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Event-Triggered Fixed-Time Consensus of Second-Order Nonlinear Multi-Agent systems with Delay and Switching Topologies

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Journal of Shanghai Jiaotong university(science) 2024年第4期29卷 625-639页

作者： XING Youjing GAO Jinfeng LIU Xiaoping WU Ping School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China Department of Systems and Computer Engineering Carleton UniversityOttawaK1S 5B6Canada

To address fixed-time consensus problems of a class of leader-follower second-order nonlinear multi-agent systems with uncertain external disturbances,the event-triggered fixed-time consensus protocol is ***,the virtual velocity is designed based on the backstepping control method to achieve the system consensus and the bound on convergence time only depending on the system ***,an event-triggered mechanism is presented to solve the problem of frequent communication between agents,and triggered condition based on state information is given for each *** is available to save communication resources,and the Zeno behaviors are ***,the delay and switching topologies of the system are also ***,the system stabilization is analyzed by Lyapunov stability ***,simulation results demonstrate the validity of the presented method.

关键词： event-triggered mechanism fixed-time consensus multi-agent systems switching topologies

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Mutual character dialogue generation with semi-supervised multitask learners and awareness

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1357-1363页

作者： Ahrari Khalaf, Ayesheh Hassan Abdalla Hashim, Aisha Olowolayemo, Akeem Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia Department of Computer Science Faculty of Information and Communication Technology International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia

Consistent efforts have been ongoing to improve the friendliness and reliability of informal dialogue systems. However, most research focuses solely on mimicking human-like answers. Therefore, the interlocutors’ awareness features of the dialogue system are left unexplored. Meanwhile, cognitive science research reveals that awareness is a crucial indicator of an effective, high-quality informal conversation. This research aims to boost the quality of the conversational generation system by factoring in awareness of the interlocutors in the design and training of the dialogue system model. The Generative Pre-Trained Transformer-2 (GPT-2) model was implemented into the Persona Perception (P²) Bot to achieve the objectives of this study. This was to precisely develop model's understanding, P² Bot was implemented using a transmitter–receiver-based structure. The P² Bot leverages mutual persona awareness to improve the quality of customized dialogue generation. GPT-2 is a 1.5B parameter transformer model that produces state-of-the-art accuracy in a zero-shot setting on seven of the eight evaluated language modeling datasets. The observations of the proposed model on a sizable open-source dataset, PERSONA-CHAT, proved successful, with improvement above the state-of-the-art baselines in both automatic measures and human assessments. The model has achieved 82.2% accuracy on Hits@1 performance metrics in the original data and 68.8% on the revised data. On the human evaluation, the model scored an average of 2.66, pointing out that the responses provided were coherent and informative. A dialogue generation model with character and awareness which can communicate like an informative human expert was introduced. This study presents the submerging of GPT-2 model on a mutual persona perception dialogue generating model. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Cognitive science Conversational agent Dialogue generation Generative Pre-trained Transformer 2 (GPT-2) Natural language understanding (NLU)

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Clustering column-mean quantile median: a new methodology for imputing missing data

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Journal of engineering and Applied science 2022年第1期69卷 113页

作者： Yehia, Nourhan Wahed, Manal Abdel Mabrouk, Mai Said Biomedical Engineering Department Faculty of Engineering Misr University for Science and Technology University October City Egypt Systems and Biomedical Engineering Department Faculty of Engineering Cairo University Cairo Egypt

DNA microarray data sets have been widely explored and used to analyze data without any previous biological background. However, analyzing them becomes challenging if data are missing. Thus, machine learning techniques are applied because microarray technology is promising in genomics, especially in the analysis of gene expression data. Furthermore, gene expression data can describe the transcription and translation processes of each genetic information in detail. In this study, a new system was proposed to impute more realizable values for missing data in a microarray dataset. This system was validated and evaluated on 42 samples of rectal cancer. Several evaluation tests were also conducted to confirm the effectiveness of the new system and compare it with highly known imputing algorithms. The proposed clustering column-mean quantile median technique could predict highly informative missing genes, thereby reducing the difference between the original and imputed datasets and demonstrating its efficiency. © 2022, The Author(s).

关键词： Machine learning

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A salient feature establishment tactic for cassava disease recognition

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Artificial Intelligence in Agriculture 2024年第4期14卷 115-132页

作者： Jjayu Zhang Baohua Zhang Zixuan Chen Innocent Nyalala Kunjie Chen Junfeng Gao College of Engineering Nanjing Agricultural UniversityNanjing210000 Jjiangsu ProvinceChina Department of Computer Science University of AberdeenUK Faculty of Science Department of Computer ScienceEgerton UniversityNjoroKenya Nanjing Institute of Agricultural Mechanization Ministry of Agriculture and Rural AffairsNanjingJiangsu 210014China

