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30th International Conference on computers in Education Conference, ICCE 2022

作者： Zakaria, Noor Syamilah Subarimaniam, Neerushah Saripan, M. Iqbal Ismail, Alyani Department of Counselor Education and Counseling Psychology Faculty of Educational Studies Universiti Putra Malaysia Malaysia Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Malaysia

ISBN: (纸本)9786269689002

Ethics incorporate the sources of human standards and values. However, there is a consistent struggle to locate both concepts of social condition and human values;and the question of what comprises counseling ethics education is complex and intriguing. Therefore, there is an urgency to transform teaching and learning practices of counseling ethics education by integrating educational gamification to complement the conservative classroom teaching and learning methods. A prototype called EthoshuntTM is developed from gamification ideation as an educational gamification-based app that can be utilized in teaching and learning of counseling ethics education. The elements of game dynamics, game mechanics, functionality, and learning flow of EthoshuntTM emerged as unique aspects which enable the students engrossed with learning objectives and charted with learning outcomes, digitally. The researchers advance that EthoshuntTM may bring a new gamut of talent for educators in developing technology-mediated learning to augment teaching and learning activities in counseling ethics education. EthoshuntTM holds the potential to inspire students while making counseling ethics education learning more fun and enjoyable. This offers possibilities to revolutionize existing counseling ethics education teaching and learning practices in line with the needs of the 4th Industrial Revolution. © ICCE *** rights reserved.

关键词： Students

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HSCM: A New Framework For Handwritten Signature Verification Using ConvMixer

HSCM: A New Framework For Handwritten Signature Verification...

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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Mona Alaa Fathy Amr E. Mohamed Sameh A. Salem Computer & Systems Engineering Dept Faculty of Engineering Helwan University Cairo Egypt Computer & Systems Engineering Dept Faculty of Engineering Helwan University Cairo Egypt Egyptian Computer Emergency Readiness Team (EG-CERT) NTRA Egypt

As nations attempt to recover from the coronavirus disease (COVID-19) epidemic, digital solutions facilitate economic change and put economies on the path to green growth. Therefore, identifying an individual based on biometric characteristics has rapidly become an urgent and essential subject. Banks, intelligence agencies, and high-profile institutions are frequently use handwritten signature verification to authenticate the identification of an individual. This paper introduces an innovative model (HSCM) for the authentication of online handwritten signatures based on the ConvMixer Network. The main idea of the proposed model is to divide the signature into several patches known as strokes, which are then processed using ConvMixer to generate signature features. These features are processed using Feed-Forward network. Experimental results on the real-world dataset show that the proposed model achieved 11.6% equal error rate (EER) compared with other competitors. This vindicates the efficacy and reliability of the proposed model in signature verification).

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Optimal Resource Allocation Method for Device-to-Device Communication in 5G Networks

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computers, Materials & Continua 2022年第4期71卷 1-15页

作者： Fahd N.Al-Wesabi Imran Khan Saleem Latteef Mohammed Huda Farooq Jameel Mohammad Alamgeer Ali M.Al-Sharafi Byung Seo Kim Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Electrical Engineering University of Engineering and TechnologyPeshawarPakistan Department of Medical Instrumentation Techniques Engineering Electrical Engineering Technical CollegeMiddle Technical UniversityBaghdad10013Iraq Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA Department of Software and Communications Engineering Hongik UniversityKorea

With the rapid development of the next-generation mobile network,the number of terminal devices and applications is growing ***,how to obtain a higher data rate,wider network coverage and higher resource utilization in the limited spectrum resources has become the common research goal of ***-to-Device(D2D)communication technology and other frontier communication technologies have ***-to-Device communication technology is the technology that devices in proximity can communicate directly in cellular *** has become one of the key technologies of the fifth-generation mobile communications system(5G).D2D communication technology which is introduced into cellular networks can effectively improve spectrum utilization,enhance network coverage,reduce transmission delay and improve system throughput,but it would also bring complicated and various interferences due to reusing cellular resources at the same *** resource management is one of the most challenging and importing issues to give full play to the advantages of D2D *** resource allocation is an important factor that needs to be addressed in D2D ***,this paper proposes an optimization method based on the game-matching *** main idea is to model the optimization problem of the quality-of-experience based on user fairness and solve it through game-matching *** results show that the proposed algorithm effectively improved the resource allocation and utilization as compared with existing algorithms.

