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Prof. Wadii Boulila
Manouba University, Tunisia

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Research Keywords & Expertise

0 Uncertainty Analysis
0 Big Data Analysis and Data Science
0 Machine Learning Application
0 Envinronmental applications
0 Remote Sesning

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Uncertainty Analysis

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Short Biography

WADII BOULILA received a B.Eng. (highest 1st Class Honours with distinction) in Computer Science from the Aviation School of Borj El Amri, in 2005, an MSc. from the National School of Computer Science (ENSI), University of Manouba, Tunisia, in 2007, and a Ph.D. conjointly from the ENSI and Telecom-Bretagne, University of Rennes 1, France, in 2012. He is currently an Associate Professor of Computer Science with the IS Department, College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia. He is also a Senior Researcher with the RIADI Laboratory, University of Manouba, and previously a Senior Research Fellow with the ITI Department, University of Rennes 1, France. He has participated in many research and industrial-funded projects. His primary research interests include big data analytics, deep learning, cybersecurity, data mining, artificial intelligence, uncertainty modeling, and remote sensing images. He has served as a Chair, a Reviewer, and a TPC member for many leading international conferences and journals. He is an IEEE member and a Senior Fellow of the Higher Education Academy (SFHEA), U.K.

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Journal article
Published: 28 August 2021 in Applied Sciences
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Spreading rumors in social media is considered under cybercrimes that affect people, societies, and governments. For instance, some criminals create rumors and send them on the internet, then other people help them to spread it. Spreading rumors can be an example of cyber abuse, where rumors or lies about the victim are posted on the internet to send threatening messages or to share the victim’s personal information. During pandemics, a large amount of rumors spreads on social media very fast, which have dramatic effects on people’s health. Detecting these rumors manually by the authorities is very difficult in these open platforms. Therefore, several researchers conducted studies on utilizing intelligent methods for detecting such rumors. The detection methods can be classified mainly into machine learning-based and deep learning-based methods. The deep learning methods have comparative advantages against machine learning ones as they do not require preprocessing and feature engineering processes and their performance showed superior enhancements in many fields. Therefore, this paper aims to propose a Novel Hybrid Deep Learning Model for Detecting COVID-19-related Rumors on Social Media (LSTM–PCNN). The proposed model is based on a Long Short-Term Memory (LSTM) and Concatenated Parallel Convolutional Neural Networks (PCNN). The experiments were conducted on an ArCOV-19 dataset that included 3157 tweets; 1480 of them were rumors (46.87%) and 1677 tweets were non-rumors (53.12%). The findings of the proposed model showed a superior performance compared to other methods in terms of accuracy, recall, precision, and F-score.

ACS Style

Mohammed Al-Sarem; Abdullah Alsaeedi; Faisal Saeed; Wadii Boulila; Omair AmeerBakhsh. A Novel Hybrid Deep Learning Model for Detecting COVID-19-Related Rumors on Social Media Based on LSTM and Concatenated Parallel CNNs. Applied Sciences 2021, 11, 7940 .

AMA Style

Mohammed Al-Sarem, Abdullah Alsaeedi, Faisal Saeed, Wadii Boulila, Omair AmeerBakhsh. A Novel Hybrid Deep Learning Model for Detecting COVID-19-Related Rumors on Social Media Based on LSTM and Concatenated Parallel CNNs. Applied Sciences. 2021; 11 (17):7940.

Chicago/Turabian Style

Mohammed Al-Sarem; Abdullah Alsaeedi; Faisal Saeed; Wadii Boulila; Omair AmeerBakhsh. 2021. "A Novel Hybrid Deep Learning Model for Detecting COVID-19-Related Rumors on Social Media Based on LSTM and Concatenated Parallel CNNs." Applied Sciences 11, no. 17: 7940.

Review
Published: 16 August 2021 in Computers in Biology and Medicine
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Obesity is considered a principal public health concern and ranked as the fifth foremost reason for death globally. Overweight and obesity are one of the main lifestyle illnesses that leads to further health concerns and contributes to numerous chronic diseases, including cancers, diabetes, metabolic syndrome, and cardiovascular diseases. The World Health Organization also predicted that 30% of death in the world will be initiated with lifestyle diseases in 2030 and can be stopped through the suitable identification and addressing of associated risk factors and behavioral involvement policies. Thus, detecting and diagnosing obesity as early as possible is crucial. Therefore, the machine learning approach is a promising solution to early predictions of obesity and the risk of overweight because it can offer quick, immediate, and accurate identification of risk factors and condition likelihoods. The present study conducted a systematic literature review to examine obesity research and machine learning techniques for the prevention and treatment of obesity from 2010 to 2020. Accordingly, 93 papers are identified from the review articles as primary studies from an initial pool of over 700 papers addressing obesity. Consequently, this study initially recognized the significant potential factors that influence and cause adult obesity. Next, the main diseases and health consequences of obesity and overweight are investigated. Ultimately, this study recognized the machine learning methods that can be used for the prediction of obesity. Finally, this study seeks to support decision-makers looking to understand the impact of obesity on health in the general population and identify outcomes that can be used to guide health authorities and public health to further mitigate threats and effectively guide obese people globally.

ACS Style

Mahmood Safaei; Elankovan A. Sundararajan; Maha Driss; Wadii Boulila; Azrulhizam Shapi'I. A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity. Computers in Biology and Medicine 2021, 136, 104754 .

AMA Style

Mahmood Safaei, Elankovan A. Sundararajan, Maha Driss, Wadii Boulila, Azrulhizam Shapi'I. A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity. Computers in Biology and Medicine. 2021; 136 ():104754.

