Marwan Albahar - Academia.edu (original) (raw)

Papers by Marwan Albahar

International Journal of Environmental Research and Public Health, Dec 15, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

The complexity and interconnection of smart cities provide huge political, technical, and socioec... more The complexity and interconnection of smart cities provide huge political, technical, and socioeconomic challenges for the designers, integrators, and organisations who are responsible for the administration of these new entities. A growing number of studies are concentrating their attention on the safety, privacy, and potential dangers that exist within smart cities. These studies are drawing attention to the dangers that are associated with information security as well as the challenges that smart city infrastructure faces in the management and processing of personal data. This state of the art review of the literature analyses a number of issues pertaining to smart homes, offers a helpful synthesis of the important information found in the primary research, and creates a model for the interaction between smart cities. An overview of smart home research incorporating machine learning, including everything from definition to current research state. The state of art begins with a sm...

Bioengineering

Omicron, so-called COVID-2, is an emerging variant of COVID-19 which is proved to be the most fat... more Omicron, so-called COVID-2, is an emerging variant of COVID-19 which is proved to be the most fatal amongst the other variants such as alpha, beta and gamma variants (α, β, γ variants) due to its stern and perilous nature. It has caused hazardous effects globally in a very short span of time. The diagnosis and medication of Omicron patients are both challenging undertakings for researchers (medical experts) due to the involvement of various uncertainties and the vagueness of its altering behavior. In this study, an algebraic approach, interval-valued fuzzy hypersoft set (iv-FHSS), is employed to assess the conditions of patients after the application of suitable medication. Firstly, the distance measures between two iv-FHSSs are formulated with a brief description some of its properties, then a multi-attribute decision-making framework is designed through the proposal of an algorithm. This framework consists of three phases of medication. In the first phase, the Omicron-diagnosed pa...

Bioengineering

Susceptibility analysis is an intelligent technique that not only assists decision makers in asse... more Susceptibility analysis is an intelligent technique that not only assists decision makers in assessing the suspected severity of any sort of brain tumour in a patient but also helps them diagnose and cure these tumours. This technique has been proven more useful in those developing countries where the available health-based and funding-based resources are limited. By employing set-based operations of an arithmetical model, namely fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS), this study seeks to develop a robust multi-attribute decision support mechanism for appraising patients’ susceptibility to brain tumours. The FPCIFHSS is regarded as more reliable and generalised for handling information-based uncertainties because its complex components and fuzzy parameterisation are designed to deal with the periodic nature of the data and dubious parameters (sub-parameters), respectively. In the proposed FPCIFHSS-susceptibility model, some suitable types of brain ...

International Journal of Environmental Research and Public Health

Facilitating the navigation of visually impaired people in indoor environments requires detecting... more Facilitating the navigation of visually impaired people in indoor environments requires detecting indicating signs and informing them. In this paper, we proposed an indoor sign detection based on a lightweight anchor-free object detection model called FAM-centerNet. The baseline model of this work is the centerNet, which is an anchor-free object detection model with high performance and low computation complexity. A Foreground Attention Module (FAM) was introduced to extract target objects in real scenes with complex backgrounds. This module segments the foreground to extract relevant features of the target object using midground proposal and boxes-induced segmentation. In addition, the foreground module provides scale information to improve the regression performance. Extensive experiments on two datasets prove the efficiency of the proposed model for detecting general objects and custom indoor signs. The Pascal VOC dataset was used to test the performance of the proposed model for...

Bioengineering, 2023

Susceptibility analysis is an intelligent technique that not only assists decision makers in ass... more Susceptibility analysis is an intelligent technique that not only assists decision makers in assessing the suspected severity of any sort of brain tumour in a patient but also helps them diagnose and cure these tumours. This technique has been proven more useful in those developing countries where the available health-based and funding-based resources are limited. By employing set-based operations of an arithmetical model, namely fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS), this study seeks to develop a robust multi-attribute decision support mechanism for appraising patients’ susceptibility to brain tumours. The FPCIFHSS is regarded as more reliable and generalised for handling information-based uncertainties because its complex components and fuzzy parameterisation are designed to deal with the periodic nature of the data and dubious parameters (sub-parameters), respectively. In the proposed FPCIFHSS-susceptibility model, some suitable types of brain tumours are approximated with respect to the most relevant symptoms (parameters) based on the expert opinions of decision makers in terms of complex intuitionistic fuzzy numbers (CIFNs). After determining the fuzzy parameterised values of multi-argument-based tuples and converting the CIFNs into fuzzy values, the scores for such types of tumours are computed based on a core matrix which relates them with fuzzy parameterised multi-argument-based tuples. The sub-intervals within [0, 1] denote the susceptibility degrees of patients corresponding to these types of brain tumours. The susceptibility of patients is examined by observing the membership of score values in the sub-intervals.