关键词： D2D communication resource allocation optimization networks

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Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

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computers, Materials & Continua 2023年第1期74卷 141-152页

作者： Nidhi Agarwal M.Gokilavani S.Nagarajan S.Saranya Hadeel Alsolai Sami Dhahbi Amira Sayed Abdelaziz Department of Information Technology KIET Group of InstitutionsDelhi201206India Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for WomenNew DelhiDelhi110006India Department of Computer Science and Engineering KL UniversityVaddeswaramAndhra Pradesh522502India Department of Electronics&Communication Engineering Saveetha Engineering CollegeChennai602105India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112India Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

In recent times,wireless sensor network(WSN)finds their suitability in several application areas,ranging from military to commercial *** nodes in WSN are placed arbitrarily in the target field,node localization(NL)becomes essential where the positioning of the nodes can be determined by the aid of anchor *** goal of any NL scheme is to improve the localization accuracy and reduce the localization error *** this motivation,this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme(IAOAB-NLS)for *** presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the *** addition,the IAOAB-NLS model is stimulated by the behaviour of *** IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the *** guaranteeing the proficient NL process of the IAOAB-NLS model,widespread experimentation takes place to assure the betterment of the IAOAB-NLS *** resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.

关键词： Aquila optimizer node localization WSN intelligent models unknown nodes anchor nodes

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A survey on RGB images classification using convolutional neural network (CNN) architectures: applications and challenges

A survey on RGB images classification using convolutional ne...

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Circuit, systems and Communication (ICCSC), International Conference on

作者： El Kharrachi Khayya Ahmed El Oirrak Toufik Datsi Computer Systems Engineering Laboratory Cadi Ayyad University Faculty of sciences Semlalia Marrakech Morocco

ISBN: (数字)9798350365306

ISBN: (纸本)9798350365313

The process of classifying RGB images is a basic and noxious activity in computer vision with an array of applications in face recognition, traffic analysis, and security protocols. An important part of this mission encompasses Convolutional Neural Networks, deemed to be core technologies, as they are capable in many domains. This short survey paper aims at conducting an in-depth analysis of CNN-based RGB image classification approaches in a way that considers complex architectures, multiple uses, and the difficulties that are entailed in them. The motivation behind this survey is to talk about the ever-increasing importance of RGB image classification across different applications. It hopes to explore and attend to the challenges emerging in association with this technology while also discussing the latest architectures and techniques advancing the field. In this paper, we go through different methods used while building the CNN architectures, thereby explaining their way of handling RGB image classification tasks. Moreover, it gives a detailed discussion about various other areas where CNNs seem to be efficient, such as agriculture and veterinary. This short survey will also dwell on the main challenges faced when dealing with RGB image classification, such as dataset variability, model robustness, and computational complexity issues. We construct the comprehensive understanding of CNN-based RGB image classification by synthesizing ideas from well-known datasets, such as CIFAR-10 and others, that will provide the foundation for future research in this area

关键词： Surveys computer vision Accuracy Architecture Transfer learning computer architecture Transformers

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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

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Iran Journal of computer Science 2024年第3期7卷 699-721页

作者： Nurjahan Mahbub-Or-Rashid, Md. Satu, Md. Shahriare Tammim, Sanjana Ruhani Sunny, Farhana Akter Moni, Mohammad Ali Department of IoT and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakair Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Bangladesh Department of Management Information Systems Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Software Engineering Auburn University Auburn AL United States Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Faculty of Health and Behavioural Sciences School of Health and Rehabilitation Sciences The University of Queensland St Lucia Australia

