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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Fuhaidi, Belal Farae, Zainab Al-Sorori, Wedad Maqtary, Naseebah Al-Ashmoery, Yahya Al-Fuhaidy, Farouk Al-Taweel, Sadik Faculty of Computing and IT University of Science and Technology Sana’a Yemen Department of IT Al-Razi University Sana’a Yemen Dept. Mathematics & Computer Faculty of Science Sana’a University Sana’a Yemen Department of Electrical Engineering Faculty of Engineering Ibb University Ibb Yemen

ISBN: (纸本)9798331533557

Intrusion detection systems (IDSs) are a necessary principle in WSN security, which can successfully prevent various hackers' and intruders' attempts to hack the network. In this research, we address the problem of achieving high accuracy in detecting intrusions in WSNs due to specific characteristics of WSN data, including the appropriate dataset, the drawbacks of feature selection, and choosing the proper algorithms for the classification process. In this paper, we proposed the anomaly-based IDS model using the DNN algorithm and mutual information (MI) technology to select features. The proposed model has been implemented using the Python language used in the Anaconda platform and relying on the standard NSL-KDD dataset. The experimental results showed the capability of the proposed model to achieve high-performance accuracy in intrusion detection using the DNN algorithm compared to the state-of-the-art. The proposed model outperforms other previous relevant works by 3.65% enhancement in terms of accuracy. © 2024 IEEE.

关键词： Network intrusion

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Dark Forest Algorithm:A Novel Metaheuristic Algorithm for Global Optimization Problems

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computers, Materials & Continua 2023年第5期75卷 2775-2803页

作者： Dongyang Li Shiyu Du Yiming Zhang Meiting Zhao Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo315000China Engineering Laboratory of Advanced Energy Materials Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo315000China School of Material and Chemical Engineering Ningbo UniversityNingbo315000China

Metaheuristic algorithms,as effective methods for solving optimization problems,have recently attracted considerable attention in science and engineering *** are popular and have broad applications owing to their high efficiency and low *** algorithms are generally based on the behaviors observed in nature,physical sciences,or *** study proposes a novel metaheuristic algorithm called dark forest algorithm(DFA),which can yield improved optimization results for global optimization *** DFA,the population is divided into four groups:highest civilization,advanced civilization,normal civilization,and low *** civilization has a unique way of *** verify DFA’s capability,the performance of DFA on 35 well-known benchmark functions is compared with that of six other metaheuristic algorithms,including artificial bee colony algorithm,firefly algorithm,grey wolf optimizer,harmony search algorithm,grasshopper optimization algorithm,and whale optimization *** results show that DFA provides solutions with improved efficiency for problems with low dimensions and outperforms most other algorithms when solving high dimensional *** applied to five engineering projects to demonstrate its *** results show that the performance of DFA is competitive to that of current well-known metaheuristic ***,potential upgrading routes for DFA are proposed as possible future developments.

关键词： Metaheuristic algorithm global optimization

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Early-Stage Segmentation and Characterization of Brain Tumor

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computers, Materials & Continua 2022年第10期73卷 1001-1017页

作者： Syed Nauyan Rashid Muhammad Hanif Usman Habib Akhtar Khalil Omair Inam Hafeez Ur Rehman Department of Computer Science National University of Computer and Emerging Sciences IslamabadPeshawar CampusPakistan Faculty of Computer Science and Engineering Ghulam Ishaq Khan(GIK)Institute of Engineering Sciences and TechnologyTopiPakistan IFahja Pvt Limited PeshawarPakistan Department of Electrical and Computer Engineering COMSATS University IslamabadPakistan

Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain *** life expectancy of patients diagnosed with gliomas decreases *** gliomas are diagnosed in later stages,resulting in imminent *** average,patients do not survive 14 months after *** only way to minimize the impact of this inevitable disease is through early *** Magnetic Resonance Imaging(MRI)scans,because of their better tissue contrast,are most frequently used to assess the brain *** manual classification of MRI scans takes a reasonable amount of time to classify brain *** this,dealing with MRI scans manually is also cumbersome,thus affects the classification *** eradicate this problem,researchers have come up with automatic and semiautomatic methods that help in the automation of brain tumor classification ***,many techniques have been devised to address this issue,the existing methods still struggle to characterize the enhancing *** is because of low variance in enhancing region which give poor contrast in MRI *** this study,we propose a novel deep learning based method consisting of a series of steps,namely:data pre-processing,patch extraction,patch pre-processing,and a deep learning model with tuned hyper-parameters to classify all types of gliomas with a focus on enhancing *** trained model achieved better results for all glioma classes including the enhancing *** improved performance of our technique can be attributed to several ***,the non-local mean filter in the pre-processing step,improved the image detail while removing irrelevant ***,the architecture we employ can capture the non-linearity of all classes including the enhancing ***,the segmentation scores achieved on the Dice Similarity Coefficient(DSC)metric for normal,necrosis,edema,enhancing and non-enhancing tumor classes are 0.95,0.97,0.91,0.93,0.95;respectively.

关键词： Segmentation CNN characterization brain tumor MRI

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Composing Synergistic Macro Actions for Reinforcement Learning Agents

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IEEE Transactions on Neural Networks and Learning Systems 2024年第5期35卷 7251-7258页

作者： Chen, Yu-Ming Chang, Kaun-Yu Liu, Chien Hsiao, Tsu-Ching Hong, Zhang-Wei Lee, Chun-Yi Hsinchu300 Taiwan Avery Design Systems Inc. Taipei100 Taiwan University of Rostock Faculty of Computer Science and Electrical Engineering Rostock18051 Germany National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan Massachusetts Institute of Technology CambridgeMA02139 United States

Macro actions have been demonstrated to be beneficial for the learning processes of an agent and have encouraged a variety of techniques to be developed for constructing more effective ones. However, previous techniques usually do not further consider combining macro actions to form a synergistic macro action ensemble, in which synergism exhibits when the constituent macro actions are favorable to be jointly used by an agent during evaluation. Such a synergistic macro action ensemble may potentially allow an agent to perform even better than the individual macro actions within it. Motivated by the recent advances of neural architecture search (NAS), in this brief, we formulate the construction of a synergistic macro action ensemble as a Markov decision process (MDP) and evaluate the constructed macro action ensemble as a whole. Such a problem formulation enables synergism to be taken into account by the proposed evaluation procedure. Our experimental results demonstrate that the proposed framework is able to discover the synergistic macro action ensembles. Furthermore, we also highlight the benefits of these macro action ensembles through a set of analytical cases. © 2012 IEEE.

关键词： Reinforcement learning

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Learning coordinated emotion representation between voice and face

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Applied Intelligence 2023年第11期53卷 14470-14492页

作者： Fang, Zheng Liu, Zhen Hung, Chih-Chieh Sekhavat, Yoones A. Liu, Tingting Wang, Xun Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315000 China National Chung Hsing University Taichung City Taiwan Faculty of Multimedia Tabriz Islamic Art University Tabriz Iran College of Science and Technology Ningbo University Cixi China

Voice and face information are two most important perceptual modalities for human. In recent years, many researchers show great interest in learning cross-modal representations for different face-voice association tasks. However, these existing methods focus on the various biological attributions but rarely take emotion semantics between voice and face into account. In this paper, we present a novel two-stream model, called Emotion Representation Learning Network (EmoRL-Net), to learn the cross-modal coordinated emotion representations for various downstream matching and retrieval tasks. Within the proposed approach, we first propose two sub-network architectures that learn two unimodal features from the two modalities. Afterwards, we train EmoRL-Net by an objective loss function which includes one explicit and two implicit constraints. Meanwhile, an online semi-hard negative mining strategy is utilized to construct triplet units in a mini-batch manner, thereby stabilize and speeding up the learning process. Extensive experiments demonstrate that the proposed method can benefit various face-voice emotion tasks, including cross-modal verification, 1:2 matching, 1:N matching, and retrieval scenarios. The experiment results also show the proposed method outperforms the state-of-the-art approaches. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Semantics

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Classification of Arrhythmia ECG Signals using Convolutional Neural Network 30

Classification of Arrhythmia ECG Signals using Convolutional...

