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A GSO-based multi-objective technique for performance optimization of blockchain-based industrial Internet of things

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International Journal of communication systems 2024年第15期37卷

作者： Zanbouri, Kouros Darbandi, Mehdi Nassr, Mohammad Heidari, Arash Navimipour, Nima Jafari Yalcın, Senay Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Pôle Universitaire Léonard de Vinci Paris France Communication Technology Engineering Department Tartous University Tartus Syria Department of Mathematics and Natural Sciences Gulf University for Science and Technology Mishref Campus Kuwait Department of Computer Engineering Faculty of Engineering and Natural Science İstanbul Atlas University Turkey Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Research Center of High Technologies and Innovative Engineering Western Caspian University Baku Azerbaijan Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Energy System Engineering School of Engineering and Natural Sciences Bahçeşehir University Istanbul Turkey

The latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain-based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain-based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain-based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision-making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain-based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations. © 2024 John Wiley & Sons Ltd.

关键词： Multiobjective optimization

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Large Scale Model Enabled Semantic communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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IEEE Conference on Global communications (GLOBECOM)

作者： Kuiyuan Ding Fangfang Liu Yang Yang Mingzhe Chen Caili Guo Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and the Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC.

关键词： Transmitters Computational modeling Noise Autoencoders Receivers Semantic communication Transformers Feature extraction Robustness Decoding

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Brain-computer Interface system Based on Common Spatial Patterns for Inner Speech Recognition from Electroencephalography Signal by Using a Convolutional Neural Networks

Brain-Computer Interface System Based on Common Spatial Patt...

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IEEE International Conference on systems, Man and Cybernetics

作者： Hussna Elnoor M. Abdalla Syed Abdul Rahman Al-Haddad Hamidon Bin Basri Ishak Bin Aris Abdul Hanif Khan Bin Yusofv Hisham Neyaz University Putra Malaysia Malaysia & Sudan International University Sudan Department of Computer and Communication System Engineering University Putra Malaysia Department of Medicine Faculty of Medicine and Health Sciences University Putra Malaysia Department of Electrical and Electronic Engineering University Putra Malaysia Malaysia Department of Medicine Faculty of Medicine and Health Sciences University Putra Malaysia

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Inner speech recognition is a modern advancement in Brain computer interfaces (BCI) that facilitates a communication between the computer and the brain in a direct way. It is particularly beneficial for individuals who face communication issues due to speech disability. In this research, an end-to-end brain-computer interface system was developed that uses Electroencephalography (EEG) signals to recognize inner speech straight from the brain. The study utilized an open-access dataset presented on four Spanish words from ten participants. The proposed system involved preprocessing to clean and enhance the signal. Common Spatial Pattern (CSP) was utilized to extract features to improve the discrimination between the selected four words. A Convolutional Neural Networks (CNNs) deep learning model was used to enhance the inner speech recognition performance from spatial features. The result shows that the model can decode the inner speech with an average accuracy of 81.40% for the un-seeing dataset and 89.80% for the entire dataset, indicating that the proposed method outperforms previous works that used the same dataset.

关键词： Deep learning Face recognition Speech recognition Feature extraction Brain modeling Brain-computer interfaces Electroencephalography Convolutional neural networks Cybernetics Biological neural networks

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Review of 3D Networks-On-Chip Simulators and Plugins

Review of 3D Networks-On-Chip Simulators and Plugins

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2021 IEEE Conference on Postgraduate Research in Microelectronics and Electronics, PRIMEASIA 2021

作者： Al-Hchaimi, Ahmed Abbas Jasim Flayyih, Wameedh Nazar Hashim, Fazirulhisyam Rusli, Mohd Shahrizal Rokhani, Fakhrul Zaman Universiti Putra Malaysia Department of Computer and Communication Systems Engineering Malaysia University of Baghdad College of Engineering Iraq Universiti Teknologi Malaysia School of Electrical Engineering Malaysia Universiti Putra Malaysia System-On-Chip Research Center Malaysia

ISBN: (纸本)9781665439374

A comprehensive review focuses on 3D network-on-chip (NoC) simulators and plugins while paying attention to the 2D simulators as the baseline is presented. Discussions include the programming languages, installation configuration, platforms and operating systems for the respective simulators. In addition, the simulator's properties and plugins for design metrics evaluations are addressed. This review is intended for the early career researchers starting in 3D NoC, offering selection guidelines on the right tools for the targeted NoC architecture, design, and requirements. © 2021 IEEE.

关键词： Network-on-chip

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Predictive Performance and Clinical Implications of Machine Learning in Early Coronary Heart Disease Detection

Predictive Performance and Clinical Implications of Machine ...

