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Articles 361 - 390 of 677
Full-Text Articles in Computer Sciences
Deep Learning-Based Segmentation And Classification Of Leaf Images For Detection Of Tomato Plant Disease, Muhammad Shoaib, Tariq Hussain, Babar Shah, Ihsan Ullah, Sayyed Mudassar Shah, Farman Ali, Sang Hyun Park
Deep Learning-Based Segmentation And Classification Of Leaf Images For Detection Of Tomato Plant Disease, Muhammad Shoaib, Tariq Hussain, Babar Shah, Ihsan Ullah, Sayyed Mudassar Shah, Farman Ali, Sang Hyun Park
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Plants contribute significantly to the global food supply. Various Plant diseases can result in production losses, which can be avoided by maintaining vigilance. However, manually monitoring plant diseases by agriculture experts and botanists is time-consuming, challenging and error-prone. To reduce the risk of disease severity, machine vision technology (i.e., artificial intelligence) can play a significant role. In the alternative method, the severity of the disease can be diminished through computer technologies and the cooperation of humans. These methods can also eliminate the disadvantages of manual observation. In this work, we proposed a solution to detect tomato plant disease using a …
Multicriteria Decision Making For Carbon Dioxide (Co2) Emission Reduction, Rahman Ali, Farkhund Iqbal, Muhammad Sadiq Hassan Zada
Multicriteria Decision Making For Carbon Dioxide (Co2) Emission Reduction, Rahman Ali, Farkhund Iqbal, Muhammad Sadiq Hassan Zada
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The fast industrial revolution all over the world has increased emission of carbon dioxide (CO2), which has badly affected the atmosphere. Main sources of CO2 emission include vehicles and factories, which use oil, gas, and coal. Similarly, due to the increased mobility of automobiles, CO2 emission increases day-by-day. Roughly, 40% of the world’s total CO2 emission is due to the use of personal cars on busy and congested roads, which burn more fuel. In addition to this, the unavailability of parking in all parts of the cities and the use of conventional methods for searching parking areas have added more …
Multi-Bsm: An Anomaly Detection And Position Falsification Attack Mitigation Approach In Connected Vehicles, Zouheir Trabelsi, Syed Sarmad Shah, Kadhim Hayawi
Multi-Bsm: An Anomaly Detection And Position Falsification Attack Mitigation Approach In Connected Vehicles, Zouheir Trabelsi, Syed Sarmad Shah, Kadhim Hayawi
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With the dawn of the emerging technologies in the field of vehicular environment, connected vehicles are advancing at a rapid speed. The advancement of such technologies helps people daily, whether it is to reach from one place to another, avoid traffic, or prevent any hazardous incident from occurring. Safety is one of the main concerns regarding the vehicular environment when it comes to developing applications for connected vehicles. Connected vehicles depend on messages known as basic safety messages (BSMs) that are repeatedly broadcast in their communication range in order to obtain information regarding their surroundings. Different kinds of attacks can …
Rootasrole: A Security Module To Manage The Administrative Privileges For Linux, Ahmad Samer Wazan, David W Chadwick, Remi Venant, Eddie Billoir, Romain Laborde, Liza Ahmad, Mustafa Kaiiali
Rootasrole: A Security Module To Manage The Administrative Privileges For Linux, Ahmad Samer Wazan, David W Chadwick, Remi Venant, Eddie Billoir, Romain Laborde, Liza Ahmad, Mustafa Kaiiali
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Today, Linux users use sudo/su commands to attribute Linux’s administrative privileges to their programs. These commands always give the whole list of administrative privileges to Linux programs, unless there are pre-installed default policies defined by Linux Security Modules(LSM). LSM modules require users to inject the needed privileges into the memory of the process and to declare the needed privileges in an LSM policy. This approach can work for users who have good knowledge of the syntax of LSM modules’ policies. Adding or editing an existing policy is a very time-consuming process because LSM modules require adding a complete list of …
Problematic Internet Usage: The Impact Of Objectively Recorded And Categorized Usage Time, Emotional Intelligence Components And Subjective Happiness About Usage, Sameha Alshakhsi, Khansa Chemnad, Mohamed Basel Almourad, Majid Altuwairiqi, John Mcalaney, Raian Ali
