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Improving Information Delivery Effectiveness and Efficiency Using WebVR Technology 23

Improving Information Delivery Effectiveness and Efficiency ...

引用

8th International Conference on Sustainable Information engineering and Technology, SIET 2023

作者： Pratama, Ardiyan Nur Akbar, Muhammad Aminul Pinandito, Aryo Informatics Engineering Department Universitas Brawijaya Faculty of Computer Science Indonesia Information System Department Universitas Brawijaya Faculty of Computer Science Indonesia

ISBN: (纸本)9798400708503

In this digital era, attracting new students from outside the region to enter a college or university is difficult. This problem can be solved with good marketing and information dissemination techniques. Information is a type of data that has been processed and organized so that it has relevant meaning for the recipient. Useful information can be used to help make decisions, gain new knowledge, or solve problems. One medium for providing information is by using videos that can be uploaded to YouTube for dissemination. However, problems arise when the information depends on the point of view of the video content creator, who may not provide enough information sought by users in their videos. Virtual reality (VR) technology can be pursued as one of the solutions to overcome the lack of detail and control in videos for users to get the expected information. One of the technologies in VR that allows people to explore a three-dimensional virtual world on a web platform is the 360° WebVR technology. Since users have full control over what can be seen using WebVR, it is expected that this technology can assist users in getting deeper and detailed information related to their needs. This study aims to determine the effectiveness and efficiency of using YouTube videos and WebVR in distributing information and delivering detailed information to users. The results show that WebVR technology has the potential to provide greater control over more detailed information to users who use it, in contrast to traditional two-dimensional video playback technology. © 2023 ACM.

关键词： Virtual reality

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Performance Analysis of 3D Rendering Method on Web-Based Augmented Reality Application Using WebGL and OpenGL Shading Language 23

Performance Analysis of 3D Rendering Method on Web-Based Aug...

引用

8th International Conference on Sustainable Information engineering and Technology, SIET 2023

作者： Hamzaturrazak, Multohadi Jonemaro, Eriq Muhammad Adams Pinandito, Aryo Informatics Engineering Department Universitas Brawijaya Faculty of Computer Science Indonesia Information System Department Universitas Brawijaya Faculty of Computer Science Indonesia

ISBN: (纸本)9798400708503

In recent years, web programming has increased to the extent where it is capable of rendering 3D objects on the web. Its rendering performance varied depending on where it ran and what library was used to render the objects. This study aims to investigate the rendering performance of several 3D renderer libraries that render 3D objects in an augmented reality application. WebGL is one of the APIs that uses the Javascript programming language to render 3D objects in web applications. To create augmented reality on the web, a JavaScript library called *** is needed, which allows developers to create augmented reality on the web without the need to use additional plugins. *** supports various types of augmented reality, i.e., bookmarks, images, and location-based types. It also allows developers to incorporate visual and audio effects into augmented reality. This study aims to investigate the rendering performance of WebGL raw shaders and GLSL shaders in an HTML-based augmented reality application. The results suggested that both WebGL and GLSL could render low-poly and medium-poly 3D objects well. However, according to the test, WebGL performance drops significantly when rendering high-polygon 3D objects. Nonetheless, the GLSL library could render the same high-poly 3D objects at a similar level with native library rendering performance. Thus, suggest GLSL shaders as the recommended library to render 3D objects on the web. © 2023 ACM.

关键词： Augmented reality

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DDoS Cyber-Attacks Detection-Based Hybrid CNN-LSTM 3rd

DDoS Cyber-Attacks Detection-Based Hybrid CNN-LSTM

引用

3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Khaleel, Thura Jabbar Shiltagh, Nadia Adnan Iraqi Commission for Computers and Information/Informatics Institute for Postgraduate Studies Baghdad Iraq Computer Engineering Department College of Engineering University of Baghdad Baghdad Iraq

ISBN: (纸本)9789819708918

Protecting software-defined networking (SDN) against cyber-attacks has become crucial in an expanding digital threat environment. Distributed Denial-of-Service (DDoS) attacks are risky since they may seriously interrupt operations. To mitigate these risks, this study introduces an anomaly detection method that utilizes a hybrid convolutional and short-term memory (CNN-LSTM) deep neural network. This model merges the CNN's ability to automatically extract spatial features with the LSTM's proficiency in sequence modeling, thereby enhancing the detection of anomalies in network traffic metadata. The model also integrates an autoencoder structure to facilitate representation learning and reduce dimensionality. The model's effectiveness was tested using publicly accessible SDN datasets, and the results were remarkable. The model identified DDoS attacks with an accuracy rate of over 99%, surpassing the performance of previous shallow learning models. Moreover, the model proved highly adaptable, successfully detecting attacks across various data samples. This deep learning-based detection system is a significant advancement, providing precise and efficient analytics that bolster real-time cybersecurity monitoring. However, it's crucial to continue research in deployment, interpretability, and the potential of combinatorial learning with other advanced technologies. We can only fully harness the great potential of artificial intelligence for adequate cyber protection by looking into these areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Denial-of-service attack

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Hand Sign Detection and Voice Conversion for the Hearing and Speech Impaired Using Convolutional Neural Networks 4th

Hand Sign Detection and Voice Conversion for the Hearing and...

