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Blockchain and IIoT Enabled Solution for Social Distancing and Isolation Management to Prevent Pandemics

computers, Materials & Continua 2023年第7期76卷 687-709页

作者： Muhammad Saad Maaz Bin Ahmad Muhammad Asif Muhammad Khalid Khan Toqeer Mahmood Elsayed Tag Eldin Hala Abdel Hameed College of Computing and Information Sciences Karachi Institute of Economics&TechnologyKarachiPakistan Department of Computer Science Lahore Garrison UniversityLahorePakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Faculty of Engineering and Technology Future University in Egypt New Cairo11835Egypt Faculty of Computer and Information Systems Fayoum UniversityFaiyumEgypt Khaybar Applied College Taibah UniversityMedinaSaudi Arabia

Pandemics have always been a nightmare for humanity,especially in developing *** lockdowns are considered one of the effective ways to deal with spreading such ***,developing countries cannot afford such solutions because these may severely damage the country’s ***,this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations *** novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things(IIoT)and blockchain-enabled *** to existing studies,the immutable and tamper-proof contact tracing and quarantine management solution is *** use of advanced technologies and information security is a critical area for practitioners in the internet of things(IoT)and corresponding ***,this study also emphasizes information security,end-to-end solution,and experimental ***,a wearable wristband is proposed,incorporating 4G-enabled ultra-wideband(UWB)technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization(WHO).Secondly,distributed ledger technology(DLT)omits the centralized dependency for transmitting contact tracing ***,a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication ***,based on geofencing techniques,blockchain-enabled machine-to-machine(M2M)technology is proposed for quarantine *** step-by-step methodology and test results are proposed to ensure contact tracing and quarantine *** existing research studies,the security aspect is also considered in the realm of *** practical implementation of the proposed solution also obtains the *** results indicate the successful implementation of blockchain-enabled contac

关键词： Blockchain contact tracing distributed ledger technology geo-fencing internet of things industrial internet of things isolation management social distancing ultra-wideband

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EOG acquisition system based on ATmega AVR microcontroller

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16589-16605页

作者： Abdel-Samei, Abdel-Gawad A. Shaaban, Abdel-Salam Brisha, Ayman M. El-Samie, Fathi E. Abd Ali, Ahmed S. Department of Electronics Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Department of Process of Control Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Department of Mechanical Engineering Mechatronics Division Faculty of Engineering Assiut University Assiut Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

This paper presents a hardware and software of an electrooculogram (EOG) acquisition system based on ATmega AVR microcontroller for the acquisition of vertical and horizontal eye movements. The presented system is used to acquire a dataset of eye movements for volunteers. This system gives two channels representing vertical and horizontal EOG signals. The frequency range of the EOG signal is known to be 0.1 to 10 Hz, and hence this frequency range is isolated with a High-Pass Filter (HPF) with a cutoff frequency of 0.1 Hz followed by a Low-Pass Filter (LPF) with a cutoff frequency of 10 Hz. The EOG acquisition system is interfaced with an ATmega AVR microcontroller to acquire a dataset that can be used for controlling hardware such as Light Emitting Diodes (LEDs), wheelchair, and robot arm. The presented system is composed of EOG signal acquisition, Ag/AgCl electrodes, analog-to-digital converter through Arduino Mega 2560 board microcontroller unit, trainer board, laptop, keypad, and Liquid Crystal Display (LCD). The eye movement is detected by measuring the potential difference between cornea and retina using five Ag–Agcl disposable electrodes. Different volunteers of different ages at different times have been treated with the presented system to obtain data. Classified vertical and horizontal EOG signals and the basic eye movements e.g., open eye, left, right, up, and down can be used to control robots and wheelchairs for rehabilitation purposes. © 2023, The Author(s).

关键词： Electrodes

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Security and Reliability Performance Analysis of Cognitive NOMA Network Under Outage Constraint of Multiple Primary Users 23

Security and Reliability Performance Analysis of Cognitive N...

