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11th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Daoudi, Abdelaziz Mahmoudi, Saïd Tahri Mohamed University Faculty of Exact Sciences Computer Science Department Bechar Algeria University of Mons Faculty of Engineering Computer Science Department Mons7000 Belgium

ISBN: (纸本)9798350362541

In the last few arrays, cardiovascular disorders are a major concern for global health. The prevalence of these heart conditions is constantly increasing, posing a significant challenge for healthcare systems worldwide. Considering this reality, our work aims to develop a precise and reliable diagnostic tool that is essential for effectively detecting these disorders. In this paper, we propose a hybrid approach that combines two processing methods. The first method involves symlet-4 wavelet analysis, while the second method utilizes modeling through a naive Bayesian network. The objective of this work is to detect cardiovascular disorders from ECG signals and determine the classes of arrhythmias present, distinguishing them from normal ones. Our method has shown promising results and will contribute to improving the management of patients with cardiovascular disorders while reducing potential complications associated with these serious conditions. © 2024 IEEE.

关键词： Diseases

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Relationship of IQ with Causal Connections Between Brain Salient Network Regions 31

Relationship of IQ with Causal Connections Between Brain Sal...

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31st National and 9th International Iranian Conference on Biomedical engineering, ICBME 2024

作者： Ghoorchian, Mina Soltanian-Zadeh, Hamid Pedram, Mir Mohsen Sadeghi, Mohsen Mir Mohammad Institute for Cognitive Science Studies Department of Cognitive Science Tehran Iran University of Tehran School of Electrical and Computer Engineering Tehran Iran Kharazmi University Department of Electrical and Computer Engineering Faculty of Engineering Tehran Iran

ISBN: (纸本)9798331529710

IQ is one of the indicators that has always been of interest to psychiatrists, doctors and cognitive science researchers. Since this index plays a key role in people's lives and also in the occurrence of brain abnormalities, many studies have been conducted on it. In this paper, we used the resting state magnetic resonance imaging data available in the HCP database to classify people into three groups with low, medium, and high IQ and applied machine learning methods on the matrix of causal connections between related network regions. We applied brain highlights. We calculated the matrix of causal relationships in this network by the spDCM algorithm and then obtained the differences of the three groups using the ANOVA statistical test and 6 edges out of 169 edges with p-value © 2024 IEEE.

关键词： Support vector regression

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The Impact of CyRIS in Ascertaining Accurate Implementation of Projects 14

The Impact of CyRIS in Ascertaining Accurate Implementation ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sharifi, Ahma Zia Walid, Md. Abul Ala Zia, Ghezal Ahmad Jan Salehin, Md. Nazmus Safi, Ihsan Ul Haq Al Islam, Iqbal Faculty of Computer Science Nangarhar Univeristy Department of Networking and Communication Afghanistan Department of Computer Science and Engineering Khulna9203 Bangladesh Berlin Germany Department of Computer Science and Engineering Natore Bangladesh Nangarhar University Department of Networking and Communication Faculty of Computer Science Afghanistan Department of Computer Science & Engineering Savar Dhaka Bangladesh

ISBN: (纸本)9798350335095

This systematic review seeks to evaluate the impact of CyRIS in ascertaining accurate implementation of projects with a primary focus on the merits of its functionalities. Core to the study is the view that CyRIS-driven project implementation reduces the risks of inaccuracy to an acceptable level. Although CyRIS is an extensively researched area in cyber security, a comprehensive review of subcategories, specifically, the accurate implementation of projects using the tool remains understudied. The proposed systematic review of literature provides insights and future directions from selected databases, including scienceDirect, Springer Journals, SAGE Journals, and Wiley Online Library. Moreover, the limitations and strengths of each article collected are assessed using the PRISMA model to identify risks of bias. The research, which consists of journal articles, conferences, and symposium papers, is analyzed using "Cyber-Range Instantiation Systems and accurate implementation of projects"as the key phrase and "Cyber-Risk Models", "Cyber Range Instantiation", and "Cyber Security Training", and "Accurate Implementation of Projects"as the keywords. The findings of the study will bridge the awareness gap in using CyRIS-led Cyber-Risk Models to execute accurate training scenarios, improve weaknesses of implementation, simulate real-time attack scenarios, and highlight existing gaps as well as prospects for future research. © 2023 IEEE.

