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Articles 331 - 360 of 677
Full-Text Articles in Computer Sciences
Computational Intelligence And Soft Computing Paradigm For Cheating Detection In Online Examinations, Sanaa Kaddoura, Shweta Vincent, D. Jude Hemanth
Computational Intelligence And Soft Computing Paradigm For Cheating Detection In Online Examinations, Sanaa Kaddoura, Shweta Vincent, D. Jude Hemanth
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Covid-19 has been a life-changer in the sphere of online education. With complete lockdown in various countries, there has been a tumultuous increase in the need for providing online education, and hence, it has become mandatory for examiners to ensure that a fair methodology is followed for evaluation, and academic integrity is met. A plethora of literature is available related to methods to mitigate cheating during online examinations. A systematic literature review (SLR) has been followed in our article which aims at introducing the research gap in terms of the usage of soft computing techniques to combat cheating during online …
Application Of A Gene Modular Approach For Clinical Phenotype Genotype Association And Sepsis Prediction Using Machine Learning In Meningococcal Sepsis, Asrar Rashid, Arif R. Anwary, Feras Al-Obeidat, Joe Brierley, Mohammed Uddin, Hoda Alkhzaimi, Amrita Sarpal, Mohammed Toufiq, Zainab A. Malik, Raziya Kadwa, Praveen Khilnani, M. Guftar Shaikh, Govind Benakatti, Javed Sharief, Syed Ahmed Zaki, Abdulrahman Zeyada, Ahmed Al-Dubai, Wael Hafez, Amir Hussain
Application Of A Gene Modular Approach For Clinical Phenotype Genotype Association And Sepsis Prediction Using Machine Learning In Meningococcal Sepsis, Asrar Rashid, Arif R. Anwary, Feras Al-Obeidat, Joe Brierley, Mohammed Uddin, Hoda Alkhzaimi, Amrita Sarpal, Mohammed Toufiq, Zainab A. Malik, Raziya Kadwa, Praveen Khilnani, M. Guftar Shaikh, Govind Benakatti, Javed Sharief, Syed Ahmed Zaki, Abdulrahman Zeyada, Ahmed Al-Dubai, Wael Hafez, Amir Hussain
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Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The research used Weighted Gene Co-expression Network Analysis (WGCNA) to establish links between gene expression and clinical parameters in infants admitted to the Pediatric Critical Care Unit with MSS. Additionally, various machine learning (ML) algorithms, including Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Artificial Neural Network (ANN) were implemented to predict sepsis survival. The findings revealed a transition …
Fuzzy Logic-Based Approach For Location Identification And Routing In The Outdoor Environment, Saravjeet Singh, Jaiteg Singh, Sukhjit Singh Sehra, Babar Shah, Farman Ali, Daehan Kwak
Fuzzy Logic-Based Approach For Location Identification And Routing In The Outdoor Environment, Saravjeet Singh, Jaiteg Singh, Sukhjit Singh Sehra, Babar Shah, Farman Ali, Daehan Kwak
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Finding the precise and accurate location of devices on road networks is challenging in remote areas with poor internet connectivity and Global Positioning System coverage. Navigation applications that completely depend on the internet and reference spatial data for location identification and mapping do not perform well in case of frequent internet disconnection. These reference spatial data sources have many associated challenges like large size, errors in data, and restricted access. To address these challenges, this paper provides an approach for localization and routing using self-generated reference data using likelihood estimation. According to the proposed approach, the trajectory information is used …
Of Stances, Themes, And Anomalies In Covid-19 Mask-Wearing Tweets, Jwen Fai Low, Benjamin C.M. Fung, Farkhund Iqbal, Ebrahim Bagheri
Of Stances, Themes, And Anomalies In Covid-19 Mask-Wearing Tweets, Jwen Fai Low, Benjamin C.M. Fung, Farkhund Iqbal, Ebrahim Bagheri