Processes, 2023

What is presented in this research is an intelligent system for detecting the volume percentage o... more What is presented in this research is an intelligent system for detecting the volume percentage of three-phase fluids passing through oil pipes. The structure of the detection system consists of an X-ray tube, a Pyrex galss pipe, and two sodium iodide detectors. A three-phase fluid of water, gas, and oil has been simulated inside the pipe in two flow regimes, annular and stratified. Different volume percentages from 10 to 80% are considered for each phase. After producing and emitting X-rays from the source and passing through the pipe containing a three-phase fluid, the intensity of photons is recorded by two detectors. The simulation is introduced by a Monte Carlo N-Particle (MCNP) code. After the implementation of all flow regimes in different volume percentages, the signals recorded by the detectors were recorded and labeled. Three frequency characteristics and five wavelet transform characteristics were extracted from the received signals of each detector, which were collected in a total of 16 characteristics from each test. The feature selection system based on the particle swarm optimization (PSO) algorithm was applied to determine the best combination of extracted features. The result was the introduction of seven features as the best features to determine volume percentages. The introduced characteristics were considered as the input of a Multilayer Perceptron (MLP) neural network, whose structure had seven input neurons (selected characteristics) and two output neurons (volume percentage of gas and water). The highest error obtained in determining volume percentages was equal to 0.13 as MSE, a low error compared with previous works. Using the PSO algorithm to select the most optimal features, the current research’s accuracy in determining volume percentages has significantly increased.

Mathematics

The standard optimization of open-pit mine design and production scheduling, which is impacted by... more The standard optimization of open-pit mine design and production scheduling, which is impacted by a variety of factors, is an essential part of mining activities. The metal uncertainty, which is connected to supply uncertainty, is a crucial component in optimization. To address uncertainties regarding the economic value of mining blocks and the general problem of mine design optimization, a minimum-cut network flow algorithm is employed to give the optimal ultimate pit limits and pushback designs under uncertainty. A structure that is computationally effective and can manage the joint presentation and treatment of the economic values of mining blocks under various circumstances is created by the push re-label minimum-cut technique. In this study, the algorithm is put to the test using a copper deposit and shows similarities to other stochastic optimizers for mine planning that have already been created. Higher possibilities of reaching predicted production targets are created by the...

Mathematics

Rivers play a major role within ecosystems and society, including for domestic, industrial, and a... more Rivers play a major role within ecosystems and society, including for domestic, industrial, and agricultural uses, and in power generation. Forecasting of suspended sediment yield (SSY) is critical for design, management, planning, and disaster prevention in river basin systems. It is difficult to forecast the SSY using conventional methods because these approaches cannot handle complicated non-stationarity and non-linearity. Artificial intelligence techniques have gained popularity in water resources due to handling complex problems of SSY. In this study, a fully automated generalized single hybrid intelligent artificial neural network (ANN)-based genetic algorithm (GA) forecasting model was developed using water discharge, temperature, rainfall, SSY, rock type, relief, and catchment area data of eleven gauging stations for forecasting the SSY. It is applied at individual gauging stations for SSY forecasting in the Mahanadi River which is one of India’s largest peninsular rivers. A...

Computers, Materials & Continua

There has been an increase in attacks on mobile devices, such as smartphones and tablets, due to ... more There has been an increase in attacks on mobile devices, such as smartphones and tablets, due to their growing popularity. Mobile malware is one of the most dangerous threats, causing both security breaches and financial losses. Mobile malware is likely to continue to evolve and proliferate to carry out a variety of cybercrimes on mobile devices. Mobile malware specifically targets Android operating system as it has grown in popularity. The rapid proliferation of Android malware apps poses a significant security risk to users, making static and manual analysis of malicious files difficult. Therefore, efficient identification and classification of Android malicious files is crucial. Several Convolutional Neural Network (CNN) based methods have been proposed in this regard; however, there is still room for performance improvement. In this work, we propose a transfer learning and stacking approach to efficiently detect the Android malware files by utilizing two wellknown machine learning models, ResNet-50 and Support Vector Machine (SVM). The proposed model is trained on the DREBIN dataset by transforming malicious APK files into grayscale images. Our model yields higher performance measures than state-of-the-art works on the DREBIN dataset, where the reported measures are accuracy, recall, precision, and F1 measures of 97.8%, 95.8%, 95.7%, and 95.7%, respectively.

