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Articles 301 - 330 of 677
Full-Text Articles in Computer Sciences
Developing Young Science And Technology Parks: Recent Findings From Industrial Nations Using The Data-Driven Approach, Charles Mondal, Mousa Al-Kfairy, Robert B. Mellor
Developing Young Science And Technology Parks: Recent Findings From Industrial Nations Using The Data-Driven Approach, Charles Mondal, Mousa Al-Kfairy, Robert B. Mellor
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Science and technology parks (STPs) are curated locations where new technology-based firms (NTBFs) and other SMEs and firms can conglomerate and promote a culture of innovation. Overall, the aim is to construct a sustainable high-value tech entrepreneurship ecosystem, and to this end we present here some recent and novel concepts derived from approaches using a data-driven statistical foundation. This paper considers studies on the organic growth of young start-up science and technology parks by authors who have used big data, econometric analyses, panel data and computer simulations. The results and concepts are derived from industrialized countries, notably Sweden and the …
Sebd: A Stream Evolving Bot Detection Framework With Application Of Pac Learning Approach To Maintain Accuracy And Confidence Levels, Eiman Alothali, Kadhim Hayawi, Hany Alashwal
Sebd: A Stream Evolving Bot Detection Framework With Application Of Pac Learning Approach To Maintain Accuracy And Confidence Levels, Eiman Alothali, Kadhim Hayawi, Hany Alashwal
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A simple supervised learning model can predict a class from trained data based on the previous learning process. Trust in such a model can be gained through evaluation measures that ensure fewer misclassification errors in prediction results for different classes. This can be applied to supervised learning using a well-trained dataset that covers different data points and has no imbalance issues. This task is challenging when it integrates a semi-supervised learning approach with a dynamic data stream, such as social network data. In this paper, we propose a stream-based evolving bot detection (SEBD) framework for Twitter that uses a deep …
Cardiac Arrhythmia Disease Classifier Model Based On A Fuzzy Fusion Approach, Fatma Taher, Hamoud Alshammari, Lobna Osman, Mohamed Elhoseny, Abdulaziz Shehab, Eman Elayat
Cardiac Arrhythmia Disease Classifier Model Based On A Fuzzy Fusion Approach, Fatma Taher, Hamoud Alshammari, Lobna Osman, Mohamed Elhoseny, Abdulaziz Shehab, Eman Elayat
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Cardiac diseases are one of the greatest global health challenges. Due to the high annual mortality rates, cardiac diseases have attracted the attention of numerous researchers in recent years. This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases. The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms. An ensemble of classifiers is then applied to the fusion’s results. The proposed model classifies the arrhythmia dataset from the University of California, Irvine into normal/abnormal classes as well as 16 classes of arrhythmia. Initially, at the preprocessing steps, …
An Advanced Deep Learning Models-Based Plant Disease Detection: A Review Of Recent Research, Muhammad Shoaib, Babar Shah, Shaker Ei-Sappagh, Akhtar Ali, Asad Ullah, Fayadh Alenezi, Tsanko Gechev, Tariq Hussain, Farman Ali
An Advanced Deep Learning Models-Based Plant Disease Detection: A Review Of Recent Research, Muhammad Shoaib, Babar Shah, Shaker Ei-Sappagh, Akhtar Ali, Asad Ullah, Fayadh Alenezi, Tsanko Gechev, Tariq Hussain, Farman Ali
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Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on …
A Fog Computing Framework For Intrusion Detection Of Energy-Based Attacks On Uav-Assisted Smart Farming, Junaid Sajid, Kadhim Hayawi, Asad Waqar Malik, Zahid Anwar, Zouheir Trabelsi
A Fog Computing Framework For Intrusion Detection Of Energy-Based Attacks On Uav-Assisted Smart Farming, Junaid Sajid, Kadhim Hayawi, Asad Waqar Malik, Zahid Anwar, Zouheir Trabelsi
