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7th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

作者： Lytvynenko, Volodtmyr Olszewski, Serge Osypenko, Volodymyr Lurie, Iryna Demchenko, Violeta Dontsova, Dariia Kherson National Technical University Department of Informatics and Computer Science Kherson Ukraine Taras Shevchenko National University Department of Radio Engineering and Electronic Systems Kyiv Ukraine Kyiv National University of Technology and Design Department of Computer Science Kyiv Ukraine State Institution Kundiiev Institute of Occupational Health National Academy of Medical Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798331534141

This paper presents a comparative analysis of the application of Variational Autoencoders (VAE) and Wasserstein Generative Adversarial Networks (WGAN) for detecting anomalous images. The performance evaluation of the models is conducted using metrics such as AUC, Precision, Recall, and F1-Score. The results indicate that VAEs provide high Recall in anomaly detection but with low Precision, whereas WGANs demonstrate more balanced results with fewer false positives. Recommendations are proposed for selecting an appropriate model depending on the specific task requirements and the need to minimize false alarms or achieve high detection completeness. © 2024 IEEE.

关键词： Generative adversarial networks

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Machine Learning based Network Security in Healthcare System

Machine Learning based Network Security in Healthcare System

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2022 International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2022

作者： Aweke, Desalegn Genale, Assefa Senbato Sundaram, B. Barani Pandey, Amit Janga, Vijaykumar Karthika, P. College of Informatics Bule Hora University Bule hora Ethiopia Bule Hora University Department of Computer Science Bule hora Ethiopia College of Informatics Bule Hora University Computer Science Department Bule hora Ethiopia Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (纸本)9781665478847

The world is filled with exciting technologies and ideas;scientists build machines to avoid human intervention in completing work. It is highly challenging to complete the task without the Machine Learning (ML) Technology intervention. With the technological development, certain processes or consultations are performed with the aid of doctors available around the world. In this scenario, it could be noticed that health care is one of the world's expected domains that require the most incredible attention in data security while performing data transfer. Nodes in the network are considered based on the weakest link to overcome the cyber attacker's issues. Besides building the software for data storage, a better mechanism has to be incorporated to provide security to the stored data. This process is a delicate task for every network engineer. This paper will explain such concepts related to health prediction and health care by building the most robust network security systems. Finally, the discussion would cross over human-looping systems, which act as one of the common problems that are affected mentally for a person. According to the results, the suggested model achieved the accuracy of 98.89%, that is 4.76% greater than the previous model. © 2022 IEEE.

关键词： Health care

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Impact of Channel Numbers and Training Epochs on U-Net's Polyp Segmentation Performance 7

Impact of Channel Numbers and Training Epochs on U-Net's Pol...

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7th International Conference on Signal Processing and Information Security, ICSPIS 2024

作者： Ji, Zhanlin Ganchev, Ivan Zhejiang A&F University College of Mathematics and Computer Science Hangzhou China University of Limerick Limerick Telecommunications Research Centre Ireland University of Plovdiv 'Paisii Hilendarski' Plovdiv Bulgaria Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (纸本)9798350368673

Medical image segmentation plays an extremely crucial role in the modern field of medicine. It is a process that involves the precise delineation and labeling of medical images through computer technology. However, during the training of segmentation models, insufficient computational power is often a challenge. Despite having superior models, the lack of hardware resources may hinder the widespread implementation of these models. Therefore, in this paper, the U-Net model is used as a case study to investigate the impact of the number of channels and training epochs on the segmentation performance. The experimental results show that by reducing the number of channels in the U-Net model and increasing the number of training epochs, the model's parameter counts can be successfully decreased, while also achieving satisfactory segmentation results. © 2024 IEEE.