The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness, which affects countless people daily worldwide. Unfortunately, no specific therapeutic drugs or vaccines are available to cure patients completely. Therefore, early identification of infected individuals and their isolation can help reduce community transmission of COVID-19. Despite being commonly used, the reverse transcription polymerase chain reaction (RT-PCR) test is costly, time-consuming, and requires suitable kits, which are not always readily available. An alternative solution for the traditional RT-PCR test is machine learning and deep learning-based COVID-19 detection that utilizes clinical features of chest X-rays and computed tomography (CT) images. In this study, we conducted a detailed review of more than 100 recently published works to detect COVID-19-infected patients. Thus, different data preprocessing, data augmentation, image enhancement, feature extraction, machine learning, deep learning, Explainable Artificial Intelligence (AI) methods, etc. were explored to understand how these techniques were used to process images for identifying COVID-19. Additionally, we identified the current challenges in this field and suggested further research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Chest X-ray Computed tomography COVID-19 Deep learning Machine learning

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Empirical Literature on Economic Growth, 1991–2020: Uncovering Extant Gaps and Avenues for Future Research

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Global Journal of Emerging Market Economies 2023年第1期15卷 7-37页

作者： Doré, Natalia I. Teixeira, Aurora A. C. Centro de Economias e Finanças da UP (CEF.UP) Faculty of Economics University of Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal

The factors required to achieve sustainable economic growth in a country are debated for decades, and empirical research in this regard continues to grow. Given the relevance of the topic and the absence of a comprehensive, systematic literature review, we used bibliometric techniques to examine and document several aspects in the empirical literature related to growth, from 1991 to 2020. Five main results are worth highlighting: (a) the share of empirical articles on economic growth show a clear upward trend;(b) among all the groups of countries considered, the emerging economies (EEs) have received the most scientific attention;(c) the economic growth processes of the Latin American and Caribbean EEs have observed negligible scientific attention;(d) the very long-run studies comprise a residual share among the empirical literature on growth;(e) the extant empirical studies on economic growth have addressed mainly the impact of “macroeconomic conditions.” Our findings suggest there is a need to redirect the empirical growth agenda, so as to encourage more scientific attention devoted to the analysis of key determinants of economic growth in the very long run. There should also be increased scrutiny of the processes of economic growth in Latin American and Caribbean EEs © 2021 Emerging Markets Forum, Washington DC.

关键词： bibliometric Economic growth emerging economies systematic survey

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Factored Task and Motion Planning with Combined Optimization, Sampling and Learning

arXiv

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arXiv 2024年

作者： Ortiz-Haro, Joaquim TU Berlin Faculty IV - Electrical Engineering and Computer Science Learning and Intelligent Systems Germany

Modern robots excel at performing simple and repetitive tasks in controlled environments;however, future applications, such as robotic construction and assistance, will require long-term planning of physical interactions. These problems can be formulated as Task and Motion Planning (TAMP). The goal is to find how the robot should move to solve complex tasks requiring multiple interactions with objects in the environment, such as building furniture or cleaning and organizing the kitchen. However, TAMP is notoriously difficult to solve because it involves a tight combination of task planning and motion planning, considering geometric and physical constraints. In this thesis, we aim to improve the performance of TAMP algorithms from three complementary perspectives. First, we investigate the integration of discrete task planning with continuous trajectory optimization. Our main contribution is a conflict-based solver that automatically discovers why a task plan might fail when considering the constraints of the physical world. This information is then fed back into the task planner, resulting in an efficient, bidirectional, and intuitive interface between task and motion, capable of solving TAMP problems with multiple objects, robots, and tight physical constraints. Traditionally, there have been two competing approaches to solving TAMP problems: sample-based and optimization-based methods. In the second part, we first illustrate that, given the wide range of tasks and environments within TAMP, neither sampling nor optimization is superior in all settings. To combine the strengths of both approaches, we have designed meta-solvers for TAMP, adaptive solvers that automatically select which algorithms and computations to use and how to best decompose each problem to find a solution faster. A third promising direction to improve TAMP algorithms is to learn from previous solutions to similar problems. In the third part, we combine deep learning architectures with model-base