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COVID-19 is an opportunity to study public acceptance of a ‘‘new’’ healthcare intervention, universal masking, which unlike vaccination, is mostly alien to the Anglosphere public despite being practiced in ages past. Using a collection of over two million tweets, we studied the ways in which proponents and opponents of masking vied for influence as well as the themes driving the discourse. Pro-mask tweets encouraging others to mask up dominated Twitter early in the pandemic though its continued dominance has been eroded by anti-mask tweets criticizing others for their masking behavior. Engagement, represented by the counts of likes, retweets, and replies, …
Design And Delivery Of National Housing In The Uae: An Alternative Approach, Basem Eid Mohamed, Mohamed Elkaftangui, Rana Zureikat, Rund Hiyasat
Design And Delivery Of National Housing In The Uae: An Alternative Approach, Basem Eid Mohamed, Mohamed Elkaftangui, Rana Zureikat, Rund Hiyasat
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The provision of national housing to citizens in the United Arab Emirates (UAE) is considered a crucial topic. Over the past four decades, the process of developing national housing has emerged into multiple housing programs and schemes, all with the same aim of offering affordable and high-quality housing to citizens, in addition to meeting the needs of local families regarding spatial configurations while maintaining cultural values. However, despite all these efforts, the question has always remained: are the offered housing practices suited for family needs, socioeconomic trends, and environmental challenges? This study aims to offer an alternative approach for the …
Overhead Based Cluster Scheduling Of Mixed Criticality Systems On Multicore Platform, Amjad Ali, Asad Masood Khattak, Shahid Iqbal, Omar Alfandi, Bashir Hayat, Muhammad Hameed Siddiqi, Adil Khan
Overhead Based Cluster Scheduling Of Mixed Criticality Systems On Multicore Platform, Amjad Ali, Asad Masood Khattak, Shahid Iqbal, Omar Alfandi, Bashir Hayat, Muhammad Hameed Siddiqi, Adil Khan
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The cluster-based technique is gaining focus for scheduling tasks of mixed-criticality (MC) real-time multicore systems. In this technique, the cores of the MC system are distributed in groups known as clusters. When all cores are distributed in clusters, the tasks are partitioned into clusters, which are scheduled on the cores within each cluster using a global approach. In this study, a cluster-based technique is adopted for scheduling tasks of real-time mixed-criticality systems (MCS). The Decreasing Criticality Decreasing Utilization with the worst-fit (DCDU-WF) technique is used for partitioning of tasks to clusters, whereas a novel mixed-criticality cluster-based boundary fair (MC-Bfair) scheduling …
Need For Affect, Problematic Social Media Use And The Mediating Role Of Fear Of Missing Out In European And Arab Samples, Areej Babiker, Mohamed Basel Almourad, Constantina Panourgia, Sameha Alshakhsi, Christian Montag, Raian Ali
Need For Affect, Problematic Social Media Use And The Mediating Role Of Fear Of Missing Out In European And Arab Samples, Areej Babiker, Mohamed Basel Almourad, Constantina Panourgia, Sameha Alshakhsi, Christian Montag, Raian Ali
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Purpose: The growing awareness and concern about the excessive use of social media have led to an increasing number of studies investigating the underlying factors contributing to this behavior. In the literature, it is discussed that problematic social media use (PSMU) can impact individuals’ mental health and well-being. Drawing on the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, this study aimed to examine the association between the need for affect (affect approach and affect avoidance) and PSMU (operationalized via the social media disorder scale), as well as the mediating role of fear of missing out (FoMO) in that relation. Participants and Methods: …
3d Indoor Modeling And Game Theory Based Navigation For Pre And Post Covid-19 Situation, Jaiteg Singh, Noopur Tyagi, Saravjeet Singh, Babar Shah, Farman Ali, Ahmad Ali Alzubi, Abdulrhman Alkhanifer