Problematic Internet Usage: The Impact Of Objectively Recorded And Categorized Usage Time, Emotional Intelligence Components And Subjective Happiness About Usage, Sameha Alshakhsi, Khansa Chemnad, Mohamed Basel Almourad, Majid Altuwairiqi, John Mcalaney, Raian Ali
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Most research on Problematic Internet Usage (PIU) relied on self-report data when measuring the time spent on the internet. Self-reporting of use, typically done through a survey, showed discrepancies from the actual amount of use. Studies exploring the association between trait emotional intelligence (EI) components and the subjective feeling on technology usage and PIU are also limited. The current cross-sectional study aims to examine whether the objectively recorded technology usage, taking smartphone usage as a representative, components of trait EI (sociability, emotionality, well-being, self-control), and happiness with phone use can predict PIU and its components (obsession, neglect, and control disorder). …
An Approach For Improved Students’ Performance Prediction Using Homogeneous And Heterogeneous Ensemble Methods, Edmund Evangelista, Benedict Sy
An Approach For Improved Students’ Performance Prediction Using Homogeneous And Heterogeneous Ensemble Methods, Edmund Evangelista, Benedict Sy
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Web-based learning technologies of educational institutions store a massive amount of interaction data which can be helpful to predict students’ performance through the aid of machine learning algorithms. With this, various researchers focused on studying ensemble learning methods as it is known to improve the predictive accuracy of traditional classification algorithms. This study proposed an approach for enhancing the performance prediction of different single classification algorithms by using them as base classifiers of homogeneous ensembles (bagging and boosting) and heterogeneous ensembles (voting and stacking). The model utilized various single classifiers such as multilayer perceptron or neural networks (NN), random forest …
Augmented Reality And Gps-Based Resource Efficient Navigation System For Outdoor Environments: Integrating Device Camera, Sensors, And Storage, Saravjeet Singh, Jaiteg Singh, Babar Shah, Sukhjit Singh Sehra, Farman Ali
Augmented Reality And Gps-Based Resource Efficient Navigation System For Outdoor Environments: Integrating Device Camera, Sensors, And Storage, Saravjeet Singh, Jaiteg Singh, Babar Shah, Sukhjit Singh Sehra, Farman Ali
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Contemporary navigation systems rely upon localisation accuracy and humongous spatial data for navigational assistance. Such spatial-data sources may have access restrictions or quality issues and require massive storage space. Affordable high-performance mobile consumer hardware and smart software have resulted in the popularity of AR and VR technologies. These technologies can help to develop sustainable devices for navigation. This paper introduces a robust, memory-efficient, augmented-reality-based navigation system for outdoor environments using crowdsourced spatial data, a device camera, and mapping algorithms. The proposed system unifies the basic map information, points of interest, and individual GPS trajectories of moving entities to generate and …
Predicting The Level Of Respiratory Support In Covid-19 Patients Using Machine Learning, Hisham Abdeltawab, Fahmi Khalifa, Yaser Elnakieb, Ahmed Elnakib, Fatma Taher, Norah Saleh Alghamdi, Harpal Singh Sandhu, Ayman El-Baz
Predicting The Level Of Respiratory Support In Covid-19 Patients Using Machine Learning, Hisham Abdeltawab, Fahmi Khalifa, Yaser Elnakieb, Ahmed Elnakib, Fatma Taher, Norah Saleh Alghamdi, Harpal Singh Sandhu, Ayman El-Baz
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In this paper, a machine learning-based system for the prediction of the required level of respiratory support in COVID-19 patients is proposed. The level of respiratory support is divided into three classes: class 0 which refers to minimal support, class 1 which refers to non-invasive support, and class 2 which refers to invasive support. A two-stage classification system is built. First, the classification between class 0 and others is performed. Then, the classification between class 1 and class 2 is performed. The system is built using a dataset collected retrospectively from 3491 patients admitted to tertiary care hospitals at the …