关键词： Cybersecurity

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Next-Generation Cyber Defense: Deep Q-Learning-Based Adversarial Detection and Prevention in Autonomous Vehicle Networks 4

Next-Generation Cyber Defense: Deep Q-Learning-Based Adversa...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Srivastava, Shubhi Bhargav, H.K. Gurani, Vijayakumar Chaturvedi, Arpana Gandhi, Ankita Dharangutti, Yogesh New Horizon College of Engineering Department of Information Science and Engineering Bangalore India Shridevi Institute of Engineering and Technology Department of Computer Science and Engineering Tumakuru India Karnatak University Department of Computer Science Dharwad India New Delhi Institute of Management Department of IT/AI/ML New Delhi India Parul Institute of Engineering and Technology Department of Computer science and Engineering Gujarat India Faculty of Law Pune India

ISBN: (纸本)9798350352931

This paper presents a novel AI-powered super-resolution technique for real-time video processing, which significantly enhances the visual quality of low-resolution video frames. The proposed framework employs a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Generative Adversarial Networks (GANs) to reconstruct high-resolution frames with minimal computational overhead. Experimental results on benchmark video datasets show that the proposed method achieves a peak signal-to-noise ratio (PSNR) of 34.56 dB and a structural similarity index (SSIM) of 0.94, outperforming state-of-the-art methods by 12.3% in PSNR and 8.7% in SSIM. Additionally, the system demonstrates an average processing speed of 30 frames per second (fps) on a GTX 1080Ti GPU, making it suitable for real-time applications. The qualitative and quantitative results validate the effectiveness of the proposed method in preserving fine details, reducing artifacts, and enhancing visual clarity in various real-time video processing scenarios. © 2024 IEEE.

关键词： Generative adversarial networks

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Prediction of Heart Disease Severity Using Hierarchically-Structured Machine-Learning Models with Feature Space Reduction

Prediction of Heart Disease Severity Using Hierarchically-St...

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17th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Kiuchi, Ayami Fujita, Tomoya Yamana, Hayato Department of Computer Science and Engineering Waseda University Tokyo Japan Department of Computer Science and Communications Engineering Waseda University Tokyo Japan Faculty of Science and Engineering Waseda University Tokyo Japan

Heart disease is the primary cause of death worldwide according to the 2019 statistics published by the World Health Organization (WHO), with roughly 8.9 million people dying annually. Predicting the likelihood and severity of this disease leads to earlier detection and helps reduce the workload of medical professionals. Previous studies have adopted a one-time classification that is insufficient to predict heart disease severity. This study proposes a novel classification method to enhance the prediction accuracy of heart disease by using: 1) a hierarchical binary-classification technique to classify the severity in order from the lowest level and 2) a data-preprocessing technique to transform continuous values into binary values based on medical knowledge and statistics information to decrease the feature space. An experimental evaluation of the heartdisease dataset from the UC Irvine (UCI) machine-learning repository confirms that the proposed method achieves the highest accuracy at 100% in predicting the presence of heart disease and at 93.13% in its severity level. In addition, the proposed method achieved 96.67%, 91.25%, 90.59%, and 93.64% accuracy for severity prediction in the Cleveland, Hungarian, Long-Beach-VA, and Switzerland datasets, respectively. © 2024 by SCITEPRESS–science and Technology Publications, Lda.

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Adaptive Butterfly Optimization Algorithm(ABOA)Based Feature Selection and Deep Neural Network(DNN)for Detection of Distributed Denial-of-Service(DDoS)Attacks in Cloud

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computer Systems science & engineering 2023年第10期47卷 1109-1123页

作者： S.Sureshkumar G.K.D.Prasanna Venkatesan R.Santhosh Department of Computer Science and Engineering Karpagam Academy of Higher EducationCoimbatore641021India Dean Engineering Faculty EngineeringKarpagam Academy of Higher EducationCoimbatore641021India Department of Computer Science and Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatore641021India

Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly *** this,with its distributed and dynamic nature and shortcomings in virtualization deployment,the cloud environment is exposed to a wide variety of cyber-attacks and security *** Intrusion Detection System(IDS)is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various *** attacks are becoming more frequent and powerful,and their attack pathways are continually changing,which requiring the development of new detection *** the purpose of the study is to improve detection *** Selection(FS)is *** the same time,the IDS’s computational problem is limited by focusing on the most relevant elements,and its performance and accuracy *** this research work,the suggested Adaptive butterfly optimization algorithm(ABOA)framework is used to assess the effectiveness of a reduced feature subset during the feature selection phase,that was motivated by this motive *** classification is not compromised by using an ABOA *** design of Deep Neural Networks(DNN)has simplified the categorization of network traffic into normal and DDoS threat ***’s parameters can be finetuned to detect DDoS attacks better using specially built *** reconstruction error,no exploding or vanishing gradients,and reduced network are all benefits of the changes outlined in this *** it comes to performance criteria like accuracy,precision,recall,and F1-Score are the performance measures that show the suggested architecture outperforms the other existing *** the proposed ABOA+DNN is an excellent method for obtaining accurate predictions,with an improved accuracy rate of 99.05%compared to other existing approaches.

关键词： Cloud computing distributed denial of service intrusion detection system adaptive butterfly optimization algorithm deep neural network

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A Yet Powerful Routing Protocol for Vehicular Ad-hoc Networks (VANETs) Utilizing Software Defined Network (SDN) and Fog Computing 8

A Yet Powerful Routing Protocol for Vehicular Ad-hoc Network...

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8th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2024

作者： Darabkh, Khalid A. Al-Mistarihi, Mamoun F. Al-Maaitah, Mera Ismail School of Engineering The University of Jordan Computer Engineering Department Amman11942 Jordan Jordan University of Science and Technology Faculty of Engineering Electrical Engineering Department Irbid22110 Jordan

ISBN: (纸本)9798350368970

In this article, we propose a novel protocol that employs different technologies together, such as Software Defined Network (SDN) for the purpose of offering scalability, programmability, and global network information, and Fog computing for the reason of providing location services that meet V ANET standards. Additionally, we propose a novel cluster head selection algorithm for the purpose of enhancing the efficiency of the protocol by considering three different dimensions, namely, lifetime, average distance, and signal-to-interference noise ratio. The novelty of this proposed algorithm comes from reducing the number of control overheads, reducing long-distance communications, and reducing packet loss due to collisions and contentions inside each cluster, respectively. The proposed protocol has been assessed using N s3 simulator whereas its results are so promising and worth publishing. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Cross-Layer Distributed Attack Detection Model for the IoT

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International Journal of Ambient Computing and Intelligence 2022年第1期13卷 1-17页

作者： Ahmed, Hassan I. Nasr, Abdurrahman A. Abdel-Mageid, Salah M. Aslan, Heba K. Informatics Department Electronics Research Institute Cairo Egypt Computer Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Computer Engineering Department College of Computer Science and Engineering Taibah University Saudi Arabia Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Egypt

The security of IoT that is based on layered approaches has shortcomings such as the redundancy, inflexibility, and inefficiently of security solutions. There are many harmful attacks in IoT networks such as DoS and DDoS attacks, which can compromise the IoT architecture in all layers. Consequently, cross layer approach is proposed as an effective and practical security defending mechanism. Cross-layer distributed attack detection model (CLDAD) is proposed to enhance security solutions for IoT environments. CLDAD presents a general detection method of DDoS in sensing layer, network layer, and application layer. CLDAD is based on big data analytics techniques, which enable the detection process to be performed in a distributed way, so the model can detect DDoS attacks in any layer on-the-fly and the model support the scalability of the IoT environment. CLDAD is tested based on three datasets, namely artificial jamming attack dataset, BoT-IoT dataset, and BoT-IoT-based HTTP. The results showed that the proposed model is efficient in detecting attacks in the three layers of the IoT. Copyright © 2022, IGI Global.