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Precision agriculture and smart farming have received significant attention due to the advancements made in remote sensing technology to support agricultural efficiency. In large-scale agriculture, the role of unmanned aerial vehicles (UAVs) has increased in remote monitoring and collecting farm data at regular intervals. However, due to an open environment, UAVs can be hacked to malfunction and report false data. Due to limited battery life and flight times requiring frequent recharging, a compromised UAV wastes precious energy when performing unnecessary functions. Furthermore, it impacts other UAVs competing for charging times at the station, thus disrupting the entire data collection mechanism. …
User-Centered Software Design: User Interface Redesign For Blockly–Electron, Artificial Intelligence Educational Software For Primary And Secondary Schools, Chenghong Cen, Guang Luo, Lujia Li, Yilin Liang, Kang Li, Tan Jiang, Qiang Xiong
User-Centered Software Design: User Interface Redesign For Blockly–Electron, Artificial Intelligence Educational Software For Primary And Secondary Schools, Chenghong Cen, Guang Luo, Lujia Li, Yilin Liang, Kang Li, Tan Jiang, Qiang Xiong
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According to the 2021 and 2022 Horizon Report, AI is emerging in all areas of education, in various forms of educational aids with various applications, and is carving out a similarly ubiquitous presence across campuses and classrooms. This study explores a user-centered approach used in the design of the AI educational software by taking the redesign of the user interface of AI educational software Blockly–Electron as an example. Moreover, by analyzing the relationship between the four variables of software usability, the abstract usability is further certified so as to provide ideas for future improvements to the usability of AI educational …
Responses To Sad Emotion In Autistic And Normal Developing Children: Is There A Difference?, Mohamed Basel Almourad, Emad Bataineh, Zelal Wattar
Responses To Sad Emotion In Autistic And Normal Developing Children: Is There A Difference?, Mohamed Basel Almourad, Emad Bataineh, Zelal Wattar
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This paper describes how the gazing pattern differ between the responses of Normal Developing (ND) and Autistic (AP) children to sad emotion. We employed an eye tracking technology to collect and track the participants’ eye movements by showing a dynamic stimulus (video) that showed a gradual transition from pale emotions to melancholy facial expressions in both female and male faces. The location of the child's gaze in the stimulus was the focus of our data analysis. We deduced that there was a distinction between the two groups based on this. ND children predominantly concentrated on the eyes and mouth region …
User Acceptance And Adoption Of Smart Homes: A Decade Long Systematic Literature Review, Ibrahim Mashal, Ahmed Shuhaiber, Ayman Wael Al-Khatib
User Acceptance And Adoption Of Smart Homes: A Decade Long Systematic Literature Review, Ibrahim Mashal, Ahmed Shuhaiber, Ayman Wael Al-Khatib
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This survey aims to provide a coherent and bibliometric overview of the theories and constructs employed in smart homes acceptance and adoption literature. To achieve the study aims, we con-ducted a systematic search for every article related to the SH concept, services and applications, user acceptance and adoption, and integrated IoT home appliances and devices, in 10 major library databases, namely, IEEE Digital Library, ACM Digital Library, Association for Information Systems (AIS), Elsevier, Emerald, Taylor and Francis, Wiley InterScience, Springer, Inderscience, and Hindawi. These databases contain literature focusing on smart home adoption using IoT tech-nology. 40 research articles of journal …
The Rising Trend Of Metaverse In Education: Challenges, Opportunities, And Ethical Considerations, Sanaa Kaddoura, Fatima Al Husseiny
The Rising Trend Of Metaverse In Education: Challenges, Opportunities, And Ethical Considerations, Sanaa Kaddoura, Fatima Al Husseiny