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30th International Conference on Systems, Signals and Image Processing, IWSSIP 2023

作者： Habijan, Marija Galic, Irena Pizurica, Aleksandra Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek31000 Croatia Ghent University TELIN-GAIM Faculty of Engineering and Architecture Ghent Belgium

ISBN: (纸本)9798350337297

The electrocardiogram (ECG) has been established as a reliable tool for monitoring cardiovascular health. Vast amount of ECG recordings can pose a challenge for its processing and analysis and seeking out experts to analyze such a large amount of ECG data can deplete valuable medical resources. Recently, there has been a growing interest in automatic methods for accurate heartbeat categorization. Our motivation behind this work is to improve efficiency and accuracy for arrhythmia classification based on convolutional neural networks (CNNs). We first pre-process the input signals by normalizing them using a z-score. Following the data segmentation, the under-represented classes are oversampled using random duplication. By randomly duplicating the samples of underrepresented categories and eliminating instances from the overrepresented categories, this approach minimizes the imbalance in the training data. We propose an efficient 12-layer 1D-CNN to classify cardiac arrhythmia into five classes. We demonstrate the oversampling technique's impact on the proposed CNN's performance by comparing its performance before and after its application. © 2023 IEEE.

关键词： Electrocardiograms

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Rethinking Transferable Adversarial Attacks With Double Adversarial Neuron Attribution

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 354-364页

作者： Zhu, Zhiyu Jin, Zhibo Wang, Xinyi Zhang, Jiayu Chen, Huaming Choo, Kim-Kwang Raymond University of Sydney School of Electrical and Computer Engineering DarlingtonNSW2008 Australia University of Malaya Faculty of Computer Science and Information Technology KualaLumpur50603 Malaysia SuZhou Yierqi Jiangsu Suzhou215000 China The University of Texas at San Antonio San AntonioTX78249 United States

Transferable adversarial attacks are a threat to deep neural networks, in particular, for black-box scenarios where access to model information is limited. One can, for example, exploit the intermediate layer neurons to generate transferable adversarial samples. However, current works show limitations in providing feature-level attack mechanisms across multiple victim models. In light of the attribution methods, in this article, we investigate the attribution similarity across different models. We leverage the similarity to incorporate different attribution properties to enhance the sample transferability, for the first time, formulating a novel neuron attribution-based transferable attack termed DANAA++. Specifically, we utilize a range of adversarial attack methods to generate different baseline points through adversarial training. The attribution results are thus obtained along both linear and nonlinear integration paths. In our experiments, the baseline points and integration paths significantly help improve the transferability of adversarial samples. Our approach provides novel insights for building effective attribution-based feature-level adversarial attacks. https://***/LMBTough/DANAAPP. © 2020 IEEE.

关键词： Iterative methods

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Design of Medical Privacy Protection Circuit Based on Cross Obfuscation 9

Design of Medical Privacy Protection Circuit Based on Cross ...

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9th International Conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Chen, Zhikang Zhang, Yuejun Ren, Mingze Xia, Donghao Wang, Pengjun Zhang, Huihong Wen, Liang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China College of Electrical and Electronic Engineering Wenzhou University Wenzhou China China Coast Guard Academy Department of Electronic Technology Ningbo China

