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Articles 1 - 30 of 83
Full-Text Articles in Physical Sciences and Mathematics
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 …
Irreversibility Minimization Analysis Of Ferromagnetic Oldroyd-B Nanofluid Flow Under The Influence Of A Magnetic Dipole, Muhammad Ramzan, Fares Howari, Jae Dong Chung, Seifedine Kadry, Yu Ming Chu
Irreversibility Minimization Analysis Of Ferromagnetic Oldroyd-B Nanofluid Flow Under The Influence Of A Magnetic Dipole, Muhammad Ramzan, Fares Howari, Jae Dong Chung, Seifedine Kadry, Yu Ming Chu
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© 2021, The Author(s). Studies highlighting nanoparticles suspensions and flow attributes in the context of their application are the subject of current research. In particular, the utilization of these materials in biomedical rheological models has gained great attention. Magneto nanoparticles have a decisive role in the ferrofluid flows to regulate their viscoelastic physiognomies. Having such substantial interest in the flow of ferrofluids our objective is to elaborate the melting heat transfer impact in a stretched Oldroyd-B flow owing to a magnetic dipole in the presence of entropy generation optimization. Buongiorno nanofluid model expounding thermophoretic and Brownian features are considered. Moreover, …
Utilization Of The Uae Date Palm Leaf Biochar In Carbon Dioxide Capture And Sequestration Processes, Imen Ben Salem, Maisa El Gamal, Manish Sharma, Suhaib Hameedi, Fares M. Howari
Utilization Of The Uae Date Palm Leaf Biochar In Carbon Dioxide Capture And Sequestration Processes, Imen Ben Salem, Maisa El Gamal, Manish Sharma, Suhaib Hameedi, Fares M. Howari
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This paper evaluates the potential use of date palm leaf biochar as a climate change solution through CO2 capture and sequestration. The pyrolysis of date palm leaf was performed at different temperatures 300°, 400°, 500°, and 600 °C. The physicochemical characteristics of the synthesized biochar were examined using Scanning Electron Microscopy (SEM) with Energy Dispersive X-Ray Analysis (EDX), Fourier transforms infrared spectroscopy (FTIR), Thermogravimetric analysis (TGA), and X-ray diffraction analysis (XRD). Direct gas-solid interaction was carried out in an integrated Fluidized Bed Reactor (FBR), connected with a gas analyzer for maximum and effective mixing between the biochar and CO2. LabView …
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 …
The Use Of Mobile Payment Systems In Post-Covid-19 Economic Recovery: Primary Research On An Emerging Market For Experience Goods, Maiya M. Suyunchaliyeva, Raghav Nautiyal, Aijaz A. Shaikh, Ravishankar Sharma
The Use Of Mobile Payment Systems In Post-Covid-19 Economic Recovery: Primary Research On An Emerging Market For Experience Goods, Maiya M. Suyunchaliyeva, Raghav Nautiyal, Aijaz A. Shaikh, Ravishankar Sharma
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This study investigated whether mobile payment services could drive post-COVID-19 pandemic recovery in the ‘experience goods’ sector (e.g., tourism) utilising Bandura’s self-efficacy or social cognitive theory. It explored the factors influencing the intention to continue using mobile payment services and the intention to recommend these to others. An empirical survey was conducted to assess the study variables, and the data obtained therefrom were analysed using the industry-standard Cross-Industry Standard Process for Data Mining method. The study results suggest that personal innovativeness and perceived trust influence consumers’ intention to continue using mobile payment services and that perceived trust, personal innovativeness and …
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 …
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 …
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 …
Application Of Response Surface Methodology On The Nanofluid Flow Over A Rotating Disk With Autocatalytic Chemical Reaction And Entropy Generation Optimization, Tahir Mehmood, Muhammad Ramzan, Fares Howari, Seifedine Kadry, Yu Ming Chu