Electronics

Today, one of the most popular ways organizations use to provide their services, or broadly speak... more Today, one of the most popular ways organizations use to provide their services, or broadly speaking, interact with their customers, is through web applications. Those applications should be protected and meet all security requirements. Penetration testers need to make sure that the attacker cannot find any weaknesses to destroy, exploit, or disclose information on the Web. Therefore, using automated vulnerability assessment tools is the best and easiest part of web application pen-testing, but these tools have strengths and weaknesses. Thus, using the wrong tool may lead to undetected, expected, or known vulnerabilities that may open doors for cyberattacks. This research proposes an empirical comparison of pen-testing tools for detecting web app vulnerabilities using approved standards and methods to facilitate the selection of appropriate tools according to the needs of penetration testers. In addition, we have proposed an enhanced benchmarking framework that combines the latest r...

Applied Computational Intelligence and Soft Computing

Emerging technologies provide a highly compatible platform for analyzing substantial data volumes... more Emerging technologies provide a highly compatible platform for analyzing substantial data volumes relating to crowd management. These technologies are not only effective in providing remedial solutions but they are also cutting-edge, saving both time and money, and user-friendly. About twenty percent of the world’s Muslims are granted the ability to take part in religious observances on an annual basis. An in-depth analysis of the technologies and applications used in the Hajj and Umrah systems is required given the importance of utilizing and adapting technology to assist pilgrims performing the Hajj and Umrah. Both of these pilgrimages are considered acts of worship by Muslims. As a result, the services provided during Umrah and Hajj will be significantly enhanced if the numerous technological advancements that are evaluated in this paper are implemented. The majority of previous research is shown to revolve around artificial intelligence and embedded Internet of Things technologi...

Computers, Materials & Continua

The coronavirus disease 2019 (COVID-19) pandemic has had a devastating impact on the health and w... more The coronavirus disease 2019 (COVID-19) pandemic has had a devastating impact on the health and welfare of the global population. A key measure to combat COVID-19 has been the effective screening of infected patients. A vital screening process is the chest radiograph. Initial studies have shown irregularities in the chest radiographs of COVID-19 patients. The use of the chest X-ray (CXR), a leading diagnostic technique, has been encouraged and driven by several ongoing projects to combat this disease because of its historical effectiveness in providing clinical insights on lung diseases. This study introduces a dilated bi-branched convoluted neural network (CNN) architecture, VGG-COVIDNet, to detect COVID-19 cases from CXR images. The front end of the VGG-COVIDNet consists of the first 10 layers of VGG-16, where the convolutional layers in these layers are reduced to two to minimize latency during the training phase. The last two branches of the proposed architecture consist of dilated convolutional layers to reduce the model's computational complexity while retaining the feature maps' spatial information. The simulation results show that the proposed architecture is superior to all the state-of-the-art architecture in accuracy and sensitivity. The proposed architecture's accuracy and sensitivity are 96.5% and 96%, respectively, for each infection type.

International Journal of Advanced Computer Science and Applications

Digital transformation has been accelerated in recent years, and COVID-19 has resulted in a rise ... more Digital transformation has been accelerated in recent years, and COVID-19 has resulted in a rise in overall internet spending. Businesses must take measures in order to ensure that customers have a safe and enjoyable online purchasing experience. In this paper, customers' security perceptions regarding the most popular e-commerce applications in Saudi Arabia are explored. Surveys were distributed online via Google Form to 200 participants in total as part of a crosssectional research design using quantitative methodology. The main findings were related to confirming eight main hypotheses of the research that were related to testing if some factors were important to forming perceived trust by customers. Five factors (trust, security, reputation, benefits, and convenience) were found to have a positive effect, and the remaining three were not (familiarity, size, and usefulness). Finally, this study recommends various actions for practitioners and policymakers to take in order to improve customer perceptions of payment methods and security in Saudi Arabia.

Computational Intelligence and Neuroscience

It is critical to successfully identify, mitigate, and fight against Android malware assaults, si... more It is critical to successfully identify, mitigate, and fight against Android malware assaults, since Android malware has long been a significant threat to the security of Android applications. Identifying and categorizing dangerous applications into categories that are similar to one another are especially important in the development of a safe Android app ecosystem. The categorization of malware families may be used to improve the efficiency of the malware detection process as well as to systematically identify malicious trends. In this study, we proposed a modified ResNeXt model by embedding a new regularization technique to improve the classification task. In addition, we present a comprehensive evaluation of the Android malware classification and detection using our modified ResNeXt. The nonintuitive malware’s features are converted into fingerprint images in order to extract the rich information from the input data. In addition, we applied fine-tuned deep learning (DL) based on...