Chicago/Turabian Style

Mahmood Safaei; Elankovan A. Sundararajan; Maha Driss; Wadii Boulila; Azrulhizam Shapi'I. 2021. "A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity." Computers in Biology and Medicine 136, no. : 104754.

Research article
Published: 09 August 2021 in Computational Intelligence and Neuroscience
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Wireless mesh networks (WMNs) have emerged as a scalable, reliable, and agile wireless network that supports many types of innovative technologies such as the Internet of Things (IoT), Wireless Sensor Networks (WSN), and Internet of Vehicles (IoV). Due to the limited number of orthogonal channels, interference between channels adversely affects the fair distribution of bandwidth among mesh clients, causing node starvation in terms of insufficient bandwidth distribution, which impedes the adoption of WMN as an efficient access technology. Therefore, a fair channel assignment is crucial for the mesh clients to utilize the available resources. However, the node starvation problem due to unfair channel distribution has been vastly overlooked during channel assignment by the extant research. Instead, existing channel assignment algorithms equally distribute the interference reduction on the links to achieve fairness which neither guarantees a fair distribution of the network bandwidth nor eliminates node starvation. In addition, the metaheuristic-based solutions such as genetic algorithm, which is commonly used for WMN, use randomness in creating initial population and selecting the new generation usually leading the search to local minima. To this end, this study proposes a Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique (FA-SCGA-CAA) to solve node starvation problem in wireless mesh networks. FA-SCGA-CAA maximizes link fairness while minimizing link interference using a genetic algorithm (GA) with a novel nonlinear fairness-oriented fitness function. The primary chromosome with powerful genes is created based on multicriterion links ranking channel assignment algorithm. Such a chromosome was used with a proposed semichaotic technique to create a strong population that directs the search towards the global minima effectively and efficiently. The proposed semichaotic technique was also used during the mutation and parent selection of the new genes. Extensive experiments were conducted to evaluate the proposed algorithm. A comparison with related work shows that the proposed FA-SCGA-CAA reduced the potential node starvation by 22% and improved network capacity utilization by 23%. It can be concluded that the proposed FA-SCGA-CAA is reliable to maintain high node-level fairness while maximizing the utilization of the network resources, which is the ultimate goal of many wireless networks.

ACS Style

Fuad A. Ghaleb; Bander Ali Saleh Al-Rimy; Wadii Boulila; Faisal Saeed; Maznah Kamat; Mohd. Foad Rohani; Shukor Abd Razak. Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique for Node Starvation Problem in Wireless Mesh Networks. Computational Intelligence and Neuroscience 2021, 2021, 1 -19.

AMA Style

Fuad A. Ghaleb, Bander Ali Saleh Al-Rimy, Wadii Boulila, Faisal Saeed, Maznah Kamat, Mohd. Foad Rohani, Shukor Abd Razak. Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique for Node Starvation Problem in Wireless Mesh Networks. Computational Intelligence and Neuroscience. 2021; 2021 ():1-19.

Chicago/Turabian Style

Fuad A. Ghaleb; Bander Ali Saleh Al-Rimy; Wadii Boulila; Faisal Saeed; Maznah Kamat; Mohd. Foad Rohani; Shukor Abd Razak. 2021. "Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique for Node Starvation Problem in Wireless Mesh Networks." Computational Intelligence and Neuroscience 2021, no. : 1-19.

Research article
Published: 17 June 2021 in Wireless Communications and Mobile Computing
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Wireless sensor network (WSN) is an integral part of Internet of Things (IoT). The sensor nodes in WSN generate large sensing data which is disseminated to intelligent servers using multiple wireless networks. This large data is prone to attacks from malicious nodes which become part of the network, and it is difficult to find these adversaries. The work in this paper presents a mechanism to detect adversaries for the IEEE 802.15.4 standard which is a central medium access protocol used in WSN-based IoT applications. The collisions and exhaustion attacks are detected based on a soft decision-based algorithm. In case the QoS of the network is compromised due to large data traffic, the proposed protocol adaptively varies the duty cycle of the IEEE 802.15.4. Simulation results show that the proposed intrusion detection and adaptive duty cycle algorithm improves the energy efficiency of a WSN with a reduced network delay.

ACS Style

Abdulfattah Noorwali; Ahmad Naseem Alvi; Mohammad Zubair Khan; Muhammad Awais Javed; Wadii Boulila; Priyadarshini A. Pattanaik. A Novel QoS-Oriented Intrusion Detection Mechanism for IoT Applications. Wireless Communications and Mobile Computing 2021, 2021, 1 -10.

AMA Style

Abdulfattah Noorwali, Ahmad Naseem Alvi, Mohammad Zubair Khan, Muhammad Awais Javed, Wadii Boulila, Priyadarshini A. Pattanaik. A Novel QoS-Oriented Intrusion Detection Mechanism for IoT Applications. Wireless Communications and Mobile Computing. 2021; 2021 ():1-10.

Chicago/Turabian Style

Abdulfattah Noorwali; Ahmad Naseem Alvi; Mohammad Zubair Khan; Muhammad Awais Javed; Wadii Boulila; Priyadarshini A. Pattanaik. 2021. "A Novel QoS-Oriented Intrusion Detection Mechanism for IoT Applications." Wireless Communications and Mobile Computing 2021, no. : 1-10.