Application Of Response Surface Methodology On The Nanofluid Flow Over A Rotating Disk With Autocatalytic Chemical Reaction And Entropy Generation Optimization, Tahir Mehmood, Muhammad Ramzan, Fares Howari, Seifedine Kadry, Yu Ming Chu
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© 2021, The Author(s). The role of nanofluids is of fundamental significance in the cooling process of small electronic devices including microchips and other associated gadgets in microfluidics. With such astounding applications of nanofluids in mind, it is intended to examine the flow of magnetohydrodynamic nanofluid comprising a novel combination of multi-walled carbon nanotubes and engine oil over a stretched rotating disk. The concentration equation is modified by considering the autocatalytic chemical reaction. The succor of the bvp4c numerical technique amalgamated with the response surface methodology is secured for the solution of a highly nonlinear system of equations. The sensitivity …
Clustered, Stacked And Imbricated Large Coastal Rock Clasts On Ludao Island, Southeast Taiwan, And Their Application To Palaeotyphoon Intensity Assessment, James P. Terry, A.Y. Annie Lau, Kim Anh Nguyen, Yuei-An Liou, Adam D. Switzer
Clustered, Stacked And Imbricated Large Coastal Rock Clasts On Ludao Island, Southeast Taiwan, And Their Application To Palaeotyphoon Intensity Assessment, James P. Terry, A.Y. Annie Lau, Kim Anh Nguyen, Yuei-An Liou, Adam D. Switzer
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This work investigated the characteristics of a boulder field on the exposed south east coast of Ludao Island (Green Island) in southern Taiwan. Although the region regularly experiences seasonal Pacific typhoons, fieldwork on Ludao was prompted following the double-strike of Typhoon Tembin in August 2012, which followed an unusual looping track and was one of the strongest storms to affect the island in recent decades. In Wen Cuen Bay, large limestone and volcanic clasts (103–105 kg) occur both as isolated individuals and also grouped into distinct clusters across the gently-sloping emerged reef platform of Holocene age. Some individuals reach megaclast …
Hybrid Approach For Resource Allocation In Cloud Infrastructure Using Random Forest And Genetic Algorithm, Madhusudhan H S, Satish Kumar T, S.M.F D Syed Mustapha, Punit Gupta, Rajan Prasad Tripathi
Hybrid Approach For Resource Allocation In Cloud Infrastructure Using Random Forest And Genetic Algorithm, Madhusudhan H S, Satish Kumar T, S.M.F D Syed Mustapha, Punit Gupta, Rajan Prasad Tripathi
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In cloud computing, the virtualization technique is a significant technology to optimize the power consumption of the cloud data center. In this generation, most of the services are moving to the cloud resulting in increased load on data centers. As a result, the size of the data center grows and hence there is more energy consumption. To resolve this issue, an efficient optimization algorithm is required for resource allocation. In this work, a hybrid approach for virtual machine allocation based on genetic algorithm (GA) and the random forest (RF) is proposed which belongs to a class of supervised machine learning …
Inverse Properties Of A Class Of Seven-Diagonal (Near) Toeplitz Matrices, Bakytzhan Kurmanbek, Yogi Erlangga, Yerlan Amanbek
Inverse Properties Of A Class Of Seven-Diagonal (Near) Toeplitz Matrices, Bakytzhan Kurmanbek, Yogi Erlangga, Yerlan Amanbek
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This paper presents the explicit inverse of a class of seven-diagonal (near) Toeplitz matrices, which arises in the numerical solutions of nonlinear fourth-order differential equation with a finite difference method. A non-recurrence explicit inverse formula is derived using the Sherman-Morrison formula. Related to the fixed-point iteration used to solve the differential equation, we show the positivity of the inverse matrix and construct an upper bound for the norms of the inverse matrix, which can be used to predict the convergence of the method.
