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Augmenting Internet of Medical Things Security:Deep Ensemble Integration and Methodological Fusion

computer Modeling in engineering & sciences 2024年第12期141卷 2185-2223页

作者： Hamad Naeem Amjad Alsirhani Faeiz MAlserhani Farhan Ullah Ondrej Krejcar Faculty of Informatics and Management Center for Basic and Applied ResearchUniversity of Hradec KraloveHradec Kralove50003Czech Republic Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Cybersecurity Center Prince Mohammad Bin Fahd UniversityAl JawharahKhobarDhahran34754Saudi Arabia

When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network *** protect IoMT devices and networks in healthcare and medical settings,our proposed model serves as a powerful tool for monitoring IoMT *** study presents a robust methodology for intrusion detection in Internet of Medical Things(IoMT)environments,integrating data augmentation,feature selection,and ensemble learning to effectively handle IoMT data *** rigorous preprocessing,including feature extraction,correlation removal,and Recursive Feature Elimi-nation(RFE),selected features are standardized and reshaped for deep learning *** using the BAT algorithm enhances dataset *** deep learning models,Transformer-based neural networks,self-attention Deep Convolutional Neural Networks(DCNNs),and Long Short-Term Memory(LSTM)networks,are trained to capture diverse data *** predictions form a meta-feature set for a subsequent meta-learner,which combines model *** classifiers validate meta-learner features for broad algorithm *** comprehensive method demonstrates high accuracy and robustness in IoMT intrusion *** were conducted using two datasets:the publicly available WUSTL-EHMS-2020 dataset,which contains two distinct categories,and the CICIoMT2024 dataset,encompassing sixteen *** results showcase the method’s exceptional performance,achieving optimal scores of 100%on the WUSTL-EHMS-2020 dataset and 99%on the CICIoMT2024.

关键词： Cyberattack ensemble learning feature selection intrusion detection smart cities machine learning BAT augmentation

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Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm

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computers, Materials & Continua 2022年第6期71卷 4719-4736页

作者： Ayman Altameem Sandeep Kumar Ramesh Chandra Poonia Abdul Khader Jilani Saudagar Department of Computer Science College of Applied StudiesKing Saud UniversityRiyadh11495Saudi Arabia Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Information Systems Department Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Since the beginning of time,humans have relied on plants for food,energy,and *** are recognized by leaf,flower,or fruit and linked to their suitable *** methods are used to extract and select traits that are helpful in identifying a *** plant leaf image categorization,each plant is assigned a label according to its *** purpose of classifying plant leaf images is to enable farmers to recognize plants,leading to the management of plants in several *** study aims to present a modified whale optimization algorithm and categorizes plant leaf images into *** modified algorithm works on different sets of plant *** proposed algorithm examines several benchmark functions with adequate *** ten plant leaf images,this classification method was *** proposed model calculates precision,recall,F-measurement,and accuracy for ten different plant leaf image datasets and compares these parameters with other existing *** on experimental data,it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%.

关键词： Bag-of-features feature optimization plant leaf classification swarm intelligence nature-inspired algorithm

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Complementary memtransistors for neuromorphic computing: How, what and why

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Journal of Semiconductors 2024年第6期45卷 64-80页

作者： Qi Chen Yue Zhou Weiwei Xiong Zirui Chen Yasai Wang Xiangshui Miao Yuhui He School of Integrated Circuits Huazhong University of Science and TechnologyWuhan 430074China Department of Electrical and Computer Engineering University of CaliforniaSan DiegoUSA Dipartimento di Elettronica e Informazione and *** Politecnico di MilanoMilanoItaly Department of Applied Physics Hong Kong Polytechnic UniversityHong KongChina

Memtransistors in which the source-drain channel conductance can be nonvolatilely manipulated through the gate signals have emerged as promising components for implementing neuromorphic *** the other side,it is known that the complementary metal-oxide-semiconductor(CMOS)field effect transistors have played the fundamental role in the modern integrated circuit ***,will complementary memtransistors(CMT)also play such a role in the future neuromorphic circuits and chips?In this review,various types of materials and physical mechanisms for constructing CMT(how)are inspected with their merits and need-to-address challenges *** the unique properties(what)and poten-tial applications of CMT in different learning algorithms/scenarios of spiking neural networks(why)are reviewed,including super-vised rule,reinforcement one,dynamic vision with in-sensor computing,*** exploiting the complementary structure-related novel functions,significant reduction of hardware consuming,enhancement of energy/efficiency ratio and other advan-tages have been gained,illustrating the alluring prospect of design technology co-optimization(DTCO)of CMT towards neuro-morphic computing.