Journal article
Published: 06 June 2021 in Remote Sensing
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Satellite images have drawn increasing interest from a wide variety of users, including business and government, ever since their increased usage in important fields ranging from weather, forestry and agriculture to surface changes and biodiversity monitoring. Recent updates in the field have also introduced various deep learning (DL) architectures to satellite imagery as a means of extracting useful information. However, this new approach comes with its own issues, including the fact that many users utilize ready-made cloud services (both public and private) in order to take advantage of built-in DL algorithms and thus avoid the complexity of developing their own DL architectures. However, this presents new challenges to protecting data against unauthorized access, mining and usage of sensitive information extracted from that data. Therefore, new privacy concerns regarding sensitive data in satellite images have arisen. This research proposes an efficient approach that takes advantage of privacy-preserving deep learning (PPDL)-based techniques to address privacy concerns regarding data from satellite images when applying public DL models. In this paper, we proposed a partially homomorphic encryption scheme (a Paillier scheme), which enables processing of confidential information without exposure of the underlying data. Our method achieves robust results when applied to a custom convolutional neural network (CNN) as well as to existing transfer learning methods. The proposed encryption scheme also allows for training CNN models on encrypted data directly, which requires lower computational overhead. Our experiments have been performed on a real-world dataset covering several regions across Saudi Arabia. The results demonstrate that our CNN-based models were able to retain data utility while maintaining data privacy. Security parameters such as correlation coefficient (−0.004), entropy (7.95), energy (0.01), contrast (10.57), number of pixel change rate (4.86), unified average change intensity (33.66), and more are in favor of our proposed encryption scheme. To the best of our knowledge, this research is also one of the first studies that applies PPDL-based techniques to satellite image data in any capacity.

ACS Style

Munirah Alkhelaiwi; Wadii Boulila; Jawad Ahmad; Anis Koubaa; Maha Driss. An Efficient Approach Based on Privacy-Preserving Deep Learning for Satellite Image Classification. Remote Sensing 2021, 13, 2221 .

AMA Style

Munirah Alkhelaiwi, Wadii Boulila, Jawad Ahmad, Anis Koubaa, Maha Driss. An Efficient Approach Based on Privacy-Preserving Deep Learning for Satellite Image Classification. Remote Sensing. 2021; 13 (11):2221.

Chicago/Turabian Style

Munirah Alkhelaiwi; Wadii Boulila; Jawad Ahmad; Anis Koubaa; Maha Driss. 2021. "An Efficient Approach Based on Privacy-Preserving Deep Learning for Satellite Image Classification." Remote Sensing 13, no. 11: 2221.

Journal article
Published: 23 May 2021 in Ecological Informatics
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Time-series remote sensing data offer a rich source of information that can be used in a wide range of applications, from monitoring changes in land cover to surveillance of crops, coastal changes, flood risk assessment, and urban sprawl. In this paper, time-series satellite images are used to predict urban expansion. As the ground truth is not available in time-series satellite images, an unsupervised image segmentation method based on deep learning is used to generate the ground truth for training and validation. The automated annotated images are then manually validated using Google Maps to generate the ground truth. The remaining data were then manually annotated. Prediction of urban expansion is achieved by using a ConvLSTM network, which can learn the global spatio-temporal information without shrinking the size of spatial feature maps. The ConvLSTM based model is applied on the time-series satellite images and the results of prediction are compared with Pix2pix and Dual GAN networks. In this paper, experimental results are conducted using several multi-date satellite images representing the three largest cities in Saudi Arabia, namely: Riyadh, Jeddah, and Dammam. The evaluation results show that the proposed ConvLSTM based model produced better prediction results in terms of Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and overall classification accuracy as compared to Pix2pix and Dual GAN. Moreover, the training time of the proposed architecture is less than the Dual GAN architecture.

ACS Style

Wadii Boulila; Hamza Ghandorh; Mehshan Ahmed Khan; Fawad Ahmed; Jawad Ahmad. A novel CNN-LSTM-based approach to predict urban expansion. Ecological Informatics 2021, 64, 101325 .

AMA Style

Wadii Boulila, Hamza Ghandorh, Mehshan Ahmed Khan, Fawad Ahmed, Jawad Ahmad. A novel CNN-LSTM-based approach to predict urban expansion. Ecological Informatics. 2021; 64 ():101325.

Chicago/Turabian Style

Wadii Boulila; Hamza Ghandorh; Mehshan Ahmed Khan; Fawad Ahmed; Jawad Ahmad. 2021. "A novel CNN-LSTM-based approach to predict urban expansion." Ecological Informatics 64, no. : 101325.

Journal article
Published: 21 March 2021 in Sensors
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Machine learning (ML)-based algorithms are playing an important role in cancer diagnosis and are increasingly being used to aid clinical decision-making. However, these commonly operate as ‘black boxes’ and it is unclear how decisions are derived. Recently, techniques have been applied to help us understand how specific ML models work and explain the rational for outputs. This study aims to determine why a given type of cancer has a certain phenotypic characteristic. Cancer results in cellular dysregulation and a thorough consideration of cancer regulators is required. This would increase our understanding of the nature of the disease and help discover more effective diagnostic, prognostic, and treatment methods for a variety of cancer types and stages. Our study proposes a novel explainable analysis of potential biomarkers denoting tumorigenesis in non-small cell lung cancer. A number of these biomarkers are known to appear following various treatment pathways. An enhanced analysis is enabled through a novel mathematical formulation for the regulators of mRNA, the regulators of ncRNA, and the coupled mRNA–ncRNA regulators. Temporal gene expression profiles are approximated in a two-dimensional spatial domain for the transition states before converging to the stationary state, using a system comprised of coupled-reaction partial differential equations. Simulation experiments demonstrate that the proposed mathematical gene-expression profile represents a best fit for the population abundance of these oncogenes. In future, our proposed solution can lead to the development of alternative interpretable approaches, through the application of ML models to discover unknown dynamics in gene regulatory systems.

