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IEEE International Conference on Big Data

作者： Pawel Benecki Daniel Kostrzewa Piotr Grzesik Bohdan Shubyn Jia-Hao Syu Jerry Chun-Wei Lin Vaidy Sunderam Dariusz Mrozek Department of Applied Informatics Silesian University of Technology Gliwice Poland Department of Telecommunications Lviv Polytechnic National University Lviv Ukraine Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Distributed Systems and Informatics Devices Silesian University of Technology Gliwice Poland Department of Computer Science Emory University Atlanta GA USA

Detection and prediction of failures in Automated Guided Vehicles (AGV) are essential for the uninterrupted operation of production plants. Anomaly detection is usually achieved by comparing expected measurement values with actual observations. Thus, it is crucial to predict telemetry signals properly. In this paper, we research the prediction of energy consumption using state-of-the-art Artificial Neural Networks architectures (SCINet) compared with other Recurrent Neural Network (RNN) approaches on the data streams acquired from CoBotAGV. We especially focus on the possibility of applying feature weighting. We show that it can improve prediction capabilities. We also investigate resource utilization in terms of time to fit the embedded AGV environment.

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

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Telecommunications Conference (ITC-Egypt), International

作者： Rana M. NourEldeen Rahma A. Elshshtawy Omnia A. Elgohary Fatma Alzahraa A. Wahba Marco K. Attia Zeinab B. Abo elazm Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %.

关键词： 6G mobile communication Deep learning Multiple sclerosis Accuracy Internet of Medical Things Telecommunications Convolutional neural networks

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Sectoral Cybersecurity Skills Gap: The Case of Maritime Cybersecurity Certification Training

Sectoral Cybersecurity Skills Gap: The Case of Maritime Cybe...

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IEEE International Conference on engineering, Technology and Innovation

作者： Pinelopi Kyranoudi Nineta Polemi Paresh Rathod Ricardo Gregorio Lugo Sotiris Ioannidis Paulinus Ofem Department of Informatics University of Piraeus Piraeus Greece Department of Informatics University of Piraeus Greece & trustilio BV The Netherlands Cybersecurity Thematic RDI Laurea University of Applied Sciences Espoo Finland Estonian Maritime Academy Tallinn University of Technology Tallinn Estonia School of Electrical and Computer Engineering Technical University of Crete Chania Greece

ISBN: (数字)9798350362435

ISBN: (纸本)9798350362442

Cybersecurity is crucial for businesses as they face a shortage of specialists globally. This paper explores the growing concern of the skills gap in both the private and public sectors, leading to initiatives by the EU, national governments, academia, and industry to address the European skills shortage. This work identifies skill gaps in knowledge areas for the critical sectors including a maritime sector with a particular emphasis on certifications. The focus on certifications stems from the maritime industry's reliance on non-EU certification providers. This paper focuses on providing solutions by bridging the gap in Maritime Cybersecurity, proposing a syllabus of a cybersecurity certification practical training that may serve as a module curricula in a professional training programme.

关键词： Industries Technological innovation Government Europe computer security Certification Faces Business

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Segmentation and Multi-facet Classification of Individual Logs in Wooden Piles 18th

Segmentation and Multi-facet Classification of Individual Lo...

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18th International Conference on computer Aided Systems Theory, EUROCAST 2022

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany

ISBN: (纸本)9783031253119

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work, a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analysis of wooden piles by foresters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

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Telecommunications Conference (ITC-Egypt), International

作者： Mohamed M. El-Sheikh Aya Bayoumy Nada M. Ahmed Amany Mohamed Eldeeb Fatma I. Rabea Amira A. Nawar Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively.

