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Articles 421 - 450 of 677
Full-Text Articles in Computer Sciences
Autonomous Driving And Connected Mobility Modeling: Smart Dynamic Traffic Monitoring And Enforcement System For Connected And Autonomous Mobility, Dimitrios Zavantis, Fatma Outay, Youssef El-Hansali, Ansar Yasar, Elhadi Shakshuki, Haroon Malik
Autonomous Driving And Connected Mobility Modeling: Smart Dynamic Traffic Monitoring And Enforcement System For Connected And Autonomous Mobility, Dimitrios Zavantis, Fatma Outay, Youssef El-Hansali, Ansar Yasar, Elhadi Shakshuki, Haroon Malik
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In recent years, autonomous vehicles (AVs), connected vehicles (CVs) and all relative technology have been in the spotlight, being intensively researched and developed. There is high anticipation on the benefits of automation and the overall reform it will bring to the transport sector, with some optimistic estimates considering it as a reality within the next few years. Evidently, AVs and CVs are attracting considerable attention and are developed very rapidly, cultivating great expectations for traffic safety improvements. While their potential is enormous and undeniable, benefits are not automatically guaranteed as there are parameters that currently appear unforeseen. This paper investigates …
A Two-Tier Framework Based On Googlenet And Yolov3 Models For Tumor Detection In Mri, Farman Ali, Sadia Khan, Arbab Waseem Abbas, Babar Shah, Tariq Hussain, Dongho Song, Shaker Ei-Sappagh, Jaiteg Singh
A Two-Tier Framework Based On Googlenet And Yolov3 Models For Tumor Detection In Mri, Farman Ali, Sadia Khan, Arbab Waseem Abbas, Babar Shah, Tariq Hussain, Dongho Song, Shaker Ei-Sappagh, Jaiteg Singh
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Medical Image Analysis (MIA) is one of the active research areas in computer vision, where brain tumor detection is the most investigated domain among researchers due to its deadly nature. Brain tumor detection in magnetic resonance imaging (MRI) assists radiologists for better analysis about the exact size and location of the tumor. However, the existing systems may not efficiently classify the human brain tumors with significantly higher accuracies. In addition, smart and easily implementable approaches are unavailable in 2D and 3D medical images, which is the main problem in detecting the tumor. In this paper, we investigate various deep learning …
Leveraging Natural Language Processing To Analyse The Temporal Behavior Of Extremists On Social Media, May El Barachi, Sujith Samuel Mathew, Farhad Oroumchian, Imene Ajala, Saad Lutfi, Rand Yasin
Leveraging Natural Language Processing To Analyse The Temporal Behavior Of Extremists On Social Media, May El Barachi, Sujith Samuel Mathew, Farhad Oroumchian, Imene Ajala, Saad Lutfi, Rand Yasin
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Aiming at achieving sustainability and quality of life for citizens, future smart cities adopt a data-centric approach to decision making in which assets, people, and events are constantly monitored to inform decisions. Public opinion monitoring is of particular importance to governments and intelligence agencies, who seek to monitor extreme views and attempts of radicalizing individuals in society. While social media platforms provide increased visibility and a platform to express public views freely, such platforms can also be used to manipulate public opinion, spread hate speech, and radicalize others. Natural language processing and data mining techniques have gained popularity for the …
Smart Application For Every Car (Saec). (Ar Mobile Application), Murad Al-Rajab, Samia Loucif, Ossama Kousi, Mohamad Bassem Irani
Smart Application For Every Car (Saec). (Ar Mobile Application), Murad Al-Rajab, Samia Loucif, Ossama Kousi, Mohamad Bassem Irani
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Technology is continuously evolving at an exponential rate. Fast technological advances are being made, especially in the field of smart phones, that facilitate the conduct of our daily activities in many areas such as driving. The ever-increasing number of vehicles on roads increases the likelihood of traffic accidents, resulting in higher number of deaths and serious injuries to drivers, passengers, and pedestrians. Among the main causes of road accidents are over speeding, unsafe lane jumping, and failure to keep a safe distance between vehicles, to name a few. In an attempt to contribute to the improvement of road traffic safety, …
Secure Storage Model For Digital Forensic Readiness, Avinash Singh, Richard Adeyemi Ikuesan, Hein Venter
Secure Storage Model For Digital Forensic Readiness, Avinash Singh, Richard Adeyemi Ikuesan, Hein Venter
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Securing digital evidence is a key factor that contributes to evidence admissibility during digital forensic investigations, particularly in establishing the chain of custody of digital evidence. However, not enough is done to ensure that the environment and access to the evidence are secure. Attackers can go to extreme lengths to cover up their tracks, which is a serious concern to digital forensics – particularly digital forensic readiness. If an attacker gains access to the location where evidence is stored, they could easily alter the evidence (if not remove it altogether). Even though integrity checks can be performed to ensure that …