Role Of Imaging And Ai In The Evaluation Of Covid-19 Infection: A Comprehensive Survey, Mayada Elgendy, Hossam Magdy Balaha, Mohamed Shehata, Ahmed Alksas, Mahitab Ghoneim, Fatma Sherif, Ali Mahmoud, Ahmed Elgarayhi, Fatma Taher, Mohammed Sallah, Mohammed Ghazal, Ayman El-Baz
Role Of Imaging And Ai In The Evaluation Of Covid-19 Infection: A Comprehensive Survey, Mayada Elgendy, Hossam Magdy Balaha, Mohamed Shehata, Ahmed Alksas, Mahitab Ghoneim, Fatma Sherif, Ali Mahmoud, Ahmed Elgarayhi, Fatma Taher, Mohammed Sallah, Mohammed Ghazal, Ayman El-Baz
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Coronavirus disease 2019 (COVID-19) is a respiratory illness that started and rapidly became the pandemic of the century, as the number of people infected with it globally exceeded 253.4 million. Since the beginning of the pandemic of COVID-19, over two years have passed. During this hard period, several defies have been coped by the scientific society to know this novel disease, evaluate it, and treat affected patients. All these efforts are done to push back the spread of the virus. This article provides a comprehensive review to learn about the COVID-19 virus and its entry mechanism, its main repercussions on …
Detecting High-Risk Factors And Early Diagnosis Of Diabetes Using Machine Learning Methods, Zahid Ullah, Farrukh Saleem, Mona Jamjoom, Bahjat Fakieh, Faris Kateb, Abdullah Marish Ali, Babar Shah
Detecting High-Risk Factors And Early Diagnosis Of Diabetes Using Machine Learning Methods, Zahid Ullah, Farrukh Saleem, Mona Jamjoom, Bahjat Fakieh, Faris Kateb, Abdullah Marish Ali, Babar Shah
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Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, eye damage, and nerve damage. There are several risk factors involved in causing this disease, with some of the most common being obesity, age, insulin resistance, and hypertension. Therefore, early detection of these risk factors is vital in helping patients reverse diabetes from the early stage to live healthy lives. Machine learning (ML) is a useful tool that can easily detect diabetes from several risk factors and, based on the findings, provide a decision-based model that can …
Nft Certificates And Proof Of Delivery For Fine Jewelry And Gemstones, Noura Alnuaimi, Alanoud Almemari, Mohammad Madine, Khaled Salah, Hamda Al Breiki, Raja Jayaraman
Nft Certificates And Proof Of Delivery For Fine Jewelry And Gemstones, Noura Alnuaimi, Alanoud Almemari, Mohammad Madine, Khaled Salah, Hamda Al Breiki, Raja Jayaraman
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Fine jewelry is a unique class of ornaments composed of precious metals and gemstones. Premium-grade metals such as gold, platinum, and sliver, and gemstones such as pearls, diamonds, rubies, and emeralds are used use to make fine jewelry. Paper-based certificates are typically issued by retailers and producers for fine jewelry and gemstones as a proof of origin, sale, ownership, history, and quality. However, paper certificates are subject to counterfeiting, loss, or theft. In this paper, we show how non-fungible tokens (NFTs) and Ethereum blockchain can be used for digital certification, proof of ownership, sale history, and quality, as well as …
Triggers And Tweets: Implicit Aspect-Based Sentiment And Emotion Analysis Of Community Chatter Relevant To Education Post-Covid-19, Heba Ismail, Ashraf Khalil, Nada Hussein, Rawan Elabyad
Triggers And Tweets: Implicit Aspect-Based Sentiment And Emotion Analysis Of Community Chatter Relevant To Education Post-Covid-19, Heba Ismail, Ashraf Khalil, Nada Hussein, Rawan Elabyad
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This research proposes a well-being analytical framework using social media chatter data. The proposed framework infers analytics and provides insights into the public's well-being relevant to education throughout and post the COVID-19 pandemic through a comprehensive Emotion and Aspect-based Sentiment Analysis (ABSA). Moreover, this research aims to examine the variability in emotions of students, parents, and faculty toward the e-learning process over time and across different locations. The proposed framework curates Twitter chatter data relevant to the education sector, identifies tweets with the sentiment, and then identifies the exact emotion and emotional triggers associated with those feelings through implicit ABSA. …
