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Proof of Activity Protocol for IoMT Data Security

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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Ontology population using CNN model: Application to COVID-19 diagnosis 6

Ontology population using CNN model: Application to COVID-19...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Mellal, Nacima Saighi, Asma Institute of Applied Science & Technology University of Oum Research Laboratory of Artificial Intelligence And Autonomous Objects Dept. Networks And Telecommunication El Bouaghi Algeria University of Oum Research Laboratory of Artificial Intelligence And Autonomous Objects Dept. Computer Science El Bouaghi Algeria

ISBN: (纸本)9798350350265

AI models and especially deep learning models have found applications across various medical domains. While many studies focus on using electronic health records (EHRs) data to train AI models, implementing the reverse process poses several challenges. Also, the heterogeneous nature of the used data in EHRs, such as diagnoses, clinical reports, test results, prescriptions, and medical images, makes their interpretation difficult for the practitioners which require a unified understanding. Medical ontologies are useful to improve data interpretability and semantics. In this paper, we aim to create a synergy between ontologies and deep learning techniques to enhance efficiency, data understanding, and provide healthcare quality. We propose a system in which our developed ontology is populated automatically using the predictions of an AI model. We have developed and trained a CNN model to aid in the COVID-19 diagnosis. The initial obtained results are promising with an accuracy of 98%. © 2024 IEEE.

关键词： COVID-19

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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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Interpreting Black-box Machine Learning Models for High Dimensional Datasets 10

Interpreting Black-box Machine Learning Models for High Dime...

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10th IEEE International Conference on Data science and Advanced Analytics, DSAA 2023

作者： Karim, Md. Rezaul Shajalal, Md Gras, Alexander Dohmen, Till Chala, Sisay Adugna Boden, Alexander Beecks, Christian Decker, Stefan Rwth Aachen University Computer Science 5 - Information Systems and Databases Germany Fraunhofer - Institute for Applied Information Technology Fit Germany University of Siegen Germany Bonn-Rhein-Sieg University of Applied Sciences Germany University of Hagen Germany

ISBN: (纸本)9798350345032

Many datasets are of increasingly high dimension- ality, where a large number of features could be irrelevant to the learning task. The inclusion of such features would not only introduce unwanted noise but also increase computational complexity. Deep neural networks (DNNs) outperform machine learning (ML) algorithms in a variety of applications due to their effectiveness in modelling complex problems and handling high-dimensional datasets. However, due to non-linearity and higher-order feature interactions, DNN models are unavoidably opaque, making them black-box methods. In contrast, an interpretable model can identify statistically significant features and explain the way they affect the model's outcome. In this paper, we propose a novel method to improve the interpretability of blackbox models in the case of high-dimensional datasets. First, a black-box model is trained on full feature space that learns useful embeddings on which the classification is performed. To decompose the inner principles of the black-box and to identify top-k important features (global explainability), probing and perturbing techniques are applied. An interpretable surrogate model is then trained on top-k feature space to approximate the black-box. Finally, decision rules and counterfactuals are derived from the surrogate to provide local decisions. Our approach outperforms tabular learners, e.g., TabNet and XGboost, and SHAP-based interpretability techniques, when tested on a number of datasets having dimensionality between 54 and 20,53111GitHub: https://***/rezacsedu/DeepExplainHidim. © 2023 IEEE.

关键词： Deep neural networks

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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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Italian-Chinese Neural Machine Translation: results and lessons learnt 23

Italian-Chinese Neural Machine Translation: results and less...

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3rd ACM Conference on Information technology for Social Good, GoodIT 2023

作者： Delnevo, Giovanni Im, Marcus Tse, Rita Lam, Chan-Tong Tang, Su-Kit Salomoni, Paola Pau, Giovanni Ghini, Vittorio Mirri, Silvia Department of Computer Science and Engineering University of Bologna Cesena Italy Faculty of Applied Sciences Macao Polytechnic University China Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Ministry of Education Macao Polytechnic University China

ISBN: (纸本)9798400701160

Today, access to the Internet provides access to various forms of knowledge like free online lecture series offered by prestigious universities, massive open online courses, films and books, and Wikipedia. In addition, it is possible to join online communities on any topic of interest, get to know people with common interests, exchange thoughts and participate in debates. To enable access to these unprecedented knowledge bases, it is crucial to be able to translate texts into any language known by users. For this reason, Machine Translation has been a very active research field for the last thirty years. In this paper, we investigate the task of Chinese-Italian translations by exploiting Neural Machine Translation approaches. We trained several deep neural networks starting from two already available datasets containing Chinese-Italian parallel corpora. Then, we compared their performance against some of the most common machine translation services freely available online. In particular, we take advantage of Microsoft Translator, Google Translate, DeepL, and ModernMT. © 2023 Owner/Author.

关键词： Neural machine translation

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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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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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Optimizing Weighted Ensemble ML IDS Using Parallelization and Batch Learning in Healthcare Networks

Optimizing Weighted Ensemble ML IDS Using Parallelization an...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Ward, Blake Gonzalez, Daniel Alt, Jake Fang, Dongfeng Xu, Shengjie The Department of Computer Science and Software Engineering College of Engineering California Polytechnic State University San Luis ObispoCA93407 United States The Department of Cyber Intelligence and Information Operations College of Applied Science and Technology University of Arizona TucsonAZ85721 United States

ISBN: (纸本)9798350350548

The increasing adoption of IoT devices in healthcare necessitates the development of lightweight and fast intrusion detection systems (IDS). This paper proposes a weighted ensemble ML IDS with parallelized batch learning to optimize runtime while maintaining ML performance. The proposed method is compared to other solutions, evaluating ML performance and runtime across three different datasets. Experimental results demonstrate that although batch learning introduces feedback delay that slightly degrades ML performance, parallel computing significantly reduces runtime. © 2024 IEEE.

关键词： Network intrusion

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Design Considerations for Hybrid Underwater Target Tracking: Integrating Particle Filters and Deep Learning for Enhanced Decision

Design Considerations for Hybrid Underwater Target Tracking:...

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Decision Aid sciences and Application (DASA), International Conference on

作者： Abdullatif Baba Basil Alothman Maha Shabon Ziad Salem Computer Science and Engineering Department Kuwait College of Science and Technology (KCST) Kuwait Computer Engineering Department University of Turkish Aeronautical Association (UTAA) Ankara Türkiye Computer Science Department Center for Applied Mathematics and Bioinformatics (CAMB) Gulf University for Science and Technology (GUST) Hawally Kuwait

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This paper delves into the key characteristics of Autonomous underwater vehicle (AUV) design, highlighting considerations such as hull structure, hydrodynamics, propulsion systems, and sensor integration. The main contribution of this paper is the tracking of underwater dynamic targets, distinguishing between reactive and deliberative tracking levels by proposing a novel hybrid tracking approach, the Multiple Model Particle Filter (MMPF), that integrates deep learning to predict dynamic target trajectory. Finally, a few simulations are delivered to demonstrate the efficiency of the proposed approach in tracking underwater maneuvering targets under different trajectory scenarios.

关键词： Deep learning Autonomous underwater vehicles Target tracking Simultaneous localization and mapping Navigation Particle filters Trajectory Safety Security Vehicle dynamics

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