D2gen: A Decentralized Device Genome Based Integrity Verification Mechanism For Collaborative Intrusion Detection Systems, Imran Makhdoom, Kadhim Hayawi, Mohammed Kaosar, Sujith Samuel Mathew, Pin-Han Ho
D2gen: A Decentralized Device Genome Based Integrity Verification Mechanism For Collaborative Intrusion Detection Systems, Imran Makhdoom, Kadhim Hayawi, Mohammed Kaosar, Sujith Samuel Mathew, Pin-Han Ho
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Collaborative Intrusion Detection Systems are considered an effective defense mechanism for large, intricate, and multilayered Industrial Internet of Things against many cyberattacks. However, while a Collaborative Intrusion Detection System successfully detects and prevents various attacks, it is possible that an inside attacker performs a malicious act and compromises an Intrusion Detection System node. A compromised node can inflict considerable damage on the whole collaborative network. For instance, when a malicious node gives a false alert of an attack, the other nodes will unnecessarily increase their security and close all of their services, thus, degrading the system’s performance. On the contrary, …
Sustainable Maritime Crude Oil Transportation: A Split Pickup And Split Delivery Problem With Time Windows, Hiba Yahyaoui, Nadia Dahmani, Saoussen Krichen
Sustainable Maritime Crude Oil Transportation: A Split Pickup And Split Delivery Problem With Time Windows, Hiba Yahyaoui, Nadia Dahmani, Saoussen Krichen
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This paper studies a novel sustainable vessel routing problem modeling considering the multi-compartment, split pickup and split delivery, and time windows concepts. In the presented problem, oil tankers transport crude oil from supply ports to demand ports around the globe. The objective is to find ship routes, as well as port arrival and departure times, in a way that minimizes transportation costs. As a second objective, we considered the sustainability aspect by minimizing the vessel energy efficiency operational indicator. Multiple products are transported by a heterogeneous fleet of tankers. Small realistic test instances are solved with the exact method.
A New Case Of Separability In A Quartic Hénon-Heiles System, Nicola Sottocornola
A New Case Of Separability In A Quartic Hénon-Heiles System, Nicola Sottocornola
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There are four quartic integrable Hénon-Heiles systems. Only one of them has been separated in the generic form while the other three have been solved only for particular values of the constants. We consider two of them, related by a canonical transformation, and we give their separation coordinates in a new case.
Automatic Cerebrovascular Segmentation Methods - A Review, Fatma Taher, Neema Prakash
Automatic Cerebrovascular Segmentation Methods - A Review, Fatma Taher, Neema Prakash
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Cerebrovascular diseases are one of the serious causes for the increase in mortality rate in the world which affect the blood vessels and blood supply to the brain. In order, diagnose and study the abnormalities in the cerebrovascular system, accurate segmentation methods can be used. The shape, direction and distribution of blood vessels can be studied using automatic segmentation. This will help the doctors to envisage the cerebrovascular system. Due to the complex shape and topology, automatic segmentation is still a challenge to the clinicians. In this paper, some of the latest approaches used for segmentation of magnetic resonance angiography …
Solar Cycle-Modulated Deformation Of The Earth–Ionosphere Cavity, Tamás Bozóki, Gabriella Sátori, Earle Williams, Irina Mironova, Péter Steinbach, Emma C. Bland, Alexander Koloskov, Yuri M. Yampolski, Oleg V. Budanov, Mariusz Neska, Ashwini K. Sinha, Rahul Rawat, Mitsuteru Sato, Ciaran D. Beggan, Sergio Toledo-Redondo, Yakun Liu, Robert Boldi
Solar Cycle-Modulated Deformation Of The Earth–Ionosphere Cavity, Tamás Bozóki, Gabriella Sátori, Earle Williams, Irina Mironova, Péter Steinbach, Emma C. Bland, Alexander Koloskov, Yuri M. Yampolski, Oleg V. Budanov, Mariusz Neska, Ashwini K. Sinha, Rahul Rawat, Mitsuteru Sato, Ciaran D. Beggan, Sergio Toledo-Redondo, Yakun Liu, Robert Boldi
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The Earth–ionosphere cavity resonator is occupied primarily by the electromagnetic radiation of lightning below 100 Hz. The phenomenon is known as Schumann resonances (SR). SR intensity is an excellent indicator of lightning activity and its distribution on global scales. However, long-term measurements from high latitude SR stations revealed a pronounced in-phase solar cycle modulation of SR intensity seemingly contradicting optical observations of lightning from satellite, which do not show any significant solar cycle variation in the intensity and spatial distribution of lightning activity on the global scale. The solar cycle-modulated local deformation of the Earth–ionosphere cavity by the ionization of …