Evaluation Of Machine Learning Algorithm On Drinking Water Quality For Better Sustainability, Sanaa Kaddoura
Evaluation Of Machine Learning Algorithm On Drinking Water Quality For Better Sustainability, Sanaa Kaddoura
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Water has become intricately linked to the United Nations' sixteen sustainable development goals. Access to clean drinking water is crucial for health, a fundamental human right, and a component of successful health protection policies. Clean water is a significant health and development issue on a national, regional, and local level. Investments in water supply and sanitation have been shown to produce a net economic advantage in some areas because they reduce adverse health effects and medical expenses more than they cost to implement. However, numerous pollutants are affecting the quality of drinking water. This study evaluates the efficiency of using …
Vector Auto-Regression-Based False Data Injection Attack Detection Method In Edge Computing Environment, Yi Chen, Kadhim Hayawi, Qian Zhao, Junjie Mou, Ling Yang, Jie Tang, Qing Li, Hong Wen
Vector Auto-Regression-Based False Data Injection Attack Detection Method In Edge Computing Environment, Yi Chen, Kadhim Hayawi, Qian Zhao, Junjie Mou, Ling Yang, Jie Tang, Qing Li, Hong Wen
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With the wide application of advanced communication and information technology, false data injection attack (FDIA) has become one of the significant potential threats to the security of smart grid. Malicious attack detection is the primary task of defense. Therefore, this paper proposes a method of FDIA detection based on vector auto-regression (VAR), aiming to improve safe operation and reliable power supply in smart grid applications. The proposed method is characterized by incorporating with VAR model and measurement residual analysis based on infinite norm and 2-norm to achieve the FDIA detection under the edge computing architecture, where the VAR model is …
Explainable Artificial Intelligence Applications In Cyber Security: State-Of-The-Art In Research, Zhibo Zhang, Hussam Al Hamadi, Ernesto Damiani, Chan Yeob Yeun, Fatma Taher
Explainable Artificial Intelligence Applications In Cyber Security: State-Of-The-Art In Research, Zhibo Zhang, Hussam Al Hamadi, Ernesto Damiani, Chan Yeob Yeun, Fatma Taher
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This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning and Deep Learning has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most Machine Learning-based techniques and Deep Learning-based techniques are deployed in …
Forensic Investigation Of Google Meet For Memory And Browser Artifacts, Farkhund Iqbal, Zainab Khalid, Andrew Marrington, Babar Shah, Patrick C.K. Hung
Forensic Investigation Of Google Meet For Memory And Browser Artifacts, Farkhund Iqbal, Zainab Khalid, Andrew Marrington, Babar Shah, Patrick C.K. Hung
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Web applications have experienced a widespread adaptation owing to the agile Service Oriented Architecture (SOA) reflecting the ever-changing software needs of users. Google Meet is one of the top video conferencing applications, especially in the post-COVID19 era. Security and privacy concerns are therefore critical. This paper presents an extensive digital forensic analysis of Google Meet running on multiple browsers and software platforms including Google Chrome, Mozilla Firefox, and Microsoft Edge browsers in Windows 10 and Linux. Artifacts, traces of potential evidence, are extracted from different locations on a client's desktop, including the memory and browser. These include meeting records, communication …
A Gpu-Based Machine Learning Approach For Detection Of Botnet Attacks, Michal Motylinski, Áine Macdermott, Farkhund Iqbal, Babar Shah
A Gpu-Based Machine Learning Approach For Detection Of Botnet Attacks, Michal Motylinski, Áine Macdermott, Farkhund Iqbal, Babar Shah
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Rapid development and adaptation of the Internet of Things (IoT) has created new problems for securing these interconnected devices and networks. There are hundreds of thousands of IoT devices with underlying security vulnerabilities, such as insufficient device authentication/authorisation making them vulnerable to malware infection. IoT botnets are designed to grow and compete with one another over unsecure devices and networks. Once infected, the device will monitor a Command-and-Control (C&C) server indicating the target of an attack via Distributed Denial of Service (DDoS) attack. These security issues, coupled with the continued growth of IoT, presents a much larger attack surface for …