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12th International Symposium on information and Communication technology, SOICT 2023

作者： Pham Huu, Tung Tran, Hung Tran, Duc-Tan Dang, Viet-Hung Nhan Vo, Van Nguyen Dinh, Viet Faculty of Information Technology Hanoi University of Civil Engineering Hanoi Viet Nam Faculty of Computer Science Phenikaa Univerity Hanoi Viet Nam Faculty of Electrical and Electronic Engineering Phenikaa Univerity Hanoi Viet Nam Faculty of Information Technology Duy Tan University Da Nang Viet Nam Faculty of Information Technology Vnu University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9798400708916

In this paper, we conduct an investigation into the security and reliability aspects of a cognitive non-orthogonal multiple access (NOMA) network operating under outage constraints imposed by multiple primary users (PUs). Specifically, we examine a scenario where secondary users (SUs) have the capability to operate in NOMA mode, allowing them to harness the licensed spectrum band of multiple PUs, provided that the interference generated by SUs remains below a predefined threshold. Given that context, both SUs and PUs are susceptible in this spectrum-sharing environment, a potential threat arises in the form of an eavesdropper who seeks to exploit the secrecy of messages exchanged among SUs. Given this setting, we derive a power allocation policy for the SUs, formulate closed-form expressions for outage probability (OP) and intercept probability (IP) to assess system performance, and analyze the trade-off between reliability and security. Finally, we supplement our findings with numerical examples and discussions to illustrate the proposed concepts. © 2023 ACM.

关键词： Economic and social effects

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Improved Accuracy of Decision Tree with XGBoost Technique in Cat Skin Disease Diagnosis 4

Improved Accuracy of Decision Tree with XGBoost Technique in...

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4th International Conference of Science and information technology in Smart Administration, ICSINTESA 2024

作者： Safrina, Nanda Lydia, Maya Silvi Nasution, Benny Benyamin Universitas Sumatera Utara Master of Informatics Faculty of Computer Science and Information Technology Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia

ISBN: (纸本)9798350376111

This research aims to develop an expert system for initial diagnoses of skin diseases in cats using the Decision Tree method. It assists cat owners in identifying skin diseases based on observed symptoms. Data from expert veterinarian drh. Ismi Azima, including various types of cat skin diseases and their symptoms, were used. The research employs 80% training data and 20% testing data to develop the classification model. Testing results indicate the Decision Tree model achieves up to 90% average accuracy in diagnosing cat skin diseases based on 50 datasets. This expert system aids in diagnosis and provides treatment and prevention solutions, beneficial for cat owners, especially in areas with limited veterinary access. In conclusion, this Decision Tree-based expert system effectively provides initial diagnoses of skin diseases in cats with high accuracy. © 2024 IEEE.

关键词： Cat Skin Diseases Decision Tree Expert System XGBoost

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Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification

Contrastive Masked Autoencoders for Character-Level Open-Set...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Jiang, Xiaowei Ma, Wenhao Duan, Yiqun Do, Thomas Lin, Chin-Teng Australian Ai Institute School of Computer Science University of Technology GrapheneX-UTS Human-centric Ai Centre Faculty of Engineering and Information Technology Sydney Australia

ISBN: (纸本)9781665410205

In the realm of digital forensics and document authentication, writer identification plays a crucial role in determining the authors of documents based on handwriting styles. The primary challenge in writer-id is the 'open-set scenario', where the goal is accurately recognizing writers unseen during the model training. To overcome this challenge, representation learning is the key. This method can capture unique handwriting features, enabling it to recognize styles not previously encountered during training. Building on this concept, this paper introduces the Contrastive Masked Auto-Encoders (CMAE) for Character-level Open-Set Writer Identification. We merge Masked Auto-Encoders (MAE) with Contrastive Learning (CL) to simultaneously and respectively capture sequential information and distinguish diverse handwriting styles. Demonstrating its effectiveness, our model achieves state-of-the-art (SOTA) results on the CASIA online handwriting dataset, reaching an impressive precision rate of 89.7%. Our study advances universal writer-id with a sophisticated representation learning approach, contributing substantially to the ever-evolving landscape of digital handwriting analysis, and catering to the demands of an increasingly interconnected world. © 2024 IEEE.