Knowledge and Information Systems

Massive open online courses (MOOCs) have emerged as a great resource for learners. Numerous chall... more Massive open online courses (MOOCs) have emerged as a great resource for learners. Numerous challenges remain to be addressed in order to make MOOCs more useful and convenient for learners. One such challenge is how to automatically extract a set of keyphrases from MOOC video lectures that can help students quickly identify the right knowledge they want to learn and thus expedite their learning process. In this paper, we propose SemKeyphrase, an unsupervised cluster-based approach for keyphrase extraction from MOOC video lectures. SemKeyphrase incorporates a new semantic relatedness metric and a ranking algorithm, called PhraseRank, that involves two phases on ranking candidates. We conducted experiments on a real-world dataset of MOOC video lectures, and the results show that our proposed approach outperforms the state-of-the-art keyphrase extraction methods.

Computers Materials & Continua, 2021

Malicious Portable Document Format (PDF) files represent one of the largest threats in the comput... more Malicious Portable Document Format (PDF) files represent one of the largest threats in the computer security space. Significant research has been done using handwritten signatures and machine learning based on detection via manual feature extraction. These approaches are time consuming, require substantial prior knowledge, and the list of features must be updated with each newly discovered vulnerability individually. In this study, we propose two models for PDF malware detection. The first model is a convolutional neural network (CNN) integrated into a standard deviation based regularization model to detect malicious PDF documents. The second model is a support vector machine (SVM) based ensemble model with three different kernels. The two models were trained and tested on two different datasets. The experimental results show that the accuracy of both models is approximately 100%, and the robustness against evasive samples is excellent. Further, the robustness of the models was evaluated with malicious PDF documents generated using Mimicus. Both models can distinguish the different vulnerabilities exploited in malicious files and achieve excellent performance in terms of generalization ability, accuracy, and robustness.

IET Information Security, 2021

Nowadays, social media platforms such as Twitter have become a popular medium for people to sprea... more Nowadays, social media platforms such as Twitter have become a popular medium for people to spread and consume news because of their easy access and the rapid proliferation of news. However, the credibility of the news posted on these platforms has become a significant issue. In other words, written news that contains inaccurate information aiming to mislead readers has been rapidly disseminated on these platforms. In the literature, this news is called fake news. Detecting such news on social media platforms has become a challenging task. One of the main challenges is identifying useful information that is exploited as a way to detect fake news. A hybrid model comprising a recurrent neural network (RNN) and support vector machine (SVM) is incorporated to detect real and fake news. An RNN with bidirectional gated recurrent units was used to encode textual data, including news content and comments, to numerical feature vectors. The encoded features were fed to an SVM with radial basis function kernel to classify the given input of real and fake news. Experiments on the real-world dataset yield encouraging results and demonstrate that the proposed framework outperforms state-of-the-art methods. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

IEEE/WIC/ACM International Conference on Web Intelligence, 2019

The Massive Open Online Courses (MOOCs) have emerged as a great resource for learners. Numerous c... more The Massive Open Online Courses (MOOCs) have emerged as a great resource for learners. Numerous challenges remain to be addressed in order to make MOOCs more useful and convenient for learners. One such challenge is how to automatically extract a set of keyphrases from MOOC video lectures that can help students quickly identify a suitable knowledge without spending too much time and expedite their learning process. In this paper, we propose SemKeyphrase, an unsupervised cluster-based approach for keyphrase extraction from MOOC video lectures. SemKeyphraseincorporates a new ranking algorithm, called PhaseRank, that involves two phases on ranking candidate keyphrases. Experiment results on a real-world dataset of MOOC video lectures show that our proposed approach outperforms the state-of-the-art methods by 16% or more in terms of F1 score.

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2019

Nowadays, more and more web applications start to offer the multiple sign-in feature, allowing us... more Nowadays, more and more web applications start to offer the multiple sign-in feature, allowing users to sign into multiple accounts simultaneously from the same browser. This feature significantly improves user experience. Unfortunately, if such a feature is not designed and implemented properly, it could lead to security, privacy, or usability issues. In this paper, we perform the first comprehensive study of the multiple sign-in feature among various web applications, including Google, Dropbox. Our results show that the problem is quite worrisome. All analyzed products that provide the multiple sign-in feature either suffer from potential security/privacy threats or are sacrificing usability to some extent. We present all issues found in these applications, and analyze the root cause by identifying four different implementation models. Finally, based on our analysis results, we design a client-side proof-of-concept solution, called G-Remember, to mitigate these issues. Our experiments show that G-Remember can successfully provide adequate context information for web servers to recognize users' intended accounts, and thus effectively address the presented multiple sign-in threat.