ACS Style

Muhamed Farouq; Wadii Boulila; Zain Hussain; Asrar Rashid; Moiz Shah; Sajid Hussain; Nathan Ng; Dominic Ng; Haris Hanif; Mohamad Shaikh; Aziz Sheikh; Amir Hussain. A Novel Coupled Reaction-Diffusion System for Explainable Gene Expression Profiling. Sensors 2021, 21, 2190 .

AMA Style

Muhamed Farouq, Wadii Boulila, Zain Hussain, Asrar Rashid, Moiz Shah, Sajid Hussain, Nathan Ng, Dominic Ng, Haris Hanif, Mohamad Shaikh, Aziz Sheikh, Amir Hussain. A Novel Coupled Reaction-Diffusion System for Explainable Gene Expression Profiling. Sensors. 2021; 21 (6):2190.

Chicago/Turabian Style

Muhamed Farouq; Wadii Boulila; Zain Hussain; Asrar Rashid; Moiz Shah; Sajid Hussain; Nathan Ng; Dominic Ng; Haris Hanif; Mohamad Shaikh; Aziz Sheikh; Amir Hussain. 2021. "A Novel Coupled Reaction-Diffusion System for Explainable Gene Expression Profiling." Sensors 21, no. 6: 2190.

Original research
Published: 17 February 2021 in Journal of Ambient Intelligence and Humanized Computing
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Service-Oriented Computing (SOC) describes a specific paradigm of computing that utilizes Web services as reusable components in order to develop new software applications. SOC allows distributed applications to work together via the Internet without direct human intervention. In this work, we propose a new SOC-based approach to ensure application development. This approach ensures the discovery, selection, and composition of the most appropriate Web services. With this approach, various requirements (both functional and non-functional) are specified by the developer to satisfy QoS, QoE, and QoBiz parameters and Web services are selected and composed to meet these requirements. Our approach is implemented using the Req-WSComposer (Requirements-based Web Services Composer) platform, whose functionalities are tested using an extended and enriched version of the OWLS-TC dataset, which includes around 10,830 semantic Web services descriptions. The results of our experiments demonstrate that the proposed approach enables users to extract the most appropriate composition solution that satisfies the developer's pre-determined requirements.

ACS Style

Maha Driss; Safa Ben Atitallah; Amal Albalawi; Wadii Boulila. Req-WSComposer: a novel platform for requirements-driven composition of semantic web services. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -17.

AMA Style

Maha Driss, Safa Ben Atitallah, Amal Albalawi, Wadii Boulila. Req-WSComposer: a novel platform for requirements-driven composition of semantic web services. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-17.

Chicago/Turabian Style

Maha Driss; Safa Ben Atitallah; Amal Albalawi; Wadii Boulila. 2021. "Req-WSComposer: a novel platform for requirements-driven composition of semantic web services." Journal of Ambient Intelligence and Humanized Computing , no. : 1-17.

Journal article
Published: 12 February 2021 in Computers and Electronics in Agriculture
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Developments in remote sensing technology have led to a continuous increase in the volume of remote-sensing data, which can be qualified as big remote sensing data. A wide range of potential applications is using these data including land cover classification, regional planning, catastrophe prediction and management, and climate-change estimation. Big remote sensing data are characterized by different types of resolutions (radiometric, spatial, spectral, and temporal), modes of imaging, and sensor types, and this range of options often makes the process of analyzing and interpreting such data more difficult. In this paper, which is the first study of its kind, we propose a novel distributed deep learning-based approach for the classification of big remote sensing images. Specifically, we propose Distributed Convolutional-Neural-Networks for handling RS image classification (RS-DCNN). The first step is to prepare the training dataset for RS-DCNN. Then, to ensure a data-parallel training on the top of the Apache Spark framework, a pixel-based convolutional-neural-network model across the big data cluster is performed using BigDL. Experiments are conducted on a real dataset covering many regions of Saudi Arabia and the results demonstrate high classification accuracy at a faster speed than other state-of-the-art classification methods.

ACS Style

Wadii Boulila; Mokhtar Sellami; Maha Driss; Mohammed Al-Sarem; Mahmood Safaei; Fuad A. Ghaleb. RS-DCNN: A novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification. Computers and Electronics in Agriculture 2021, 182, 106014 .

AMA Style

Wadii Boulila, Mokhtar Sellami, Maha Driss, Mohammed Al-Sarem, Mahmood Safaei, Fuad A. Ghaleb. RS-DCNN: A novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification. Computers and Electronics in Agriculture. 2021; 182 ():106014.

Chicago/Turabian Style

Wadii Boulila; Mokhtar Sellami; Maha Driss; Mohammed Al-Sarem; Mahmood Safaei; Fuad A. Ghaleb. 2021. "RS-DCNN: A novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification." Computers and Electronics in Agriculture 182, no. : 106014.

Journal article
Published: 01 February 2021 in International Journal of Electrical and Computer Engineering (IJECE)
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Population-based approaches regularly are better than single based (local search) approaches in exploring the search space. However, the drawback of population-based approaches is in exploiting the search space. Several hybrid approaches have proven their efficiency through different domains of optimization problems by incorporating and integrating the strength of population and local search approaches. Meanwhile, hybrid methods have a drawback of increasing the parameter tuning. Recently, population-based local search was proposed for a university course-timetabling problem with fewer parameters than existing approaches, the proposed approach proves its effectiveness. The proposed approach employs two operators to intensify and diversify the search space. The first operator is applied to a single solution, while the second is applied for all solutions. This paper aims to investigate the performance of population-based local search for the nurse rostering problem. The INRC2010 database with a dataset composed of 69 instances is used to test the performance of PB-LS. A comparison was made between the performance of PB-LS and other existing approaches in the literature. Results show good performances of proposed approach compared to other approaches, where population-based local search provided best results in 55 cases over 69 instances used in experiments.