Crowdsensing Application On Coalition Game Using Gps And Iot Parking In Smart Cities, Hasan Abu Hilal, Narmeen Abu Hilal, Ala’ Abu Hilal, Tariq Abu Hilal
Crowdsensing Application On Coalition Game Using Gps And Iot Parking In Smart Cities, Hasan Abu Hilal, Narmeen Abu Hilal, Ala’ Abu Hilal, Tariq Abu Hilal
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This paper provides an overview of crowdsensing and some of its applications. Crowdsensing is a part of the collecting data situations also; it’s built on a data system on multiple customer interactions. Moreover, writing the general information of the smart cities can be used to boost to received number frequency to send messages. This work mentioned the Crowdsensing layers that describe Mobile crowdsensing. The article focuses on crowdsensing layers, developed an application in Coalition Game using crowdsensing in terms of GPS. In addition, this paper discussed the Mobile crowdsensing system and how important the cloud is in serving the wireless …
On The Use Of Allen’S Interval Algebra In The Coordination Of Resource Consumption By Transactional Business Processes, Zakaria Maamar, Fadwa Yahya, Lassaad Ben Ammar
On The Use Of Allen’S Interval Algebra In The Coordination Of Resource Consumption By Transactional Business Processes, Zakaria Maamar, Fadwa Yahya, Lassaad Ben Ammar
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This paper presents an approach to coordinate the consumption of resources by transactional business processes. Resources are associated with consumption properties known as unlimited, limited, limited-but-extensible, shareable, and non-shareable restricting their availabilities at consumption-time. And, processes are associated with transactional properties known as pivot, retriable, and compensatable restricting their execution outcomes in term of either success or failure. To consider the intrinsic characteristics of both consumption properties and transactional properties when coordinating resource consumption by processes, the approach adopts Allen’s interval algebra through different time-interval relations like before, overlaps, and during to set up the coordination, which should lead to …
On Modelling And Analyzing Composite Resources’ Consumption Cycles Using Time Petri-Nets, Amel Benna, Fatma Masmoudi, Mohamed Sellami, Zakaria Maamar, Rachid Hadjidj
On Modelling And Analyzing Composite Resources’ Consumption Cycles Using Time Petri-Nets, Amel Benna, Fatma Masmoudi, Mohamed Sellami, Zakaria Maamar, Rachid Hadjidj
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ICT community cornerstones (IoT in particular) gain competitive advantage from using physical resources. This paper adopts Time Petri-Nets (TPNs) to model and analyze the consumption cycles of composite resources. These resources consist of primitive, and even other composite, resources that are associated with consumption properties and could be subject to disruptions. These properties are specialized into unlimited, shareable, limited, limited-but-renewable, and non-shareable, and could impact the availability of resources. This impact becomes a concern when disruptions suspend ongoing consumption cycles to make room for the unplanned consumptions. Resuming the suspended consumption cycles depends on the resources’ consumption properties. To ensure …
Improved Reptile Search Optimization Algorithm Using Chaotic Map And Simulated Annealing For Feature Selection In Medical Filed, Zenab Elgamal, Aznul Qalid Md Sabri, Mohammad Tubishat, Dina Tbaishat, Sharif Naser Makhadmeh, Osama Ahmad Alomari
Improved Reptile Search Optimization Algorithm Using Chaotic Map And Simulated Annealing For Feature Selection In Medical Filed, Zenab Elgamal, Aznul Qalid Md Sabri, Mohammad Tubishat, Dina Tbaishat, Sharif Naser Makhadmeh, Osama Ahmad Alomari
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The increased volume of medical datasets has produced high dimensional features, negatively affecting machine learning (ML) classifiers. In ML, the feature selection process is fundamental for selecting the most relevant features and reducing redundant and irrelevant ones. The optimization algorithms demonstrate its capability to solve feature selection problems. Reptile Search Algorithm (RSA) is a new nature-inspired optimization algorithm that stimulates Crocodiles’ encircling and hunting behavior. The unique search of the RSA algorithm obtains promising results compared to other optimization algorithms. However, when applied to high-dimensional feature selection problems, RSA suffers from population diversity and local optima limitations. An improved metaheuristic …
A Brief Comparison Of K-Means And Agglomerative Hierarchical Clustering Algorithms On Small Datasets, Hassan I. Abdalla
A Brief Comparison Of K-Means And Agglomerative Hierarchical Clustering Algorithms On Small Datasets, Hassan I. Abdalla
