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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Widzisz, Karolina Kania, Mateusz Zyla, Joanna Polański, Andrzej Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice Poland Department of Applied Informatics Silesian University of Technology Akademicka 16 Gliwice Poland Department of Data Science and Engineering Silesian University of Technology Akademicka 16 Gliwice Poland

The primary objective of this study was to test the hypothesis that the binary information on the presence or absence of gene expression can sufficiently capture the inherent heterogeneity within single-cell RNA se quencing (scRNA-seq) data. This hypothesis posits that even without detailed expression levels, valuable insights about cellular diversity can be obtained. Utilizing this method can be particularly advantageous when analyzing large datasets, a common scenario in the field of scRNA-seq. In this paper, we evaluate clustering performance and cluster separability of a variety of model-based algorithms and distance-based methods to an alyze both expression level data and threshold-encoded binarized data. We examined the performance of the Bernoulli-mixture model and Gaussian-mixture model. These were compared against traditional clustering techniques such as hierarchical clustering, K-means, and the Louvain algorithm on a range of scRNA-seq datasets. Our findings reveal that mixture models exhibit a lower dependence on the specific dataset compared to distance-based methods. Mixture models, particularly, demonstrate greater efficacy in accurately estimat ing the number of clusters present within the data. Among analyzed algorithms, the Bernoulli-mixture model stands out, outperforming distance-based approaches in several key aspects. Binary data, presence/absence of gene expression, seem to be indeed adequate to capture the heterogeneity of scRNA-seq data when clus tering with methods specifically designed for binary datasets. The implications of this finding are significant, as it opens up new possibilities for simplifying data analysis in scRNA-seq studies without compromising the accuracy of the results. © 2025 by SCITEPRESS– science and technology Publications, Lda.

关键词： Data accuracy

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Compression through extraction of learned parameters from images in de-correlated image space

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Iran Journal of computer science 2024年第2期7卷 259-277页

作者： Hasnat, Abul Halder, Santanu Bhattacharjee, Debotosh Government College of Engineering and Textile Technology West Bengal Berhampore India Government College of Engineering and Leather Technology West Bengal Kolkata India Computer Science and Engineering Jadavpur University Kolkata 700032 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove 500 03 Czech Republic

Image compression is a class of algorithms that reduces the storage space requirement for a digital image. Lossy image compression techniques achieve higher compression but the visual quality of the decompressed image is degraded many times. Decompressed images lose their visual appeal due to compression artifacts. These compression artifacts are introduced due to the quantization step of the compression phase. We developed a lossy image compression technique that works on the spatial domain and de-correlated color model. For the luminance channel compression, the modified Vector Quantization method is used. In the case of chrominance channels, a feature vector is built for each pixel using the neighborhood statistics and cluster information of the pixel. For all the pixels of the image, using these feature vectors, a training dataset is formed. For the training of an artificial neural network (ANN), a feature vector of a pixel is used as the input and its respective chrominance value is used as the target output. Two training datasets are used to train two ANNs separately—one for the Cb channel and one for the Cr channel. These two trained ANNs are stored as the compressed form for the chrominance channels. During the decompression process, first, the luminance channel is reconstructed. Later, for each chrominance channel, the respective trained ANN predicts the chrominance values for each pixel. Thus, the whole image is reconstructed. The method has been tested on the benchmark images and also color images from the UCID v.2 database. The experimental result shows that the method successfully avoids the blocking artifacts in the reconstructed images. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： JPEG JPEG2000 Lossy image compression Neighborhood Statistics PSNR SSIM Vector quantization

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Generation of synthetic data with low-dimensional features for condition monitoring utilizing Generative Adversarial Networks 26

Generation of synthetic data with low-dimensional features f...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Wagner, Fabian König, Timo Benninger, Moritz Kley, Markus Liebschner, Marcus Faculty for Electronics and Computer Science University of Applied Sciences Aalen Aalen73430 Germany Institute for Drive Technology University of Applied Sciences Aalen Aalen73430 Germany

Condition monitoring of machine elements for end-of-line quality control is an essential part of product assurance in many production areas. Therefore, the monitored machine elements are often separated in different condition classes and are classified via conventional signal processing and machine learning methods. Especially for machine learning algorithms, collection of a sufficient amount of data from each class is particularly relevant so that balanced training datasets, regarding the condition classes, are available. In reality, however, in most cases significantly more data is captured from good system states. This results in unbalanced data sets, which can be counteracted with synthetically generated data. Generative Adversarial Networks (GAN) are a suitable approach to generate synthetic measurement data. In the scope of this paper, a use case is considered, in which bearings are monitored in an end-of-line control via acoustic signals. The generation of such data requires a high computational effort. To reduce this effort, a suitable signal pre-processing method is presented, which allows the reduction of the generated feature dimension. The synthetic data based on low-dimensional features is subsequently evaluated regarding its suitability for the condition classification. It is shown that with synthetically generated data with low-dimensional features a similar classification accuracy can be achieved as with real data, making the synthetic data suitable for data augmentation in the use case. © 2022 The Authors. Published by Elsevier B.V.