ACS Style

Anmar Abuhamdah; Wadii Boulila; Ghaith Jaradat; Anas M. Quteishat; Mutasem K. Alsmadi; Ibrahim A. Almarashdeh. A novel population-based local search for nurse rostering problem. International Journal of Electrical and Computer Engineering (IJECE) 2021, 11, 471 -480.

AMA Style

Anmar Abuhamdah, Wadii Boulila, Ghaith Jaradat, Anas M. Quteishat, Mutasem K. Alsmadi, Ibrahim A. Almarashdeh. A novel population-based local search for nurse rostering problem. International Journal of Electrical and Computer Engineering (IJECE). 2021; 11 (1):471-480.

Chicago/Turabian Style

Anmar Abuhamdah; Wadii Boulila; Ghaith Jaradat; Anas M. Quteishat; Mutasem K. Alsmadi; Ibrahim A. Almarashdeh. 2021. "A novel population-based local search for nurse rostering problem." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1: 471-480.

Review
Published: 18 January 2021 in Sensors
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Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity can be simultaneously recorded using electroencephalography (EEG). However, TMS-evoked EEG potentials (TEPs) do not only reflect transcranial neural stimulation as they can be contaminated by artifacts. Over the last two decades, significant developments in EEG amplifiers, TMS-compatible technology, customized hardware and open source software have enabled researchers to develop approaches which can substantially reduce TMS-induced artifacts. In TMS-EEG experiments, various physiological and external occurrences have been identified and attempts have been made to minimize or remove them using online techniques. Despite these advances, technological issues and methodological constraints prevent straightforward recordings of early TEPs components. To the best of our knowledge, there is no review on both TMS-EEG artifacts and EEG technologies in the literature to-date. Our survey aims to provide an overview of research studies in this field over the last 40 years. We review TMS-EEG artifacts, their sources and their waveforms and present the state-of-the-art in EEG technologies and front-end characteristics. We also propose a synchronization toolbox for TMS-EEG laboratories. We then review subject preparation frameworks and online artifacts reduction maneuvers for improving data acquisition and conclude by outlining open challenges and future research directions in the field.

ACS Style

Giuseppe Varone; Zain Hussain; Zakariya Sheikh; Adam Howard; Wadii Boulila; Mufti Mahmud; Newton Howard; Francesco Morabito; Amir Hussain. Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures. Sensors 2021, 21, 637 .

AMA Style

Giuseppe Varone, Zain Hussain, Zakariya Sheikh, Adam Howard, Wadii Boulila, Mufti Mahmud, Newton Howard, Francesco Morabito, Amir Hussain. Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures. Sensors. 2021; 21 (2):637.

Chicago/Turabian Style

Giuseppe Varone; Zain Hussain; Zakariya Sheikh; Adam Howard; Wadii Boulila; Mufti Mahmud; Newton Howard; Francesco Morabito; Amir Hussain. 2021. "Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures." Sensors 21, no. 2: 637.

Journal article
Published: 22 December 2020 in IEEE Access
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With recent advancements in multimedia technologies, the security of digital data has become a critical issue. To overcome the vulnerabilities of current security protocols, researchers tend to focus their efforts on modifying existing protocols. Over the last few decades, though, several proposed encryption algorithms have been proven insecure, leading to major threats against important data. Using the most appropriate encryption algorithm is a very important means of protection against such attacks, but which algorithm is most appropriate in any particular situation will also be dependent on what sort of data is being secured. However, testing potential cryptosystems one by one to find the best option can take up an important processing time. For a fast and accurate selection of appropriate encryption algorithms, we propose a security level detection approach for image encryption algorithms by incorporating a support vector machine (SVM). In this work, we also create a dataset using standard encryption security parameters, such as entropy, contrast, homogeneity, peak signal to noise ratio, mean square error, energy, and correlation. These parameters are taken as features extracted from different cipher images. Dataset labels are divided into three categories based on their security level: strong, acceptable, and weak. To evaluate the performance of our proposed model, we have performed different analyses (f1-score, recall, precision, and accuracy), and our results demonstrate the effectiveness of this SVM-supported system.

ACS Style

Arslan Shafique; Jameel Ahmed; Wadii Boulila; Hamzah Ghandorh; Jawad Ahmad; Mujeeb Ur Rehman. Detecting the Security Level of Various Cryptosystems Using Machine Learning Models. IEEE Access 2020, 9, 9383 -9393.

AMA Style

Arslan Shafique, Jameel Ahmed, Wadii Boulila, Hamzah Ghandorh, Jawad Ahmad, Mujeeb Ur Rehman. Detecting the Security Level of Various Cryptosystems Using Machine Learning Models. IEEE Access. 2020; 9 ():9383-9393.

Chicago/Turabian Style

Arslan Shafique; Jameel Ahmed; Wadii Boulila; Hamzah Ghandorh; Jawad Ahmad; Mujeeb Ur Rehman. 2020. "Detecting the Security Level of Various Cryptosystems Using Machine Learning Models." IEEE Access 9, no. : 9383-9393.