Using Deep Learning To Detect Social Media ‘Trolls’, Áine Macdermott, Michal Motylinski, Farkhund Iqbal, Kellyann Stamp, Mohammed Hussain, Andrew Marrington
Using Deep Learning To Detect Social Media ‘Trolls’, Áine Macdermott, Michal Motylinski, Farkhund Iqbal, Kellyann Stamp, Mohammed Hussain, Andrew Marrington
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Detecting criminal activity online is not a new concept but how it can occur is changing. Technology and the influx of social media applications and platforms has a vital part to play in this changing landscape. As such, we observe an increasing problem with cyber abuse and ‘trolling’/toxicity amongst social media platforms sharing stories, posts, memes sharing content. In this paper we present our work into the application of deep learning techniques for the detection of ‘trolls’ and toxic content shared on social media platforms. We propose a machine learning solution for the detection of toxic images based on embedded …
Augmented Reality-Based English Language Learning: Importance And State Of The Art, Mohammad Wedyan, Jannat Falah, Omar Elshaweesh, Salsabeel F. M. Alfalah, Moutaz Alazab
Augmented Reality-Based English Language Learning: Importance And State Of The Art, Mohammad Wedyan, Jannat Falah, Omar Elshaweesh, Salsabeel F. M. Alfalah, Moutaz Alazab
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Augmented reality is increasingly used in the educational domain. However, little is known concerning the actual importance of AR for learning English skills. The weakness of the English language among English as a foreign Language (EFL) students is widespread in different educational institutions. Accordingly, this paper aims at exploring the importance of AR for learning English skills from the perspectives of English language teachers and educators. Mixed qualitative methods were used. To achieve the objective of this study, 12 interviews were conducted with English teachers concerning the topic under investigation. Second, a systematic literature review (SLR) that demonstrates the advantages, …
Twenty Years Of Mobile Banking Services Development And Sustainability: A Bibliometric Analysis Overview (2000–2020), Ayman A. Alsmadi, Ahmed Shuhaiber, Loai N. Alhawamdeh, Rasha Alghazzawi, Manaf Al-Okaily
Twenty Years Of Mobile Banking Services Development And Sustainability: A Bibliometric Analysis Overview (2000–2020), Ayman A. Alsmadi, Ahmed Shuhaiber, Loai N. Alhawamdeh, Rasha Alghazzawi, Manaf Al-Okaily
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The current paper aims to analyze the keywords related to mobile banking (otherwise known as m-banking) issues by focusing on its development from 2000 to 2020, of which the first publication about this issue appeared in the Scopus database. This paper explored and analyzed 1206 research papers using the Scopus database. Bibliometric analysis and content analysis had been conducted through Excel and VOS viewer software to obtain the results. In addition, the findings of this paper reveal that the universal trends and increased production at a global level led to many changes, and the most rampant topic associated with m-banking …
Sel-Covidnet: An Intelligent Application For The Diagnosis Of Covid-19 From Chest X-Rays And Ct-Scans, Ahmad Al Smadi, Ahed Abugabah, Ahmad Mohammad Al-Smadi, Sultan Almotairi
Sel-Covidnet: An Intelligent Application For The Diagnosis Of Covid-19 From Chest X-Rays And Ct-Scans, Ahmad Al Smadi, Ahed Abugabah, Ahmad Mohammad Al-Smadi, Sultan Almotairi
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COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). In which …
Did Usage Of Mental Health Apps Change During Covid-19? A Comparative Study Based On An Objective Recording Of Usage Data And Demographics, Maryam Aziz, Aiman Erbad, Mohamed Basel Almourad, Majid Altuwairiqi, John Mcalaney, Raian Ali
Did Usage Of Mental Health Apps Change During Covid-19? A Comparative Study Based On An Objective Recording Of Usage Data And Demographics, Maryam Aziz, Aiman Erbad, Mohamed Basel Almourad, Majid Altuwairiqi, John Mcalaney, Raian Ali