关键词： Condition monitoring

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Proof of Activity Protocol for IoMT Data Security

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computer Systems science & Engineering 2023年第1期44卷 339-350页

作者： R.Rajadevi K.Venkatachalam Mehedi Masud Mohammed A.AlZain Mohamed Abouhawwash Department of Information Technology Kongu Engineering CollegeErode638060TamilnaduIndia Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The Internet of Medical Things(IoMT)is an online device that senses and transmits medical data from users to physicians within a time ***,recent years,IoMT has rapidly grown in the medicalfield to provide healthcare services without physical *** the use of sensors,IoMT applications are used in healthcare *** such applications,one of the most important factors is data security,given that its transmission over the network may cause *** data security in IoMT systems,blockchain is used due to its numerous blocks for secure data *** this study,Blockchain-assisted secure data management framework(BSDMF)and Proof of Activity(PoA)protocol using malicious code detection algorithm is used in the proposed data security for the healthcare *** main aim is to enhance the data security over the *** PoA protocol enhances high security of data from the literature *** replacing the malicious node from the block,the PoA can provide high security for medical data in the *** with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio,precision ratio,security,and efficiency and less response time for Blockchain-enabled healthcare systems.

关键词： Blockchain IoMT malicious code detection security secure data management framework data management PoA

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HTPS: Heterogeneous Transferring Prediction System for Healthcare Datasets

HTPS: Heterogeneous Transferring Prediction System for Healt...

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31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Fojcik, Marcin Cupek, Rafal National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland

ISBN: (纸本)9798350337631

Medical internet of things leads to revolutionary improvements in medical services, also known as smart healthcare. With the big healthcare data, data mining and machine learning can assist wellness management and intelligent diagnosis, and achieve the P4-medicine. However, healthcare data has high sparsity and heterogeneity. In this paper, we propose a Heterogeneous Transferring Prediction System (HTPS). Feature engineering mechanism transforms the dataset into sparse and dense feature matrices, and autoencoders in the embedding networks not only embed features but also transfer knowledge from heterogeneous datasets. Experimental results show that the proposed HTPS outperforms the benchmark systems on various prediction tasks and datasets, and ablation studies present the effectiveness of each designed mechanism. Experimental results demonstrate the negative impact of heterogeneous data on benchmark systems and the high transferability of the proposed HTPS. © 2023 IEEE.

关键词： Health care

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Resource Allocation in Edge Computing Environment Using Deterministic Policy Gradient Algorithm

Resource Allocation in Edge Computing Environment Using Dete...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： C. P. Shabariram N. Shanthi P. Priya Ponnuswamy V. Subramaniyaswamy V. Sathana Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Computer Science and Engineering School of Computing SASTRA University Thanjavur India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In recent years, the edge computing paradigm enables the movement of processing units and storage nearer to the data available locations. The mechanism completes the computation in a short span of time in minimum bandwidth. Edge ecosystem is a type of distributed computing that is sensitive to topology and geography; the Internet of Things is a prime instance of this. Rather than referring to a single technology, Edge computing refers to an architecture. This paper proposes a resource allocation methodology that will enliven the situation between users and edge servers. By creating continuous control at the edge servers to determine resource allocation, edge computing improves reaction time, provides high security with decreased risk, scalability, lowers transmission costs, and versatility (offload targets, migration bandwidth and computing resources). The Deterministic Policy Gradient, Deep learning and Quality Network concepts are combined in the proposed system. The continuous action space is achieved by a deterministic policy gradient. The experience relay includes a quality network. In the proposed system, the actor-critic network produces a single continuous action instead of resulting probability based actions. The critic-part uses Q-value from a quality network based on current status and activity. The goal of the proposed system is to develop a Deep Deterministic Policy Gradient methodology to allocate servers for mobile users with the help of the Edge computing while taking computation resources, offloading goals and migration bandwidth into consideration. The simulation result indicates that deterministic policy gradients integrated deep learning models improve the system performance compared with Game theory.

