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10th International Conference on Education and Technology, ICET 2024

作者： Wardaya, Utara Setya Bachtiar, Fitra Abdurrachman Pradana, Fajar Marutschke, Daniel Moritz Brawijaya University Faculty of Computer Science Information System Department Malang Indonesia Brawijaya University Faculty of Computer Science Informatics Department Malang Indonesia Kyoto University of Advanced Science Faculty of Economics and Business Administration Kyoto Japan

ISBN: (纸本)9798331522483

Learning is a process that involves the progressive transfer of knowledge, encompassing both the physical and psychological components of the learner. Emotions play a crucial role in the success of learning, yet they are often overlooked in the context of online education. Facial emotion detection technology and emotion analysis offer a solution for effectively recognizing and accommodating students' emotions in digital learning environments. The use of a Learning Management System (LMS) based on the MERN architecture (MongoDB, ***, ***, ***) is also critical in supporting the flexibility and responsiveness of online learning platforms. This research employed the Agile model, which was systematically planned and organized. The Agile model is an approach that allows for continuous adjustments during development. The aim of this study is to integrate artificial intelligence into an e-learning system to detect and evaluate students' emotions, thereby enabling the development of more effective online learning methods. The design process following the Agile Method involved needs analysis, modeling with UML (Use Case, Activity Diagram, Sequence Diagram, and Class Diagram), as well as database and code implementation. The backend development focused on database management, server configuration, and API integration, while the frontend was designed for a responsive user interface. Testing was conducted using Black Box and White Box methods, which demonstrated 100% validity for system functionality, while user feedback through UAT yielded a 94% validity score. The results indicate that emotion detection functions effectively in e-learning, and emotion analysis can enhance the quality of learning, with positive emotional states leading to better outcomes. © 2024 IEEE.

关键词： Black-box testing

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Deep Convolutional - Generative Adversarial Network for Autism Prediction 3

Deep Convolutional - Generative Adversarial Network for Auti...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Cholaraja, K. Kumar, Nithish Selva Kumar, T. Akash, K. Ananth, G. Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India

ISBN: (纸本)9798350343632

autism spectrum disorder (ASD) is a multifaceted developmental condition characterized by enduring challenges in social communication, restricted interests, and other associated symptoms. This presents a consistent behavioral pattern. It is important to note that autism is a lifelong condition, but the extent of functional impediment varies among individuals. Early signs of this condition can be identified by parents, caregivers, or pediatricians before a child reaches their first birthday. Extensive research underscores that early intervention correlates with improved outcomes for individuals with autism in their later lives. As a result, there is an increasing demand for ASD prediction in order to detect the condition at an early stage. The effort to detect autism spectrum disorder (ASD) using machine learning has reached a notable milestone, with a notable accuracy of roughly 86.5%. Machine learning's prowess in ASD prediction has been unwavering, giving increased accuracy with less data while gradually declining when presented with large datasets. To overcome such constraints and raise the accuracy criteria for ASD prediction, this study delves into the domain of deep learning, that focuses on learning and making predictions from vast and complicated datasets using artificial neural networks. Deep learning techniques learn representations or features directly from raw data, as opposed to typical machine learning algorithms, which rely on constructed features. In this ambitious project, we begin on a paradigm shift from traditional machine learning approaches to deep learning, leveraging the limitless potential of advanced artificial neural networks. The method to this prediction is to use the Deep Convolutional GAN deep learning algorithm, with the goal of achieving higher precision. © 2023 IEEE.

关键词： autism spectrum disorder (ASD) Deep Learning Machine Learning

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An Educational Human Digital Twin Proposed Model for Personalized E-Learning 1

An Educational Human Digital Twin Proposed Model for Persona...