关键词： Motion planning

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A 1D Residual CNN with SMOTE-Tomek and Cosine Annealing for Enhanced ECG Arrhythmia Classification

A 1D Residual CNN with SMOTE-Tomek and Cosine Annealing for ...

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IEEE Latin American Symposium on Circuits and systems (LASCAS)

作者： Feng Jiang Yang Wei Lim Siti Anom Ahmad Faisul Arif Ahmad Luthffi Idzhar Ismail Ahmed Faeq Hussein Fakhrul Zaman Rokhani Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Department of Electrical and Electronics Engineering Faculty of Engineering Universiti Putra Malaysia Department of Biomedical Engineering Faculty of Engineering Al-Nahrain University Baghdad Iraq

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

This paper proposes a 1D residual convolutional neural network (CNN) for classifying arrhythmias based on electrocardiogram (ECG) signals. The additional residual blocks and skip connections effectively alleviate the gradient problem in deep neural networks by providing a direct path for gradient propagation and enhancing the feature information propagation. In addition, the SMOTE-Tomek technique was demonstrated in this work to mitigate the effects of class imbalance caused by a large amount of data during model training and effectively increase the SNR of the training set. The robustness of the model is increased by adding the cosine annealing technique and L 2 regularization. The proposed model was evaluated with various performance metrics, resulting in an accuracy of $\mathbf{9 8. 7 4 \%}$ , sensitivity of $\mathbf{9 8. 3 5 \%}$ , specificity of $\mathbf{9 1. 8 6 \%}$ , precision of $\mathbf{9 7. 9 5 \%}$ , and F1 score of $\mathbf{9 8. 1 4 \%}$ on the MIT-BIH Arrhythmia Database. The result shows better performance for classifying previously unseen data for the proposed approach as compared to the state-of-the-art works.

关键词： Training Annealing Sensitivity Arrhythmia Electrocardiography Robustness Convolutional neural networks Signal to noise ratio Residual neural networks Overfitting

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ResNet50 Pretrained Model Based Pneumonia Detection System

ResNet50 Pretrained Model Based Pneumonia Detection System

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AI IoT Congress (AIIoT), World

作者： Mohale Tshwale Pius Owolawi Moses Olaifa Vusi Malele Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria South Africa

ISBN: (数字)9798350387803

ISBN: (纸本)9798350387810

Pneumonia among other pulmonary diseases is considered to one of the major causes of death around the globe. For rapid intervention and better treatment outcomes, it is crucial that this disorder is timely and accurately detected. The X-Ray based approach to detection is tedious and result interpretation are characterized by differences in interpretation and opinions. Clinical workflow and integration of AI-based technologies for pneumonia and lung disease identification are in their infancy. This study is focused on the application of convolutional neural networks (CNN) in the classification and detection of pneumonia. The goal is to create user friendly end-to-end closed system, interfaces and workflows with an underpinning machine learning technique, making it easier for both physicians and patients to use. Django framework is employed in the development of the overall system and integration. The efficiency of the proposed system and techniques has been tested on a large data set generated from various publicly available datasets of diverse image sizes and quality. The test conducted shows that the system achieved good results and generalization. Accuracy scores of 98.9%, Receiver Operating characteristic (ROC) of 0.99, and F1 Score of 0.95 were achieved after we trained the model with ResNet50 using deep learning approaches.

关键词： Performance evaluation Pneumonia Accuracy Medical services System integration Receivers Convolutional neural networks

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