3d Indoor Modeling And Game Theory Based Navigation For Pre And Post Covid-19 Situation, Jaiteg Singh, Noopur Tyagi, Saravjeet Singh, Babar Shah, Farman Ali, Ahmad Ali Alzubi, Abdulrhman Alkhanifer
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The COVID-19 pandemic has greatly affected human behavior, creating a need for individuals to be more cautious about health and safety protocols. People are becoming more aware of their surroundings and the importance of minimizing the risk of exposure to potential sources of infection. This shift in mindset is particularly important in indoor environments, especially hospitals, where there is a greater risk of virus transmission. The implementation of route planning in these areas, aimed at minimizing interaction and exposure, is crucial for positively influencing individual behavior. Accurate maps of buildings help provide location-based services, prepare for emergencies, and manage infrastructural …
Understanding Influencers Of College Major Decision: The Uae Case, Mohammad Amin Kuhail, Joao Negreiros, Haseena Al Katheeri, Sana Khan, Shurooq Almutairi
Understanding Influencers Of College Major Decision: The Uae Case, Mohammad Amin Kuhail, Joao Negreiros, Haseena Al Katheeri, Sana Khan, Shurooq Almutairi
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This study aims to understand and analyze what influences female students to choose a college major in the United Arab Emirates (UAE). To accomplish our target, we conducted a survey with mostly female first-year undergraduate students (N = 496) at Zayed University to understand the personal, social, and financial factors influencing students’ major choices. Further, this study also asked students to specify their actions before deciding on their major and assessed the information that could be helpful for future students to decide on their majors. Last, the study investigated how Science, Technology, Engineering, and Mathematics (STEM) students differ from other …
Using Blockchain For Enabling Transparent, Traceable, And Trusted University Ranking Systems, Ammar Battah, Khaled Salah, Raja Jayaraman, Ibrar Yaqoob, Ashraf Khalil
Using Blockchain For Enabling Transparent, Traceable, And Trusted University Ranking Systems, Ammar Battah, Khaled Salah, Raja Jayaraman, Ibrar Yaqoob, Ashraf Khalil
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Ranking systems have proven to improve the quality of education and help build the reputation of academic institutions. Each of the current academic ranking systems is based on different methodologies, criteria, and standards of measurement. Academic and employer reputations are subjective indicators of some rankings determined through surveying that is neither transparent nor traceable. The current academic ranking systems fall short of providing transparency and traceability features for both subjective and objective indicators that are used to calculate the ranking. Also, the ranking systems are managed and controlled in a centralized manner by specific entities. This raises concerns about fairness …
Covid-19 Pandemic And The Cyberthreat Landscape: Research Challenges And Opportunities, Heba Saleous, Muhusina Ismail, Saleh H. Aldaajeh, Nisha Madathil, Saed Alrabaee, Kim Kwang Raymond Choo, Nabeel Al-Qirim
Covid-19 Pandemic And The Cyberthreat Landscape: Research Challenges And Opportunities, Heba Saleous, Muhusina Ismail, Saleh H. Aldaajeh, Nisha Madathil, Saed Alrabaee, Kim Kwang Raymond Choo, Nabeel Al-Qirim
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Although cyber technologies benefit our society, there are also some related cybersecurity risks. For example, cybercriminals may exploit vulnerabilities in people, processes, and technologies during trying times, such as the ongoing COVID-19 pandemic, to identify opportunities that target vulnerable individuals, organizations (e.g., medical facilities), and systems. In this paper, we examine the various cyberthreats associated with the COVID-19 pandemic. We also determine the attack vectors and surfaces of cyberthreats. Finally, we will discuss and analyze the insights and suggestions generated by different cyberattacks against individuals, organizations, and systems.