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Metaverse is invading the educational sector and will change human-computer interaction techniques. Prominent technology executives are developing novel ways to turn the Metaverse into a learning environment, considering the rapid growth of technology. Since the COVID-19 outbreak, people have grown accustomed to teleworking, telemedicine, and numerous other forms of distance interaction. Recently, the Metaverse has been the focus of many educators. With Facebook’s statement that it was rebranding and promoting itself as Meta, this field saw a surge in interest in the areas of computer science and education. There is a literature gap in studying the Metaverse’s role in education. …
Towards Machine Learning-Based Fpga Backend Flow: Challenges And Opportunities, Imran Taj, Umer Farooq
Towards Machine Learning-Based Fpga Backend Flow: Challenges And Opportunities, Imran Taj, Umer Farooq
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Field-Programmable Gate Array (FPGA) is at the core of System on Chip (SoC) design across various Industry 5.0 digital systems—healthcare devices, farming equipment, autonomous vehicles and aerospace gear to name a few. Given that pre-silicon verification using Computer Aided Design (CAD) accounts for about 70% of the time and money spent on the design of modern digital systems, this paper summarizes the machine learning (ML)-oriented efforts in different FPGA CAD design steps. With the recent breakthrough of machine learning, FPGA CAD tasks—high-level synthesis (HLS), logic synthesis, placement and routing—are seeing a renewed interest in their respective decision-making steps. We focus …
Prediction Of Wilms’ Tumor Susceptibility To Preoperative Chemotherapy Using A Novel Computer-Aided Prediction System, Israa Sharaby, Ahmed Alksas, Ahmed Nashat, Hossam Magdy Balaha, Mohamed Shehata, Mallorie Gayhart, Ali Mahmoud, Mohammed Ghazal, Ashraf Khalil, Rasha T. Abouelkheir, Ahmed Elmahdy, Ahmed Abdelhalim, Ahmed Mosbah, Ayman El-Baz
Prediction Of Wilms’ Tumor Susceptibility To Preoperative Chemotherapy Using A Novel Computer-Aided Prediction System, Israa Sharaby, Ahmed Alksas, Ahmed Nashat, Hossam Magdy Balaha, Mohamed Shehata, Mallorie Gayhart, Ali Mahmoud, Mohammed Ghazal, Ashraf Khalil, Rasha T. Abouelkheir, Ahmed Elmahdy, Ahmed Abdelhalim, Ahmed Mosbah, Ayman El-Baz
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Wilms’ tumor, the most prevalent renal tumor in children, is known for its aggressive prognosis and recurrence. Treatment of Wilms’ tumor is multimodal, including surgery, chemotherapy, and occasionally, radiation therapy. Preoperative chemotherapy is used routinely in European studies and in select indications in North American trials. The objective of this study was to build a novel computer-aided prediction system for preoperative chemotherapy response in Wilms’ tumors. A total of 63 patients (age range: 6 months–14 years) were included in this study, after receiving their guardians’ informed consent. We incorporated contrast-enhanced computed tomography imaging to extract the texture, shape, and functionality-based …
The Impact Of Objectively Recorded Smartphone Usage And Emotional Intelligence On Problematic Internet Usage, Sameha Alshakhsi, Khansa Chemnad, Mohamed Basel Almourad, Majid Altuwairiqi, John Mcalaney, Raian Ali
The Impact Of Objectively Recorded Smartphone Usage And Emotional Intelligence On Problematic Internet Usage, Sameha Alshakhsi, Khansa Chemnad, Mohamed Basel Almourad, Majid Altuwairiqi, John Mcalaney, Raian Ali
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This study examined the effects of gender, age, objective smartphone usage data, and Emotional Intelligence (EI) on Problematic Internet Use (PIU) and its components (obsession, neglect, and control disorder). The study relied on objective data of smartphone usage as a representative of technology use collected by a monitoring application of smartphone usage. PIU and EI were measured through the Problematic Internet Usage Questionnaire short form (PIUQ-SF-6) and Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF), respectively. The current cross-sectional study was carried out with 268 participants (Female: 61.6%, ages from 15 to 64) from ten different countries. The analysis was performed using …
Carla+: An Evolution Of The Carla Simulator For Complex Environment Using A Probabilistic Graphical Model, Sumbal Malik, Manzoor Ahmed Khan, Aadam, Hesham El-Sayed, Farkhund Iqbal, Jalal Khan, Obaid Ullah