Tweet-To-Act: Towards Tweet-Mining Framework For Extracting Terrorist Attack-Related Information And Reporting, Farkhund Iqbal, Rabia Batool, Benjamin C. M. Fung, Saiqa Aleem, Ahmed Abbasi, Abdul Rehman Javed
Tweet-To-Act: Towards Tweet-Mining Framework For Extracting Terrorist Attack-Related Information And Reporting, Farkhund Iqbal, Rabia Batool, Benjamin C. M. Fung, Saiqa Aleem, Ahmed Abbasi, Abdul Rehman Javed
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The widespread popularity of social networking is leading to the adoption of Twitter as an information dissemination tool. Existing research has shown that information dissemination over Twitter has a much broader reach than traditional media and can be used for effective post-incident measures. People use informal language on Twitter, including acronyms, misspelled words, synonyms, transliteration, and ambiguous terms. This makes incident-related information extraction a non-trivial task. However, this information can be valuable for public safety organizations that need to respond in an emergency. This paper proposes an early event-related information extraction and reporting framework that monitors Twitter streams, synthesizes event-specific …
A Novel Numerical Method For Solving Fractional Diffusion-Wave And Nonlinear Fredholm And Volterra Integral Equations With Zero Absolute Error, Mutaz Mohammad, Alexandre Trounev, Mohammed Alshbool
A Novel Numerical Method For Solving Fractional Diffusion-Wave And Nonlinear Fredholm And Volterra Integral Equations With Zero Absolute Error, Mutaz Mohammad, Alexandre Trounev, Mohammed Alshbool
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In this work, a new numerical method for the fractional diffusion-wave equation and nonlinear Fredholm and Volterra integro-differential equations is proposed. The method is based on Euler wavelet approximation and matrix inversion of an M × M collocation points. The proposed equations are presented based on Caputo fractional derivative where we reduce the resulting system to a system of algebraic equations by implementing the Gaussian quadrature discretization. The reduced system is generated via the truncated Euler wavelet expansion. Several examples with known exact solutions have been solved with zero absolute error. This method is also applied to the Fredholm and …
Explicit Inverse Of Near Toeplitz Pentadiagonal Matrices Related To Higher Order Difference Operators, Bakytzhan Kurmanbek, Yogi Erlangga, Yerlan Amanbek
Explicit Inverse Of Near Toeplitz Pentadiagonal Matrices Related To Higher Order Difference Operators, Bakytzhan Kurmanbek, Yogi Erlangga, Yerlan Amanbek
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This paper analyzes the inverse of near Toeplitz pentadiagonal matrices, arising from a finite-difference approximation to the fourth-order nonlinear beam equation. Explicit non-recursive inverse matrix formulas and bounds of norms of the inverse matrix are derived for the clamped–free and clamped–clamped boundary conditions. The bound of norms is then used to construct a convergence bound for the fixed-point iteration of the form u=f(u) for solving the nonlinear equation. Numerical computations presented in this paper confirm the theoretical results.
Boolean Logic Algebra Driven Similarity Measure For Text Based Applications, Hassan I. Abdalla, Ali A. Amer
Boolean Logic Algebra Driven Similarity Measure For Text Based Applications, Hassan I. Abdalla, Ali A. Amer
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In Information Retrieval (IR), Data Mining (DM), and Machine Learning (ML), similarity measures have been widely used for text clustering and classification. The similarity measure is the cornerstone upon which the performance of most DM and ML algorithms is completely dependent. Thus, till now, the endeavor in literature for an effective and efficient similarity measure is still immature. Some recently-proposed similarity measures were effective, but have a complex design and suffer from inefficiencies. This work, therefore, develops an effective and efficient similarity measure of a simplistic design for text-based applications. The measure developed in this work is driven by Boolean …
Classifier Performance Evaluation For Lightweight Ids Using Fog Computing In Iot Security, Belal Sudqi Khater, Ainuddin Wahid Abdul Wahab, Mohd Yamaniidna Idris, Mohammed Abdulla Hussain, Ashraf Ahmed Ibrahim, Mohammad Arif Amin, Hisham A. Shehadeh