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In this work, the agglomerative hierarchical clustering and K-means clustering algorithms are implemented on small datasets. Considering that the selection of the similarity measure is a vital factor in data clustering, two measures are used in this study - cosine similarity measure and Euclidean distance - along with two evaluation metrics - entropy and purity - to assess the clustering quality. The datasets used in this work are taken from UCI machine learning depository. The experimental results indicate that k-means clustering outperformed hierarchical clustering in terms of entropy and purity using cosine similarity measure. However, hierarchical clustering outperformed k-means clustering …
Interacting With Educational Chatbots: A Systematic Review, Mohammad Amin Kuhail, Nazik Alturki, Salwa Alramlawi, Kholood Alhejori
Interacting With Educational Chatbots: A Systematic Review, Mohammad Amin Kuhail, Nazik Alturki, Salwa Alramlawi, Kholood Alhejori
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Chatbots hold the promise of revolutionizing education by engaging learners, personalizing learning activities, supporting educators, and developing deep insight into learners’ behavior. However, there is a lack of studies that analyze the recent evidence-based chatbot-learner interaction design techniques applied in education. This study presents a systematic review of 36 papers to understand, compare, and reflect on recent attempts to utilize chatbots in education using seven dimensions: educational field, platform, design principles, the role of chatbots, interaction styles, evidence, and limitations. The results show that the chatbots were mainly designed on a web platform to teach computer science, language, general education, …
Analysis Of Blockchain Solutions For E-Voting: A Systematic Literature Review, Ali Benabdallah, Antoine Audras, Louis Coudert, Nour El Madhoun, Mohamad Badra
Analysis Of Blockchain Solutions For E-Voting: A Systematic Literature Review, Ali Benabdallah, Antoine Audras, Louis Coudert, Nour El Madhoun, Mohamad Badra
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To this day, abstention rates continue to rise, largely due to the need to travel to vote. This is why remote e-voting will increase the turnout by allowing everyone to vote without the need to travel. It will also minimize the risks and obtain results in a faster way compared to a traditional vote with paper ballots. In fact, given the high stakes of an election, a remote e-voting solution must meet the highest standards of security, reliability, and transparency to gain the trust of citizens. In literature, several remote e-voting solutions based on blockchain technology have been proposed. Indeed, …
Loguad: Log Unsupervised Anomaly Detection Based On Word2vec, Jin Wang, Changqing Zhao, Shiming He, Yu Gu, Osama Alfarraj, Ahed Abugabah
Loguad: Log Unsupervised Anomaly Detection Based On Word2vec, Jin Wang, Changqing Zhao, Shiming He, Yu Gu, Osama Alfarraj, Ahed Abugabah
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System logs record detailed information about system operation and are important for analyzing the system's operational status and performance. Rapid and accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more and more complex, and the number of system logs gradually increases, which brings challenges to analyze system logs. Some recent studies show that logs can be unstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a long time to train models. Therefore, to reduce the computational …
Classification Of Parkinson Disease Based On Patient’S Voice Signal Using Machine Learning, Imran Ahmed, Sultan Aljahdali, Muhammad Shakeel Khan, Sanaa Kaddoura
Classification Of Parkinson Disease Based On Patient’S Voice Signal Using Machine Learning, Imran Ahmed, Sultan Aljahdali, Muhammad Shakeel Khan, Sanaa Kaddoura
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Parkinson’s disease (PD) is a nervous system disorder first described as a neurological condition in 1817. It is one of the more prevalent diseases in the elderly, and Alzheimer’s is the second most common neurodegenerative illness. It impacts the patient’s movement. Symptoms start gradually with tremors, stiffness in movement, and speech and voice disorders. Researches proved that 89% of patients with Parkinson’s has speech disorder including uncertain articulation, hoarse and breathy voice and monotone pitch. The cause behind this voice change is the reduction of dopamine due to damage of neurons in the substantia nigra responsible for dopamine production. In …
Trajectory Design For Uav-Based Data Collection Using Clustering Model In Smart Farming, Tariq Qayyum, Zouheir Trabelsi, Asad Malik, Kadhim Hayawi
Trajectory Design For Uav-Based Data Collection Using Clustering Model In Smart Farming, Tariq Qayyum, Zouheir Trabelsi, Asad Malik, Kadhim Hayawi