关键词： Image segmentation

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A robust multi-utility neural network technique integrated with discriminators for bone health decisioning to facilitate clinical-driven processes

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Research on Biomedical Engineering 2023年第1期39卷 139-157页

作者： Ramaraj, Kottaimalai Amiya, Gautam Rajasekaran, Murugan Pallikonda Govindaraj, Vishnuvarthanan Vasudevan, Muneeswaran Thirumurugan, M. Zhang, Yu-Dong Abdullah, S. Sheik Thiyagarajan, Arunprasath Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Biomedical Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Consultant Orthopaedic Surgeon MGR Medical University Tamilnadu Chennai India School of Informatics University of Leicester LeicesterLE1 7RH United Kingdom

Purpose: Osteoporosis (OP) is a malformation of the bones caused by the loss of bone mass and its mineral density, and also deterioration in bone quality or structures, causing an increased risk of fractures. Apart from bone cracks, damage to hip, spine vertebrae and wrist are the most prevalent. Method: This work proposes a hybrid strategy to categorise the normal and abnormal bone mineral density (BMD) values obtained from 140 patients. Owing to the smaller sample and the large number of variables in a high-dimensional data classification issue, classical linear discriminant analysis (LDA) performs poorly, resulting in the instability and singularity of the sample covariance matrix. To suppress this, a normalised radial basis function neural network (NRBFNN) coupled with a modified version of LDA (MLDA) is being proposed through this paper for BMD evaluations. In this study, we propose a novel modified version of LDA (MLDA) based on a robust estimator for high-dimensional covariance matrices, and has been proved to be asymptotically highly reliable than the sample covariance matrix. Results: Instead of employing un-normalised RBF, NRBFNN is used for BMD classification. The suggested hybrid algorithm MLDA-NRBFNN achieved a classification accuracy of 97.5% on comparing with ANN, PAC-ANN and RBFN. Conclusion: With the intervention of the proposed method, the doctors could tend to have a precursor to assay the patients with varied bone ailments and to understand the recovery impact of knee replacement as well. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.

关键词： Deterioration

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CentIER:Accurate centromere identification for plant genomes

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Plant Communications 2024年第10期5卷 12-15页

作者： Dong Xu Jinbao Yang Huaming Wen Wenle Feng Xiaohui Zhang Xingqi Hui Junyang Yue Yun Xu Fei Chen Weihua Pan Shenzhen Branch Guangdong Laboratory of Lingnan Modern AgricultureGenome Analysis Laboratory of the Ministry of Agriculture and Rural AffairsAgricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhen 518120China Rubber Research Institute Chinese Academy of Tropical Agricultural ScienceHaikouHainan 571101China School of Computer Science and Technology University of Science and Technology of ChinaHefeiAnhui 230026China College of Informatics Huazhong Agricultural UniversityWuhanHubei 430070China School of Horticulture Anhui Agricultural UniversityHefeiAnhui 230026China Nan Fan College Hainan UniversitySanyaHainan 572000China

Dear Editor,Centromeres,the basis for cell division,offer essential insights into cell dynamics,genome stability,and evolutionary processes(McKinley and Cheeseman,2016).Because of ultra-high complexity,high-quality sequences of centromeric regions have long been difficult to obtain,hindering studies of centromere function,evolution,and variation.

关键词： hinder insight stability

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RGB-guided hyperspectral image super-resolution with deep progressive learning

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 679-694页

作者： Tao Zhang Ying Fu Liwei Huang Siyuan Li Shaodi You Chenggang Yan School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Satellite Information Intelligent Processing and Application Research Laboratory Beijing Institute of Remote SensingBeijingChina Key Laboratory of Spectral Imaging Technology Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of SciencesXi'anChina Institute of Informatics University of AmsterdamAmsterdamNetherlands School of Communication Engineering Hangzhou Dianzi UniversityHangzhouChina

Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal ***,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image *** approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted ***,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing *** this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image ***-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image ***,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing *** proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial *** has a good general-isation capability,especially for blind HS image *** experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

关键词： computer vision deep neural networks image processing image resolution unsupervised learning

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Analysis of UAVs' Coverage Efficiency in Flight and Static Mode 4

Analysis of UAVs' Coverage Efficiency in Flight and Static M...