International Journal of Environmental Research and Public Health, Dec 15, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

The complexity and interconnection of smart cities provide huge political, technical, and socioec... more The complexity and interconnection of smart cities provide huge political, technical, and socioeconomic challenges for the designers, integrators, and organisations who are responsible for the administration of these new entities. A growing number of studies are concentrating their attention on the safety, privacy, and potential dangers that exist within smart cities. These studies are drawing attention to the dangers that are associated with information security as well as the challenges that smart city infrastructure faces in the management and processing of personal data. This state of the art review of the literature analyses a number of issues pertaining to smart homes, offers a helpful synthesis of the important information found in the primary research, and creates a model for the interaction between smart cities. An overview of smart home research incorporating machine learning, including everything from definition to current research state. The state of art begins with a sm...

Bioengineering

Omicron, so-called COVID-2, is an emerging variant of COVID-19 which is proved to be the most fat... more Omicron, so-called COVID-2, is an emerging variant of COVID-19 which is proved to be the most fatal amongst the other variants such as alpha, beta and gamma variants (α, β, γ variants) due to its stern and perilous nature. It has caused hazardous effects globally in a very short span of time. The diagnosis and medication of Omicron patients are both challenging undertakings for researchers (medical experts) due to the involvement of various uncertainties and the vagueness of its altering behavior. In this study, an algebraic approach, interval-valued fuzzy hypersoft set (iv-FHSS), is employed to assess the conditions of patients after the application of suitable medication. Firstly, the distance measures between two iv-FHSSs are formulated with a brief description some of its properties, then a multi-attribute decision-making framework is designed through the proposal of an algorithm. This framework consists of three phases of medication. In the first phase, the Omicron-diagnosed pa...

Bioengineering

Susceptibility analysis is an intelligent technique that not only assists decision makers in asse... more Susceptibility analysis is an intelligent technique that not only assists decision makers in assessing the suspected severity of any sort of brain tumour in a patient but also helps them diagnose and cure these tumours. This technique has been proven more useful in those developing countries where the available health-based and funding-based resources are limited. By employing set-based operations of an arithmetical model, namely fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS), this study seeks to develop a robust multi-attribute decision support mechanism for appraising patients’ susceptibility to brain tumours. The FPCIFHSS is regarded as more reliable and generalised for handling information-based uncertainties because its complex components and fuzzy parameterisation are designed to deal with the periodic nature of the data and dubious parameters (sub-parameters), respectively. In the proposed FPCIFHSS-susceptibility model, some suitable types of brain ...

International Journal of Environmental Research and Public Health

Facilitating the navigation of visually impaired people in indoor environments requires detecting... more Facilitating the navigation of visually impaired people in indoor environments requires detecting indicating signs and informing them. In this paper, we proposed an indoor sign detection based on a lightweight anchor-free object detection model called FAM-centerNet. The baseline model of this work is the centerNet, which is an anchor-free object detection model with high performance and low computation complexity. A Foreground Attention Module (FAM) was introduced to extract target objects in real scenes with complex backgrounds. This module segments the foreground to extract relevant features of the target object using midground proposal and boxes-induced segmentation. In addition, the foreground module provides scale information to improve the regression performance. Extensive experiments on two datasets prove the efficiency of the proposed model for detecting general objects and custom indoor signs. The Pascal VOC dataset was used to test the performance of the proposed model for...

Bioengineering, 2023

Susceptibility analysis is an intelligent technique that not only assists decision makers in ass... more Susceptibility analysis is an intelligent technique that not only assists decision makers in assessing the suspected severity of any sort of brain tumour in a patient but also helps them diagnose and cure these tumours. This technique has been proven more useful in those developing countries where the available health-based and funding-based resources are limited. By employing set-based operations of an arithmetical model, namely fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS), this study seeks to develop a robust multi-attribute decision support mechanism for appraising patients’ susceptibility to brain tumours. The FPCIFHSS is regarded as more reliable and generalised for handling information-based uncertainties because its complex components and fuzzy parameterisation are designed to deal with the periodic nature of the data and dubious parameters (sub-parameters), respectively. In the proposed FPCIFHSS-susceptibility model, some suitable types of brain tumours are approximated with respect to the most relevant symptoms (parameters) based on the expert opinions of decision makers in terms of complex intuitionistic fuzzy numbers (CIFNs). After determining the fuzzy parameterised values of multi-argument-based tuples and converting the CIFNs into fuzzy values, the scores for such types of tumours are computed based on a core matrix which relates them with fuzzy parameterised multi-argument-based tuples. The sub-intervals within [0, 1] denote the susceptibility degrees of patients corresponding to these types of brain tumours. The susceptibility of patients is examined by observing the membership of score values in the sub-intervals.