Forecasting Energy Consumption Demand Of Customers In Smart Grid Using Temporal Fusion Transformer (Tft), Amril Nazir, Abdul Khalique Shaikh, Abdul Salam Shah, Ashraf Khalil
Forecasting Energy Consumption Demand Of Customers In Smart Grid Using Temporal Fusion Transformer (Tft), Amril Nazir, Abdul Khalique Shaikh, Abdul Salam Shah, Ashraf Khalil
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Energy consumption prediction has always remained a concern for researchers because of the rapid growth of the human population and customers joining smart grids network for smart home facilities. Recently, the spread of COVID-19 has dramatically increased energy consumption in the residential sector. Hence, it is essential to produce energy per the residential customers' requirements, improve economic efficiency, and reduce production costs. The previously published papers in the literature have considered the overall energy consumption prediction, making it difficult for production companies to produce energy per customers' future demand. Using the proposed study, production companies can accurately have energy per …
Short Term Energy Consumption Forecasting Using Neural Basis Expansion Analysis For Interpretable Time Series, Abdul Khalique Shaikh, Amril Nazir, Imran Khan, Abdul Salam Shah
Short Term Energy Consumption Forecasting Using Neural Basis Expansion Analysis For Interpretable Time Series, Abdul Khalique Shaikh, Amril Nazir, Imran Khan, Abdul Salam Shah
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Smart grids and smart homes are getting people's attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart …
The Role Of Radiomics And Ai Technologies In The Segmentation, Detection, And Management Of Hepatocellular Carcinoma, Dalia Fahmy, Ahmed Alksas, Ahmed Elnakib, Ali Mahmoud, Heba Kandil, Ashraf Khalil, Mohammed Ghazal, Eric Van Bogaert, Sohail Contractor, Ayman El-Baz
The Role Of Radiomics And Ai Technologies In The Segmentation, Detection, And Management Of Hepatocellular Carcinoma, Dalia Fahmy, Ahmed Alksas, Ahmed Elnakib, Ali Mahmoud, Heba Kandil, Ashraf Khalil, Mohammed Ghazal, Eric Van Bogaert, Sohail Contractor, Ayman El-Baz
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Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.
Smartphone Usage Before And During Covid-19: A Comparative Study Based On Objective Recording Of Usage Data, Khansa Chemnad, Sameha Alshakhsi, Mohamed Basel Almourad, Majid Altuwairiqi, Keith Phalp, Raian Ali
Smartphone Usage Before And During Covid-19: A Comparative Study Based On Objective Recording Of Usage Data, Khansa Chemnad, Sameha Alshakhsi, Mohamed Basel Almourad, Majid Altuwairiqi, Keith Phalp, Raian Ali
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Most studies that claimed changes in smartphone usage during COVID-19 were based on self-reported usage data, e.g., that collected through a questionnaire. These studies were also limited to reporting the overall smartphone usage, with no detailed investigation of distinct types of apps. The current study investigated smartphone usage before and during COVID-19. Our study used a dataset from a smartphone app that objectively logged users’ activities, including apps accessed and each app session start and end time. These were collected during two periods: pre-COVID-19 (161 individuals with 77 females) and during COVID-19 (251 individuals with 159 females). We report on …
Interacting With A Chatbot-Based Advising System: Understanding The Effect Of Chatbot Personality And User Gender On Behavior, Mohammad Amin Kuhail, Justin Thomas, Salwa Alramlawi, Syed Jawad Hussain Shah, Erik Thornquist
Interacting With A Chatbot-Based Advising System: Understanding The Effect Of Chatbot Personality And User Gender On Behavior, Mohammad Amin Kuhail, Justin Thomas, Salwa Alramlawi, Syed Jawad Hussain Shah, Erik Thornquist
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Chatbots with personality have been shown to affect engagement and user subjective satisfaction. Yet, the design of most chatbots focuses on functionality and accuracy rather than an interpersonal communication style. Existing studies on personality-imbued chatbots have mostly assessed the effect of chatbot personality on user preference and satisfaction. However, the influence of chatbot personality on behavioral qualities, such as users’ trust, engagement, and perceived authenticity of the chatbots, is largely unexplored. To bridge this gap, this study contributes: (1) A detailed design of a personality-imbued chatbot used in academic advising. (2) Empirical findings of an experiment with students who interacted …
An Effective Deep Learning Approach For The Classification Of Bacteriosis In Peach Leave, Muneer Akbar, Mohib Ullah, Babar Shah, Rafi Ullah Khan, Tariq Hussain, Farman Ali, Fayadh Alenezi, Ikram Syed, Kyung Sup Kwak
