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14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

作者： Ucer, Serkan Ozyer, Tansel Alhajj, Reda Ankara Turkey Ankara Medipol University Department of Computer Engineering Ankara Turkey Istanbul Medipol University Department of Computer Engineering Istanbul Turkey University of Southern Denmark Department of Health Informatics Odense Denmark

ISBN: (纸本)9781665456616

In this research, we introduce a model to detect inconsistent & anomalous samples in tabular labeled datasets which are used in machine learning classification tasks, frequently. Our model, abbreviated as the ClaCO (Classes vs. Communities: SNA for Outlier Detection), first converts tabular data with labels into an attributed and labeled undirected network graph. Following the enrichment of the graph, it analyses the edge structure of the individual egonets, in terms of the class and community belongings, by introducing a new SNA metric named as 'the Consistency Score of a Node-CSoN'. Through an exhaustive analysis of the ego network of a node, CSoN tries to exhibit consistency of a node by examining the similarity of its immediate neighbors in terms of shared class and/or shared community belongings. To prove the efficiency of the proposed ClaCO, we employed it as a subsidiary method for detecting anomalous samples in the train part in the traditional ML classification task. With the help of this new consistency score, the least CSoN scored set of nodes flagged as outliers and removed from the training dataset, and remaining part fed into the ML model to see the effect on classification performance with the 'whole' dataset through competing outlier detection methods. We have shown this outlier detection model as an efficient method since it improves classification performance both on the whole dataset and reduced datasets with competing outlier detection methods, over several known both real-life and synthetic datasets. © 2022 IEEE.

关键词： Anomaly detection

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Diabetes Classification Problem with CatBoost Method and Optuna Gradient Boosting Optimization 6

Diabetes Classification Problem with CatBoost Method and Opt...

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

作者： Kurniawan, Safar Dwi Purwono, Purwono Ma'arif, Alfian Suwarno, Iswanto Politeknik Harapan Bersama Department of Computer Engineering Tegal Indonesia Universitas Harapan Bangsa Department of Informatics Purwokerto Indonesia Universitas Ahmad Dahlan Department of Electrical Engineering Yogyakarta Indonesia Universitas Muhammadiyah Yogyakarta Department of Engineer Professional Program Yogyakarta Indonesia

ISBN: (纸本)9798350315639

Diabetes mellitus is a dangerous global epidemic that can cause kidney failure, heart attack, blindness, and death. The main cause of this disease is too high blood sugar levels due to the irrelevant work of pancreatic beta cells. Rapid computer technology advances and implementations based on artificial intelligence have penetrated various fields, especially the health sector. Data mining and machine learning are technologies that continue to grow, are reliable, and can be used as research support tools in the medical field. This technology is considered capable of helping to automate diabetes prediction. Based on related research, our contribution to this research is to utilize the CatBoost method with Optuna optimization. In the data preprocessing stage, all 768 data sets have gone through removing outliers, normalizing features to reducing dimensions. The dataset is divided into 85\% composition for training data and 15\% for test data. At the feature selection stage with Correlation Pearson's, we found that Glucose correlated with diabetes outcome or disease because it had a value range of 0.47. The results' accuracy is considered less than optimal because it only produces an accuracy value below 80%, even after optimization with Optuna. It can be seen that there was only an increase of 7\% after optimizing with Optuna. © 2023 IEEE.

关键词： Data mining

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Design and Implementation of WebXR Health Learning Module Application 24

Design and Implementation of WebXR Health Learning Module Ap...

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24th International Electronics Symposium, IES 2022

作者： Hanfati, Kirana Sukaridhoto, Sritrusta Basuki, Dwi Kurnia Budiarti, Rizqi Putri Nourma Fajrianti, Evianita Dewi Hafidz, Ilham Achmad Al Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Indonesia Universitas Nahdlatul Ulama Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

The Corona-Virus Disease Pandemic of 2019 (COVID-19) has impacted every part of our lives, including schooling. Measures to prevent virus transmission have also been taken, such as the Work From Home policy. It caused various problems in understanding the lesson, especially for medical students, because most medical schools have a lot of practical lessons. Unfortunately, the practical lesson can not be done in this situation. To keep clinical learning on track in the face of a pandemic, the Environment must adjust to better train future clinicians. The authors of this work develop an immersive platform of medical learning to aid medical education in the clinical evaluation future and competency development in the healthcare education field via virtual education via a web-based extended reality (XR) experience (WebXR). This WebXR provides standard operating procedures for doing intramuscular injection and has various features like creating an account to access the Environment of intramuscular injection, giving the explanation of what VR, AR, and MR are, and also explaining more about intramuscular injection based on medical module handbooks. The authors decided to interact with the object using pointing, clicking, and dragging. This platform is a multi-cross XR platform which means it can be run on mobile VR devices, PCVR, android as AR devices, Magic Leap 1, and Hololens 2 as MR devices, so the WebXR will be a flexible web that can be accessed with various devices. Therefore, it gives advantages because of the flexibility. The authors evaluated the system using the PIECES framework and some questionnaires to give a better result. © 2022 IEEE.