ISBN: (纸本)9798331509453

With the rise of the Internet of Things, smart healthcare with remote monitoring and diagnosis enables patients to reduce the number and duration of hospitalizations, thereby reducing the pressure on doctors. However, the transmission data of disease detection chips is susceptible to cache attacks, leading to user health data leakage. Hardware level encryption design has become a research hotspot. Firstly, by adding a Mask channel at the upper level of each convolution layer, the preprocessed data is transmitted simultaneously with the obfuscated data, and the channel exchange during transmission is utilized to protect user privacy;Secondly, there is a redundancy mechanism between the input matrix and weight matrix of the storage module, and invalid values are added to the redundant parts to prevent cache attacks from stealing user information;Finally, add multi temporal obfuscation to the first input data to achieve the different lengths between the first and subsequent data. The protection circuit designed in this article achieves user privacy protection on the basis of only increasing hardware overhead by 2.5%, power consumption overhead by 3.61%, and not affecting disease monitoring accuracy. © 2024 IEEE.

关键词： Information leakage

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MEC-IoT-Healthcare: Analysis and Prospects

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computers, Materials & Continua 2023年第6期75卷 6219-6250页

作者： Hongyuan Wang Mohammed Dauwed Imran Khan Nor Samsiah Sani Hasmila Amirah Omar Hirofumi Amano Samih M.Mostafa College of Computer Science Guangdong University of Science and TechnologyDongGuan523083China Department of Medical Instrumentation Techniques Engineering Dijlah University CollegeBaghdadIraq Department of Electrical Engineering University of Engineering&TechnologyPeshawar814Pakistan Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600BangiSelangorMalaysia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Physical sensors,intelligent sensors,and output recommenda-tions are all examples of smart health technology that can be used to monitor patients’health and change their *** health is an Internet-of-Things(IoT)-aware network and sensing infrastructure that provides real-time,intelligent,and ubiquitous healthcare *** of the rapid development of cloud computing,as well as related technologies such as fog computing,smart health research is progressively moving in the right ***,fog computing,IoT sensors,blockchain,privacy and security,and other related technologies have been the focus of smart health research in recent *** the moment,the focus in cloud and smart health research is on how to use the cloud to solve the problem of enormous health data and enhance service performance,including cloud storage,retrieval,and calculation of health big *** article reviews state-of-the-art edge computing methods that has shifted to the collection,transmission,and calculation of health data,which includes various sensors and wearable devices used to collect health data,various wireless sensor technologies,and how to process health data and improve edge performance,among other ***,the typical smart health application cases,blockchain’s application in smart health,and related privacy and security issues were reviewed,as well as future difficulties and potential for smart health *** comparative analysis provides a reference for the the mobile edge computing in healthcare systems.

关键词： IoT mobile-edge computing cloud computing e-health

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Heart Sound Classification using Deep Learning 8

Heart Sound Classification using Deep Learning

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8th International Conference on Smart and Sustainable Technologies, SpliTech 2023

作者： Habijan, Marija Galić, Irena Pizurica, Aleksandra Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek31000 Croatia Ghent University TELIN-GAIM Faculty of Engineering and Architecture Ghent Belgium

ISBN: (纸本)9789532901283

Cardiac auscultation is the process of listening to the sounds of the heart with a stethoscope, which can provide important diagnostic information about a patient's heart function. It is a key component of a physical examination and can help doctors identify potential heart problems such as murmurs, arrhythmias, and valve disorders. Deep learning-based cardiac auscultation is of great interest to medical professionals as it can reduce the burden of manual auscultation by automatically detecting abnormal heartbeats. However, automatic cardiac auscultation is a challenging task due to the need for dependable and precise results, as well as the interference of background noise in heart sounds. In this work, we propose a heart sound classification method that could potentially accelerate automated cardiac auscultation. The method is based on Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN). We use heart sounds from the dataset Classifying Heart Sounds Challenge and extract input features using Mel Frequency Cepstral Coefficients (MFCCs). Further, we examine the use of a simple data augmentation technique and show that adding noise, stretching, and shifting of heart sound signals significantly improves the classification accuracy of the normal, murmur, and artifact heart sounds. © 2023 University of Split, FESB.

关键词： Heart

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