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4th International science and Technology Conference "Modern Network Technologies", MoNeTec 2022

作者： Platonova, Anna Medvedeva, Ekaterina Gaidamaka, Yuliya Applied Probability and Informatics Department Moscow117198 Russia Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Moscow119333 Russia

ISBN: (数字)9781665472463

ISBN: (纸本)9781665472463

Nowadays one of the critical tasks for analyzing the usage of unmanned aerial vehicle (UAV) as base stations is the development of effective UAV control strategies while servicing customers one the ground. The main problem is states within ultimate battery capacity, which limits the UAV's flight's duration and battery lifetime. This paper presents a scenario for placing UAVs on stationary objects, such as roofs, walls or balconies of buildings, in the process of providing communication services. Such a strategy provides drones to extend their autonomous work during the flight in the customer service area, as the main source of power consumption, the engine, can be turned off for long periods of time after arriving in a subscriber's service area. The paper compares the proposed scenario with the standard flight mode of UAVs. © 2022 IEEE.

关键词： Base stations

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Comparison Between Novel Machine and Deep Learning-based Skin Tumor Recognition Techniques

Comparison Between Novel Machine and Deep Learning-based Ski...

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E-Health and Bioengineering Conference (EHB)

作者： Tudor Barbu Lucian Murgu Institute of Computer Science Romanian Academy - Iasi Branch Iasi Romania Doctoral School of Mathematical Sciences and Informatics of SCOSAAR Romanian Academy Bucharest Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

This research work introduces and compares some novel automatic skin tumor classification frameworks. The first proposed technique recognizes successfully the skin tumors as either benign or malignant tumors, by applying a deep learning classification model. So, a Convolutional Neural Network (CNN)-based classifier is built and then trained and validated successfully on a skin tumor image dataset. Next, a machine learning - based tumor recognition approach is proposed here. It is composed of a fractal analysis-based feature extraction scheme and a Random Forest (RF) - based classification algorithm. Both techniques achieve high recognition rates. The performed method comparison illustrates that machine learning classification models can obtain comparable good results with deep learning-based schemes when applied on some powerful medical image feature vectors.

关键词： Image recognition Malignant tumors Feature extraction Skin Fractals Vectors Classification algorithms Random forests Tumors Skin cancer

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Exploring the Privacy and Security Challenges Faced by Migrant Domestic Workers in Chinese Smart Homes 25

Exploring the Privacy and Security Challenges Faced by Migra...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： He, Shijing Zhan, Xiao Lei, Yaxiong Liu, Yueyan Abu-Salma, Ruba Such, Jose Informatics King's College London London United Kingdom School of Computer Science University of St Andrews St Andrews United Kingdom School of Design and Innovation China Academy of Art Hangzhou China

ISBN: (纸本)9798400713941

The growing use of smart home devices poses considerable privacy and security challenges, especially for individuals like migrant domestic workers (MDWs) who may be surveilled by their employers. This paper explores the privacy and security challenges experienced by MDWs in multi-user smart homes through in-depth semi-structured interviews with 26 MDWs and 5 staff members of agencies that recruit and/or train domestic workers in China. Our findings reveal that the relationships between MDWs, their employers, and agencies are characterized by significant power imbalances, influenced by Chinese cultural and social factors (such as Confucianism and collectivism), as well as legal ones. Furthermore, the widespread and normalized use of surveillance technologies in China, particularly in public spaces, exacerbates these power imbalances, reinforcing a sense of constant monitoring and control. Drawing on our findings, we provide recommendations to domestic worker agencies and policymakers to address the privacy and security challenges facing MDWs in Chinese smart homes. © 2025 Copyright held by the owner/author(s).

关键词： Internet of Things (IoT) migrant domestic workers multi-user privacy smart homes

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STEM Educational Kit for Assistance of Individuals with Special Needs

STEM Educational Kit for Assistance of Individuals with Spec...

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2023 International Conference Automatics and informatics, ICAI 2023

作者： Todorov, Todor Vela, Pajtim St. Cyril and St. Methodius University of Veliko Tamovo Bulgaria Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia Bulgaria University of Kadri Zeka Faculty of Computer Science Kosovo Gjilan Serbia

ISBN: (纸本)9798350312911

The paper presents a practical application of microcontrollers and sensors to enhance learning system and create equipment that can help individuals with special needs. Throughout this study, 26 high school students volunteered as research participants. To achieve satisfactory results, several interviews were conducted to identify some of the service needs for people with special needs. As a result, a prototype was created, in which several components were integrated to finish the equipment. Finally, is made an analysis of test results about the device performance and satisfaction of the students and teachers involved in the process. © 2023 IEEE.

关键词： Learning systems

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