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communication & Computing (WCONF), World Conference on

作者： V Dankan Gowda Annepu Arudra K M Mouna Sanjog Thapa Vaishali N. Agme KDV Prasad Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Department of Computer Science & Engineering Rajiv Gandhi Institute of Technology Bangalore Karnataka India Department of Electronics & Communication Engineering BGS Institute of Technology/ACU Mandya Karnataka India Department of Information System and Analysis Lamar University Beaumont Texas USA Department of Applied Science Bharati Vidyapeeth College of Engineering Navi Mumbai Maharashtra India Department of Research Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

The identification of CHD in the early stage is crucial due to a high probability of death and ineffective treatment outcomes. This paper assesses the feasibility as well as clinical usefulness of several machine learning techniques in the early diagnosis of CHD. We employed a large set of data containing patients' features: demographic characteristics, health history, results of tests and diagnosis. The work of the study was to use Random Forest, Support Vector Machine and Neural Networks to estimate the probability of the incidence of CHD. Cohesion was performed using cross-validation for training and validation of the models with accuracy, precision, recall, F1 score, and ROC-AUC study parameters. The findings of this study show that through machine learning, the identification and diagnosis of CHD early stages can be significantly improved with the RF model giving the highest accuracy. Challenges and possibilities of using these predictive tools in clinical practice are examined, with the focus on possible enhancements of patients' treatment and outcomes. Thus, the research under consideration proved that machine learning-based predictive models can be considered a promising avenue for the early diagnosis of CHD, which should be further explored and implemented.

关键词： Heart Training Support vector machines Radio frequency Accuracy Neural networks Predictive models Vectors Random forests Diseases

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Fiber-optics IoT healthcare system based on deep reinforcement learning combinatorial constraint scheduling for hybrid telemedicine applications

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computers in Biology and Medicine 2024年 178卷 108694-108694页

作者： Lakhan, Abdullah Nedoma, Jan Mohammed, Mazin Abed Deveci, Muhammet Fajkus, Marcel Marhoon, Haydar Abdulameer Memon, Sajida Martinek, Radek Department of Cybersecurity and Computer Science Dawood University of Engineering and Technology Sindh Karachi City74800 Pakistan Department of Artificial Intelligence College of Computer Science and Information Technology University of Anbar Anbar 31001 Iraq Department of Industrial Engineering Turkish Naval Academy National Defence University Istanbul Tuzla34942 Turkey Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon College of Computer Sciences and Information Technology University of Kerbala Karbala Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Department of Computer System Engineering and Technology Dawood University of Engineering and Technology Sindh Karachi City74800 Pakistan Department of Telecommunications VSB-Technical University of Ostrava Ostrava70800 Czech Republic Department of Cybernetics and Biomedical Engineering VSB-Technical University of Ostrava Ostrava70800 Czech Republic

Telemedicine is an emerging development in the healthcare domain, where the Internet of Things (IoT) fiber optics technology assists telemedicine applications to improve overall digital healthcare performances for society. Telemedicine applications are bowel disease monitoring based on fiber optics laser endoscopy, gastrointestinal disease fiber optics lights, remote doctor-patient communication, and remote surgeries. However, many existing systems are not effective and their approaches based on deep reinforcement learning have not obtained optimal results. This paper presents the fiber optics IoT healthcare system based on deep reinforcement learning combinatorial constraint scheduling for hybrid telemedicine applications. In the proposed system, we propose the adaptive security deep q-learning network (ASDQN) algorithm methodology to execute all telemedicine applications under their given quality of services (deadline, latency, security, and resources) constraints. For the problem solution, we have exploited different fiber optics endoscopy datasets with images, video, and numeric data for telemedicine applications. The objective is to minimize the overall latency of telemedicine applications (e.g., local, communication, and edge nodes) and maximize the overall rewards during offloading and scheduling on different nodes. The simulation results show that ASDQN outperforms all telemedicine applications with their QoS and objectives compared to existing state action reward state (SARSA) and deep q-learning network (DQN) policy during execution and scheduling on different nodes. © 2024 Elsevier Ltd

关键词： Reinforcement learning

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Identifying Inaccurate Stories: Leveraging High Knowledge and Big Data

Identifying Inaccurate Stories: Leveraging High Knowledge an...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Mohammad Ahmar Khan Pushpendra Kumar Verma Shivangi Ghaldiyal N. V. Balaji G.Arun Francis Swati Bula Patil Department of Management Information System College of Commerce & Business Administration Dhofar University Country Sultanate of Oman School of Computer Science and Applications IIMT University Meerut Department of Computer Applications Invertis University Bareilly Uttar Pradesh India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore Tamil Nadu India IT Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Research on fake news has gained significant traction in a number of fields, We describe how the issue is tackled from a natural language processing standpoint in this research with the aim of developing identify false material in news. Collecting high-quality data, such as examples of false and actual news stories on a diverse range of subjects, is the key obstacle in this field of study. We examine the available before introducing the site as our lab’s gift to the. We provide the whole text of the news that have already been applied based on human evaluation of the articles’ authenticity. In order to clarify the gaps and causes of imbalance in the datasets that are now accessible and to direct ongoing attempts, we also conduct an appearance-based experiment. We urge the public to gather more information and make it accessible for study.