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Unmanned aerial vehicles (UAVs) play an important role in facilitating data collection in remote areas due to their remote mobility. The collected data require processing close to the end-user to support delay-sensitive applications. In this paper, we proposed a data collection scheme and scheduling framework for smart farms. We categorized the proposed model into two phases: data collection and data scheduling. In the data collection phase, the IoT sensors are deployed randomly to form a cluster based on their RSSI. The UAV calculates an optimum trajectory in order to gather data from all clusters. The UAV offloads the data to …
On The Design And Implementation Of An On-Board Test Bed System For V2v Road Hazard Signaling, Fatma Outay, Faouzi Kamoun, Anouar Chemek, Hichem Bargaoui, Ansar Yasar
On The Design And Implementation Of An On-Board Test Bed System For V2v Road Hazard Signaling, Fatma Outay, Faouzi Kamoun, Anouar Chemek, Hichem Bargaoui, Ansar Yasar
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This paper describes the design, implementation, and testing of an ITS-G5 prototype Road hazard Signaling (RHS) system that is inspired by the concept of crowdsourcing. Our approach enables drivers to interact with a touchscreen onboard interface to send ITS-G5 decentralized environmental notification messages (DENM) in order to warn nearby vehicles against the presence of a hazardous situation. These messages are analyzed, filtered for relevance, and presented to concerned drivers via the Onboard Units (OBUs) so that precautionary measures can be taken. We describe the design and implementation aspects of the proposed system and update the open source cargeo6 implementation of …
Comparison Of Reaction Time-Based Collaborative Velocity Control And Intelligent Driver Model For Agent-Based Simulation Of Autonomous Car, Fatma Outay, Abdeljalil Abbas-Turki, Stéphane Galland, Alexandre Lombard, Nicolas Gaud
Comparison Of Reaction Time-Based Collaborative Velocity Control And Intelligent Driver Model For Agent-Based Simulation Of Autonomous Car, Fatma Outay, Abdeljalil Abbas-Turki, Stéphane Galland, Alexandre Lombard, Nicolas Gaud
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Based on historical records, driving in hazardous weather conditions is one of the most serious causes that lead to fatal accidents on roads in general and in United Arab Emirates (UAE) highways in particular. One solution to improve road safety is to equip vehicles and infrastructure with connected and smart devices and convert them into autonomous vehicles. Before deploying a concrete solution to the field, it must be validated by simulation, and more specifically by agent-based simulation. In this paper, we propose to implement the Reaction Time-Based Collaborative Velocity Control (RT-CVC) model that was implemented in autonomous cars into an …
Social Networking Applications: A Comparative Analysis For A Collaborative Learning Through Google Classroom And Zoom, Tariq Abu Hilal, Ala’ Abu Hilal, Hasan Abu Hilal
Social Networking Applications: A Comparative Analysis For A Collaborative Learning Through Google Classroom And Zoom, Tariq Abu Hilal, Ala’ Abu Hilal, Hasan Abu Hilal
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Recently, social network applications were developed intensively due to the increasing compaction and user demands. These applications provide different services to their users like learning, awareness, chatting with friends, sharing global news, etc. Simply, this work introduces the advantages of these software applications, specifically in the field of education during the COVID 19 spread. Google Classroom and Zoom meetings had gained the attention of many educational institutes for using them as a learning platform for students and educators. This research used two methodologies SWOT analysis and the information system success model of DeLone and McLean's updated to evaluate the effectiveness …
Haptic Feedback To Assist Blind People In Indoor Environment Using Vibration Patterns, Shah Khusro, Babar Shah, Inayat Khan, Sumayya Rahman
Haptic Feedback To Assist Blind People In Indoor Environment Using Vibration Patterns, Shah Khusro, Babar Shah, Inayat Khan, Sumayya Rahman
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Feedback is one of the significant factors for the mental mapping of an environment. It is the communication of spatial information to blind people to perceive the surroundings. The assistive smartphone technologies deliver feedback for different activities using several feedback mediums, including voice, sonification and vibration. Researchers 0have proposed various solutions for conveying feedback messages to blind people using these mediums. Voice and sonification feedback are effective solutions to convey information. However, these solutions are not applicable in a noisy environment and may occupy the most important auditory sense. The privacy of a blind user can also be compromised with …
A Novel Tunicate Swarm Algorithm With Hybrid Deep Learning Enabled Attack Detection For Secure Iot Environment, Fatma Taher, Mohamed Elhoseny, Mohammed K. Hassan, Ibrahim M. El-Hasnony
A Novel Tunicate Swarm Algorithm With Hybrid Deep Learning Enabled Attack Detection For Secure Iot Environment, Fatma Taher, Mohamed Elhoseny, Mohammed K. Hassan, Ibrahim M. El-Hasnony
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No abstract provided.