Research article
Published: 19 December 2020 in International Journal of Imaging Systems and Technology
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Frequent pattern mining (FPM) algorithms are often based on graph isomorphism in order to identify common pattern occurrences. Recent research works, however, have focused on cases in which patterns can differ from their occurrences. Such cases have great potential for the analysis of noisy network data. Most existing FPM algorithms consider differences in edges and their labels, but none of them so far has considered the structural differences of vertices and their labels. Discerning how to identify cases that differ from the initial pattern by any number of vertices, edges, or labels has become the main challenge of recent research works. As a solution, we suggest a novel FMP algorithm named mining frequent approximate patterns (MFAPs) with two central new characteristics. First, we begin by using the inexact matching technique, which allows for structural differences in edge, vertices, and labels. Second, we follow the approximate matching with a focus on mining patterns within the directed graph, as opposed to the more commonly explored case of patterns being mined from the undirected graph. Our results illustrate the effectiveness of this new MFAP algorithm in identifying patterns within an optimized time.

ACS Style

Kaouthar Driss; Wadii Boulila; Aurélie Leborgne; Pierre Gançarski. Mining frequent approximate patterns in large networks. International Journal of Imaging Systems and Technology 2020, 31, 1265 -1279.

AMA Style

Kaouthar Driss, Wadii Boulila, Aurélie Leborgne, Pierre Gançarski. Mining frequent approximate patterns in large networks. International Journal of Imaging Systems and Technology. 2020; 31 (3):1265-1279.

Chicago/Turabian Style

Kaouthar Driss; Wadii Boulila; Aurélie Leborgne; Pierre Gançarski. 2020. "Mining frequent approximate patterns in large networks." International Journal of Imaging Systems and Technology 31, no. 3: 1265-1279.

Review
Published: 26 November 2020 in Computer Science Review
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This paper reviews big data and Internet of Things (IoT)-based applications in smart environments. The aim is to identify key areas of application, current trends, data architectures, and ongoing challenges in these fields. To the best of our knowledge, this is a first systematic review of its kind, that reviews academic documents published in peer-reviewed venues from 2011 to 2019, based on a four-step selection process of identification, screening, eligibility, and inclusion for the selection process. In order to examine these documents, a systematic review was conducted and six main research questions were answered. The results indicate that the integration of big data and IoT technologies creates exciting opportunities for real-world smart environment applications for monitoring, protection, and improvement of natural resources. The fields that have been investigated in this survey include smart environment monitoring, smart farming/agriculture, smart metering, and smart disaster alerts. We conclude by summarizing the methods most commonly used in big data and IoT, which we posit to serve as a starting point for future multi-disciplinary research in smart cities and environments.

ACS Style

Yosra Hajjaji; Wadii Boulila; Imed Riadh Farah; Imed Romdhani; Amir Hussain. Big data and IoT-based applications in smart environments: A systematic review. Computer Science Review 2020, 39, 100318 .

AMA Style

Yosra Hajjaji, Wadii Boulila, Imed Riadh Farah, Imed Romdhani, Amir Hussain. Big data and IoT-based applications in smart environments: A systematic review. Computer Science Review. 2020; 39 ():100318.

Chicago/Turabian Style

Yosra Hajjaji; Wadii Boulila; Imed Riadh Farah; Imed Romdhani; Amir Hussain. 2020. "Big data and IoT-based applications in smart environments: A systematic review." Computer Science Review 39, no. : 100318.

Review article
Published: 11 September 2020 in Computer Science Review
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The rapid growth of urban populations worldwide imposes new challenges on citizens’ daily lives, including environmental pollution, public security, road congestion, etc. New technologies have been developed to manage this rapid growth by developing smarter cities. Integrating the Internet of Things (IoT) in citizens’ lives enables the innovation of new intelligent services and applications that serve sectors around the city, including healthcare, surveillance, agriculture, etc. IoT devices and sensors generate large amounts of data that can be analyzed to gain valuable information and insights that help to enhance citizens’ quality of life. Deep Learning (DL), a new area of Artificial Intelligence (AI), has recently demonstrated the potential for increasing the efficiency and performance of IoT big data analytics. In this survey, we provide a review of the literature regarding the use of IoT and DL to develop smart cities. We begin by defining the IoT and listing the characteristics of IoT-generated big data. Then, we present the different computing infrastructures used for IoT big data analytics, which include cloud, fog, and edge computing. After that, we survey popular DL models and review the recent research that employs both IoT and DL to develop smart applications and services for smart cities. Finally, we outline the current challenges and issues faced during the development of smart city services.

ACS Style

Safa Ben Atitallah; Maha Driss; Wadii Boulila; Henda Ben Ghézala. Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions. Computer Science Review 2020, 38, 100303 .

AMA Style

Safa Ben Atitallah, Maha Driss, Wadii Boulila, Henda Ben Ghézala. Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions. Computer Science Review. 2020; 38 ():100303.

Chicago/Turabian Style

Safa Ben Atitallah; Maha Driss; Wadii Boulila; Henda Ben Ghézala. 2020. "Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions." Computer Science Review 38, no. : 100303.

Journal article
Published: 01 September 2020 in IEEE Access
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Visual selective image encryption can both improve the efficiency of the image encryption algorithm and reduce the frequency and severity of attacks against data. In this article, a new form of encryption is proposed based on keys derived from Deoxyribonucleic Acid (DNA) and plaintext image. The proposed scheme results in chaotic visual selective encryption of image data. In order to make and ensure that this new scheme is robust and secure against various kinds of attacks, the initial conditions of the chaotic maps utilized are generated from a random DNA sequence as well as plaintext image via an SHA-512 hash function. To increase the key space, three different single dimension chaotic maps are used. In the proposed scheme, these maps introduce diffusion in a plain image by selecting a block that have greater correlation and then it is bitwise XORed with the random matrix. The other two chaotic maps break the correlation among adjacent pixels via confusion (row and column shuffling). Once the ciphertext image has been divided into the respective units of Most Significant Bits (MSBs) and Least Significant Bit (LSBs), the host image is passed through lifting wavelet transformation, which replaces the low-frequency blocks of the host image (i.e., HL and HH) with the aforementioned MSBs and LSBs of ciphertext. This produces a final visual selective encrypted image and all security measures proves the robustness of the proposed scheme.