关键词： Deep learning Accuracy Magnetic resonance imaging Soft sensors Medical services Feature extraction Regulation

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Total Labelings of Graphs with Prescribed Weights

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Journal of Combinatorial Mathematics and Combinatorial Computing 2024年 120卷 367-376页

作者： Bača, Martin Miller, Mirka Phanalasy, Oudone Ryan, Joe Semaničová-Feňovčíková, Andrea Sillasen, Anita A. Department of Applied Mathematics and Informatics Technical University Košice Slovakia School of Mathematical and Physical Sciences The University of Newcastle Australia School of Electrical Engineering and Computer Science The University of Newcastle Australia Department of Mathematics University of West Bohemia Pilsen Czech Republic Department of Informatics King's College London United Kingdom Department of Mathematics National University of Laos Vientiane Laos Department of Mathematical Sciences Aalborg University Aalborg Denmark

The total labeling of a graph G = (V, E) is a bijection from the union of the vertex set and the edge set of G to the set {1, 2, . . . , |V(G)| + |E(G)|}. The edge-weight of an edge under a total labeling is the sum of the label of the edge and the labels of the end vertices of that edge. The vertexweight of a vertex under a total labeling is the sum of the label of the vertex and the labels of all the edges incident with that vertex. A total labeling is called edge-magic or vertex-magic when all the edge-weights or all the vertex-weights are the same, respectively. When all the edge-weights or all the vertex-weights are different then a total labeling is called edge-antimagic or vertex-antimagic total, respectively. In this paper we deal with the problem of finding a total labeling of some classes of graphs that is simultaneously vertex-magic and edge-antimagic or simultaneously vertex-antimagic and edge-magic, respectively. We show several results for stars, paths and cycles. © 2024 Charles Babbage Research Centre. All rights reserved.

关键词： Edge-antimagic total labeling Edge-magic total labeling Vertex-antimagic total labeling Vertex-magic total labeling

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First steps towards modeling the interaction of cardiovascular agents and smooth muscle activation in arterial walls

First steps towards modeling the interaction of cardiovascul...

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92nd Annual Meeting of the International Association of applied Mathematics and Mechanics, GAMM 2022

作者： Nurani Ramesh, S. Uhlmann, K. Saßmannshausen, L. Rheinbach, O. Klawonn, A. Heinlein, A. Balzani, D. Civil and Environmental Engineering Ruhr University Bochum Universitätsstraße 150 Bochum44801 Germany University of Cologne Department of Mathematics and Computer Science Cologne Germany Technische Universität Bergakademie Freiberg Department of Mathematics and Informatics Freiberg Germany Delft Institute of Applied Mathematics Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

Numerical simulation of the response of healthy and pathological arteries to cardiovascular agents can provide valuable information to the physician in the treatment of diseases such as hypertension, atherosclerosis, and the Marfan syndrome. Here, we provide a first step towards a computational framework to model the effects of antihypertensive agents on the mechanical response of arterial walls. A material model is developed by extending an existing formulation for wall tissue to incorporate the effects of calcium-ion channel blockers. The resulting coupled deformation-diffusion problem is then solved using the finite element method. Simulation results with drug activity show that, indeed, an increased lumen diameter due to reduced contraction is obtained. Additionally, a decrease in the rate of arterial contraction is observed, which is also consistent with expected behavior. Finally, we compare results for an implicit or explicit treatment of the the deformation-diffusion coupling, and we observe that both coupling schemes yield comparable results for a wide range of time step sizes. © 2023 The Authors. Proceedings in applied Mathematics & Mechanics published by Wiley-VCH GmbH.

关键词： Diseases

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A Review of Android and iOS Operating System Security

A Review of Android and iOS Operating System Security

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2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2022

作者： Khan, Shahnawaz Yusuf, Ammar Haider, Mohammad Thirunavukkarasu, K. Nand, Parma Imam Rahmani, Mohammad Khalid Faculty of Engineering Design and Information and Communications Technology Bahrain Polytechnic Isa Town Bahrain Applied Science University College of Arts and Science Bahrain Saudi Electronic University College of Computing and Informatics Dammam Saudi Arabia Galgotias University Department of Computer Science and Engineering Greater Noida India Sharda University School of Engineering and Technology Greater Noida India Saudi Electronic University College of Computing and Informatics Riyadh11673 Saudi Arabia