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1st IEEE Afro-Mediterranean Conference on Artificial Intelligence, AMCAI 2023

作者： Aboulsafa, Esraa I. Khayat, Ghada A. El Elmorsy, Shaimaa A. Alexandria University Faculty of Business Information Systems and Computers Department Alexandria Egypt Alexandria University Faculty of Science Mathematics and Computer Science Department Alexandria Egypt

ISBN: (纸本)9798350394689

Global education systems are shifting from traditional one-size-fits-all learning approaches towards a more personalized, studentcentered approach so that students can progress at their own pace. Several personalized learning techniques were used, however, these techniques depended mainly on historical data. This research proposes a framework for real-time based personalized learning system. The system depends on real time data and digital twins to provide a more effective personalized learning experience for higher education students. Depending on real-time data about students is expected to provide a more effective personalized learning experience. In addition, it is expected to make the personalized learning process more adaptive. This research states the results of phase 1 of a financed project that aims to build a prototype of a student digital twin for personalized learning. The project is also extended to the development of academic staff members' knowledge, skills and study material improvements. However, this research discusses these points briefly and focuses on the discussion of students personalized learning. In this research the appropriate attributes for building a student digital twin, and the suitable tools for capturing the realtime data of students are determined. In addition, the architecture of the digital twin based personalized learning system, the real-time personalized learning process, the potential insights and decisions are discussed. As for future work, the project continues to the modelling of algorithms for the student digital twin and actual building of the digital twin. It is then extended to setting and using the tools and testing the results. © 2023 IEEE.

关键词： Students

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Automated Autism Spectral Disorder Classification Using Optimal Machine Learning Model

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computers, Materials & Continua 2023年第3期74卷 5251-5265页

作者： Hanan Abdullah Mengash Hamed Alqahtani Mohammed Maray Mohamed K.Nour Radwa Marzouk Mohammed Abdullah Al-Hagery Heba Mohsen Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial Intelligence and Unit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Autism Spectrum Disorder (ASD) refers to a neuro-disorder wherean individual has long-lasting effects on communication and interaction *** information technologywhich employs artificial intelligence(AI) model has assisted in early identify ASD by using pattern *** advances of AI models assist in the automated identification andclassification of ASD, which helps to reduce the severity of the *** study introduces an automated ASD classification using owl searchalgorithm with machine learning (ASDC-OSAML) model. The proposedASDC-OSAML model majorly focuses on the identification and classificationof ASD. To attain this, the presentedASDC-OSAML model follows minmaxnormalization approach as a pre-processing stage. Next, the owl searchalgorithm (OSA)-based feature selection (OSA-FS) model is used to derivefeature subsets. Then, beetle swarm antenna search (BSAS) algorithm withIterative Dichotomiser 3 (ID3) classification method was implied for ASDdetection and classification. The design of BSAS algorithm helps to determinethe parameter values of the ID3 classifier. The performance analysis of theASDC-OSAML model is performed using benchmark dataset. An extensivecomparison study highlighted the supremacy of the ASDC-OSAML modelover recent state of art approaches.

关键词： Autism spectral disorder machine learning owl search algorithm feature selection id3 classifier

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Breaking Tester Stereotypes: who is testing and why it matters 37

Breaking Tester Stereotypes: who is testing and why it matte...

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37th International BCS Human-computer Interaction Conference, BCS HCI 2024

作者： Evans, Isabel Porter, Chris Micallef, Mark Dept. of Computer Information Systems Faculty of ICT University of Malta Msida Malta Dept. of Computer Science Faculty of ICT University of Malta Msida Malta

Testing is essential for successful delivery of software solutions and is not always performed by specialist testers. In earlier studies, we noted a wide diversity in the backgrounds of the testers participating in these studies, prompting our research question: Who is Testing? We conducted a qualitative study of over 70 industry testers, covering testers from multiple countries and domains, with information about their backgrounds, hobbies, roles and characteristics. We show testers are from a wide range of backgrounds, with differing needs, characteristics, and problem-solving preferences. Their roles in software projects are multi-faceted, requiring a high cognitive skill level. We discuss how to break stereotyping and best support diversity in testers’ backgrounds and characteristics. We consider whether software testers are different from other software practitioners, and how understanding tester personas helps support of testers and testing. © Evans et al.