An Effective Deep Learning Approach For The Classification Of Bacteriosis In Peach Leave, Muneer Akbar, Mohib Ullah, Babar Shah, Rafi Ullah Khan, Tariq Hussain, Farman Ali, Fayadh Alenezi, Ikram Syed, Kyung Sup Kwak
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Bacteriosis is one of the most prevalent and deadly infections that affect peach crops globally. Timely detection of Bacteriosis disease is essential for lowering pesticide use and preventing crop loss. It takes time and effort to distinguish and detect Bacteriosis or a short hole in a peach leaf. In this paper, we proposed a novel LightWeight (WLNet) Convolutional Neural Network (CNN) model based on Visual Geometry Group (VGG-19) for detecting and classifying images into Bacteriosis and healthy images. Profound knowledge of the proposed model is utilized to detect Bacteriosis in peach leaf images. First, a dataset is developed which consists …
Hybrid Feature Selection Based On Principal Component Analysis And Grey Wolf Optimizer Algorithm For Arabic News Article Classification, Osama Ahmad Alomari, Ashraf Elnagar, Imad Afyouni, Ismail Shahin, Ali Bou Nassif, Ibrahim Abaker Hashem, Mohammad Tubishat
Hybrid Feature Selection Based On Principal Component Analysis And Grey Wolf Optimizer Algorithm For Arabic News Article Classification, Osama Ahmad Alomari, Ashraf Elnagar, Imad Afyouni, Ismail Shahin, Ali Bou Nassif, Ibrahim Abaker Hashem, Mohammad Tubishat
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The rapid growth of electronic documents has resulted from the expansion and development of internet technologies. Text-documents classification is a key task in natural language processing that converts unstructured data into structured form and then extract knowledge from it. This conversion generates a high dimensional data that needs further analusis using data mining techniques like feature extraction, feature selection, and classification to derive meaningful insights from the data. Feature selection is a technique used for reducing dimensionality in order to prune the feature space and, as a result, lowering the computational cost and enhancing classification accuracy. This work presents a …
Why People Choose Apps: An Evaluation Of The Ecology And User Experience Of Mobile Applications, Ons Al-Shamaileh, Alistair Sutcliffe
Why People Choose Apps: An Evaluation Of The Ecology And User Experience Of Mobile Applications, Ons Al-Shamaileh, Alistair Sutcliffe
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Purpose To investigate the reasons for users’ choice of mobile applications and how their choice relates to their experience of use. Method A mixed methods study of the factors influencing users’ choice to adopt or abandon mobile applications. Seventy-nine respondents completed a questionnaire recording their top four favourite applications, the frequency of use and user experience measures: aesthetics, content, usability, pleasurable interaction, and overall experience. They also reported up to four abandoned Apps, with any alternatives considered and the reasons for use or abandoning. Follow-up interviews probed the reasons for users’ choice of specific applications. Results/Conclusions Social media was the …
Crowdpower: A Novel Crowdsensing-As-A-Service Platform For Real-Time Incident Reporting, Sujith Samuel Mathew, May El Barachi, Mohammad Amin Kuhail
Crowdpower: A Novel Crowdsensing-As-A-Service Platform For Real-Time Incident Reporting, Sujith Samuel Mathew, May El Barachi, Mohammad Amin Kuhail
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Crowdsensing using mobile phones is a novel addition to the Internet of Things applications suite. However, there are many challenges related to crowdsensing, including (1) the ability to manage a large number of mobile users with varying devices’ capabilities; (2) recruiting reliable users available in the location of interest at the right time; (3) handling various sensory data collected with different requirements and at different frequencies and scales; (4) brokering the relationship between data collectors and consumers in an efficient and scalable manner; and (5) automatically generating intelligence reports after processing the collected sensory data. No comprehensive end-to-end crowdsensing platform …
The Impact Of Cdio's Dimensions And Values On It Learner's Attitude And Behavior: A Regression Model Using Partial Least Squares, Ahmed Shuhaiber, Monther Aldwairi
The Impact Of Cdio's Dimensions And Values On It Learner's Attitude And Behavior: A Regression Model Using Partial Least Squares, Ahmed Shuhaiber, Monther Aldwairi