引用

4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Belgaum, Mohammad Riyaz Sowmya, Kurni Sireesha, Kuruva Priyanka, Tatagari Tony Vyshnavi, Uppara Uravakonda Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (纸本)9789819994410

Sign Language Recognition (SLR) tries to convert sign language into written or spoken form to enable communication between a deaf-mute person and a normal person. The task of communicating with disabled and impaired persons is extremely challenging, owing to its complexity and variety of hand motions that have a major societal impact. In order to depict sign language motions, SLR approaches are increasingly being used. These classification models are based on hand-crafted characteristics. Building reliable features that can adapt to the diverse range of hand signs is nonetheless challenging. To address this issue, the authors proposed a special 3D convolutional neural network (CNN) to ease the communication between normal and mute communities. Our proposed model eliminates the requirement to create features by automatically extracting discriminative spatial–temporal features from the actual livestream without any prior information. To improve performance, the 3D CNN is fed with multiple channels of video feeds that include colour, depth, trajectory information, joint body locations, and depth clues. We illustrate the proposed model's superiority to the conventional methods based on hand-crafted characteristics by testing them against a real dataset collected using a depth camera. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Audition

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Enhancing Machine Learning Algorithms Using Recursive Feature Elimination For Student Performance Prediction

Enhancing Machine Learning Algorithms Using Recursive Featur...

引用

2024 International of Seminar on Application for Technology of Information and Communication, iSemantic 2024

作者： Wijiyanto Marjuni, Aris Fanani, Ahmad Zainul Basuki, Ruri Suko Dian Nuswantoro University Department of Informatics Engineering Semarang Indonesia Duta Bangsa University Fakulty of Computer Science Surakarta Indonesia

ISBN: (纸本)9798350366648

Prediction systems for students' performance are crucial in improving educational outcomes in higher education institutions. Big data can be sourced from various channels, such as examination centers, academic systems, registration departments, and e-learning platforms. Extracting meaningful insights from educational data is challenging, necessitating data dimensionality reduction. This study introduces an enhanced recursive feature elimination (RFE) algorithm as a wrapper feature selection technique. We adopt machine learning by combining the proposed RFE with five different classifiers, including Gradient Boosting Classifier (GB), Random Forest Classifier (RF), Support Vector Classifier (SVC), K-Neighbors Classifier (KNN), and Decision Tree Classifier (DT). The dataset was originally intended to influence factors in dropout students, and it has 329 records and 9 features. The AUC score improved by 8% compared to the model tested without the proposed feature selection method by testing the proposed methodology with the Decision Tree Classifier. Educators and academic institutions can utilize this information to develop targeted intervention strategies, tailored learning experiences, and personalized recommendations for students, ultimately promoting a more effective learning environment and improving overall educational outcomes. © 2024 IEEE.

关键词： Adversarial machine learning

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Adaptive Learning and Virtual Reality based Conceptual Framework for Dental Training 5

Adaptive Learning and Virtual Reality based Conceptual Frame...

引用

5th International Conference on computer and Applications, ICCA 2023

作者： Sherif, Meriam Barakat, Nahla Hamdy, Abeer The British University in Egypt Faculty of Informatics and Computer Sceince Department of Software Engineering Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Sceince Department of Artifical Intellegence Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Sceince Department of Software Enginerring Cairo Egypt

ISBN: (纸本)9798350303254

Recently, there has been increasing interest in utilizing virtual reality (VR) technology in dental education and student training. VR based training can provide students with sufficient training and hands-on experience before entering the clinical setting, which in turn helps avoid risks ranging from small errors to severe consequences. However, previous research in this area is limited and none of the previous work proposed a complete, scalable framework for dental students' education and training. This paper proposes an improved virtual environment for dental training, through an adaptive learning approach, integrated with the VR environment, which is - to the best of our knowledge - the first framework proposed for this purpose. The framework consists of eight modules that aim to provide students with a holistic dental training experience, equipping them with the essential skills and confidence required for their future clinical practice. Furthermore, it aims to improve patient care and safety. In order to guarantee the effectiveness and usability of the proposed framework, a thorough evaluation will be conducted, encompassing both a system perspective, which involves assessing software metrics, as well as evaluating its usability. © 2023 IEEE.

关键词： Virtual reality

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New Perspectives in e-Learning: EEG-Based Modelling of Human Cognition Individual Differences 13th

New Perspectives in e-Learning: EEG-Based Modelling of Hum...