Classifier Performance Evaluation For Lightweight Ids Using Fog Computing In Iot Security, Belal Sudqi Khater, Ainuddin Wahid Abdul Wahab, Mohd Yamaniidna Idris, Mohammed Abdulla Hussain, Ashraf Ahmed Ibrahim, Mohammad Arif Amin, Hisham A. Shehadeh
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In this article, a Host-Based Intrusion Detection System (HIDS) using a Modified Vector Space Representation (MVSR) N-gram and Multilayer Perceptron (MLP) model for securing the Internet of Things (IoT), based on lightweight techniques and using Fog Computing devices, is proposed. The Australian Defence Force Academy Linux Dataset (ADFA-LD), which contains exploits and attacks on various applications, is employed for the analysis. The proposed method is divided into the feature extraction stage, the feature selection stage, and classification modeling. To maintain the lightweight criteria, the feature extraction stage considers a combination of 1-gram and 2-gram for the system call encoding. In …
Pedestrian Attribute Recognition Using Trainable Gabor Wavelets, Imran N Junejo, Naveed Ahmed, Mohammad Lataifeh
Pedestrian Attribute Recognition Using Trainable Gabor Wavelets, Imran N Junejo, Naveed Ahmed, Mohammad Lataifeh
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Surveillance cameras are everywhere keeping an eye on pedestrians or people as they navigate through the scene. Within this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails the extraction of different attributes such as age-group, clothing style, accessories, footwear style etc. This is a multi-label problem with a host of challenges even for human observers. As such, the topic has rightly attracted attention recently. In this work, we integrate trainable Gabor wavelet (TGW) layers inside a convolution neural network (CNN). Whereas other researchers have used fixed Gabor filters with the CNN, the proposed layers …
Swarm Differential Privacy For Purpose-Driven Data-Information-Knowledge-Wisdom Architecture, Yingbo Li, Yucong Duan, Zakaria Maamar, Haoyang Che, Anamaria-Beatrice Spulber, Stelios Fuentes
Swarm Differential Privacy For Purpose-Driven Data-Information-Knowledge-Wisdom Architecture, Yingbo Li, Yucong Duan, Zakaria Maamar, Haoyang Che, Anamaria-Beatrice Spulber, Stelios Fuentes
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Privacy protection has recently been in the spotlight of attention to both academia and industry. Society protects individual data privacy through complex legal frameworks. The increasing number of applications of data science and artificial intelligence has resulted in a higher demand for the ubiquitous application of the data. The privacy protection of the broad Data-Information-Knowledge-Wisdom (DIKW) landscape, the next generation of information organization, has taken a secondary role. In this paper, we will explore DIKW architecture through the applications of the popular swarm intelligence and differential privacy. As differential privacy proved to be an effective data privacy approach, we will …
Energy-Efficient Load Balancing Algorithm For Workflow Scheduling In Cloud Data Centers Using Queuing And Thresholds, Nimra Malik, Muhammad Sardaraz, Muhammad Tahir, Babar Shah, Gohar Ali, Fernando Moreira
Energy-Efficient Load Balancing Algorithm For Workflow Scheduling In Cloud Data Centers Using Queuing And Thresholds, Nimra Malik, Muhammad Sardaraz, Muhammad Tahir, Babar Shah, Gohar Ali, Fernando Moreira
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Cloud computing is a rapidly growing technology that has been implemented in various fields in recent years, such as business, research, industry, and computing. Cloud computing provides different services over the internet, thus eliminating the need for personalized hardware and other resources. Cloud computing environments face some challenges in terms of resource utilization, energy efficiency, heterogeneous resources, etc. Tasks scheduling and virtual machines (VMs) are used as consolidation techniques in order to tackle these issues. Tasks scheduling has been extensively studied in the literature. The problem has been studied with different parameters and objectives. In this article, we address the …
Classification And Analysis Of Android Malware Images Using Feature Fusion Technique, Jaiteg Singh, Deepak Thakur, Tanya Gera, Babar Shah, Tamer Abuhmed, Farman Ali
Classification And Analysis Of Android Malware Images Using Feature Fusion Technique, Jaiteg Singh, Deepak Thakur, Tanya Gera, Babar Shah, Tamer Abuhmed, Farman Ali