Accurate classification of cassava disease,particularly in field scenarios,relies on object semantic localization to identify and precisely locate specific objects within an image based on their semantic meaning,thereby enabling targeted classification while suppressing rrelevant noise and focusing on key semantic *** advancement of deep convolutional neural networks(CNNs)paved the way for identifying cassava diseases by leveraging salient semantic features and promising high *** study proposes an approach that incorporates three innovative elements to refine feature representation for cassava disease ***,a mutualattention method is introduced to highlight semantic features and suppress irrelevant background features in the feature ***,instance batch normalization(IBN)was employed after the residual unit to construct salient semantic features using the mutualattention method,representing high-quality semantic features in the ***,the RSigELUD activation method replaced the conventional ReLU activation,enhancing the nonlinear mapping capacity of the proposed neural network and further improving fine-grained leaf disease classification performance This approach significantly aided in distinguishing subtle disease manifestations in cassava *** proposed neural network,MAIRNet-101(Mutualattention IBN RSigELUD Neural Network),achieved an accuracy of 95.30%and an F1-score of 0.9531,outperforming EfficientNet-B5 and *** evaluate the generalization capability of MAIRNet,the FGVC-Aircraft dataset was used to train MAIRNet-50,which achieved an accuracy of 83.64%.These results suggest that the proposed algorithm is well suited for cassava leaf disease classification applications and offers a robust solution for advancing agricultural technology.

关键词： Cassava disease classification Irrelevant feature depression Semantic foreground representation Salient semantic features Negative eigenvalue maintain

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A novel deep dual self-attention and Bi-LSTM fusion framework for Parkinson’s disease prediction using freezing of gait: a biometric application

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Multimedia Tools and Applications 2024年第33期83卷 80179-80200页

作者： Habib, Zeeshan Mughal, Muhammad Ali Khan, Muhammad Attique Hamza, Ameer Alturki, Nazik Jamel, Leila Department of Electrical Engineering HITEC University Taxila Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia

Parkinson’s disease (PD) disorder is caused by the imbalance of inhibitory dopamine and excitatory acetylcholine neurotransmitters, which causes hindrance in locomotion. Freezing of gait (FOG), tremors, and bradykinesia are the most debilitating and disconcerting symptoms of Parkinson’s Disease (PD). FOG episodes occur due to neurological control disorder and motorized impairments, which sternly hinder voluntary movement and forward locomotion. The computational intelligence techniques can effectively predict PD from the sensor data. To detect the FOG episodes often experienced by PD patients, this paper utilizes a 5G spectrum that operates at 4.8 GHz. The main idea of the proposed system is to extract the wireless channel characteristics information (containing amplitude variances) that can be integrated into a 5G communication system. This system collects data through wireless devices such as dipole antennas, network interface cards, and RF signal generators. Five human activities were performed: fast walking, slow walking, sitting on a chair, FOG episodes, and voluntary stopping. We proposed a novel deep dual self-attention and Bi-LSTM fusion architecture for PD prediction to train the collected dataset. Two architectures have been proposed, namely Deep Dual Attention Neural Network (D2ANN) and BiLSTM Neural Network (BNN), for the classification of PD using Chanel State Information (CSI) data. After that, the most prominent information is extracted using self-attention activation. A newly informed weighted fusion approach is proposed, and tree growth optimization is employed for the best feature selection. Finally, the selected features are classified using a multilayer full SoftMax neural network. The experimental process was conducted on FOG wireless sensor network data and obtained the highest accuracy of 98.66%. Compared to the results with state-of-the-art techniques, the proposed framework shows improved accuracy, recall, and precision rates. © The Author(s

关键词： Network architecture

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Assessment of Spring Water Security for Small Community Using Internet of Things (IoT) 7th

Assessment of Spring Water Security for Small Community Usin...

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7th International Conference on Architecture and Civil engineering, ICACE 2023

作者： Nadzri, Mohamad Nazrul Hafiz Mohd Othman, Maidiana Razali, Noor Afiza Mat Nordin, Norzaima Department of Civil Engineering Faculty of Engineering Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia Department of Computer Science Faculty of Defence Science and Technology Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia Department of Aeronautic and Aviation Engineering of Computer Science Faculty of Defence Science and Technology Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9789819707508

The securing of clean and sustainable water sources is a fundamental entitlement of individuals and an essential factor in the sustained advancement of societies. Alternative water resources, springs can serve as a primary water source for small, remote communities. The preservation of the security and quality of spring water is of the highest priority. This study provides an assessment of the security of spring water resources within the community, facilitated through the utilization of an Internet of Things (IoT) monitoring system. The study aimed to assess the variations in temperature, turbidity, pH, TDS, and electrical conductivity of spring water on the NDUM campus over the course of a day, specifically during the morning, afternoon, and evening periods. The monitoring was conducted over a span of one year to capture potential patterns and changes. The obtained data is sent to the cloud by using an IoT-based ThingSpeak application to monitor the quality of water. As a result of the collected data, the system provides an efficient water quality parameter monitoring system, and early warning can be taken to reduce any harmful effects to the environment. The outcome of this study may contribute to the proposal of a new way of monitoring water quality on campus by using a real-time system. In addition, the outcome also provides baseline information about water quality for the welfare of society and supports the green campus campaign, which may also help in future water security and sustainability planning for the NDUM campus area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Water quality