Conceptualising The Role Of The Uae Innovation Strategy In University-Industry Knowledge Diffusion Process, Mousa Al-Kfairy, Munir Majdalawieh, Saed Alrabaee
Conceptualising The Role Of The Uae Innovation Strategy In University-Industry Knowledge Diffusion Process, Mousa Al-Kfairy, Munir Majdalawieh, Saed Alrabaee
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Universities are considered one of the primary sources of knowledge and an essential component of the triple helix theory. They fuel the industries with the required expertise and pool of resources to operate efficiently. Moreover, entrepreneurial universities successfully contributed to regional development and employment growth by supporting entrepreneurial activities and incubation programmes. Thus, university-industry collaboration is vital for enhancing knowledge-based industries' knowledge diffusion as well as the regional innovation atmospheres. On the other hand, countries and regional authorities strive to stimulate their regional development by encouraging innovation and entrepreneurship activities. For example, the UAE announced its 2015 innovation strategy that …
Appliancenet: A Neural Network Based Framework To Recognize Daily Life Activities And Behavior In Smart Home Using Smart Plugs, Muhammad Fahim, S. M.Ahsan Kazmi, Asad Masood Khattak
Appliancenet: A Neural Network Based Framework To Recognize Daily Life Activities And Behavior In Smart Home Using Smart Plugs, Muhammad Fahim, S. M.Ahsan Kazmi, Asad Masood Khattak
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A smart plug can transform the typical electrical appliance into a smart multi-functional device, which can communicate over the Internet. It has the ability to report the energy consumption pattern of the attached appliance which offer the further analysis. Inside the home, smart plugs can be utilized to recognize daily life activities and behavior. These are the key elements to provide human-centered applications including healthcare services, power consumption footprints, and household appliance identification. In this research, we propose a novel framework ApplianceNet that is based on energy consumption patterns of home appliances attached to smart plugs. Our framework can process …
Estimating Fuel-Efficient Air Plane Trajectories Using Machine Learning, Jaiteg Singh, Gaurav Goyal, Farman Ali, Babar Shah, Sangheon Pack
Estimating Fuel-Efficient Air Plane Trajectories Using Machine Learning, Jaiteg Singh, Gaurav Goyal, Farman Ali, Babar Shah, Sangheon Pack
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Airline industry has witnessed a tremendous growth in the recent past. Percentage of people choosing air travel as first choice to commute is continuously increasing. Highly demanding and congested air routes are resulting in inadvertent delays, additional fuel consumption and high emission of greenhouse gases. Trajectory planning involves creation identification of cost-effective flight plans for optimal utilization of fuel and time. This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required. In this paper, an algorithm for dynamic planning of optimized flight trajectories has been proposed. The proposed algorithm …
Smart Covid-3d-Scnn: A Novel Method To Classify X-Ray Images Of Covid-19, Ahed Abugabah, Atif Mehmood, Ahmad Ali Al Zubi, Louis Sanzogni
Smart Covid-3d-Scnn: A Novel Method To Classify X-Ray Images Of Covid-19, Ahed Abugabah, Atif Mehmood, Ahmad Ali Al Zubi, Louis Sanzogni
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The outbreak of the novel coronavirus has spread worldwide, and millions of people are being infected. Image or detection classification is one of the first application areas of deep learning, which has a significant contribution to medical image analysis. In classification detection, one or more images (detection) are usually used as input, and diagnostic variables (such as whether there is a disease) are used as output. The novel coronavirus has spread across the world, infecting millions of people. Early-stage detection of critical cases of COVID-19 is essential. X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early. …
Using Interactive Technology To Develop Preservice Teachers’ Steam Competencies In Early Childhood Education Program, Areej Elsayary, Rana Zein, Lani San Antonio
Using Interactive Technology To Develop Preservice Teachers’ Steam Competencies In Early Childhood Education Program, Areej Elsayary, Rana Zein, Lani San Antonio
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Using interactive technology leads to an interactive learning environment where learners develop their STEAM competencies, including critical thinking, collaboration, communication, creativity and innovation, self-direction, connection, and the use of interactive technology tools effectively. This research aims to investigate the use of interactive technology in developing preservice teachers’ STEAM competencies. The participants were preservice teachers (n=80) in an early childhood education program at a Federal University in the United Arab Emirates. An explanatory sequential mixed-method approach used quantitative analysis (quasi-experiment) followed by a qualitative approach (a focus group discussion was conducted). An online survey was used to collect the quantitative data …