Processes, 2023

What is presented in this research is an intelligent system for detecting the volume percentage o... more What is presented in this research is an intelligent system for detecting the volume percentage of three-phase fluids passing through oil pipes. The structure of the detection system consists of an X-ray tube, a Pyrex galss pipe, and two sodium iodide detectors. A three-phase fluid of water, gas, and oil has been simulated inside the pipe in two flow regimes, annular and stratified. Different volume percentages from 10 to 80% are considered for each phase. After producing and emitting X-rays from the source and passing through the pipe containing a three-phase fluid, the intensity of photons is recorded by two detectors. The simulation is introduced by a Monte Carlo N-Particle (MCNP) code. After the implementation of all flow regimes in different volume percentages, the signals recorded by the detectors were recorded and labeled. Three frequency characteristics and five wavelet transform characteristics were extracted from the received signals of each detector, which were collected in a total of 16 characteristics from each test. The feature selection system based on the particle swarm optimization (PSO) algorithm was applied to determine the best combination of extracted features. The result was the introduction of seven features as the best features to determine volume percentages. The introduced characteristics were considered as the input of a Multilayer Perceptron (MLP) neural network, whose structure had seven input neurons (selected characteristics) and two output neurons (volume percentage of gas and water). The highest error obtained in determining volume percentages was equal to 0.13 as MSE, a low error compared with previous works. Using the PSO algorithm to select the most optimal features, the current research’s accuracy in determining volume percentages has significantly increased.

Mathematics

The standard optimization of open-pit mine design and production scheduling, which is impacted by... more The standard optimization of open-pit mine design and production scheduling, which is impacted by a variety of factors, is an essential part of mining activities. The metal uncertainty, which is connected to supply uncertainty, is a crucial component in optimization. To address uncertainties regarding the economic value of mining blocks and the general problem of mine design optimization, a minimum-cut network flow algorithm is employed to give the optimal ultimate pit limits and pushback designs under uncertainty. A structure that is computationally effective and can manage the joint presentation and treatment of the economic values of mining blocks under various circumstances is created by the push re-label minimum-cut technique. In this study, the algorithm is put to the test using a copper deposit and shows similarities to other stochastic optimizers for mine planning that have already been created. Higher possibilities of reaching predicted production targets are created by the...

Mathematics

Rivers play a major role within ecosystems and society, including for domestic, industrial, and a... more Rivers play a major role within ecosystems and society, including for domestic, industrial, and agricultural uses, and in power generation. Forecasting of suspended sediment yield (SSY) is critical for design, management, planning, and disaster prevention in river basin systems. It is difficult to forecast the SSY using conventional methods because these approaches cannot handle complicated non-stationarity and non-linearity. Artificial intelligence techniques have gained popularity in water resources due to handling complex problems of SSY. In this study, a fully automated generalized single hybrid intelligent artificial neural network (ANN)-based genetic algorithm (GA) forecasting model was developed using water discharge, temperature, rainfall, SSY, rock type, relief, and catchment area data of eleven gauging stations for forecasting the SSY. It is applied at individual gauging stations for SSY forecasting in the Mahanadi River which is one of India’s largest peninsular rivers. A...

Computers, Materials & Continua

There has been an increase in attacks on mobile devices, such as smartphones and tablets, due to ... more There has been an increase in attacks on mobile devices, such as smartphones and tablets, due to their growing popularity. Mobile malware is one of the most dangerous threats, causing both security breaches and financial losses. Mobile malware is likely to continue to evolve and proliferate to carry out a variety of cybercrimes on mobile devices. Mobile malware specifically targets Android operating system as it has grown in popularity. The rapid proliferation of Android malware apps poses a significant security risk to users, making static and manual analysis of malicious files difficult. Therefore, efficient identification and classification of Android malicious files is crucial. Several Convolutional Neural Network (CNN) based methods have been proposed in this regard; however, there is still room for performance improvement. In this work, we propose a transfer learning and stacking approach to efficiently detect the Android malware files by utilizing two wellknown machine learning models, ResNet-50 and Support Vector Machine (SVM). The proposed model is trained on the DREBIN dataset by transforming malicious APK files into grayscale images. Our model yields higher performance measures than state-of-the-art works on the DREBIN dataset, where the reported measures are accuracy, recall, precision, and F1 measures of 97.8%, 95.8%, 95.7%, and 95.7%, respectively.