关键词： Deep learning System performance Simulation Scalability Bandwidth Topology Servers Resource management Security Edge computing

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Efficient local search based on dynamic connectivity maintenance for minimum connected dominating set

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Journal of Artificial Intelligence Research 2021年 71卷 89-119页

作者： Zhang, Xindi Li, Bohan Cai, Shaowei Wang, Yiyuan State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Computer Science and Information Technology Northeast Normal University China Key Laboratory of Applied Statistics of MOE Northeast Normal University China

The minimum connected dominating set (MCDS) problem is an important extension of the minimum dominating set problem, with wide applications, especially in wireless networks. Most previous works focused on solving MCDS problem in graphs with relatively small size, mainly due to the complexity of maintaining connectivity. This paper explores techniques for solving MCDS problem in massive real-world graphs with wide practical importance. Firstly, we propose a local greedy construction method with reasoning rule called 1hopReason. Secondly and most importantly, a hybrid dynamic connectivity maintenance method (HDC+) is designed to switch alternately between a novel fast connectivity maintenance method based on spanning tree and its previous counterpart. Thirdly, we adopt a two-level vertex selection heuristic with a newly proposed scoring function called chrono-safety to make the algorithm more considerate when selecting vertices. We design a new local search algorithm called FastCDS based on the three ideas. Experiments show that FastCDS significantly outperforms five state-of-the-art MCDS algorithms on both massive graphs and classic benchmarks. © 2021 AI Access Foundation. All rights reserved.

关键词： Local search (optimization)

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A Quick Heuristic Algorithm for Enforcing the Liveness of S3PR Petri Nets 22

A Quick Heuristic Algorithm for Enforcing the Liveness of S3...

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22nd International Symposium INFOTEH-JAHORINA, INFOTEH 2023

作者： Karatkevich, Andrei Grobelna, Iwona Agh University of Science and Technology Dept. of Applied Computer Science Kraków Poland University of Zielona Góra Autom. Control Electronics and Electr. Eng. Zielona Góra Poland

ISBN: (纸本)9781665475464

The optimal deadlock avoiding, deadlock recovery, as well as deadlock detection in Petri nets are the NP-hard problems. For this reason, heuristic algorithms for finding the approximate solutions of such problems are of a great interest. In the paper a heuristic method of deadlock recovery for the S3PR nets, which are often used for modelling of manufacturing systems, is proposed. The method is based on the idea of resetting the process instances in the S3PR nets and a heuristic for breaking the cycles in the wait-for graphs. The method does not require any time consuming operations such as construction of the reachability graph, explicit detecting of all reachable deadlocks or all elementary cycles in a wait-for graph, hence it is quick. The simple recovery transitions are added to the net providing recovery from both global and local deadlocks. © 2023 IEEE.

关键词： Petri nets

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Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

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computer Systems science & Engineering 2023年第9期46卷 2947-2961页

作者： IbrahimM.Alwayle Badriyya B.Al-onazi Mohamed K.Nour Khaled M.Alalayah Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Amal S.Mehanna Abdelwahed Motwakel Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Online reviews regarding purchasing services or products offered are the main source of users’*** gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or *** practice is called review *** the last few years,various techniques have been recommended to solve the problem of spam *** spam detection study focuses on English reviews,with a lesser interest in other *** review detection in Arabic online sources is an innovative topic despite the vast amount of data ***,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion *** presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and ***,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible ***,unigram and bigram feature extractors are *** SGRU model is employed in this study to identify and classify Arabic spam *** the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU *** experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC *** extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.

关键词： Arabic text spam reviews machine learning deep learning white shark optimizer

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Pre-clustered Generative Adversarial Network Model for Mongolian Font Style Transfer 1

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7th International Conference on Optimization, Simulation and Control, COSC 2022

作者： Barintag, Saheya Zhang, Zexing Duan, Bohuai Wang, Jinghang College of Mathematics Science Inner Mongolia Normal University Huhhot China Center for Applied Mathematics Inner Mongolia Huhhot China College of Computer Science and Technology Inner Mongolia Normal University Huhhot China College of Information Science and Technology Northeast Normal University Changchun China

ISBN: (数字)9783031412295

ISBN: (纸本)9783031412288

Font style transfer has important application value in the field of data enhancement and can be used to alleviate the problem of insufficient data in fields such as handwritten character recognition, glyph inference restoration, and ancient book restoration. The complexity of traditional Mongolian has brought many challenges to character recognition and restoration of ancient books. This paper first builds a small-scale Mongolian font style dataset and a graph cluster aggregator algorithm. Secondly, an improved conditional generative adversarial neural network model with MSE loss function is proposed, and the self-built dataset is used to train the model after image aggregation. The experimental results show that the model can learn the traditional Mongolian font style and transfer it to the same semantic text in a small amount of training, and generate images with prominent style. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Restoration

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