关键词： Medical education

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Action Localization and Recognition through Unsupervised I3D and TSN 3

Action Localization and Recognition through Unsupervised I3D...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Umran, Munzir Muchtar, Kahlil Abidin, Taufik Fuadi Arnia, Fitri Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Universitas Syiah Kuala Faculty of Mathematics and Natural Sciences Department of Informatics Banda Aceh Indonesia

ISBN: (纸本)9798350321487

Since CCTV technology was widely introduced as a video surveillance system, it has become very popular due to increased security concerns. However, detecting suspicious behavior is still done manually by humans, which is inefficient in terms of accuracy and manpower (prone to distraction and fatigue). Many studies use the entire frame of a video to recognize human action, whereas in this study first localize the area of object movement using the unsupervised inflated 3D (I3D) network which produces several frames that have been determined as the region of interest (ROI) from the total frames in the video. Then the collection of ROI frames is generated into a video which will then be classified to recognize human action through the Temporal Segment Networks (TSN). For efficiency purposes, the proposed pipeline can be run separately in the edge (for localization), and cloud environment (for recognition), respectively. The results show that there are 54 classes of ROI video data sets that have an average human action recognition prediction similarity rate above 84.6% which is the average of the prediction similarity rate of the entire ROI video data. In terms of prediction accuracy, 37 classes of data (36.63%) are better than the original class, including 25 classes of data (24.75%) above the threshold, and the processing speed of classification with ROI video data is on average 5 times faster than full-frame video data. © 2023 IEEE.

关键词： Forecasting

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Questionable Fairness in Federated Learning 8

Questionable Fairness in Federated Learning

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8th International Conference on Data Science and Machine Learning Applications, CDMA 2025

作者： Alharbi, Raed Nguyen, Truc Yousefzadeh, Nooshin Aljohani, Ahmed National Renewable Energy Laboratory Computatioual Science Center GoldenCO United States University of Florida Computer and Information Science and Engineering Department GainesvilleFL United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia University of North Texas Computer Science and Engineering Denton United States

ISBN: (纸本)9798331539696

Federated learning (FL) has emerged as a powerful framework for training deep learning models across numerous distributed clients, where a central server distributes and aggregates model updates without accessing clients' raw data, ensuring privacy. However, this decentralized approach introduces significant challenges regarding fairness. Existing research shows that non-i.i.d. data distributions among clients can lead to biased global models, particularly affecting decisions based on sensitive attributes. This paper aims to investigate these fairness issues rigorously by analyzing how disparities in client data distribution impact model fairness. We conduct experiments using various models and datasets to evaluate and mitigate biases. We also propose novel data partitioning strategies to reflect real-world client data distributions accurately. Our finding shows that FL is highly vulnerable to the fairness problem due to the Non-IID property. The experiment's code can be found here: https://***/raed19/Questionable-Fairness-in-Federated-Learning. © 2025 IEEE.

关键词： Federated learning

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Evaluation of Convolutional Neural Network for Automatic Caries Detection in Digital Radiograph Panoramic on Small Dataset

Evaluation of Convolutional Neural Network for Automatic Car...

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2022 International Conference on Data and Software engineering, ICoDSE 2022

作者： Fariza, Arna Asmara, Rengga Rojaby, Muhammad Oktavian Fajar Astuti, Eha Renwi Putra, Ramadhan Hardani Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Universitas Airlangga Department of Dentistry Surabaya Indonesia

ISBN: (数字)9798350397055

ISBN: (纸本)9798350397055

Dental caries or tooth decay is damage to the hard tissues of the teeth that can occur in the enamel, dentin, and cementum areas. Panoramic radiography is a screening tool for tactile or visual examination of the oral cavity which is useful for further diagnosis and treatment. The process of segmentation of panoramic radiographs is a difficult process because there is no homogeneity between panoramic images with one another. Noise levels, vertebral column images, and low contrast are the main challenges in image processing. This study evaluates CNN to detect caries automatically on panoramic radiographs on a small dataset. The dataset consisted of manually cropped maxillary and mandibular premolars and molars. An augmentation strategy consisting of horizontal flip, vertical flip, and affine transformation is used to produce a wider variety of images. This study compares the architecture of non-pretrained and pretrained models consisting of 3-layer CNN, 3-layer CNN with batch normalization, ResNet18, and ResNeXt50 32×4d. Evaluation was carried out on 400 training data and 76 testing data. Combination of augmentation strategies and pre-trained ResNet18 and ResNeXt50 32×4d achieves high accuracy compared to other models. © 2022 IEEE.