关键词： Contrastive Learning

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Boundary Extraction for Unseen Entities Prediction Using Bidirectional Long Short-Term Memory 17th

Boundary Extraction for Unseen Entities Prediction Using Bid...

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17th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Kuntichod, Watcharet Lee, Wilaiporn Prayote, Akara Department of Computer and Information Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

ISBN: (纸本)9789819606917

Named Entities Recognition (NER), which is a task to identify and classify named entities within text, has gained significant popularity in recent years. This task often requires pre-labeled data and large datasets to achieve high accuracy. A key challenge in NER is extracting unseen entities that have not been previously labeled or are new terms, posing difficulties in adapting to rapidly changing data and the emergence of new vocabularies. In this article, we propose a model utilizing Bidirectional Long Short-Term Memory (Bi-LSTM) for boundary extraction. The model aims to enhance the detection and labeling of unseen entities by leveraging Bi-LSTM's strengths in capturing contextual information and accurately identifying entities boundaries. The model demonstrates high potential in identifying unseen names without pre-labeling. Our study demonstrates the model's showcasing its effectiveness in discovering unlabeled words and unseen entities in Thai language datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Labeled data

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Improving Prediction Accuracy in IoT Network Security Against DDoS Attacks by Combining RIPPER and KNN Algorithms 8

Improving Prediction Accuracy in IoT Network Security Agains...

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8th International Conference on Electrical, Telecommunication and computer engineering, ELTICOM 2024

作者： Juliansyah, Handrino Sihombing, Poltak Fahmi, Fahmi Universitas Sumatera Utara Faculty of Computer Science and Information Technology North Sumatera Medan Indonesia Universitas Sumatera Utara Faculty of Engineering Electrical Engineering Dept North Sumatera Medan Indonesia

ISBN: (纸本)9798331531249

The Internet of Things (IoT) is a network of linked devices comprising interconnected devices that communicate through internet, integrating sensors, software and other technologies. As the number of connected devices grows, ensuring IoT security has become increasingly critical, with cyber threats and other related risks arising. A prevalent threat is the Distributed Denial of Service (DDoS) attack, which seeks to incapacitate server systems by overwhelming the network with excessive traffic. This study explores machine learning approach to improve intrusion detection systems (IDS) performance, such as accuracy in identifying and mitigating DDoS attacks. Specifically, it employs a hybrid approach combining the well-known RIPPER algorithm or Repeated Incremental Pruning to Produce Error Reduction and the K-Nearest Neighbors (KNN) algorithm as detection models. The study utilizes the Network Security Layer-Knowledge Discovery in Database (NSL-KDD) dataset, which contains 41 features for classifying DDoS attacks. To optimize the model's performance, the data underwent preprocessing to eliminate irrelevant and redundant features while retaining essential information. The classification results demonstrate a peak accuracy of 97.32% with k=6 and an average accuracy of 96.30% across 15 trials with varying k values from 1 to 15. These findings highlight the effectiveness of the RIPPER-KNN approach in improving the prediction accuracy of DDoS attack detection in IoT networks. © 2024 IEEE.

关键词： Denial-of-service attack

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OntoDSO: an ontological-based dolphin swarm optimization (DSO) approach to perform energy efficient routing in Wireless Sensor Networks (WSNs)

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International Journal of information technology (Singapore) 2024年第3期16卷 1551-1557页

作者： Boopathi, Mythili Parikh, Swapnil Awasthi, Aishwary Malviya, Ashwini Nachappa, M.N. Mishra, Awakash Shyam, Gopal Krishna Narula, Gagandeep Singh Department of Information Technology Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Mechanical Engineering Sanskriti University UP Mathura India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science and Information Technology Jain (Deemed to be University) Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Noida India Department of Computer Science Engineering Presidency University Bengaluru India Guru Gobind Singh Indraprastha University Delhi Dwarka India