ACS Style

Jan Sher Khan; Wadii Boulila; Jawad Ahmad; Saeed Rubaiee; Atique Ur Rehman; Roobaea Alroobaea; William J. Buchanan. DNA and Plaintext Dependent Chaotic Visual Selective Image Encryption. IEEE Access 2020, 8, 159732 -159744.

AMA Style

Jan Sher Khan, Wadii Boulila, Jawad Ahmad, Saeed Rubaiee, Atique Ur Rehman, Roobaea Alroobaea, William J. Buchanan. DNA and Plaintext Dependent Chaotic Visual Selective Image Encryption. IEEE Access. 2020; 8 (99):159732-159744.

Chicago/Turabian Style

Jan Sher Khan; Wadii Boulila; Jawad Ahmad; Saeed Rubaiee; Atique Ur Rehman; Roobaea Alroobaea; William J. Buchanan. 2020. "DNA and Plaintext Dependent Chaotic Visual Selective Image Encryption." IEEE Access 8, no. 99: 159732-159744.

Journal article
Published: 01 September 2020 in Electronics
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Vehicular ad hoc networks (VANETs) play an important role as enabling technology for future cooperative intelligent transportation systems (CITSs). Vehicles in VANETs share real-time information about their movement state, traffic situation, and road conditions. However, VANETs are susceptible to the cyberattacks that create life threatening situations and/or cause road congestion. Intrusion detection systems (IDSs) that rely on the cooperation between vehicles to detect intruders, were the most suggested security solutions for VANET. Unfortunately, existing cooperative IDSs (CIDSs) are vulnerable to the legitimate yet compromised collaborators that share misleading and manipulated information and disrupt the IDSs’ normal operation. As such, this paper proposes a misbehavior-aware on-demand collaborative intrusion detection system (MA-CIDS) based on the concept of distributed ensemble learning. That is, vehicles individually use the random forest algorithm to train local IDS classifiers and share their locally trained classifiers on-demand with the vehicles in their vicinity, which reduces the communication overhead. Once received, the performance of the classifiers is evaluated using the local testing dataset in the receiving vehicle. The evaluation values are used as a trustworthiness factor and used to rank the received classifiers. The classifiers that deviate much from the box-and-whisker plot lower boundary are excluded from the set of the collaborators. Then, each vehicle constructs an ensemble of weighted random forest-based classifiers that encompasses the locally and remotely trained classifiers. The outputs of the classifiers are aggregated using a robust weighted voting scheme. Extensive simulations were conducted utilizing the network security laboratory-knowledge discovery data mining (NSL-KDD) dataset to evaluate the performance of the proposed MA-CIDS model. The obtained results show that MA-CIDS performs better than the other existing models in terms of effectiveness and efficiency for VANET.

ACS Style

Fuad A. Ghaleb; Faisal Saeed; Mohammad Al-Sarem; Bander Ali Saleh Al-Rimy; Wadii Boulila; A. E. M. Eljialy; Khalid Aloufi; Mamoun Alazab. Misbehavior-Aware On-Demand Collaborative Intrusion Detection System Using Distributed Ensemble Learning for VANET. Electronics 2020, 9, 1411 .

AMA Style

Fuad A. Ghaleb, Faisal Saeed, Mohammad Al-Sarem, Bander Ali Saleh Al-Rimy, Wadii Boulila, A. E. M. Eljialy, Khalid Aloufi, Mamoun Alazab. Misbehavior-Aware On-Demand Collaborative Intrusion Detection System Using Distributed Ensemble Learning for VANET. Electronics. 2020; 9 (9):1411.

Chicago/Turabian Style

Fuad A. Ghaleb; Faisal Saeed; Mohammad Al-Sarem; Bander Ali Saleh Al-Rimy; Wadii Boulila; A. E. M. Eljialy; Khalid Aloufi; Mamoun Alazab. 2020. "Misbehavior-Aware On-Demand Collaborative Intrusion Detection System Using Distributed Ensemble Learning for VANET." Electronics 9, no. 9: 1411.

Journal article
Published: 29 July 2020 in IEEE Access
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The evolution of wireless and mobile communication from 0G to the upcoming 5G gives rise to data sharing through the Internet. This data transfer via open public networks are susceptible to several types of attacks. Encryption is a method that can protect information from hackers and hence confidential data can be secured through a cryptosystem. Due to the increased number of cyber attacks, encryption has become an important component of modern-day communication. In this paper, a new image encryption algorithm is presented using chaos theory and dynamic substitution. The proposed scheme is based on two dimensional Henon, Ikeda chaotic maps, and substitution box (S-box) transformation. Through Henon, a random S-Box is selected and the image pixel is substituted randomly. To analyze security and robustness of the proposed algorithm, several security tests such as information entropy, histogram investigation, correlation analysis, energy, homogeneity, and mean square error are performed. The entropy values of the test images are greater than 7.99 and the key space of the proposed algorithm is 2798. Furthermore, the correlation values of the encrypted images using the the proposed scheme are close to zero when compared with other conventional schemes. The number of pixel change rate (NPCR) and unified average change intensity (UACI) for the proposed scheme are higher than 99.50% and 33, respectively. The simulation results and comparison with the state-of-the-art algorithms prove the efficiency and security of the proposed scheme.