关键词： complementary memtransistor neuromorphic computing reward-modulated spike timing-dependent plasticity remote supervise method in-sensor computing

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Optimal Number of Cardiac Cycles for Continuous Blood Pressure Estimation

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IEIE Transactions on Smart Processing and Computing 2022年第6期11卷 421-425页

作者： Sunarya, Unang Park, Cheolsoo Department of Computer Engineering Kwangwoon University Seoul Korea Republic of School of Applied Science Telkom University Bandung Indonesia

Continuous blood pressure monitoring is essential for patients with hypertension. Most studies have suggested cuffless blood pressure monitoring techniques using a single cardiac cycle based on the pulse transit time. This paper investigates feature extraction from multiple cardiac cycles to estimate blood pressure. This implementation uses electrocardiogram, photoplethysmogram, and ballistrocardiagram signals. Random forest was applied to estimate blood pressure using the leave-one-subject-out cross-validation technique. The results show that the model could achieve better performance when using three cardiac cycles with an average MAE of 3.364 ± 3.059 mmHg for the diastolic blood pressure and 4.201 ±2.355 mmHg for the systolic blood pressure. Copyrights © 2022 The Institute of Electronics and Information Engineers.

关键词： Blood pressure

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Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

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computers, Materials & Continua 2022年第12期73卷 5075-5089页

作者： Abdullah A Asiri Akhtar Badshah Fazal Muhammad Hassan A Alshamrani Khalil Ullah Khalaf A Alshamrani Samar Alqhtani Muhammad Irfan Hanan Talal Halawani Khlood M Mehdar Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Software Engineering University of MalakandDir LowerPakistan Department of Computer Science and Information Systems College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia

Electroencephalogram(EEG)is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain,measured and recorded chronologically the surface of the scalp from the *** recorded signals from the brain are rich with useful *** inference of this useful information is a challenging *** paper aims to process the EEG signals for the recognition of human emotions specifically happiness,anger,fear,sadness,and surprise in response to audiovisual *** EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp,in response to audiovisual stimuli for the mentioned *** a bandpass filter with a bandwidth of 1-100 Hz,recorded raw EEG signals are *** preprocessed signals then further analyzed and twelve selected features in different domains are *** Random forest(RF)and multilayer perceptron(MLP)algorithms are then used for the classification of the emotions through extracted *** proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80%and 88%usingMLP and RF classifiers respectively on hybrid features for experimental signals of different *** proposed model outperforms in terms of cost and accuracy.

关键词： Electroencephalography emotion classification signal processing multilayer perceptron random forest

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Cluster-preserving sampling algorithm for large-scale graphs

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science China(Information sciences) 2023年第1期66卷 60-76页

作者： Jianpeng ZHANG Hongchang CHEN Dingjiu YU Yulong PEI Yingjun DENG National Digital Switching System E&T Research Center Information Engineering University Network Systems Department of the Strategic Support Force School of Computer Science and Technology Eindhoven University of Technology Center for Applied Mathematics Tianjin University

Graph sampling is a very effective method to deal with scalability issues when analyzing largescale graphs. Lots of sampling algorithms have been proposed, and sampling qualities have been quantified using explicit properties(e.g., degree distribution) of the sample. However, the existing sampling techniques are inadequate for the current sampling task: sampling the clustering structure, which is a crucial property of the current networks. In this paper, using different expansion strategies, two novel top-leader sampling methods(i.e., TLS-e and TLS-i) are proposed to obtain representative samples, and they are capable of effectively preserving the clustering structure. The rationale behind them is to select top-leader nodes of most clusters into the sample and then heuristically incorporate peripheral nodes into the sample using specific expansion strategies. Extensive experiments are conducted to investigate how well sampling techniques preserve the clustering structure of graphs. Our empirical results show that the proposed sampling algorithms can preserve the population's clustering structure well and provide feasible solutions to sample the clustering structure from large-scale graphs.

