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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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Improving Information Delivery Effectiveness and Efficiency Using WebVR Technology 23

Improving Information Delivery Effectiveness and Efficiency ...

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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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Multi-Access Edge Computing Using Intelligent Mobile User Resource Allocation In 6G 4

Multi-Access Edge Computing Using Intelligent Mobile User Re...

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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： Saeed, Mamoon M. Saeed, Rashid A. Mokhtar, Rania A. Gaid, AbdulGuddoos S. A. Ahmed, Zeinab E. Elbasheir, Mohammed S. Faculty of Engineering Department of Communications and Electronics Engineering Yemen Taif University Faculty of Engineering Department of Electrical and Electronic Engineering Altaif Saudi Arabia College of Computers and Information Technology Taif University Department of Computer Engineering Taif21944 Saudi Arabia Taiz University Faculty of Engineering & Information Technology Dept. of Communication & Computer Engineering Taiz Yemen International Islamic University Malaysia Department of Electrical and Computer Malaysia School of Electronic Engineering Sudan University of Science and Technology Khartoum Sudan

ISBN: (纸本)9798350372632

Edge computing plays a crucial role in the advancement of Beyond 5G (B5G) and 6G networks. The substantial growth in network traffic and computational requirements necessitates the implementation of multi-access edge computing (MEC) as a viable solution. MEC aims to bring the capabilities of cloud computing closer to end-users by integrating them into the radio access network (RAN). This research introduces an optimal approach to connect mobile users with MEC hosts and access points, effectively distributing radio and computing resources to individual mobile users. The objective is achieved by lowering the total user transmit power while abiding by latency constraints in both the communication and computation phases, using the Grey Wolf Optimization technique (GWO). A comparison to alternative smart techniques demonstrates the effectiveness of the suggested approach © 2024 IEEE.

关键词： 5G mobile communication systems

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Comparison of Unsupervised Learning Algorithms for K-Means Clustering Efficiency in IoE Sensor Detection systems for Smart Agriculture 13

Comparison of Unsupervised Learning Algorithms for K-Means C...

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13th International Electrical engineering Congress, iEECON 2025

作者： Tipwong, Wallop Sirikham, Adisorn Konpang, Jessada Kirdpipat, Putthiphong Chongjarearn, Yutana Preutisrunyanont, Oradee Rajamangala University of Technology Krungthep Department of Electrical and Telecommunication Engineering Bangkok Thailand Thammasat University Faculty of Engineering Thammasat School of Engineering Department of Electrical and Computer Engineering Pathum Thani Thailand Dhurakij Pundit University Department of Robotic Engineering and Automation System Bangkok Thailand

ISBN: (纸本)9798331543952

This study investigates the application of clustering algorithms to data collected from color sensors providing frequency signals during the cultivation process. Continuous sampling allowed for real-time data collection, and variations in temperature and humidity were observed to influence signal reception. The dataset's large volume and varying sample sizes necessitated the evaluation of several clustering models for efficient data processing. The experiment tested K-Means, K-Means++, Mini-Batch K-Means, and Bisecting K-Means algorithms. The results reveal that the computation time for each model increased with the number of groups. Specifically, the K-Means, K-Means++, and Mini-Batch K-Means models exhibited shorter computation times, ranging from 0.02 to 0.03 seconds for two-group scenarios, while the Bisecting K-Means algorithm took significantly longer, approximately 0.10 seconds. Despite the variation in processing times, all models performed similarly in terms of clustering quality, with an average Silhouette Score of 0.82. This score indicates that the algorithms effectively separated data points into well-defined groups while maintaining high accuracy in clustering. Additionally, the analysis highlighted the importance of balancing computation time and clustering quality, particularly as the number of clusters increases. These findings suggest that while computational efficiency may vary across models, the clustering quality remains robust, providing valuable insights for data analysis in agricultural sensor networks. The study's results demonstrate that each clustering model is capable of handling large datasets with high accuracy in grouping, making them applicable to real-world agricultural data processing scenarios. In conclusion, the experiment confirms that all tested models, despite differing in computation time, successfully partitioned the data with high-quality results, offering promising applications for data analysis and optimization in agricult

关键词： K-means clustering

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Enhancing the Efficiency of Navigation systems: Dijkstra Algorithm Optimization Through Integration of Douglas-Peucker Line Simplification Technique 23

Enhancing the Efficiency of Navigation Systems: Dijkstra Alg...

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

作者： Haq, Muhammad Yasir Anshari Sianturi, Riswan Septriayadi 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

Smartphones have grown popular in the current digital era among people. Map-based applications are one of many that are used daily by people to acquire information about specific locations. Google Maps and Waze are two popular examples of map-based applications for travel and navigation, as they provide detailed information while the user navigates from one point to another. One of the most commonly used algorithms in map-based applications is the Dijkstra algorithm. Although the Dijkstra algorithm can determine the shortest route between two points, there is an issue when the selected route involves a large number of data points that need to be processed. The more data points involved, the longer it takes to do a route calculation in the Dijkstra algorithm. This can lead to a degradation in system performance, especially on smartphones with limited resources. To address this issue, the Douglas-Peucker algorithm is proposed as a solution to reduce the number of points involved in the calculation using the line simplification technique. Line simplification means reducing unnecessary points that are considered unimportant. This study suggested that integrating the Douglas-Peucker line simplification technique before Dijkstra could improve the system performance more than the system without integrating line simplification in terms of reducing both memory usage and the time required to compute the solution. © 2023 ACM.

关键词： Navigation systems

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Enhancing structural health monitoring with AI-ML algorithms: a focus on crack detection and prediction

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Asian Journal of Civil engineering 2025年第5期26卷 1907-1918页

作者： Bader, Ahmad Shtayat, Amir Al-Mistarehi, Bara’ Communication and Computer Department Faculty of Engineering Jadara University Irbid Jordan Department of City Planning and Design Jordan University of Science and Technology Irbid 22110 Jordan Department of Civil Engineering Jordan University of Science and Technology Irbid Jordan

SHM is a very important process in terms of the safety and durability of infrastructure. Traditional SHM often faces problems detecting minor structural defects and handling large datasets. Therefore, certain more advanced approaches are called for. The paper discussed the applications of AI and ML algorithms, such as CatBoost and the African Vultures Optimization Algorithm, for such challenges. The research is based on a unique dataset of 8,541 rows and diverse features, developing a predictive framework that enhances crack detection and forecast capabilities. The approach mainly deals with heterogeneous data using the CatBoost algorithm, given its capability for high-accuracy predictions, while AVOA optimizes feature selection, reduces the computational cost, and guarantees no loss in model performance. This methodology has resulted in a significant enhancement of the prediction accuracy, which states the importance of AI-ML integration in SHM. The key results demonstrate the effectiveness of the model in detecting structural anomalies and crack propagation to enable proactive maintenance strategies. This study’s contributions have gone toward advancing SHM with scalable and efficient AI-ML frameworks, enabling real-time monitoring for better infrastructure management. Such development might have a transforming potential to cut down on maintenance costs and enhance operational safety, thus further encouraging sustainable infrastructure systems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： African vultures optimization algorithm CatBoost Crack Detection Feature selection Machine learning Structural health monitoring

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Intrusion Detection in the IoT-Fog Adopting the GRU and CNN: A Deep Learning-Based Approach 6th

Intrusion Detection in the IoT-Fog Adopting the GRU and CNN:...

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6th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2022

作者： Al-Khuzaie, Zahraa Majeed Albermany, Salah A. K. AbdlNibe, Mohammed Ahmed Computer Science Department University of Kufa Najaf Iraq Department of Electronics and Communication Engineering Faculty of Engineering University of Kufa Kufa Iraq

ISBN: (纸本)9789811995118

The main objective of this research is to utilize the technologies of cloud computing in the applications of the IoT. The IoT network is divided into three layers: the cloud computing layer, the edge layer, and the fog layer. More than twenty-five million devices are connected to the fog layer leading to an increase in data traffic in the network in general. As a result, there will be an increase in the number of attacks. Therefore, protecting the data of the IoT and detecting attacks are indispensable. Since new types of attacks frequently emerge, intrusion detecting systems (IDSs) have to be developed to counteract the new types of attacks. However, built upon the principles of deep learning and machine learning, most of the current IDSs are based on predicting the probability of an attack. The most commonly used IDSs are the KNN and the SVN. Such IDSs suffer from low accuracy rates in attacks detection, time consumption, and a large number of false alarms. Thus, this paper suggests a new model of deep learning that is built upon combining two algorithms: the convolution neural network (CNN) and the gated recurrent unit network (GRU). Additionally, two types of datasets are utilized: the NSL-KDD and UNSW-NB15. These datasets are categorized in accordance with multiclass classification. The suggested model provides a better detection rate in comparison with the other models. It achieves a detection rate that is 1.5 more accurate than the other IDSs. Also, it exhibits fewer false alarms, and it provides less time for detection rate. The achieved results of the suggested model are due to utilizing the technologies of deep learning and machine learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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Lung sound disease detection using attention over pre-trained efficientnet architecture

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Multimedia Tools and Applications 2024年 1-35页

作者： Nair, Anuja Vadher, Himanshu Patel, Pal Vyas, Tarjni Bhatt, Chintan Bruno, Alessandro Department of Computer Science & amp Engineering School of Technology Nirma University Gujarat Ahmedabad382481 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar382007 India Department of Business Law Economics Consumer Behaviour ’Carlo A. Ricciardi’ Faculty of Communication IULM University Milan20143 Italy

Air pollution has been on the rise for quite a while now and with it is the increasing number of cases involving respiratory diseases. These respiratory diseases range from the mild ones to the most severe ones. Therefore, we need to detect these diseases. The most efficient way of checking the health of the lung is through the use of lung sounds. But the lung sounds carry with them the minute variations that a doctor or any human sometimes misses. To tackle the same, this paper proposes a deep learning model that detects a person’s lung disease by processing lung sounds. The ICBHI 2017 dataset comprising the lung sound audios and their corresponding labels is used to train the proposed model. The dataset consists of lung sounds and some additional background noise has been removed using the bandpass Butterworth filter, whose frequency range has been set to 250Hz - 2000Hz. Later, the amount of data is multiplied through data augmentation for audio signals. The augmented data is later converted to mel spectrograms, which are, in turn, fed into the deep learning model. The model that gives the best accuracy is EfficientNet-B0 + attention with a training accuracy of 99.72%, validation accuracy of 99.82%, precision, recall and f1-scores of 99.82% as well. Furthermore, a comparison of the pre-trained model’s training and inference times has been performed. Although the proposed model takes a significant amount of training time, it takes the least amount of inference time compared to the other pre-trained models that have been tested. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Butterworth filters

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Smart Weapon/Emotion Recognition system to Reduce Suicide Attempts

Smart Weapon/Emotion Recognition System to Reduce Suicide At...

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2024 Arab ICT Conference, AICTC 2024

作者： Hagecg, Christel Al Jebran, Mital Harb, John Aoun, Elio Kassem, Abdallah Hamad, Mustapha Notre Dame University-Louaize Department of Computer and Communication Engineering Faculty of Engineering Zouk Mosbeh P.O. Box: 72 Zouk Mikael Lebanon

ISBN: (纸本)9798350342086

Acting towards decreasing the numbers of suicides is a must. Suicide rates have increased notably through the years;therefore, this paper describes a system whose purpose is to decrease those rates by trying to delay people from committing suicide until help arrives. This can be done using Artificial Intelligence, Machine Learning, and Deep Learning. The result is that the a camera will detect the situation on case of suicide, a message will be sent to specific person in order to get help. © 2024 IEEE.

关键词： Emotion Recognition

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Combination of CRITIC and PROMETHEE II Methods as An Early Detection of Potential Dropout Students 6

Combination of CRITIC and PROMETHEE II Methods as An Early D...

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6th International Conference on Information and communications Technology, ICOIACT 2023

作者： Yusa, Mochammad Coastera, Funny Farady Rahmadani, Deni Sari, Julia Purnama Kusrini, Kusrini Hartanto, Anggit Dwi University of Bengkulu Faculty of Engineering Department of Informatics Bengkulu Indonesia University of Bengkulu Faculty of Engineering Department of Information System Bengkulu Indonesia Universitas Amikom Yogyakarta Magister of Informatics Engineering Faculty of Computer Science Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Department of Information System Yogyakarta Indonesia

ISBN: (纸本)9798350315639

Dropout (DO) has negative implications for individuals and educational institutions. The field of education data mining (EDM) offers valuable contributions to prevent dropout cases and improve student retention. This study focuses on implementing EDM to identify students at risk of dropping out by integrating academic activity criteria with real-time data sources. Previous studies have encountered limitations in classification and clustering techniques, with classification relying on labeled training data (supervised learning) and clustering constrained by attribute or criteria weight (unsupervised learning). This study aims to address these limitations by combining the PROMETHEE II ranking approach and the CRITIC weighting method, producing a list of students with dropout potential. The results can aid stakeholders in implementing preventive measures. The study involves several steps: 1) Collecting data through integration with academic portals;2) Filtering and selecting criteria for student academic activities;3) Transforming and normalizing the data;4) Applying automatic weighting using CRITIC;5) Calculating criteria rankings using the PROMETHEE II technique;and 6) Validating the ranking outcomes. © 2023 IEEE.

关键词： Decision support systems

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