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CDIO (Conceiving-Designing-Implementing-Operating), crowdsourcing and gamification are gaining more popularity in IT education. However, factors that influence learners' attitude toward this method are yet to be discovered. Therefore, this study aims to develop and test a conceptual model of implementing CDIO-based curriculum in IT education. For this purpose, CDIO dimensions were conceptualized and developed into questionnaire items. Then 141 students who experienced the CDIO method in information security course and lab, were sampled through action-research approach to investigate their perceptions and experiences about the learning stages, dimensions and values of this teaching method. Data gathered were analyzed by multiple regression algorithm …
Towards Effective And Efficient Online Exam Systems Using Deep Learning-Based Cheating Detection Approach, Sanaa Kaddoura, Abdu Gumaei
Towards Effective And Efficient Online Exam Systems Using Deep Learning-Based Cheating Detection Approach, Sanaa Kaddoura, Abdu Gumaei
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With the high growth of digitization and globalization, online exam systems continue to gain popularity and stretch, especially in the case of spreading infections like a pandemic. Cheating detection in online exam systems is a significant and necessary task to maintain the integrity of the exam and give unbiased, fair results. Currently, online exam systems use vision-based traditional machine learning (ML) methods and provide examiners with tools to detect cheating throughout the exam. However, conventional ML methods depend on handcrafted features and cannot learn the hierarchical representations of objects from data itself, affecting the efficiency and effectiveness of such systems. …
Hill Climbing-Based Efficient Model For Link Prediction In Undirected Graphs, Haji Gul, Feras Al-Obeidat, Adnan Amin, Fernando Moreira, Kaizhu Huang
Hill Climbing-Based Efficient Model For Link Prediction In Undirected Graphs, Haji Gul, Feras Al-Obeidat, Adnan Amin, Fernando Moreira, Kaizhu Huang
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Link prediction is a key problem in the field of undirected graph, and it can be used in a variety of contexts, including information retrieval and market analysis. By “undirected graphs”, we mean undirected complex networks in this study. The ability to predict new links in complex networks has a significant impact on society. Many complex systems can be modelled using networks. For example, links represent relationships (such as friendships, etc.) in social networks, whereas nodes represent users. Embedding methods, which produce the feature vector of each node in a graph and identify unknown links, are one of the newest …
The Uae Employees’ Perceptions Towards Factors For Sustaining Big Data Implementation And Continuous Impact On Their Organization’S Performance, S. M.F.D.Syed Mustapha
The Uae Employees’ Perceptions Towards Factors For Sustaining Big Data Implementation And Continuous Impact On Their Organization’S Performance, S. M.F.D.Syed Mustapha
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The UAE has officially launched the Big Data initiative in the year 2022; however, the interest in and adoption of Big Data technologies and strategies had started much earlier in the private and public sectors. This research aims to explore the perceptions of the UAE employees on factors needed to implement sustainable Big Data and the continuous impact on their organizational performance. A total of 257 employees were randomly selected for an online survey, and data were collected using a Likert-style five-point scale that was tested for validity and reliability. The findings indicate that employees believe that Big Data Sustainable …
Mobility-Aware Hierarchical Fog Computing Framework For Industrial Internet Of Things (Iiot), Tariq Qayyum, Zouheir Trabelsi, Asad Waqar Malik, Kadhim Hayawi
Mobility-Aware Hierarchical Fog Computing Framework For Industrial Internet Of Things (Iiot), Tariq Qayyum, Zouheir Trabelsi, Asad Waqar Malik, Kadhim Hayawi
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The Industrial Internet of Things (IIoTs) is an emerging area that forms the collaborative environment for devices to share resources. In IIoT, many sensors, actuators, and other devices are used to improve industrial efficiency. As most of the devices are mobile; therefore, the impact of mobility can be seen in terms of low-device utilization. Thus, most of the time, the available resources are underutilized. Therefore, the inception of the fog computing model in IIoT has reduced the communication delay in executing complex tasks. However, it is not feasible to cover the entire region through fog nodes; therefore, fog node selection …
Emotion Quantification Using Variational Quantum State Fidelity Estimation, Jaiteg Singh, Farman Ali, Babar Shah, Kamalpreet Singh Bhangu, Daehan Kwak
Emotion Quantification Using Variational Quantum State Fidelity Estimation, Jaiteg Singh, Farman Ali, Babar Shah, Kamalpreet Singh Bhangu, Daehan Kwak
