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Articles 1 - 30 of 123
Full-Text Articles in Physical Sciences and Mathematics
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 …
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 …
Some Properties Of Bazilevič Functions Involving Srivastava–Tomovski Operator, Daniel Breaz, Kadhavoor R. Karthikeyan, Elangho Umadevi, Alagiriswamy Senguttuvan
Some Properties Of Bazilevič Functions Involving Srivastava–Tomovski Operator, Daniel Breaz, Kadhavoor R. Karthikeyan, Elangho Umadevi, Alagiriswamy Senguttuvan
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We introduce a new class of Bazilevič functions involving the Srivastava–Tomovski generalization of the Mittag-Leffler function. The family of functions introduced here is superordinated by a conic domain, which is impacted by the Janowski function. We obtain coefficient estimates and subordination conditions for starlikeness and Fekete–Szegö functional for functions belonging to the class.
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.
Tonga Volcanic Eruption And Tsunami, January 2022: Globally The Most Significant Opportunity To Observe An Explosive And Tsunamigenic Submarine Eruption Since Ad 1883 Krakatau, James P. Terry, James Goff, Nigel Winspear, Vena Pearl Bongolan, Scott Fisher
Tonga Volcanic Eruption And Tsunami, January 2022: Globally The Most Significant Opportunity To Observe An Explosive And Tsunamigenic Submarine Eruption Since Ad 1883 Krakatau, James P. Terry, James Goff, Nigel Winspear, Vena Pearl Bongolan, Scott Fisher
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January 2022 witnessed the violent eruption of Hunga Tonga–Hunga Haʻapai submarine volcano in the South Pacific. With a volcanic explosivity index possibly equivalent to VEI 5, this represents the largest seaborne eruption for nearly one and a half centuries since Indonesia’s cataclysmic explosion of Krakatau in AD 1883. The Tongan eruption remarkably produced ocean-wide tsunamis, never documented before in the Pacific instrumental record. Volcanically generated tsunamis have been referred to as a ‘blind spot’ in our understanding of tsunami hazards, particularly in the Pacific Ocean. This event therefore presents a unique opportunity for investigating the multiple processes contributing to volcanic …
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 …
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. …
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 …
Bivariate Copulas Based On Counter-Monotonic Shock Method, Farid El Ktaibi, Rachid Bentoumi, Nicola Sottocornola, Mhamed Mesfioui
Bivariate Copulas Based On Counter-Monotonic Shock Method, Farid El Ktaibi, Rachid Bentoumi, Nicola Sottocornola, Mhamed Mesfioui
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This paper explores the properties of a family of bivariate copulas based on a new approach using the counter-monotonic shock method. The resulting copula covers the full range of negative dependence induced by one parameter. Expressions for the copula and density are derived and many theoretical properties are examined thoroughly, including explicit expressions for prominent measures of dependence, namely Spearman’s rho, Kendall’s tau and Blomqvist’s beta. The convexity properties of this copula are presented, together with explicit expressions of the mixed moments. Estimation of the dependence parameter using the method of moments is considered, then a simulation study is carried …
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 …
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 …
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 …
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 …
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 …
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 …
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 …
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). …
A Mathematical Model For The Energy Stored In Green Roofs, Maria Aguareles, Marc Calvo-Schwarzwalder, Francesc Font, Timothy G. Myers
A Mathematical Model For The Energy Stored In Green Roofs, Maria Aguareles, Marc Calvo-Schwarzwalder, Francesc Font, Timothy G. Myers
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A simple mathematical model to estimate the energy stored in a green roof is developed. Analytical solutions are derived corresponding to extensive (shallow) and intensive (deep) substrates. Results are presented for the surface temperature and energy stored in both green roofs and concrete during a typical day. Within the restrictions of the model assumptions the analytical solution demonstrates that both energy and surface temperature vary linearly with fractional leaf coverage, albedo and irradiance, while the effect of evaporation rate and convective heat transfer is non-linear. It is shown that a typical green roof is significantly cooler and stores less energy …
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 …
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 …