Carla+: An Evolution Of The Carla Simulator For Complex Environment Using A Probabilistic Graphical Model, Sumbal Malik, Manzoor Ahmed Khan, Aadam, Hesham El-Sayed, Farkhund Iqbal, Jalal Khan, Obaid Ullah
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In an urban and uncontrolled environment, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, and unprecedented dynamic events makes it challenging for the classical autonomous vehicle to navigate the traffic safely. Therefore, the realization of collaborative autonomous driving has the potential to improve road safety and traffic efficiency. However, an obvious challenge in this regard is how to define, model, and simulate the environment that captures the dynamics of a complex and urban environment. Therefore, in this research, we first define the dynamics of the envisioned environment, where we capture the dynamics relevant …
Intelligent Health Care And Diseases Management System: Multi-Day-Ahead Predictions Of Covid-19, Ahed Abugabah, Farah Shahid
Intelligent Health Care And Diseases Management System: Multi-Day-Ahead Predictions Of Covid-19, Ahed Abugabah, Farah Shahid
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The rapidly growing number of COVID-19 infected and death cases has had a catastrophic worldwide impact. As a case study, the total number of death cases in Algeria is over two thousand people (increased with time), which drives us to search its possible trend for early warning and control. In this paper, the proposed model for making a time-series forecast for daily and total infected cases, death cases, and recovered cases for the countrywide Algeria COVID-19 dataset is a two-layer dropout gated recurrent unit (TDGRU). Four performance parameters were used to assess the model’s performance: mean absolute error (MAE), root …
Augmenting Ccam Infrastructure For Creating Smart Roads And Enabling Autonomous Driving, M. Jalal Khan, Manzoor Ahmed Khan, Obaid Ullah, Sumbal Malik, Farkhund Iqbal, Hesham El-Sayed, Sherzod Turaev
Augmenting Ccam Infrastructure For Creating Smart Roads And Enabling Autonomous Driving, M. Jalal Khan, Manzoor Ahmed Khan, Obaid Ullah, Sumbal Malik, Farkhund Iqbal, Hesham El-Sayed, Sherzod Turaev
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Autonomous vehicles and smart roads are not new concepts and the undergoing development to empower the vehicles for higher levels of automation has achieved initial milestones. However, the transportation industry and relevant research communities still require making considerable efforts to create smart and intelligent roads for autonomous driving. To achieve the results of such efforts, the CCAM infrastructure is a game changer and plays a key role in achieving higher levels of autonomous driving. In this paper, we present a smart infrastructure and autonomous driving capabilities enhanced by CCAM infrastructure. Meaning thereby, we lay down the technical requirements of the …
Semantic Orientation Of Crosslingual Sentiments: Employment Of Lexicon And Dictionaries, Arslan Ali Raza, Asad Habib, Jawad Ashraf, Babar Shah, Fernando Moreira
Semantic Orientation Of Crosslingual Sentiments: Employment Of Lexicon And Dictionaries, Arslan Ali Raza, Asad Habib, Jawad Ashraf, Babar Shah, Fernando Moreira
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Sentiment Analysis is a modern discipline at the crossroads of data mining and natural language processing. It is concerned with the computational treatment of public moods shared in the form of text over social networking websites. Social media users express their feelings in conversations through cross-lingual terms, intensifiers, enhancers, reducers, symbols, and Net Lingo. However, the generic Sentiment Analysis (SA) research lacks comprehensive coverage about such abstruseness. In particular, they are inapt in the semantic orientation of Crosslingual based code switching, capitalization and accentuation of opinionative text due to the lack of annotated corpora, computational resources, linguistic processing and inefficient …
A Deep Learning Based Dual Encoder–Decoder Framework For Anatomical Structure Segmentation In Chest X-Ray Images, Ihsan Ullah, Farman Ali, Babar Shah, Shaker El-Sappagh, Tamer Abuhmed, Sang Hyun Park