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This paper aims to objectively compare the use of mental health apps between the pre-COVID-19 and during COVID-19 periods and to study differences amongst the users of these apps based on age and gender. The study utilizes a dataset collected through a smartphone app that objectively records the users' sessions. The dataset was analyzed to identify users of mental health apps (38 users of mental health apps pre-COVID-19 and 81 users during COVID-19) and to calculate the following usage metrics; the daily average use time, the average session time, the average number of launches, and the number of usage days. …
Green Intellectual Capital And Green Supply Chain Performance: Does Big Data Analytics Capabilities Matter?, Ayman Wael Al-Khatib, Ahmed Shuhaiber
Green Intellectual Capital And Green Supply Chain Performance: Does Big Data Analytics Capabilities Matter?, Ayman Wael Al-Khatib, Ahmed Shuhaiber
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In light of global environmental concerns growing, environmental awareness within firms has become more important than before, and many scholars and researchers have argued the importance of environmental management in promoting sustainable organizational performance, especially in the context of supply chains. Thus, the current study aimed at identifying the impact of the components of green intellectual capital (green human capital, green structural capital, green relational capital) on green supply chain performance in the manufacturing sector in Jordan, as well as identifying the moderating role of big data analytics capabilities. To achieve this aim, we developed a conceptual model of Structural …
Bot-Mgat: A Transfer Learning Model Based On A Multi-View Graph Attention Network To Detect Social Bots, Eiman Alothali, Motamen Salih, Kadhim Hayawi, Hany Alashwal
Bot-Mgat: A Transfer Learning Model Based On A Multi-View Graph Attention Network To Detect Social Bots, Eiman Alothali, Motamen Salih, Kadhim Hayawi, Hany Alashwal
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Twitter, as a popular social network, has been targeted by different bot attacks. Detecting social bots is a challenging task, due to their evolving capacity to avoid detection. Extensive research efforts have proposed different techniques and approaches to solving this problem. Due to the scarcity of recently updated labeled data, the performance of detection systems degrades when exposed to a new dataset. Therefore, semi-supervised learning (SSL) techniques can improve performance, using both labeled and unlabeled examples. In this paper, we propose a framework based on the multi-view graph attention mechanism using a transfer learning (TL) approach, to predict social bots. …
An Adaptive Multi-Level Quantization-Based Reinforcement Learning Model For Enhancing Uav Landing On Moving Targets, Najmaddin Abo Mosali, Syariful Syafiq Shamsudin, Salama A. Mostafa, Omar Alfandi, Rosli Omar, Najib Al-Fadhali, Mazin Abed Mohammed, R. Q. Malik, Mustafa Musa Jaber, Abdu Saif
An Adaptive Multi-Level Quantization-Based Reinforcement Learning Model For Enhancing Uav Landing On Moving Targets, Najmaddin Abo Mosali, Syariful Syafiq Shamsudin, Salama A. Mostafa, Omar Alfandi, Rosli Omar, Najib Al-Fadhali, Mazin Abed Mohammed, R. Q. Malik, Mustafa Musa Jaber, Abdu Saif
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The autonomous landing of an unmanned aerial vehicle (UAV) on a moving platform is an essential functionality in various UAV-based applications. It can be added to a teleoperation UAV system or part of an autonomous UAV control system. Various robust and predictive control systems based on the traditional control theory are used for operating a UAV. Recently, some attempts were made to land a UAV on a moving target using reinforcement learning (RL). Vision is used as a typical way of sensing and detecting the moving target. Mainly, the related works have deployed a deep-neural network (DNN) for RL, which …
An Overview Of Technologies Deployed In Gcc Countries To Combat Covid-19, Samia Loucif, Murad Al-Rajab, Reem Salem, Nadine Akkila
An Overview Of Technologies Deployed In Gcc Countries To Combat Covid-19, Samia Loucif, Murad Al-Rajab, Reem Salem, Nadine Akkila