关键词： Big data

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Mechanistic switch in corrosion behavior of magnesium alloy diamond lattice structures induced by argon plasma treatment

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Journal of Magnesium and Alloys 2025年第1期13卷 101-119页

作者： Viviana M.Posadaa Alexandru Marin Tonny Naranjo Juan Ramírez Patricia Fernández-Morales Ken and Mary Alice Lindquist Department of Nuclear Engineering Pennsylvania State UniversityPA USA Department of Mechanical Engineering School of MinesUniversidad Nacional de ColombiaBogotáColombia Department of Nuclear Plasma and Radiological EngineeringCollege of EngineeringUniversity of Illinois at Urbana-ChampaignILUSA Medicine Faculty Universidad Pontificia BolivarianaAntioquiaColombia Corporación para Investigaciones Biológicas AntioquiaColombia Industrial Engineering Faculty Universidad Pontificia BolivarianaAntioquiaColombia Materials Science and Engineering Department Pennsylvania State UniversityUniversity ParkPA16802USA Surface Analysis Laboratory Institute for Nuclear Research PitestiMioveni15400Romania Department of Electronics and Computer Science Pontificia Universidad Javeriana de CaliColombia

Advancing 3D magnesium(Mg)development beyond current limitations requires controlling Mg alloy degradation in pre-designed,low-dimension *** study reveals a mechanistic switch in the corrosion behavior of Mg alloy(3.6%Al,0.8%Zn)diamond lattice structures,induced by plasma nanosynthesis(400 eV Ar^(+)ions,fluence 1×10^(17) ions/cm^(2)).Plasma treatment of the Mg alloy increases surface Mg from 1.5%to 14.5%,enhances carbonate formation,and generates a nanostructured surface with a Mg carbonate layer over an oxide/hydroxide *** vitro and in vivo analyses over 8 wk demonstrate how this treatment fundamentally alters the degradation process and stability of these 3D *** untreated samples initially formed a protective film that subsequently diminished,DPNS-treated samples demonstrated an inverse corrosion behavior.X-ray photoelectron spectroscopy(XPS)and electrochemical impedance spectroscopy(EIS)confirmed the presence of a stable,protective layer composed of magnesium oxide,magnesium hydroxide,and magnesium carbonate on the DPNS-treated *** 14 days,the DPNS-treated sample exhibited a more positive corrosion potential(-0.69 V versus-1.36 V)and a marginally lower current density(0.73 mA/cm^(2)compared to 0.75 mA/c^(2))relative to the *** protective layer,combined with modified surface topology,initiated a core-to-periphery degradation pattern that maintained structural integrity for up to 8 wk *** findings support the conclusion that the DPNS-treated scaffold demonstrates sustained improved corrosion resistance over time compared to the untreated ***-CT revealed plasma-treated samples retained larger struts(504.9±95.3μm at 8 wk)and formed larger H_(2) pockets extending 14.2 mm from the implant center,versus 4.9 mm in *** corrosion behavior switch enhances stability but risks pore clogging,offering insights for tailoring Mg alloy degradation and H_(2) evolution in 3D architectures

关键词： Mg Corrosion resistance Cellular materials Nanotopography Nano-medicine Diirected plasma nanosynthesis

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A Framework for Skin Cancer Prediction Using CNN Ensembles 2

A Framework for Skin Cancer Prediction Using CNN Ensembles

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Samushonga, Wieslaw Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Department of Computer Science and Engineering Punjab Gharuan India University of Kufa Faculty of Computer Science and Mathematics Najaf Iraq Technical Engineering College Al-Ayen University Department of Computer Technical Engineering Thi-Qar Iraq

ISBN: (纸本)9798350370188

Skin cancer is among the deadliest diseases engulfing the world. The early detection of this disease may provide a great relief to the patients and healthcare systems worldwide. Recently, Machine Learning (ML)-based detection techniques have become popular in early skin cancer prediction. This paper comprehensively presents an overview of the latest research in the field of skin cancer prediction using ML techniques, especially the use of Convolutional Neural Networks (CNN) for the said purpose. Various methodologies employed in the past have been discussed along with the outcomes achieved, limitations, and advantages of these predictive models. A detailed review of the latest key references in the area is also discussed. At the end, a way of achieving ensemble using CNN methods for skin care has also been discussed, which we will implement in the next phase of our research. © 2024 IEEE.

关键词： Diseases

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