关键词： Software testing

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Human Activity Recognition Using AutoML Approach 4

Human Activity Recognition Using AutoML Approach

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Alboueishi, Nadeen H. Nagem, Tarek A. M. Abdelnabi, Esra A. University of Benghazi Faculty of Information Technology Department of Artificial Intelligence and Computer Systems Benghazi Libya University of Benghazi Faculty of Education Department of Computer Science Benghazi Libya

ISBN: (纸本)9798350372632

This paper focuses on improving human activity recognition using smartphones by training the model using H2O AutoML on a dataset called WISDM. This dataset was collected from smartphone users performing various human activities such as walking, jogging, standing, sitting, upstairs, and downstairs, and was used to train the model. The model's performance was improved using H2O AutoML and evaluated using various performance metrics such as classification accuracy and F1 score. The results showed significant improvement in the model's performance in identifying various human activities, indicating the effectiveness of using H2O AutoML to improve machine learning-based models for human activity recognition using smartphones. This model can be used in various applications such as fitness, healthcare, and smart application control. It is expected to improve users' quality of life and performance in various fields. © 2024 IEEE.

关键词： Smartphones

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Deep Learning Approaches for Pneumonia Classification in Healthcare 3

Deep Learning Approaches for Pneumonia Classification in Hea...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Sriram, G.K. Karunasagar, A. Sudharsan, R. Malini, A. Thiagarajar College of Engineering Department of Computer Science and Business Systems Madurai India

ISBN: (纸本)9798350336238

In the past two decades, there has been a sharp rise in the use of deep learning for medical image processing and analysis. Recent challenges, for instance, the most well-known ImageNet computer Vision competition, have almost entirely incorporated deep learning approaches for providing the best result. The concept of Image classification was later extended to Image Segmentation and Object Detection which proved to perform extremely well using state-of-the-art classification algorithms as their backbone architecture. The accuracy of the algorithm and approach has a significant impact on the medical field as there is a constant need for accurate and computationally efficient models. The existing object detection and segmentation approaches need large data for providing accurate results, unlike classification algorithms in which accuracy can be achieved with a relatively smaller amount of data. Hence, for the overall increase of model accuracy, there is a need for image augmentation to be incorporated. In this paper, several deep learning methodologies such as classification, object detection, ensemble, and segmentation for pneumonia classification and detection have been reviewed and an ensemble-based approach for the classification of Pneumonia using chest X-rays has been proposed. © 2023 IEEE.

关键词： Object detection

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Mobile Application Enabling Citizen Report Street Problems

Mobile Application Enabling Citizen Report Street Problems

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Sathya, T. Berlinda, A.J. Gayathiri Varsha, A.S. Vidhusha, M. Sri Sairam Engineering College Computer Science & Business Systems Chennai India

ISBN: (纸本)9798350333244

The lethargicness among the corporation in solving the issues and problems in the local residential areas have resulted in a drastic rise in the street problems like faulty street lights, electricity problem, bad conditioned roads, stagnant water and many in which most of the issues are not being addressed properly and some issues are not even being reported. Since the complaints registered by the residents regarding the problems faced in their street are not addressed properly, people feel unmotivated to make complaints and give voice for their peaceful living. The impact of the current state of issue in local residential areas might result in irritation and conflicts among local residents. The project aims at achieving an ideal state where residents do not feel irritated in reporting any issues faced and are assured of the rectification of their complaints in a timely manner. © 2023 IEEE.

关键词： Housing

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural Computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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Improved Beluga Whale Optimizer-Based Cluster Head Selection in FANET 3

Improved Beluga Whale Optimizer-Based Cluster Head Selection...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Venkatasubramanian, S. Saranathan College of Engineering Department of Computer Science and Business Systems Trichy India

ISBN: (纸本)9798350317022

Important issues have arisen for the reliability of communications in FANETs as of the high-speed mobility of unmanned aerial vehicles (UAVs). One of the more recent Meta-heuristic (MH) methods, makes use of the Beluga Whale optimizer (BWO) to optimize network parameters in FANET. However, BWO may get stuck in local optima and converge too quickly because of its lack of diversity. This paper provides two steps to improve the original BWO procedure. Opposition-based learning (OBL), also known as OBWO's first stage, helps to speed up the search procedure and improve the learning procedure so that a higher quality generation of candidate keys for the basic BWO can be selected. In addition, a powerful cluster maintenance algorithm for continually updating UAV clusters is proposed. The simulation findings show that the projected OBWO scheme outperforms state-of-the-art clustering techniques by a wide margin in terms of packet delivery ratio, network lifetime to cluster head altering ratio, and energy usage. © 2023 IEEE.

关键词： Beluga Whale Optimization Cluster Head Selection Energy Ratio Flying ad hoc network Opposition-Based Learning Packet delivery ratio

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