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The super packed functionalities and artificial intelligence (AI)-powered applications have made the Android operating system a big player in the market. Android smartphones have become an integral part of life and users are reliant on their smart devices for making calls, sending text messages, navigation, games, and financial transactions to name a few. This evolution of the smartphone community has opened new horizons for malware developers. As malware variants are growing at a tremendous rate every year, there is an urgent need to combat against stealth malware techniques. This paper proposes a visualization and machine learning-based framework for classifying Android …
Toward An Intelligent Driving Behavior Adjustment Based On Legal Personalized Policies Within The Context Of Connected Vehicles, Fatma Outay, Nafaa Jabeur, Hedi Haddad, Zied Bouyahia, Hana Gharrad
Toward An Intelligent Driving Behavior Adjustment Based On Legal Personalized Policies Within The Context Of Connected Vehicles, Fatma Outay, Nafaa Jabeur, Hedi Haddad, Zied Bouyahia, Hana Gharrad
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The advent of Connected Vehicles (CVs) is creating new opportunities within the transportation sector. It is, indeed, expected to improve road traffic safety, enhance mobility, reduce fuel consumption and gas emissions, as well as foster economic growth via investments and jobs. However, to motivate the deployment of CVs and maximize their related benefits, policymakers must create appropriate neutral legal frameworks. These frameworks should promote the innovation of current road infrastructures, support cooperation and interoperability between transportation systems, and encourage fair competition between companies while upholding consumer privacy as well as data protection. We argue that policymakers should also support innovative …
Automatic Detection Of Citrus Fruit And Leaves Diseases Using Deep Neural Network Model, Asad Khattak, Muhammad Usama Asghar, Ulfat Batool, Muhammad Zubair Asghar, Hayat Ullah, Mabrook Al-Rakhami, Abdu Gumaei
Automatic Detection Of Citrus Fruit And Leaves Diseases Using Deep Neural Network Model, Asad Khattak, Muhammad Usama Asghar, Ulfat Batool, Muhammad Zubair Asghar, Hayat Ullah, Mabrook Al-Rakhami, Abdu Gumaei
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Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. As a result, designing an automated detection system for citrus plant diseases is important. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, leading us to apply them to the challenge of recognizing citrus fruit and leaf diseases. In this paper, an integrated approach is used to suggest a convolutional neural networks (CNNs) model. The proposed CNN model is intended to differentiate healthy fruits and leaves from fruits/leaves with common citrus diseases such as Black spot, canker, scab, greening, and Melanose. …
Early Assessment Of Lung Function In Coronavirus Patients Using Invariant Markers From Chest X-Rays Images, Mohamed Elsharkawy, Ahmed Sharafeldeen, Fatma Taher, Ahmed Shalaby, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Ashraf Khalil, Norah Saleh Alghamdi, Ahmed Abdel Khalek Abdel Razek, Eman Alnaghy, Moumen T. El-Melegy, Harpal Singh Sandhu, Guruprasad A. Giridharan, Ayman El-Baz
Early Assessment Of Lung Function In Coronavirus Patients Using Invariant Markers From Chest X-Rays Images, Mohamed Elsharkawy, Ahmed Sharafeldeen, Fatma Taher, Ahmed Shalaby, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Ashraf Khalil, Norah Saleh Alghamdi, Ahmed Abdel Khalek Abdel Razek, Eman Alnaghy, Moumen T. El-Melegy, Harpal Singh Sandhu, Guruprasad A. Giridharan, Ayman El-Baz
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The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or …
Blockchain For Automotive: An Insight Towards The Ipfs Blockchain-Based Auto Insurance Sector, Nishara Nizamuddin, Ahed Abugabah
Blockchain For Automotive: An Insight Towards The Ipfs Blockchain-Based Auto Insurance Sector, Nishara Nizamuddin, Ahed Abugabah
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The advancing technology and industrial revolution have taken the automotive industry by storm in recent times. The auto sector’s constantly growing demand has paved the way for the automobile sector to embrace new technologies and disruptive innovations. The multi-trillion dollar, complex auto insurance sector is still stuck in the regulations of the past. Most of the customers still contact the insurance company by phone to buy new policies and process existing insurance claims. The customers still face the risk of fraudulent online brokers, as policies are mostly signed and processed on papers which often require human supervision, with a risk …