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Sentiment analysis has been instrumental in developing artificial intelligence when applied to various domains. However, most sentiments and emotions are temporal and often exist in a complex manner. Several emotions can be experienced at the same time. Instead of recognizing only categorical information about emotions, there is a need to understand and quantify the intensity of emotions. The proposed research intends to investigate a quantum-inspired approach for quantifying emotional intensities in runtime. The inspiration comes from manifesting human cognition and decision-making capabilities, which may adopt a brief explanation through quantum theory. Quantum state fidelity was used to characterize states and …
Fast Covid-19 Detection From Chest X-Ray Images Using Dct Compression, Fatma Taher, Reem T. Haweel, Usama M. H. Al Bastaki, Eman Abdelwahed, Tariq Rehman, Tarek I. Haweel
Fast Covid-19 Detection From Chest X-Ray Images Using Dct Compression, Fatma Taher, Reem T. Haweel, Usama M. H. Al Bastaki, Eman Abdelwahed, Tariq Rehman, Tarek I. Haweel
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Novel coronavirus (COVID-19) is a new strain of coronavirus, first identified in a cluster with pneumonia symptoms caused by SARS-CoV-2 virus. It is fast spreading all over the world. Most infected people will develop mild to moderate illness and recover without hospitalization. Currently, real-time quantitative reverse transcription-PCR (rqRT-PCR) is popular for coronavirus detection due to its high specificity, simple quantitative analysis, and higher sensitivity than conventional RT-PCR. Antigen tests are also commonly used. It is very essential for the automatic detection of COVID-19 from publicly available resources. Chest X-ray (CXR) images are used for the classification of COVID-19, normal, and …
Deep Learning For Religious And Continent-Based Toxic Content Detection And Classification, Ahmed Abbasi, Abdul Rehman Javed, Farkhund Iqbal, Natalia Kryvinska, Zunera Jalil
Deep Learning For Religious And Continent-Based Toxic Content Detection And Classification, Ahmed Abbasi, Abdul Rehman Javed, Farkhund Iqbal, Natalia Kryvinska, Zunera Jalil
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With time, numerous online communication platforms have emerged that allow people to express themselves, increasing the dissemination of toxic languages, such as racism, sexual harassment, and other negative behaviors that are not accepted in polite society. As a result, toxic language identification in online communication has emerged as a critical application of natural language processing. Numerous academic and industrial researchers have recently researched toxic language identification using machine learning algorithms. However, Nontoxic comments, including particular identification descriptors, such as Muslim, Jewish, White, and Black, were assigned unrealistically high toxicity ratings in several machine learning models. This research analyzes and compares …
A Bilevel Optimization Model Based On Edge Computing For Microgrid, Yi Chen, Kadhim Hayawi, Meikai Fan, Shih Yu Chang, Jie Tang, Ling Yang, Rui Zhao, Zhongqi Mao, Hong Wen
A Bilevel Optimization Model Based On Edge Computing For Microgrid, Yi Chen, Kadhim Hayawi, Meikai Fan, Shih Yu Chang, Jie Tang, Ling Yang, Rui Zhao, Zhongqi Mao, Hong Wen
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With the continuous progress of renewable energy technology and the large-scale construction of microgrids, the architecture of power systems is becoming increasingly complex and huge. In order to achieve efficient and low-delay data processing and meet the needs of smart grid users, emerging smart energy systems are often deployed at the edge of the power grid, and edge computing modules are integrated into the microgrids system, so as to realize the cost-optimal control decision of the microgrids under the condition of load balancing. Therefore, this paper presents a bilevel optimization control model, which is divided into an upper-level optimal control …
Deep Learning Methods For Malware And Intrusion Detection: A Systematic Literature Review, Rahman Ali, Asmat Ali, Farkhund Iqbal, Mohammed Hussain, Farhan Ullah
Deep Learning Methods For Malware And Intrusion Detection: A Systematic Literature Review, Rahman Ali, Asmat Ali, Farkhund Iqbal, Mohammed Hussain, Farhan Ullah
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Android and Windows are the predominant operating systems used in mobile environment and personal computers and it is expected that their use will rise during the next decade. Malware is one of the main threats faced by these platforms as well as Internet of Things (IoT) environment and the web. With time, these threats are becoming more and more sophisticated and detecting them using traditional machine learning techniques is a hard task. Several research studies have shown that deep learning methods achieve better accuracy comparatively and can learn to efficiently detect and classify new malware samples. In this paper, we …