A Deep Learning Based Dual Encoder–Decoder Framework For Anatomical Structure Segmentation In Chest X-Ray Images, Ihsan Ullah, Farman Ali, Babar Shah, Shaker El-Sappagh, Tamer Abuhmed, Sang Hyun Park
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Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct structures, variations in the anatomical structure shape among different individuals, the presence of medical tools, such as pacemakers and catheters, and various artifacts in the chest radiographic images. In this paper, we propose a robust deep learning segmentation framework for the anatomical structure in chest radiographs that utilizes a dual encoder–decoder convolutional neural network (CNN). The first network in the dual encoder–decoder structure effectively utilizes a pre-trained VGG19 …
An Efficient Hash-Based Assessment And Recovery Algorithm For Distributed Healthcare Systems, Sanaa Kaddoura, Ramzi Haraty, Sultan Al Jahdali, Mohamad Jaber
An Efficient Hash-Based Assessment And Recovery Algorithm For Distributed Healthcare Systems, Sanaa Kaddoura, Ramzi Haraty, Sultan Al Jahdali, Mohamad Jaber
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The enhancement of the information technology in many domains has had a positive impact on the healthcare sector. The ability to share medical data is one of the positive outcomes. However, this improvement comes with a number of threats. Although many threat preventive measures have been applied yet, no one can be confident that the system is safe from attacks. Thus, an algorithm needs to assess the damage occurring as a result of an attack before recovering the database. In this work, we present a distributed algorithm that uses hash tables to deal with the “information warfare” problem in healthcare …
When Chatgpt Goes Rogue: Exploring The Potential Cybersecurity Threats Of Ai-Powered Conversational Chatbots, Farkhund Iqbal, Faniel Samsom, Faouzi Kamoun, Áine Macdermott
When Chatgpt Goes Rogue: Exploring The Potential Cybersecurity Threats Of Ai-Powered Conversational Chatbots, Farkhund Iqbal, Faniel Samsom, Faouzi Kamoun, Áine Macdermott
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ChatGPT has garnered significant interest since its release in November 2022 and it has showcased a strong versatility in terms of potential applications across various industries and domains. Defensive cybersecurity is a particular area where ChatGPT has demonstrated considerable potential thanks to its ability to provide customized cybersecurity awareness training and its capability to assess security vulnerabilities and provide concrete recommendations to remediate them. However, the offensive use of ChatGPT (and AI-powered conversational agents, in general) remains an underexplored research topic. This preliminary study aims to shed light on the potential weaponization of ChatGPT to facilitate and initiate cyberattacks. We …
Lstda: Link Stability And Transmission Delay Aware Routing Mechanism For Flying Ad-Hoc Network (Fanet), Farman Ali, Khalid Zaman, Babar Shah, Tariq Hussain, Habib Ullah, Altaf Hussain, Daehan Kwak
Lstda: Link Stability And Transmission Delay Aware Routing Mechanism For Flying Ad-Hoc Network (Fanet), Farman Ali, Khalid Zaman, Babar Shah, Tariq Hussain, Habib Ullah, Altaf Hussain, Daehan Kwak
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The paper presents a new protocol called Link Stability and Transmission Delay Aware (LSTDA) for Flying Ad-hoc Network (FANET) with a focus on network corridors (NC). FANET consists of Unmanned Aerial Vehicles (UAVs) that face challenges in avoiding transmission loss and delay while ensuring stable communication. The proposed protocol introduces a novel link stability with network corridors priority node selection to check and ensure fair communication in the entire network. The protocol uses a Red-Black (R-B) tree to achieve maximum channel utilization and an advanced relay approach. The paper evaluates LSTDA in terms of End-to-End Delay (E2ED), Packet Delivery Ratio …
Explainable Machine Learning For Evapotranspiration Prediction, Bamory Koné, Rima Grati, Bassem Bouaziz, Khouloud Boukadi
Explainable Machine Learning For Evapotranspiration Prediction, Bamory Koné, Rima Grati, Bassem Bouaziz, Khouloud Boukadi
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No abstract provided.