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Since December 2019, COVID-19 and all of its variants continue to ravage the planet with consequent negative impact that has completely changed our lives within a short period of time after the outbreak of the Virus. On March 11, 2020, COVID-19 was declared a global pandemic by the World Health Organization. Since then, a group of new COVID-19 variants has emerged posing a greater danger to humanity. By the start of August 2021, the reported COVID-19 related death toll across the globe has rocketed to 4,233,139. To deal with the COVID-19 pandemic, countries across the world have rushed to develop …
Monolithic Ontological Methodology (Mom): An Effective Software Project Management Approach, Kamal Uddin Sarker, Aziz Deraman, Raza Hasan, Ali Abbas, Babar Shah, Abrar Ullah
Monolithic Ontological Methodology (Mom): An Effective Software Project Management Approach, Kamal Uddin Sarker, Aziz Deraman, Raza Hasan, Ali Abbas, Babar Shah, Abrar Ullah
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Due to rapid changes in software applications, especially incorporating the demands of self-regulating technologies becomes a major challenge in software projects. This research focuses on technological, managerial, and procedural challenges, which are believed to be the most significant factors contributing to projects failure. To address these issues, this study proposes Monolithic Ontological Methodology (MOM) which addresses the weakness in the existing benchmark methodologies including PRINCE2, Extreme Programming, and Scrum in terms of project management, quality control, and stakeholder involvement. The MOM consists of seven phases and each phase has the required number of iterations until it is approved by management. …
The Smart In Smart Cities: A Framework For Image Classification Using Deep Learning, Rabiah Al-Qudah, Yaser Khamayseh, Monther Aldwairi, Sarfraz Khan
The Smart In Smart Cities: A Framework For Image Classification Using Deep Learning, Rabiah Al-Qudah, Yaser Khamayseh, Monther Aldwairi, Sarfraz Khan
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The need for a smart city is more pressing today due to the recent pandemic, lockouts, climate changes, population growth, and limitations on availability/access to natural resources. However, these challenges can be better faced with the utilization of new technologies. The zoning design of smart cities can mitigate these challenges. It identifies the main components of a new smart city and then proposes a general framework for designing a smart city that tackles these elements. Then, we propose a technology-driven model to support this framework. A mapping between the proposed general framework and the proposed technology model is then introduced. …
Blockchain For Governments: The Case Of The Dubai Government, Shafaq Khan, Mohammed Shael, Munir Majdalawieh, Nishara Nizamuddin, Mathew Nicho
Blockchain For Governments: The Case Of The Dubai Government, Shafaq Khan, Mohammed Shael, Munir Majdalawieh, Nishara Nizamuddin, Mathew Nicho
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Blockchain technology is an innovative technology with the potential of transforming cities by augmenting the building of resilient societies and enabling the emergence of more transparent and accountable governments. To understand the capabilities of blockchain, as well as its impact on the public sector, this study conducted a review of blockchain technology and its implementations by various governments around the globe. E-government evolution is analyzed, based on empirical evidence from a Dubai government entity in the United Arab Emirates (UAE), which has utilized blockchain technology for developing end-user services, relevant to the public sector. Benefits achieved and challenges to overcome …
Segmentation Of Infant Brain Using Nonnegative Matrix Factorization, Norah Saleh Alghamdi, Fatma Taher, Heba Kandil, Ahmed Sharafeldeen, Ahmed Elnakib, Ahmed Soliman, Yaser Elnakieb, Ali Mahmoud, Mohammed Ghazal, Ayman El-Baz
Segmentation Of Infant Brain Using Nonnegative Matrix Factorization, Norah Saleh Alghamdi, Fatma Taher, Heba Kandil, Ahmed Sharafeldeen, Ahmed Elnakib, Ahmed Soliman, Yaser Elnakieb, Ali Mahmoud, Mohammed Ghazal, Ayman El-Baz
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This study develops an atlas-based automated framework for segmenting infants' brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant's brain at the isointense age (6-12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov-Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix …