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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Sirmayanti Prastyo, Pulung Hendro Mahyati Politeknik Negeri Ujung Pandang Department of Electrical Engineering Makassar Indonesia Politeknik Negeri Ujung Pandang Department of Informatics and Computer Engineering Makassar Indonesia Politeknik Negeri Ujung Pandang Department of Chemical Engineering Makassar Indonesia

ISBN: (纸本)9798350368086

Feature selection is a pivotal process in machine learning and data analysis, aimed at improving model performance by identifying and utilizing the most relevant features while eliminating redundant and irrelevant ones. The Grey Wolf Optimizer (GWO), a metaheuristic algorithm based on the social hierarchy and hunting behavior of grey wolves, has proven to be an effective method for feature selection. However, it is also easy to get trapped in the local optima. Therefore, this study presents an enhanced Grey Wolf Optimizer with Opposition, Mutation, and Local Search Strategy (GWO-OMLSS) to boost exploration and exploitation of GWO in order to avoid the algorithm falling into local optima. The proposed method was validated using ten becnhmark data sets from the UCI machine learning repository and compared with state-of-the-art metaheuristic algorithm-based feature selections, such as GWO, PSO, ESSA, and CS. The Experimental results demonstrate that GWO-OMLSS outperforms the standard of GWO and other metaheuristic algorithms on all data sets except Ionosphere and Wine. The proposed method achieves the best an accuracy of 99.7% and 99.8% on the Exactly and M-of-n data sets, respectively, while also significantly reducing the number of features. It indicates that the proposed method not only obtains high accuracy, but also reduces the dimensionality of features effectively. It means that the proposed method can be considered as a promising algorithm for feature selection. © 2024 IEEE.

关键词： Heuristic algorithms

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Shrinking VOD Traffic via Rényi-Entropic Optimal Transport

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Performance Evaluation Review 2024年第1期52卷 75-76页

作者： Lo, Chi-Jen Marina, Mahesh K. Sastry, Nishanth Xu, Kai Fadaei, Saeed Li, Yong Department of Engineering University of Cambridge United Kingdom School of Informatics The University of Edinburgh United Kingdom School of Computer Science University of Surrey United Kingdom MIT-IBM Watson AI Lab CambridgeMA United States Department of Electronic Engineering Tsinghua University China

In response to the exponential surge in Video on Demand (VOD) traffic, numerous research endeavors have concentrated on optimizing and enhancing infrastructure efficiency. In contrast, this paper explores whether users' demand patterns can be shaped to reduce the pressure on infrastructure. Our main idea is to design a mechanism that alters the distribution of user requests to another distribution which is much more cache-efficient, but still remains 'close enough' (in terms of cost) to fulfil individual user's preference. © 2024 Owner/Author.

关键词： Video on demand

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Detection of Dead Victims at Volcanic Disaster Location based on Drone and LoRa

Detection of Dead Victims at Volcanic Disaster Location base...

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2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Hadi, Moch. Zen Samsono Dafa, Achmad Abie Kristalina, Prima Department of Informatics and Computer Engineering Surabaya East Java Indonesia

ISBN: (纸本)9781665476508

When a natural disaster occurs, the process of evacuating victims after a disaster must be carried out immediately to reduce the risk due to the late evacuation process. In this evacuation process, the Search and Rescue (SAR) team played a big role in addition to focusing on the safety of victims and also paying attention to their own safety. Meanwhile, due to the large area of the disaster the search took quite a long time. In this paper, research was carried out to facilitate the evacuation process at the location of volcanic disasters by using drones carrying electronic devices to find the whereabouts of victims on the surface. This process of detecting and classifying is based on the Convolutional Neural Network (CNN) method with the MobileNetV2 model. We train the data set of disaster victims using batch sizes 16 and 32 with epochs of 40, 80 and 100. The resulting model has the greatest accuracy of 0.81 and f1-score of 0.86. © 2022 IEEE.

关键词： Drones

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Analyzing the impact and application of Augmented Reality in Education: The case of students with special educational needs 26

Analyzing the impact and application of Augmented Reality in...

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Kapetanaki, Andrianthi Krouska, Akrivi Troussas, Christos Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Greece

ISBN: (纸本)9781450398541

This study presents a systematic review of the literature which analyzes the impact and application of Augmented Reality (AR) technology in the tutoring of students with special educational needs. In recent years, Information and Communication Technology (ICT) promotes learning in a pluralistic and multisensory environment that favors students with special educational needs. Augmented Reality is an emerging technology which has been widely used in the field of education, but only few studies are related to special education. This paper investigates the research in AR educational systems addressed to students with special educational needs. In total, 26 studies between 2014 and 2022 were selected and analyzed. Specifically, this systematic review examines the advantages and limitations of AR use in Special Education, the AR platforms and tools used in learning scenarios and the different types of students with special educational needs. © 2022 ACM.

关键词： Students

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Exploiting time-series analysis to predict customers' behavioural dynamics in social networks 13

Exploiting time-series analysis to predict customers' behavi...

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13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022

作者： Rompolas, Gerasimos University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

In the recent years, companies have focused on ways to take advantage of the rich and endless data that flow from the social media. The effective exploitation of such data has a crucial role in businesses' growth and development. Businesses in order to survive in a highly competitive market need to analyze and understand customers' needs and behaviours. This research work addresses the problem of predicting the behavioural dynamics of the customers regarding a brand name, using time-series analytics in social media. While in the existing literature many researchers have focused on studying the individual analytics, in this work we propose a more coarse-grained approach that analyses the overall behavioural dynamics in social networks. In particular, a data mining model is being presented, which exploits users' linguistic and emotional characteristics in social networks, in order to predict the future collective behavioural trends. The purpose of this work is to provide an efficient and automated tool, that will enable businesses to predict the relationships with their customers. Thus, businesses will be able to strengthen and reestablish their bonds with their customers, through appropriate social media marketing campaigns in a timely manner. © 2022 IEEE.

关键词： Data mining

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Federated Learning Models for Intrusion Detection in Industrial IoT Networks 7

Federated Learning Models for Intrusion Detection in Industr...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Prasad, Sanjana Sharma, Ishu Rajendraprasad, Deepashree HKBK College of Engineering Department of Electronics and Communication Engineering Bangalore India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India Boston University Department of Health Informatics BostonMA United States

ISBN: (纸本)9798350372816

The Industrial Internet of Things has emerged as an essential tool for building Industry 4.0 and Industry 5.0 where timely information can be retrieved from different scenarios. These devices are highly vulnerable to cyberattacks as heterogeneous types of devices can be used in the infrastructure that may or may not be equipped with standardized security protocols. Artificial intelligence-based methodologies can effectively identify these types of attacks on a prior basis for taking mitigation action. This method raises concerns about data privacy as building a machine learning-based method requires the sharing of network data that can reveal the actual information of industries. The proposed Federated Learning based framework handles this concern by preserving each device's critical data by utilizing the benefits of the decentralized model aggregation. This research work presents the comparison of the proposed framework on the CICIoT2023 dataset with federated averaging and federated proximal techniques for achieving a global model. The performance evaluation of these two aggregation techniques is performed based on metrics of accuracy, loss, precision and recall. The results prove that the federated proximal method achieves higher accuracy in comparison to the federated averaging method. © 2024 IEEE.

关键词： Network intrusion

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Intelligent Modeling Method to Optimal Predictive Model for Metal Powder Injection Molding Gears Using Fuzzy-Logic-Based Multi Objective Design

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International Journal of Fuzzy Systems 2023年第6期25卷 2338-2355页

作者： Huang, Wei-Tai Kung, Chien-Yu Chou, Jyh-Horng Department of Mechanical Engineering National Pingtung University of Science and Technology Pingtung Taiwan Department of Mechanical and Computer-Aided Engineering Feng Chia University Taichung407 Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung807 Taiwan

This study will use intelligent modeling to optimize the metal powder injection molding gear process. The robust process combined with fuzzy theory would be used to find process parameters for optimizing process single and multiple quality characteristics. And establish a reverse transfer neural network and adaptive neuro-fuzzy system prediction model. The model hyperparameter structure will be optimized during the modeling process to improve accuracy. The industry can use it to reduce process uncertainty, cost, and extremely a process innovation. The experimental results are single-objective optimization. After optimizing the parameters, the process target displacement, volume shrinkage rate, and powder concentration, compared with the manufacturer's original process, the improvements are increased by 5.79%, 5.66%, and 1.57%. Optimizing multiple quality characteristics has risen by 5.37%, 5.66%, and 0.31%. In the prediction model building part, after the optimization experiment of hyperparameter setting, the accuracy of the backward pass neural network model can reach 96.99%;the accuracy of the adaptive neuro-fuzzy system model can reach 97.45%. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.

关键词： Injection molding

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Deploying YOLOV10 for Affordable Real-Time Handgun Detection 22

Deploying YOLOV10 for Affordable Real-Time Handgun Detection

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22nd IEEE International Symposium on Intelligent Systems and informatics, SISY 2024

作者： Zigulic, Nikola Glucina, Matko Frank, Domagoj Lorencin, Ivan Sverko, Zoran Matika, Dario University of Rijeka Faculty of Engineering Rijeka Croatia Istrian University of Applied Sciences Department of Engineering Pula Croatia University North Department of Computer Science and Informatics Koprivnica Croatia Juraj Dobrila University of Pula Faculty of Informatics Pula Croatia University North Department of Electrical engineering Varaždin Croatia

ISBN: (纸本)9798350385601

The development of artificial intelligence is one of the most significant technological innovations that contributes to humanity with its characteristics and facilitates, secures, and improves everyday life. However the challenge arises when the programmer-engineer learns the algorithm of artificial intelligence to perform the proposed task, that is, the challenge lies in the issue of available hardware resources. Artificial intelligence algorithms perform many human-impossible tasks, such as detection and counting, i.e. calculating the interrelationships of individual objects, segmentation of tumors and other malignant diseases, i.e. tissues, classification of specific states of classes of a scene, and many other similar technologies. This research paper examines the possibility of implementing the You Only Look Once algorithm of the tenth generation on certain devices such as an affordable Raspberry Pi, and will discuss the advantages and disadvantages of changes in detection parameters, i.e. inferences to the applied model. In addition, a mock-up of the device will be shown, which will serve to provide timely information about criminals and suspicious persons who possess firearms in different situations, such as normal weather conditions in a populated place or in shops where petty robberies are frequent. The testing will be done using recorded videos in real-time scenarios. Finally, real-time inference or detection in real-time will be tested and the actions that Raspberry will perform will be simulated. The results indicate that the optimal model achieved a precision of 0.938, recall of 9.863, mAP50 of 0.91, and mAP50-95 of 0.739. This was achieved using an image size of 640, IOU threshold of 0.7, confidence threshold of 0.6, and training for 600 iterations with the Stochastic Gradient Descent optimizer, without augmentations, and employing the ONNX inference format. © 2024 IEEE.

关键词： Stochastic systems

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RequestAtlas: Supporting the Slow and Iterative Process of Requesting Public Records

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Proceedings of the ACM on Human-computer Interaction 2025年第2期9卷 1-35页

作者： Warren, Rachel B. Pickoff-White, Lisa Parameswaran, Aditya G. Salehi, Niloufar University of California Irvine School of Informatics & Computer Sciences IrvineCA United States KQED San Francisco United States University of California Berkeley Department of Electrical Engineering and Computer Sciences Berkeley United States University of California Berkeley School of Information BerkeleyCA United States

Public records requests are a central mechanism for government transparency. In practice, they are slow, complex processes that require analyzing large amounts of messy, unstructured data. In this paper, we introduce RequestAtlas, a system that helps investigative journalists review large quantities of unstructured data that result from submitting many public records requests. RequestAtlas was developed through a yearlong participatory design collaboration with the California Reporting Project (CRP), a journalistic collective researching police use of force and police misconduct in California. RequestAtlas helps journalists evaluate the results of public records requests for completeness and negotiate with agencies for additional information. RequestAtlas has had significant real-world impact. It has been deployed for more than a year to identify missing data in response to public records requests and to facilitate negotiation with public records request officers. Through the process of designing and observing the use of RequestAtlas, we explore the technical challenges associated with the public records request process and the design needs of investigative journalists more generally. We argue that public records requests represent an instance of an adversarial technical relationship in which two entities engage in a prolonged, iterative, often adversarial exchange of information. Technologists can support information-gathering efforts within these adversarial technical relationships by building flexible local solutions that help both entities account for the state of the ongoing information exchange. Additionally, we offer insights on ways to design applications that can assist investigative journalists in the inevitably significant data cleaning phase of processing large documents while supporting journalistic norms of verification and human review. Finally, we reflect on the ways that this participatory design process, despite its success, lays bare some of the l

关键词： adversarial technical relationships participatory design public records requests

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An Explainable Artificial Intelligence model in the assessment of Brain MRI Lesions in Multiple Sclerosis using Amplitude Modulation - Frequency Modulation multi-scale feature sets 24

An Explainable Artificial Intelligence model in the assessme...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Nicolaou, Andria Kakas, Antonis Pattichis, Constantinos S. Pattichis, Marios S. Fotso, Kevin Loizou, Christos P. Pantzaris, Marios Department of Computer Science University of Cyprus Nicosia Cyprus Department of Electrical Engineering and Computer Engineering University of New Mexico United States Department of Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Cyprus Institute of Neurology and Genetics Nicosia Cyprus

ISBN: (纸本)9798350339598

The objective of this study was to implement an explainable artificial intelligence (AI) model with embedded rules to assess Multiple Sclerosis (MS) disease evolution based on brain Magnetic Resonance Imaging (MRI) multi-scale lesion evaluation. Amplitude Modulation-Frequency Modulation (AM-FM) features were extracted from manually segmented brain MS lesions obtained using MRI and were labeled with the Expanded Disability Status Scale (EDSS). Machine learning models were used to classify the MS subjects with a benign course of the disease and subjects with advanced accumulating disability. Rules were extracted from the selected model with high accuracy and then were modified to perform argumentation-based reasoning. It is demonstrated that the proposed explainable AI modeling can distinguish MS subjects and give meaningful information to track the progression of the disease. Future research will examine more subjects and add new feature sets and models. © 2023 IEEE.

关键词： Frequency modulation

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