关键词： Image segmentation

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Motor Imagery Approach for BCI Game Development 7

Motor Imagery Approach for BCI Game Development

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7th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Prapas, Georgios Glavas, Kosmas Tzallas, Alexandros T. Tzimourta, Katerina D. Giannakeas, Nikolaos Tsipouras, Markos G. University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece University of Ioannina Department of Informatics and Telecommunication Arta Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

Brain-computer interface (BCI) is a rapidly growing field with various applications in many domains such as medical, gaming and lifestyle. This paper presents a 3D non-invasive BCI game. Muse 2 headband is used for acquiring electroencephalogram (EEG) data and OpenViBE platform for processing the raw signals and classification. The game is developed in Unity game Engine. Several subjects are included in the study and EEG signals are recorded for three different mental states i.e. left and right Motor Imagery and eye blink, before playing the game for ten times, aiming to collect coins. Average classification result is 94.86% and average coins collected from the users is 30.8 out of 50 coins. Furthermore, longer periods of playing the game leads to increased control over the game. © 2022 IEEE.

关键词： Electroencephalography

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Pseudo-Random Generator based on a Photonic Neuromorphic Physical Unclonable Function

arXiv

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arXiv 2024年

作者： Dermanis, Dimitris Rizomiliotis, Panagiotis Bogris, Adonis Mesaritakis, Charis The department of Information and Communication Systems Engineering University of the Aegean Greece The department of Informatics and Computer Engineering University of West Attica Greece The department of Informatics and Telematics Harokopio University Greece

In this work we provide numerical results concerning a silicon-on-insulator photonic neuromorphic circuit configured as a physical unclonable function. The proposed scheme is enhanced with the capability to be operated as an unconventional deterministic pseudo-random number generator, suitable for cryptographic applications that alleviates the need for key storage in non-volatile digital media. The proposed photonic neuromorphic scheme is able to offer NIST test compatible numbers with an extremely low false positive/negative probability below 10-14. The proposed scheme offers multi-functional capabilities due to the fact that it can be simultaneously used as an integrated photonic accelerator for machine-learning applications and as a hardware root of trust. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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Gray-Level Co-Occurrence Matrix and Geometric Shape for Classification of Rubber Tree Maturity 2

Gray-Level Co-Occurrence Matrix and Geometric Shape for Clas...

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2nd International Seminar on Machine Learning, Optimization, and Data Science, ISMODE 2022

作者： Saputra, Wanvy Arifha Ahyati, Inayatul Ulya Yunanto, Andhik Ampuh Noor, Syamsudin Asyikin, Arifin Noor Permadi, Dimas Fanny Hebrasianto Politeknik Negeri Banjarmasin Department of Informatics Engineering Banjarmasin Indonesia Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Politeknik Negeri Banjarmasin Department of Electrical Engineering Banjarmasin Indonesia Politeknik Negeri Banjarmasin Department of Smart Cities Information System Banjarmasin Indonesia Institut Teknologi Telkom Purwokerto Department of Informatics Engineering Purwokerto Indonesia

ISBN: (纸本)9781665455640

Rubber trees can be said to be mature by having a trunk circumference of more than 45 cm at the height of 130 cm from the ground. It influences the use of digital imagery to determine the classification of 'mature rubber trees' and 'immature rubber trees'. The challenge in the image of rubber trees is that they have similar colour characteristics between tree trunks and the ground, and multi-object rubber trees in one picture. We propose a method using the gray-level co-occurrence matrix (GLCM) and geometric shape for the classification of Rubber Tree Maturity. GLCM is used to measure neighbouring pixels with grey intensity, distance, and angle in solving the first characteristic problem. Geometric shapes used to solve the second characteristic problem with the help of determining a region of interest (ROI). The research results show that the proposed method was successfully carried out with the strongest evidence on the support vector machine (SVM), namely 0.800 f1-score, 0.778 precision and 0.824 recall. © 2022 IEEE.

关键词： Support vector machines

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Computing Challenges of UAV Networks: A Comprehensive Survey

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computers, Materials & Continua 2024年第11期81卷 1999-2051页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Xianxuan Lin Farman Ali Ahmad Ali AlZubi School of Computer Science&Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science Community CollegeKing Saud UniversityRiyadh11495Saudi Arabia

Devices and networks constantly upgrade,leading to rapid technological ***-dimensional(3D)point cloud transmission plays a crucial role in aerial computing terminology,facilitating information *** network types,including sensor networks and 5G mobile networks,support this ***,Flying Ad hoc Networks(FANETs)utilize Unmanned Aerial Vehicles(UAVs)as nodes,operating in a 3D environment with Six Degrees of Freedom(6DoF).This study comprehensively surveys UAV networks,focusing on models for Light Detection and Ranging(LiDAR)3D point cloud compression/*** topics covered include autonomous navigation,challenges in video streaming infrastructure,motivations for Quality of Experience(QoE)enhancement,and avenues for future ***,the paper conducts an extensive review of UAVs,encompassing current wireless technologies,applications across various sectors,routing protocols,design considerations,security measures,blockchain applications in UAVs,contributions to healthcare systems,and integration with the Internet of Things(IoT),Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL).Furthermore,the paper thoroughly discusses the core contributions of LiDAR 3D point clouds in UAV systems and their future prediction along with mobility *** also explores the prospects of UAV systems and presents state-of-the-art solutions.

关键词： Autonomous vehicles UAV systems UAV transmission UAV in healthcare future of UAV system security measures

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