Facial Expression Recognition Using Lightweight Deep Learning Modeling, Mubashir Ahmad, Saira, Omar Alfandi, Asad Masood Khattak, Syed Furqan Qadri, Iftikhar Ahmed Saeed, Salabat Khan, Bashir Hayat, Arshad Ahmad
Facial Expression Recognition Using Lightweight Deep Learning Modeling, Mubashir Ahmad, Saira, Omar Alfandi, Asad Masood Khattak, Syed Furqan Qadri, Iftikhar Ahmed Saeed, Salabat Khan, Bashir Hayat, Arshad Ahmad
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Facial expression is a type of communication and is useful in many areas of computer vision, including intelligent visual surveillance, human-robot interaction and human behavior analysis. A deep learning approach is presented to classify happy, sad, angry, fearful, contemptuous, surprised and disgusted expressions. Accurate detection and classification of human facial expression is a critical task in image processing due to the inconsistencies amid the complexity, including change in illumination, occlusion, noise and the over-fitting problem. A stacked sparse auto-encoder for facial expression recognition (SSAE-FER) is used for unsupervised pre-training and supervised fine-tuning. SSAE-FER automatically extracts features from input images, and …
Bypassing Multiple Security Layers Using Malicious Usb Human Interface Device, Mathew Nicho, Ibrahim Sabry
Bypassing Multiple Security Layers Using Malicious Usb Human Interface Device, Mathew Nicho, Ibrahim Sabry
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The Universal Serial Bus (USB) enabled devices acts as a trusted tool for data interchange, interface, and storage for the computer systems through Human Interface Devices (HID) namely the keyboard, mouse, headphone, storage media and peripherals that use the USB port. However, with billions of USB enabled devices currently in use today, the attacker’s potential to seamlessly leverage this device to perform malicious activities by bypassing security layers presents serious risk to systems administrators. The paper thus presents a comprehensive review of the multiple attacks that can be leveraged using USB devices and the corresponding vulnerabilities including countermeasures. This is …
Harvesting Publication Data To The Institutional Repository From Scopus, Web Of Science, Dimensions And Unpaywall Using A Custom R Script, Yrjo Lappalainen, Nikesh Narayanan
Harvesting Publication Data To The Institutional Repository From Scopus, Web Of Science, Dimensions And Unpaywall Using A Custom R Script, Yrjo Lappalainen, Nikesh Narayanan
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Institutional repositories are established tools for archiving and increasing the visibility and availability of academic outputs. Although the potential benefits of institutional repositories are well researched and many funders and institutions already mandate open access publishing via gold or green open access routes, institutional repositories often struggle with lack of growth and sustained workflows for content recruitment. Institutions have come up with various (and often creative) workflows for populating their repositories, including institutional open access mandates, library-mediated self-archiving, fully or partially automated content harvesting and integrations between repositories and Current Research Information Systems (CRIS). Zayed University launched the ZU Scholars …
Social Media Bot Detection With Deep Learning Methods: A Systematic Review, Kadhim Hayawi, Susmita Saha, Mohammad Mehedy Masud, Sujith Samuel Mathew, Mohammed Kaosar
Social Media Bot Detection With Deep Learning Methods: A Systematic Review, Kadhim Hayawi, Susmita Saha, Mohammad Mehedy Masud, Sujith Samuel Mathew, Mohammed Kaosar
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Social bots are automated social media accounts governed by software and controlled by humans at the backend. Some bots have good purposes, such as automatically posting information about news and even to provide help during emergencies. Nevertheless, bots have also been used for malicious purposes, such as for posting fake news or rumour spreading or manipulating political campaigns. There are existing mechanisms that allow for detection and removal of malicious bots automatically. However, the bot landscape changes as the bot creators use more sophisticated methods to avoid being detected. Therefore, new mechanisms for discerning between legitimate and bot accounts are …