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Energy and Bandwidth Based Link Stability Routing Algorithm for IoT

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computers, Materials & Continua 2022年第2期70卷 3875-3890页

作者： D.Kothandaraman A.Balasundaram R.Dhanalakshmi Arun Kumar Sivaraman S.Ashokkumar Rajiv Vincent M.Rajesh School of Computer Science and Artificial Intelligence SR UniversityWarangalTSIndia School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)ChennaiIndia Department of Computer Science and Engineering KCG College of TechnologyChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology(VIT)ChennaiIndia Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennaiIndia

Internet of Things(IoT)is becoming popular nowadays for collecting and sharing the data from the nodes and among the nodes using internet ***,some of the nodes in IoT are mobile and dynamic in *** maintaining the link among the nodes,efficient bandwidth of the links among the mobile nodes with increased life time is a big challenge in IoT as it integrates mobile nodes with static nodes for data *** such networks,many routing-problems arise due to difficulties in energy and bandwidth based quality of *** to the mobility and finite nature of the nodes,transmission links between intermediary nodes may fail frequently,thus affecting the routing-performance of the network and the accessibility of the *** existing protocols do not focus on the transmission links and energy,bandwidth and link stability of the nodes,but node links are significant factors for enhancing the quality of the *** stability helps us to define whether the node is within or out of a coverage *** paper proposed an Optimal Energy and bandwidth based Link Stability Routing(OEBLS)algorithm,to improve the link stable route with minimized error rate and *** this paper,the optimal route from the source to the sink is determined based on the energy and bandwidth,link stability *** the existing routes,the sink node will choose the optimal route which is having less link stability *** stable link is determined by evaluating link stability value using distance and ***-energy of the node is estimated using the current energy and the consumed *** energy is estimated using transmitted power and the received *** bandwidth in the link is estimated using the idle time and channel capacity with the consideration of probability of collision.

关键词： Link stability internet of things optimal energy optimal bandwidth residual energy

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Analysis and Classification of Fake News Using Sequential Pattern Mining

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Big Data Mining and Analytics 2024年第3期7卷 942-963页

作者： M.Zohaib Nawaz M.Saqib Nawaz Philippe Fournier-Viger Yulin He College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060 Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Department of Computer Science Faculty of Computing and Information TechnologyUnivesity of SargodhaSargodha 40100Pakistan

Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual *** introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and *** this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper ***,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic *** obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of ***,the discovered frequent patterns are used as features for fake news *** framework is evaluated with eight classifiers,and their performance is assessed with various *** experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.

关键词： disinformation fake news sequential pattern mining(SPM) frequent patterns classification

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Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

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computers, Materials & Continua 2023年第1期74卷 801-815页

作者： Yongjie Yang Shanshan Tu Raja Hashim Ali Hisham Alasmary Muhammad Waqas Muhammad Nouman Amjad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Information Security and Cybersecurity Unit King Khalid UniversityAbhaSaudi Arabia Computer Engineering Department College of Information TechnologyUniversity of Bahrain32038Bahrain School of Engineering Edith Cowan UniversityJoondalup PerthWA6027Australia School of Engineering University of Management and TechnologyLahorePakistan

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing *** computing has traditionally played an important role in establishing ***,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic ***,IoT networks are vulnerable to unwanted assaults because of their open and shared *** a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is *** this study,we examined the time-related aspects of network traffic *** presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark *** showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

关键词： Fog computing intrusion detection bi-LSTM attention mechanism

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GRIPPY: A VR Grip Controller for Combating Sarcopenia in Elderly

GRIPPY: A VR Grip Controller for Combating Sarcopenia in Eld...

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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Leong, Hoilam Wu, Jiang Lai, Yoke Chin Faulon, Loïc Sun, Xu Lee, Boon Giin University of Nottingham Faculty of Science and Engineering Department of Mechanical Materials and Manufacturing Engineering Ningbo China School of Computer Science China

ISBN: (纸本)9798331514846

As the global population ages, sarcopenia - age-related muscle decline - demands innovative solutions. This paper introduces GRIPPY, a VR grip controller that transforms basic handgrip exercises into immersive, gamified tasks. By integrating sensor-based interaction and VR gameplay, older adults can strengthen grip while maintaining higher motivation. A preliminary test with three participants (ages 68-80) revealed key usability insights, underscoring GRIPPY's potential for enhanced rehabilitation. Future work will refine its design and validate long-term efficacy. © 2025 IEEE.

关键词： Elderly Rehabilitation Grip Strength Immersive Exercise Interactive Training Sarcopenia Virtual Reality

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