ACS Style

Abdullah Qayyum; Jawad Ahmad; Wadii Boulila; Saeed Rubaiee; Arshad; Fawad Masood; Fawad Khan; William J. Buchanan. Chaos-Based Confusion and Diffusion of Image Pixels Using Dynamic Substitution. IEEE Access 2020, 8, 140876 -140895.

AMA Style

Abdullah Qayyum, Jawad Ahmad, Wadii Boulila, Saeed Rubaiee, Arshad, Fawad Masood, Fawad Khan, William J. Buchanan. Chaos-Based Confusion and Diffusion of Image Pixels Using Dynamic Substitution. IEEE Access. 2020; 8 (99):140876-140895.

Chicago/Turabian Style

Abdullah Qayyum; Jawad Ahmad; Wadii Boulila; Saeed Rubaiee; Arshad; Fawad Masood; Fawad Khan; William J. Buchanan. 2020. "Chaos-Based Confusion and Diffusion of Image Pixels Using Dynamic Substitution." IEEE Access 8, no. 99: 140876-140895.

Journal article
Published: 11 June 2020 in Remote Sensing
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Aerial photography involves capturing images from aircraft and other flying objects, including Unmanned Aerial Vehicles (UAV). Aerial images are used in many fields and can contain sensitive information that requires secure processing. We proposed an innovative new cryptosystem for the processing of aerial images utilizing a chaos-based private key block cipher method so that the images are secure even on untrusted cloud servers. The proposed cryptosystem is based on a hybrid technique combining the Mersenne Twister (MT), Deoxyribonucleic Acid (DNA), and Chaotic Dynamical Rossler System (MT-DNA-Chaos) methods. The combination of MT with the four nucleotides and chaos sequencing creates an enhanced level of security for the proposed algorithm. The system is tested at three separate phases. The combined effects of the three levels improve the overall efficiency of the randomness of data. The proposed method is computationally agile, and offered more security than existing cryptosystems. To assess, this new system is examined against different statistical tests such as adjacent pixels correlation analysis, histogram consistency analyses and its variance, visual strength analysis, information randomness and uncertainty analysis, pixel inconsistency analysis, pixels similitude analyses, average difference, and maximum difference. These tests confirmed its validity for real-time communication purposes.

ACS Style

Fawad Masood; Wadii Boulila; Jawad Ahmad; Arshad Arshad; Syam Sankar; Saeed Rubaiee; William Buchanan. A Novel Privacy Approach of Digital Aerial Images Based on Mersenne Twister Method with DNA Genetic Encoding and Chaos. Remote Sensing 2020, 12, 1893 .

AMA Style

Fawad Masood, Wadii Boulila, Jawad Ahmad, Arshad Arshad, Syam Sankar, Saeed Rubaiee, William Buchanan. A Novel Privacy Approach of Digital Aerial Images Based on Mersenne Twister Method with DNA Genetic Encoding and Chaos. Remote Sensing. 2020; 12 (11):1893.

Chicago/Turabian Style

Fawad Masood; Wadii Boulila; Jawad Ahmad; Arshad Arshad; Syam Sankar; Saeed Rubaiee; William Buchanan. 2020. "A Novel Privacy Approach of Digital Aerial Images Based on Mersenne Twister Method with DNA Genetic Encoding and Chaos." Remote Sensing 12, no. 11: 1893.

Journal article
Published: 23 March 2020 in IEEE Access
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The evolution of Service-Oriented Computing (SOC) provides more efficient software development methods for building and engineering new value-added service-based applications. SOC is a computing paradigm that relies on Web services as fundamental elements. Research and technical advancements in Web services composition have been considered as an effective opportunity to develop new service-based applications satisfying complex requirements rapidly and efficiently. In this paper, we present a novel approach enhancing the composition of semantic Web services. The novelty of our approach, as compared to others reported in the literature, rests on: i) mapping user’s/organization’s requirements with Business Process Modeling Notation (BPMN) and semantic descriptions using ontologies, ii) considering functional requirements and also different types of non-functional requirements, such as quality of service (QoS), quality of experience (QoE), and quality of business (QoBiz), iii) using Formal Concept Analysis (FCA) technique to select the optimal set of Web services, iv) considering composability levels between sequential Web services using Relational Concept Analysis (RCA) technique to decrease the required adaptation efforts, and finally, v) validating the obtained service-based applications by performing an analytical technique, which is the monitoring. The approach experimented on an extended version of the OWLS-TC dataset, which includes more than 10830 Web services descriptions from various domains. The obtained results demonstrate that our approach allows to successfully and effectively compose Web services satisfying different types of user’s functional and non-functional requirements.

ACS Style

Maha Driss; Amani Aljehani; Wadii Boulila; Hamza Ghandorh; Mohammad Al-Sarem. Servicing Your Requirements: An FCA and RCA-Driven Approach for Semantic Web Services Composition. IEEE Access 2020, 8, 59326 -59339.

AMA Style

Maha Driss, Amani Aljehani, Wadii Boulila, Hamza Ghandorh, Mohammad Al-Sarem. Servicing Your Requirements: An FCA and RCA-Driven Approach for Semantic Web Services Composition. IEEE Access. 2020; 8 (99):59326-59339.

Chicago/Turabian Style

Maha Driss; Amani Aljehani; Wadii Boulila; Hamza Ghandorh; Mohammad Al-Sarem. 2020. "Servicing Your Requirements: An FCA and RCA-Driven Approach for Semantic Web Services Composition." IEEE Access 8, no. 99: 59326-59339.