Electronics

Today, one of the most popular ways organizations use to provide their services, or broadly speak... more Today, one of the most popular ways organizations use to provide their services, or broadly speaking, interact with their customers, is through web applications. Those applications should be protected and meet all security requirements. Penetration testers need to make sure that the attacker cannot find any weaknesses to destroy, exploit, or disclose information on the Web. Therefore, using automated vulnerability assessment tools is the best and easiest part of web application pen-testing, but these tools have strengths and weaknesses. Thus, using the wrong tool may lead to undetected, expected, or known vulnerabilities that may open doors for cyberattacks. This research proposes an empirical comparison of pen-testing tools for detecting web app vulnerabilities using approved standards and methods to facilitate the selection of appropriate tools according to the needs of penetration testers. In addition, we have proposed an enhanced benchmarking framework that combines the latest r...

Applied Computational Intelligence and Soft Computing

Emerging technologies provide a highly compatible platform for analyzing substantial data volumes... more Emerging technologies provide a highly compatible platform for analyzing substantial data volumes relating to crowd management. These technologies are not only effective in providing remedial solutions but they are also cutting-edge, saving both time and money, and user-friendly. About twenty percent of the world’s Muslims are granted the ability to take part in religious observances on an annual basis. An in-depth analysis of the technologies and applications used in the Hajj and Umrah systems is required given the importance of utilizing and adapting technology to assist pilgrims performing the Hajj and Umrah. Both of these pilgrimages are considered acts of worship by Muslims. As a result, the services provided during Umrah and Hajj will be significantly enhanced if the numerous technological advancements that are evaluated in this paper are implemented. The majority of previous research is shown to revolve around artificial intelligence and embedded Internet of Things technologi...

Computers, Materials & Continua

The coronavirus disease 2019 (COVID-19) pandemic has had a devastating impact on the health and w... more The coronavirus disease 2019 (COVID-19) pandemic has had a devastating impact on the health and welfare of the global population. A key measure to combat COVID-19 has been the effective screening of infected patients. A vital screening process is the chest radiograph. Initial studies have shown irregularities in the chest radiographs of COVID-19 patients. The use of the chest X-ray (CXR), a leading diagnostic technique, has been encouraged and driven by several ongoing projects to combat this disease because of its historical effectiveness in providing clinical insights on lung diseases. This study introduces a dilated bi-branched convoluted neural network (CNN) architecture, VGG-COVIDNet, to detect COVID-19 cases from CXR images. The front end of the VGG-COVIDNet consists of the first 10 layers of VGG-16, where the convolutional layers in these layers are reduced to two to minimize latency during the training phase. The last two branches of the proposed architecture consist of dilated convolutional layers to reduce the model's computational complexity while retaining the feature maps' spatial information. The simulation results show that the proposed architecture is superior to all the state-of-the-art architecture in accuracy and sensitivity. The proposed architecture's accuracy and sensitivity are 96.5% and 96%, respectively, for each infection type.

International Journal of Advanced Computer Science and Applications

Digital transformation has been accelerated in recent years, and COVID-19 has resulted in a rise ... more Digital transformation has been accelerated in recent years, and COVID-19 has resulted in a rise in overall internet spending. Businesses must take measures in order to ensure that customers have a safe and enjoyable online purchasing experience. In this paper, customers' security perceptions regarding the most popular e-commerce applications in Saudi Arabia are explored. Surveys were distributed online via Google Form to 200 participants in total as part of a crosssectional research design using quantitative methodology. The main findings were related to confirming eight main hypotheses of the research that were related to testing if some factors were important to forming perceived trust by customers. Five factors (trust, security, reputation, benefits, and convenience) were found to have a positive effect, and the remaining three were not (familiarity, size, and usefulness). Finally, this study recommends various actions for practitioners and policymakers to take in order to improve customer perceptions of payment methods and security in Saudi Arabia.

Computational Intelligence and Neuroscience

It is critical to successfully identify, mitigate, and fight against Android malware assaults, si... more It is critical to successfully identify, mitigate, and fight against Android malware assaults, since Android malware has long been a significant threat to the security of Android applications. Identifying and categorizing dangerous applications into categories that are similar to one another are especially important in the development of a safe Android app ecosystem. The categorization of malware families may be used to improve the efficiency of the malware detection process as well as to systematically identify malicious trends. In this study, we proposed a modified ResNeXt model by embedding a new regularization technique to improve the classification task. In addition, we present a comprehensive evaluation of the Android malware classification and detection using our modified ResNeXt. The nonintuitive malware’s features are converted into fingerprint images in order to extract the rich information from the input data. In addition, we applied fine-tuned deep learning (DL) based on...