A Hybrid Machine Learning Framework For Predicting Students’ Performance In Virtual Learning Environment, Edmund Evangelista
A Hybrid Machine Learning Framework For Predicting Students’ Performance In Virtual Learning Environment, Edmund Evangelista
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Virtual Learning Environments (VLE), such as Moodle and Blackboard, store vast data to help identify students' performance and engagement. As a result, researchers have been focusing their efforts on assisting educational institutions in providing machine learning models to predict at-risk students and improve their performance. However, it requires an efficient approach to construct a model that can ultimately provide accurate predictions. Consequently, this study proposes a hybrid machine learning framework to predict students' performance using eight classification algorithms and three ensemble methods (Bagging, Boosting, Voting) to determine the best-performing predictive model. In addition, this study used filter-based and wrapper-based feature …
Estimation And Interpretation Of Machine Learning Models With Customized Surrogate Model, Mudabbir Ali, Asad Masood Khattak, Zain Ali, Bashir Hayat, Muhammad Idrees, Zeeshan Pervez, Kashif Rizwan, Tae Eung Sung, Ki Il Kim
Estimation And Interpretation Of Machine Learning Models With Customized Surrogate Model, Mudabbir Ali, Asad Masood Khattak, Zain Ali, Bashir Hayat, Muhammad Idrees, Zeeshan Pervez, Kashif Rizwan, Tae Eung Sung, Ki Il Kim
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Machine learning has the potential to predict unseen data and thus improve the productivity and processes of daily life activities. Notwithstanding its adaptiveness, several sensitive applications based on such technology cannot compromise our trust in them; thus, highly accurate machine learning models require reason. Such models are black boxes for end-users. Therefore, the concept of interpretability plays the role if assisting users in a couple of ways. Interpretable models are models that possess the quality of explaining predictions. Different strategies have been proposed for the aforementioned concept but some of these require an excessive amount of effort, lack generalization, are …
Modelling Customers Credit Card Behaviour Using Bidirectional Lstm Neural Networks, Maher Ala’Raj, Maysam F. Abbod, Munir Majdalawieh
Modelling Customers Credit Card Behaviour Using Bidirectional Lstm Neural Networks, Maher Ala’Raj, Maysam F. Abbod, Munir Majdalawieh
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With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this paper is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behaviour with respect to two aspects: the probability of single and consecutive …
Theoretical Models Of Integration Of Interactive Learning Technologies Into Teaching: A Systematic Literature Review, Laila Mohebi
Theoretical Models Of Integration Of Interactive Learning Technologies Into Teaching: A Systematic Literature Review, Laila Mohebi
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With the fast progress of technology and the vast amount of research papers related to technology integration in education being published yearly, a study that reviews models used in these papers is needed. Therefore, this paper (1) reviewed and analysed theoretical frameworks with models used for integration of technology in classrooms, (2) reviewed studies that discussed the impact of technology integration on students' learning capabilities, and (3) discussed the importance of preparing teachers to effectively integrate technology in teaching. The models reviewed were: Teacher Thoughts and Action Process (TTAP), Theory of Planned Behavior, Expectancy-Value Theory of Achievement Motivation (EVAM), Substitution …
Hybrid Feature Selection Approach To Identify Optimal Features Of Profile Metadata To Detect Social Bots In Twitter, Eiman Alothali, Kadhim Hayawi, Hany Alashwal
Hybrid Feature Selection Approach To Identify Optimal Features Of Profile Metadata To Detect Social Bots In Twitter, Eiman Alothali, Kadhim Hayawi, Hany Alashwal
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The last few years have revealed that social bots in social networks have become more sophisticated in design as they adapt their features to avoid detection systems. The deceptive nature of bots to mimic human users is due to the advancement of artificial intelligence and chatbots, where these bots learn and adjust very quickly. Therefore, finding the optimal features needed to detect them is an area for further investigation. In this paper, we propose a hybrid feature selection (FS) method to evaluate profile metadata features to find these optimal features, which are evaluated using random forest, naïve Bayes, support vector …