引用

13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Trigka, Maria Dritsas, Elias Mylonas, Phivos Athena Research and Innovation Center Patras26504 Greece Department of Informatics and Computer Engineering University of West Attica Aigaleo Greece

ISBN: (纸本)9783031632266

Learning is the process of acquiring knowledge, skills and values, through lifelong education. Learning abilities are affected by the subject’s cognitive skills showing how they think, process information, pay attention and remember things. The dynamic nature of a human’s mental state usually impacts the aforementioned structural characteristics of human cognition, hindering learning performance as well. Nowadays, technological advances in Brain-computer Interface (BCI) systems, in combination with advanced processing methods, have paved the way for the highly accurate capturing of human brain activity, helping to decode cognitive and mental status to adapt the learning process. This paper aims to present the first research outcomes of a study in progress. More specifically, the main proposition lies in extending the basic functionalities of our electroencephalography (EEG)-based e-learning prototype system to deliver personalized solutions by taking into account the individual cognitive differences of the learners. Some primary findings are showcased that investigate the potential association of cognitive style with the power spectral features of brain activity in a specific context where the subjects execute a visual task. It is anticipated that the real-time recognition of the learner’s cognitive style will help educators adapt and advance the learning process. © IFIP International Federation for Information Processing 2024.

关键词： Brain computer interface

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Quaternionic multilayer bottleneck autoencoder for color image compression 27

Quaternionic multilayer bottleneck autoencoder for color ima...

引用

27th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2024

作者： Petrovsky, Nick Vashkevich, Maxim Belarusian State University of Informatics and Radioelectronics Department of Computer Engineering 6 P. Brovky st Minsk220013 Belarus

ISBN: (纸本)9788362065486

This paper presents a study on the multilayer bottleneck autoencoder applied for thumbnail color image compression task using quaternionic neural nets (QNN). Existing state-of-the-art real-valued autoencoder architectures are weak for reconstruction of images with high-frequency components. However, experiments shows that hypercomplex representation of color and activation functions, gives better results in terms of pixellevel quality metrics PSNR and SSIM. Designed hypercomplex autoencoders are simplest type of the QNN - fully connected quaternionic layers. Investigated one- and two-layer QNN structures for encoder/decoders in compare with real-valued ones provides better PSNR by approx. 2.5 dB and 0.11 SSIM (Real valued: PSNR - 26.36 dB, SSIM - 0.856;Quaternionic: PSNR 31.28 dB, SSIM - 0.951) for 3:1 compression ratio and equal or less number of hyperparameters. © 2024 Division of Signal Processing and Electronic Systems, Poznan University of Technology (DSPES PUT).

关键词： Image compression

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A spatio-temporal matrix representation for trajectory classification 24

A spatio-temporal matrix representation for trajectory class...

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32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024

作者： Kontopoulos, Ioannis Varlamis, Iraklis Makris, Antonios Tserpes, Konstantinos Department of Informatics and Telematics Harokopio University of Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9798400711077

Fish piracy remains widespread globally despite national and international efforts. Experts estimate it accounts for about 20% of the total seafood catch worldwide. Technology is playing a key role in detecting illegal fishing, with satellite imagery and sensors being used to track vessels and monitor fishing practices. Since fishing boats broadcast their positions using a vessel tracking system, this data can be processed to detect illegal activity. This study focuses on classifying fishing vessel trajectories using only positional data. A novel trajectory representation and a Convolutional Neural Network is employed, showing promising results compared to traditional methods. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Enhancing Solar Farm Operations: Machine Learning for Equipment Fault Detection and Classification

Enhancing Solar Farm Operations: Machine Learning for Equipm...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Hamza, Ali Ali, Zunaib Dudley, Sandra Saleem, Komal Christofides, Nicholas School of Engineering LondonSE1 0AA United Kingdom Department of Engineering and Construction London United Kingdom Frederick University Department of Electrical Engineering Computer Engineering and Informatics Cyprus

ISBN: (纸本)9798350376067

Photovoltaic (PV) energy is considered one of the most widespread renewable sources. In this study, the vulnerability of solar PV systems to various faults, leading to potential performance degradation, has been addressed. A robust fault detection and classification strategy is proposed, employing Gradient Boosting Machine Learning (ML) algorithms (Light Gradient Boosting Method (LGBM), Categorical Boosting (Cat-boost), and AdaBoost). The methodology involved formulating a comprehensive PV system to create a synthetic fault database, utilizing diverse features. To simulate normal conditions and various fault scenarios, a MATLAB/Simulink-based PV System is developed. The optimization of hyperparameters of ML algorithms has been achieved by grid search optimization technique, resulting in enhanced performance, and reduced computational cost/time. The study involved multiple independent runs on ML algorithms and the application of Principal Component Analysis (PCA) for dimensionality reduction in the context of fault classification to assess the accuracy and consistency. Cross-validation is implemented to ensure the generalization capability of the ML algorithms to unseen data. Comparison has been established with Random Forest (RF) algorithms to show the performance accuracy of ML in fault diagnosis of PV systems. © 2024 IEEE.

关键词： Dimensionality reduction

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