ISBN: (数字)9781665469197

ISBN: (纸本)9781665469197

Mobile devices are an inseparable part of our lives. They have made it possible to access all the information and services anywhere at any time. Almost all of the organizations try to provide a mobile device-based solution to its users. However, this convenience has arisen the risk of losing personal information and has increased the threat to security. It has been observed recently that some of the mobile device manufacturers and mobile apps developers have lost the private information of their users to hackers. It has risen a great concern among mobile device users about their personal information. Android and iOS are the major operating systems for mobile devices and share over 99% of the mobile device market. This research aims to conduct a comparative analysis of the security of the components in the Android and iOS operating systems. It analyses the security from several perspectives such as memory randomization, application sandboxing, isolation, encryption, built-in antivirus, and data storage. From the analysis, it is evident that iOS is more secure than Android operating system. However, this security comes with a cost of losing the freedom. © 2022 IEEE.

关键词： Android (operating system)

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Cybersecurity Revolution via Large Language Models and Explainable AI

Cybersecurity Revolution via Large Language Models and Expla...

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Security of Information and Networks (SIN), International Conference on

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Walid Abushiba Munir Ahmad Anuam Ihsan Nidal A. Al-Dmour College of Arts & Science Applied Science University Manama Kingdom of Bahrain Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Selangor Malaysia Al-Khawarizimi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul Republic of Korea Department of Computer Engineering College of Engineering Mutah University Jordan

ISBN: (数字)9798331509736

ISBN: (纸本)9798331509743

Integrating Groundbreaking advancements in AI, like language models, interpretable AI, and machine learning, opens up a world of exciting new possibilities. The Evolving face of cybersecurity and Modern cyber threats are complex and well crafted; hence, conventional cybersecurity mechanisms show difficulty in staying relevant. LLMs, especially based on Transformer architecture will noticeably increase the accuracy and speed of detecting threats. Transparency and trust are increased by XAI approaches like SHAP and LIME, which offer facts about ML model predictions. This paper explores the literature that demonstrates the integration between XAI and LLMs in cybersecurity, exemplifying how this trinity of models has the potential to help attenuate errors producing reduced false positives and improve how we detect threats. Thinking about the possibilities the challenges including performance Explainability trade-offs, the need for common evaluation metrics, and the black-box nature of AI Models, remain in place. Solving these will help to enhance AI-driven solutions in cybersecurity.

关键词： Measurement Machine learning algorithms Explainable AI Large language models Predictive models Transformers Threat assessment computer security computer crime Faces

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An application of some machine learning methods for biological data modeling 15

An application of some machine learning methods for biologic...

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15th Conference of the Euro-American Consortium for Promoting the Application of Mathematics in Technical and Natural Sciences, AMiTaNS 2023

作者： Sapundzhi, Fatima Lazarova, Meglena Dzimbova, Tatyana Georgiev, Slavi Department of Communication and Computer Engineering Faculty of Engineering South-West University Neofit Rilski 66 Ivan Myhailov Str. Blagoevgrad2700 Bulgaria Faculty of Applied Mathematics and Informatics Technical University of Sofia 8 St. Kliment Ohridski Blvd. Sofia1000 Bulgaria Institute of Molecular Biology Bulgarian Academy of Sciences Acad. G.Bonchev Str bl. 21 Sofia1113 Bulgaria Department of Information Modeling Institute of Mathematics and Informatics Bulgarian Academy of Sciences Acad. Georgi Bonchev Str. bl. 8 Sofia1113 Bulgaria Department of Applied Mathematics and Statistics Faculty of Natural Sciences and Education University of Ruse 8 Studentska Str. Ruse7004 Bulgaria

The development of fast and reliable methods for predicting the biological activity of the substances in computational biology is of a great importance. This improves the development of some new compounds while keeping costs low. Among many scientists, an attractive target for docking experiments is the Delta-opioid receptor (DOR) and delta-opioid ligands (DOL). Their biological efficacy can be measured by various techniques, which could facilitate the establishment of the relationship between the structure of the compounds and their biological effect. The relationship between the results of the computer experiments and the biological activity of these compounds is modelled by using machine learning regressors. The primary goal of this study is to determine the most appropriate neural network for modelling the relationship between in vitro and in silico results for DOR and delta-opioid ligands. © Published under licence by IOP Publishing Ltd.

关键词： Bioactivity

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