The aim of the paper is to perform an energy-efficient routing for moving nodes in Wireless Sensor Networks (WSNs) by using an ontological-based dolphin swarm optimization (DSO) approach. It makes use of echolocation and ontology to locate and represent cluster nodes close to the sink thereby maintaining optimal energy consumption during simulation. A comparative analysis for 20 samples (N = 10 each) is performed between OntoDSO, Ad-hoc On-demand Distance Vector (AODV) algorithm and existing literature review studies by taking several parameters namely mean delay, mean packet delivery ratio and mean energy consumption into consideration. The simulation of results is performed using MATLAB Simulink with implementation parameters such as number of nodes (n) and initial battery life. As a result, it is found that the OntoDSO approach identifies a set of optimized routes that can satisfy delay constraints and consume less energy, thereby achieving higher performance than AODV and existing studies in the context of WSNs. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Ad-hoc On-demand Distance Vector (AODV) algorithm Dolphin Swarm Optimization (DSO) algorithm Packet delivery ratio and energy consumption Wireless Sensor Network (WSN)

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Combining Entropy Optimization and Sobel Operator for Medical Image Fusion

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computer Systems Science & engineering 2023年第1期44卷 535-544页

作者： Nguyen Tu Trung Tran Thi Ngan Tran Manh Tuan To Huu Nguyen Faculty of Computer Science and Engineering Thuyloi University175 Tay SonDong DaHanoi010000Vietnam University of Information and Communication Technology Thai Nguyen UniversityThai Nguyen240000Vietnam

Fusing medical images is a topic of interest in processing medical *** is achieved to through fusing information from multimodality images for the purpose of increasing the clinical diagnosis *** fusion aims to improve the image quality and preserve the specific *** methods of medical image fusion generally use knowledge in many differentfields such as clinical medicine,computer vision,digital imaging,machine learning,pattern recognition to fuse different medical *** are two main approaches in fusing image,including spatial domain approach and transform domain *** paper proposes a new algorithm to fusion multimodal *** algorithm is based on Entropy optimization and the Sobel *** transform is used to split the input images into components over the low and high frequency ***,two fusion rules are used for obtaining the fusing *** rule,based on the Sobel operator,is used for high frequency *** second rule,based on Entropy optimization by using Particle Swarm Optimization(PSO)algorithm,is used for low frequency *** algorithm is implemented on the images related to central nervous system *** experimental results of the paper show that the proposed algorithm is better than some recent methods in term of brightness level,the contrast,the entropy,the gradient and visual informationfidelity for fusion(VIFF),Feature Mutual information(FMI)indices.

关键词： Medical image fusion wavelet entropy optimization PSO Sobel operator

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An Improved Convolutional Neural Network Model for DNA Classification

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computers, Materials & Continua 2022年第3期70卷 5907-5927页

作者： Naglaa.F.Soliman Samia M.Abd-Alhalem Walid El-Shafai Salah Eldin S.E.Abdulrahman N.Ismaiel El-Sayed M.El-Rabaie Abeer D.Algarni Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt

Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(CNN)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DNA)*** any CNN model,convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features.A novel method based on downsampling and CNNs is introduced for feature *** downsampling is an improved form of the existing pooling layer to obtain better classification *** two-dimensional discrete transform(2D DT)and two-dimensional random projection(2D RP)methods are applied for *** convert the high-dimensional data to low-dimensional data and transform the data to the most significant feature ***,there are parameters which directly affect how a CNN model is *** this paper,some issues concerned with the training of CNNs have been *** CNNs are examined by changing some hyperparameters such as the learning rate,size of minibatch,and the number of *** and assessment of the performance of CNNs are carried out on 16S rRNA bacterial *** results indicate that the utilization of a CNN based on wavelet subsampling yields the best trade-off between processing time and accuracy with a learning rate equal to 0.0001,a size of minibatch equal to 64,and a number of epochs equal to 20.

关键词： DNA classification CNN downsampling hyperparameters DL 2D DT 2D RP

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