关键词： graph sampling clustering structure top-leader nodes expansion strategies large-scale graphs

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Application of Machine Learning for Online Reputation Systems

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International Journal of Automation and computing 2021年第3期18卷 492-502页

作者： Ahmad Alqwadri Mohammad Azzeh Fadi Almasalha Department of Computer Science Applied Science Private UniversityAmman 11931Jordan Department of Software Engineering Applied Science Private UniversityAmman 11931Jordan

Users on the Internet usually require venues to provide better purchasing *** can be provided by a reputation system that processes ratings to provide *** rating aggregation process is a main part of reputation systems to produce global opinions about the product *** methods that are frequently used do not consider consumer profiles in their calculations and cannot discover unfair ratings and trends emerging in new *** sophisticated rating aggregation methods that use a weighted average technique focus on one or a few aspects of consumers′profile *** paper proposes a new reputation system using machine learning to predict reliability of consumers from their *** particular,we construct a new consumer profile dataset by extracting a set of factors that have a great impact on consumer reliability,which serve as an input to machine learning *** predicted weight is then integrated with a weighted average method to compute product reputation *** proposed model has been evaluated over three Movie Lens benchmarking datasets,using 10-folds cross ***,the performance of the proposed model has been compared to previous published rating aggregation *** obtained results were promising which suggest that the proposed approach could be a potential solution for reputation *** results of the comparison demonstrated the accuracy of our ***,the proposed approach can be integrated with online recommendation systems to provide better purchasing recommendations and facilitate user experience on online shopping markets.

关键词： Reputation system rating aggregation machine learning consumer reliability user trust

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Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

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computer Systems science & engineering 2023年第3期44卷 2395-2409页

作者： Ashit Kumar Dutta Basit Qureshi Yasser Albagory Majed Alsanea Manal Al Faraj Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Science Prince Sultan UniversityRiyadh11586Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determine fake *** recently developed machine learning(ML)models can be employed for the detection and classification of fake *** study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine(CAS-WELM)for Cybersecurity Fake News Detection and *** goal of the CAS-WELM technique is to discriminate news into fake and *** CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embed-ding ***,N-gram based feature extraction technique is derived to gen-erate feature ***,WELM model is applied for the detection and classification of fake news,in which the weight value of the WELM model can be optimally adjusted by the use of CAS *** performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several *** experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.

关键词： Cybersecurity cybercrime fake news data classification machine learning metaheuristics

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Assessing Hallucination in Large Language Models Under Adversarial Attacks 9

Assessing Hallucination in Large Language Models Under Adver...

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9th International Conference on Mobile and Secure Services, MOBISECSERV 2024

作者： Tripathi, Shivangi Griffith, Henry Rathore, Heena Texas State University Department of Computer Science San MarcosTX United States San Antonio College Department of Math and Engineering San AntonioTX United States

ISBN: (纸本)9798350390896

Large Language Models (LLMs) are powerful tools capable of handling various tasks in natural language processing and beyond. Despite their advanced capabilities, LLMs are prone to generating hallucinations - responses that are fabricated from parameterized memorization rather than being factually accurate. Recent studies have showcased that hallucinations can not only naturally arise from issues related to the training data, training process, or inference stage, but can also result from adversarial attacks. This study extends the previous state-of-the-art by testing it on adversarial datasets. We conduct experiments using open-source models such as Vicuna-7B and LLaMA2-7B-chat and use performance metrics such as attack success rate and loss over epoch to evaluate the work on weak semantic and out-of-distribution attacks. The results showcase that the dataset selection plays a critical role in influencing the model susceptibility to attacks. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine Learning for Indoor Localization using CSI Technique 21

Machine Learning for Indoor Localization using CSI Technique

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Lee, Wilaiporn Suwanasri, Cattareeya Pipanmekaporn, Luepol Srisomboon, Kanabadee King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand

ISBN: (纸本)9798350377088

In this research, we propose a low-cost indoor localization technique using the CSI. By using CSI signal as input data, different locations and human activities are classified effectively using machine learning models. According to a low-cost constraint where a single transmitter (Tx) and receiver (Rx) are utilized, we investigate the appropriate transmitter location for achieving accurate position estimation in an indoor environment under two patterns of room environment. Moreover, an appropriate machine learning model for position estimation is examined where decision tree, KNN, Naïve bayes, and ANN are considered. The experimental results demonstrate that the highest accuracy can be achieved using the ANN technique. The shortest distance between a Tx and Rx provides the highest precision for determining the human location in an empty room, whereas placing the Tx and Rx in opposing positions is advantageous for a section with items positioned against the wall. When determining an average accuracy, placing the Tx and Rx in opposing positions shows the highest accuracy among others. © 2024 IEEE.

关键词： Adversarial machine learning

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