Flexible Global Aggregation And Dynamic Client Selection For Federated Learning In Internet Of Vehicles, Tariq Qayyum, Zouheir Trabelsi, Asadullah Tariq, Muhammad Ali, Kadhim Hayawi, Irfan Ud Din
Flexible Global Aggregation And Dynamic Client Selection For Federated Learning In Internet Of Vehicles, Tariq Qayyum, Zouheir Trabelsi, Asadullah Tariq, Muhammad Ali, Kadhim Hayawi, Irfan Ud Din
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Federated Learning (FL) enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles (IoV) realm. While FL effectively tackles privacy concerns, it also imposes significant resource requirements. In traditional FL, trained models are transmitted to a central server for global aggregation, typically in the cloud. This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server. The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments. These include diverse and distributed data sources, varying data quality, …
A Comparative Assessment Of Human Factors In Cybersecurity: Implications For Cyber Governance, Muhammad Umair Shah, Farkhund Iqbal, Umair Rehman, Patrick C.K. Hung
A Comparative Assessment Of Human Factors In Cybersecurity: Implications For Cyber Governance, Muhammad Umair Shah, Farkhund Iqbal, Umair Rehman, Patrick C.K. Hung
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This paper provides an extensive overview of cybersecurity awareness in the young, educated, and technology-savvy population of the United Arab Emirates (UAE), compared to the United States of America (USA) for advancing the scholarship and practice of global cyber governance. We conducted comparative empirical studies to identify differences in specific human factors that affect cybersecurity behaviour in the UAE and the USA. In addition, we employed several control variables to observe reliable results. We used Hofstede’s theoretical framework on culture to advance our investigation. The results show that the targeted population in the UAE exhibits contrasting interpretations of cybersecurity awareness …
Forecasting Networks Links With Laplace Characteristic And Geographical Information In Complex Networks, Muhammad Wasim, Feras Al-Obeidat, Fernando Moreira, Haji Gul, Adnan Amin
Forecasting Networks Links With Laplace Characteristic And Geographical Information In Complex Networks, Muhammad Wasim, Feras Al-Obeidat, Fernando Moreira, Haji Gul, Adnan Amin
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Forecasting links in a network is a crucial task in various applications such as social networks, internet traffic management, and data mining. Many studies on forecasting links in social networks and on other networks have been conducted over the last decade. In this paper, we propose a novel method based on graph Laplacian eigenmaps for predicting the geographic location of nodes in complex networks. Our method utilizes the adjacency matrix of the network and generates a scoring matrix that captures the similarity between nodes in terms of their geographic location. By transforming the distance matrices into score matrices using exponential …
Towards A Novel Approach For Smart Agriculture Predictability, Rima Grati, Myriam Aloulou, Khouloud Boukadi
Towards A Novel Approach For Smart Agriculture Predictability, Rima Grati, Myriam Aloulou, Khouloud Boukadi
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No abstract provided.
Dynamic Data Sample Selection And Scheduling In Edge Federated Learning, Mohamed Adel Serhani, Haftay Gebreslasie Abreha, Asadullah Tariq, Mohammad Hayajneh, Yang Xu, Kadhim Hayawi
Dynamic Data Sample Selection And Scheduling In Edge Federated Learning, Mohamed Adel Serhani, Haftay Gebreslasie Abreha, Asadullah Tariq, Mohammad Hayajneh, Yang Xu, Kadhim Hayawi
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Federated Learning (FL) is a state-of-the-art paradigm used in Edge Computing (EC). It enables distributed learning to train on cross-device data, achieving efficient performance, and ensuring data privacy. In the era of Big Data, the Internet of Things (IoT), and data streaming, challenges such as monitoring and management remain unresolved. Edge IoT devices produce and stream huge amounts of sample sources, which can incur significant processing, computation, and storage costs during local updates using all data samples. Many research initiatives have improved the algorithm for FL in homogeneous networks. However, in the typical distributed learning application scenario, data is generated …