关键词： Deep learning Accuracy Text categorization Training data Big Data Natural language processing Reliability Labeling Fake news

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Novel orange-red glaze based on in-doped CaFe2O4 red chromophore: Synthesis, properties and characterizations

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Ceramics International 2025年

作者： Qi, Menghang Tian, Liangsheng Wang, Qiuying Xiao, Ruoxiu Hu, Nan Shi, Lin Siritanon, Theeranun Jiang, Peng Department of Inorganic Nonmetallic Materials School of Materials Science and Engineering University of Science and Technology Beijing Beijing100083 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Institute of Surface-Earth System Science School of Earth System Science Tianjin University Tianjin300072 China School of Chemistry Institute of Science Suranaree University of Technology 111 University Avenue Muang Nakhon Ratchasima30000 Thailand

The CaFe2-xInxO4 (0 ≤ x ≤ 1) pigment with red shade was successfully synthesized through solid state reactions. The crystal structure, color development mechanism, morphology and its application in the glaze were studied. XPS analysis confirmed Fe resides in +3 oxidation state. Combined with UV–Vis–NIR analysis, it is determined that the formation of red shade of the pigment is due to the 6A1(S) → 4T2(D), 6A1(S) → 4A1, 4E(G) and 6A1(S) → 4T2(G) transitions of Fe3+ in the visible light region. The pigment was applied to the lead-free low temperature glaze to evaluate its potential in preparing new glaze color. The as-synthesized glaze showed orange-red shade. With increased In3+ doping concentration, the glaze demonstrates a stronger red tone. The glaze also shows high NIR solar reflectance. Inferred from SEM-EDS, the pigment particles are evenly scattered in the glaze. Therefore, CaFe2-xInxO4 (0 ≤ x ≤ 1) red pigment is a qualified candidate for red shade glaze preparation. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Crystal structure

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Intelligent Spectrum Sensing with ConvNet for 5G and LTE Signals Identification

Intelligent Spectrum Sensing with ConvNet for 5G and LTE Sig...

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IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Thien Huynh-The Quoc-Viet Pham Thai-Hoc Vu Daniel Benevides da Costa Van-Phuc Hoang Department of Computer and Communication Engineering HCMC University of Technology and Education Vietnam Institute of System Integration Le Quy Don Technical University Vietnam School of Computer Science and Statistics Trinity College Dublin Ireland Department of Electrical Electronic and Computer Engineering University of Ulsan Republic of Korea Technology Innovation Institute Abu Dhabi United Arab Emirates

The paper presents an intelligent spectrum sensing approach for next-generation wireless networks by exploiting deep learning, in which we develop a deep convolutional network (ConvNet) to automatically identify Fifth Generation New Radio (5G NR) and Long-Term Evolution (LTE) signals under standards-specified channel models with diversified RF impairments. In particular, we design a semantic segmentation ConvNet to detect and localize the spectral content of 5G NR and LTE in a synthetic signal featured by spectrum occupancy. A received signal is first converted by a short-time Fourier transform and represented as a wideband spectrogram image which is then passed through the ConvNet, incorporated by DeepLabv3+ and ResNet18 to improve the accuracy of pixel-wise segmentation to further increase the accuracy of signal identification. In the simulations, our ConvNet achieves around 95% mean accuracy and 91% mean intersection-over-union (IoU) at medium SNR level and demonstrates robustness under various practical channel impairments.

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Detection of Human Stress Using Optimized Feature Selection and Classification in ECG Signals

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Mathematical Problems in engineering 2023年第1期2023卷

作者： Narmadha, G. Deivasigamani, S. Vellaisamy, Muthukumar Freitas, Lídio Inácio Ahmad, Badlishah R. Sakthivel, B. Department of Electrical and Electronics Engineering Sethu Institute of Technology Tamil Nadu Virudhunagar626115 India Faculty of Engineering Technology and Built Environment UCSI University Cheras Kuala Lumpur56000 Malaysia School of Engineering Faculty of Computing and Engineering Quest International University Ipoh Perak30250 Malaysia Department of Mechanical Engineering Dili Institute of Technology Dili Timor-leste Computer and Communication System University Malaysia Perlis Arau Malaysia Department of Electronics and Communication Engineering PSY Engineering College Tamil Nadu Sivagangai630561 India

An autonomic nervous system (ANS) of humans is majorly affected by psychological stress. The changes in ANS may cause several chronic diseases in humans. The electrocardiogram (ECG) signal is used to observe the variation in ANS. Numerous techniques are presented for an ECG stress signal handling feature extraction and classification. This work managed a heart rate variability feature acquired from smaller peak waveforms such as P, Q, S, and T waves. Also, the R peak is detected, which is a significant part of the ECG waveform. In this work, the proposed stress classification work has been categorized into two main processes: feature selection (FS) and classification. The main aim of the proposed work is to propose an optimized FS and classifier model for the detection of stress in ECG signals. The Metaheuristics model of the African vulture optimization (AVO) technique is presented to perform an FS. This selection is made to choose the required features and minimize the data for classification. The AVO-based modified Elman recurrent neural network (MERNN) technique is proposed to perform an efficient classification. The AVO is used for fine-tuning the weight of the MERNN technique. The experimental result of this technique is evaluated in terms of Recall (91.56%), Accuracy (92.43%), Precision (92.78%), and F1 score (95.86%). Thus, the proposed performance achieved a superior result than the conventional techniques. © 2023 Narmadha G et al.

关键词： Classification (of information)

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