Knowledge and Information Systems

Massive open online courses (MOOCs) have emerged as a great resource for learners. Numerous chall... more Massive open online courses (MOOCs) have emerged as a great resource for learners. Numerous challenges remain to be addressed in order to make MOOCs more useful and convenient for learners. One such challenge is how to automatically extract a set of keyphrases from MOOC video lectures that can help students quickly identify the right knowledge they want to learn and thus expedite their learning process. In this paper, we propose SemKeyphrase, an unsupervised cluster-based approach for keyphrase extraction from MOOC video lectures. SemKeyphrase incorporates a new semantic relatedness metric and a ranking algorithm, called PhraseRank, that involves two phases on ranking candidates. We conducted experiments on a real-world dataset of MOOC video lectures, and the results show that our proposed approach outperforms the state-of-the-art keyphrase extraction methods.

Computers Materials & Continua, 2021

Malicious Portable Document Format (PDF) files represent one of the largest threats in the comput... more Malicious Portable Document Format (PDF) files represent one of the largest threats in the computer security space. Significant research has been done using handwritten signatures and machine learning based on detection via manual feature extraction. These approaches are time consuming, require substantial prior knowledge, and the list of features must be updated with each newly discovered vulnerability individually. In this study, we propose two models for PDF malware detection. The first model is a convolutional neural network (CNN) integrated into a standard deviation based regularization model to detect malicious PDF documents. The second model is a support vector machine (SVM) based ensemble model with three different kernels. The two models were trained and tested on two different datasets. The experimental results show that the accuracy of both models is approximately 100%, and the robustness against evasive samples is excellent. Further, the robustness of the models was evaluated with malicious PDF documents generated using Mimicus. Both models can distinguish the different vulnerabilities exploited in malicious files and achieve excellent performance in terms of generalization ability, accuracy, and robustness.

IET Information Security, 2021

Nowadays, social media platforms such as Twitter have become a popular medium for people to sprea... more Nowadays, social media platforms such as Twitter have become a popular medium for people to spread and consume news because of their easy access and the rapid proliferation of news. However, the credibility of the news posted on these platforms has become a significant issue. In other words, written news that contains inaccurate information aiming to mislead readers has been rapidly disseminated on these platforms. In the literature, this news is called fake news. Detecting such news on social media platforms has become a challenging task. One of the main challenges is identifying useful information that is exploited as a way to detect fake news. A hybrid model comprising a recurrent neural network (RNN) and support vector machine (SVM) is incorporated to detect real and fake news. An RNN with bidirectional gated recurrent units was used to encode textual data, including news content and comments, to numerical feature vectors. The encoded features were fed to an SVM with radial basis function kernel to classify the given input of real and fake news. Experiments on the real-world dataset yield encouraging results and demonstrate that the proposed framework outperforms state-of-the-art methods. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

IEEE/WIC/ACM International Conference on Web Intelligence, 2019

The Massive Open Online Courses (MOOCs) have emerged as a great resource for learners. Numerous c... more The Massive Open Online Courses (MOOCs) have emerged as a great resource for learners. Numerous challenges remain to be addressed in order to make MOOCs more useful and convenient for learners. One such challenge is how to automatically extract a set of keyphrases from MOOC video lectures that can help students quickly identify a suitable knowledge without spending too much time and expedite their learning process. In this paper, we propose SemKeyphrase, an unsupervised cluster-based approach for keyphrase extraction from MOOC video lectures. SemKeyphraseincorporates a new ranking algorithm, called PhaseRank, that involves two phases on ranking candidate keyphrases. Experiment results on a real-world dataset of MOOC video lectures show that our proposed approach outperforms the state-of-the-art methods by 16% or more in terms of F1 score.

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2019

Nowadays, more and more web applications start to offer the multiple sign-in feature, allowing us... more Nowadays, more and more web applications start to offer the multiple sign-in feature, allowing users to sign into multiple accounts simultaneously from the same browser. This feature significantly improves user experience. Unfortunately, if such a feature is not designed and implemented properly, it could lead to security, privacy, or usability issues. In this paper, we perform the first comprehensive study of the multiple sign-in feature among various web applications, including Google, Dropbox. Our results show that the problem is quite worrisome. All analyzed products that provide the multiple sign-in feature either suffer from potential security/privacy threats or are sacrificing usability to some extent. We present all issues found in these applications, and analyze the root cause by identifying four different implementation models. Finally, based on our analysis results, we design a client-side proof-of-concept solution, called G-Remember, to mitigate these issues. Our experiments show that G-Remember can successfully provide adequate context information for web servers to recognize users' intended accounts, and thus effectively address the presented multiple sign-in threat.