service providers; therefore, it should be considered during the designing of IPTV delivery networks. The authors assess request distribution, replica placement and resource allocation. These are the key problems threatening IPTV delivery networks. Round robin algorithm can become the best request distribution algorithm in case all the contents are fully replicated among homogeneous servers. The replica placement problem and the resource allocation problem are integrated for the purpose of building content‐aware IPTV delivery networks. This integration allows the service provider to adjust the required resources according to the necessitated replication scheme. To investigate the performance of the proposed IPTV content status model, the authors have tested the model on an empirical data set that is sampled according to the content popularity distribution.

ACS Style

Suliman Mohamed Fati; Putra Sumari. Content Awareness in IPTV Delivery Networks. IPTV Delivery Networks 2018, 93 -125.

AMA Style

Suliman Mohamed Fati, Putra Sumari. Content Awareness in IPTV Delivery Networks. IPTV Delivery Networks. 2018; ():93-125.

Chicago/Turabian Style

Suliman Mohamed Fati; Putra Sumari. 2018. "Content Awareness in IPTV Delivery Networks." IPTV Delivery Networks , no. : 93-125.

Journal article
Published: 16 May 2016 in Multimedia Tools and Applications
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Due to the enormous improvement in networking and multimedia, IPTV has become recently a popular means to distribute high quality TV services over IP networks. Accordingly, Telecommunication companies started the competition to provide IPTV services to increase their customer base and profit. The key concern of service providers in this hectic competition is to provide high quality service with lower cost. However, the contents’ popularity and the users’ preferences are fluctuated rapidly, which leads to resources waste and load imbalance. Thus, the contents’ status should be considered during the content replication to save resources and reduce service cost. To the best of our knowledge, there is no work investigate the impact of contents’ status on building Replica Placement Strategy. Therefore, this paper studies the impact of contents’ status on replica placement strategy over the peer-service area architecture. Two optimization models are proposed Cost Effective Model (CE), which replicates the contents partially without considering contents’ status and Cost Effective with Load Balance model (CELB), which considers the contents’ status. Both models have been solved using Hybrid Genetic Algorithm. The experimental results show that CELB model outperforms the other models in terms of Storage Saving Ratio (SSR), load distribution, and allocation cost.

ACS Style

Suliman Mohamed Fati; Putra Sumari. Content-aware replica placement strategy for IPTV services over peer-service area architecture. Multimedia Tools and Applications 2016, 76, 10041 -10065.

AMA Style

Suliman Mohamed Fati, Putra Sumari. Content-aware replica placement strategy for IPTV services over peer-service area architecture. Multimedia Tools and Applications. 2016; 76 (7):10041-10065.

Chicago/Turabian Style

Suliman Mohamed Fati; Putra Sumari. 2016. "Content-aware replica placement strategy for IPTV services over peer-service area architecture." Multimedia Tools and Applications 76, no. 7: 10041-10065.

Journal article
Published: 05 September 2012 in Multimedia Tools and Applications
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Load balancing is a crucial factor in IPTV delivery networks. Load balancing aims at utilizing the resources efficiently, maximizing the throughput, and minimizing the request rejection rate. The peer-service area is the recent architecture for IPTV delivery networks that overcomes the flaws of the previous architectures. However, it still suffers from the load imbalance problem. This paper investigates the load imbalance problem, and tries to augment the peer-service area architecture to overcome this problem. To achieve the load balancing over the proposed architecture, we suggest a new load-balancing algorithm that considers both the expected and the current load of both contents and servers. The proposed load-balancing algorithm consists of two stages. The first stage is the contents replication according to their expected load, while the second stage is the content-aware request distribution. To test the effectiveness of the proposed algorithm, we have compared it with both the traditional Round Robin algorithm and Cho algorithm. The experimental results depict that the proposed algorithm outperforms the two other algorithms in terms of load balance, throughput, and request rejection rate.

ACS Style

Suliman Mohamed Ahmed Gaber; Putra Sumari. Predictive and content-aware load balancing algorithm for peer-service area based IPTV networks. Multimedia Tools and Applications 2012, 70, 1987 -2010.

AMA Style

Suliman Mohamed Ahmed Gaber, Putra Sumari. Predictive and content-aware load balancing algorithm for peer-service area based IPTV networks. Multimedia Tools and Applications. 2012; 70 (3):1987-2010.

Chicago/Turabian Style

Suliman Mohamed Ahmed Gaber; Putra Sumari. 2012. "Predictive and content-aware load balancing algorithm for peer-service area based IPTV networks." Multimedia Tools and Applications 70, no. 3: 1987-2010.