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A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

computer Systems Science & engineering 2023年第4期45卷 329-342页

作者： Nada M.Elfatih Elmustafa Sayed Ali Maha Abdelhaq Raed Alsaqour Rashid A.Saeed Department of Electrical and Electronics Engineering Red Sea UniversityPort Sudan33311Sudan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic UniversityRiyadh93499Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Electronics Engineering Sudan University of Science and TechnologyKhartoum11111Sudan

In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection *** optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold ***,these methods do not take into account the effect of sample size and its effect on improving CoR *** general,a large sample size results in more reliable detection,but takes longer sensing time and increases ***,the locally sensed sample size is an optimization ***,optimizing the local sample size for each cognitive user helps to improve CoR *** this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)*** the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.

关键词： Cognitive radio spectrum sensing energy detection double threshold neural network machine learning optimization quality of service

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Secure Image Communication Using Galois Field,Hyper 3D Logistic Map,and B92 Quantum Protocol

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computers, Materials & Continua 2024年第12期81卷 4435-4463页

作者： De Rosal Ignatius Moses Setiadi Nova Rijati Ahmad Rofiqul Muslikh Bonifacius Vicky Indriyono Aceng Sambas Faculty of Computer Science Universitas Dian NuswantoroSemarang50131Central JavaIndonesia Research Center for Quantum Computing and Materials Informatics Faculty of Computer ScienceUniversitas Dian NuswantoroSemarang50131Central JavaIndonesia Faculty of Information Technology Universitas MerdekaMalang65146East JavaIndonesia Faculty of Economics and Business Universitas STRADA IndonesiaKediri64133East JavaIndonesia Faculty of Informatics and Computing Universiti Sultan Zainal AbidinCampus BesutBesut22200TerengganuMalaysia Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaTamansari GobrasTasikmalaya46196Indonesia

In this paper,we propose a novel secure image communication system that integrates quantum key distribution and hyperchaotic encryption techniques to ensure enhanced security for both key distribution and plaintext ***,we leverage the B92 Quantum Key Distribution(QKD)protocol to secure the distribution of encryption keys,which are further processed through Galois Field(GF(28))operations for increased *** encrypted plaintext is secured using a newly developed Hyper 3D Logistic Map(H3LM),a chaotic system that generates complex and unpredictable sequences,thereby ensuring strong confusion and diffusion in the encryption *** hybrid approach offers a robust defense against quantum and classical cryptographic attacks,combining the advantages of quantum-level key distribution with the unpredictability of hyperchaos-based *** proposed method demonstrates high sensitivity to key changes and resilience to noise,compression,and cropping attacks,ensuring both secure key transmission and robust image encryption.

关键词： Galois field hyper 3D logistic map hyperchaotic map image security improved logistic map secure communication quantum security

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Blockchain-based Energy Trading among UAVs and Base Stations for Net-Zero 10

Blockchain-based Energy Trading among UAVs and Base Stations...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Ayaz, Ferheen Nekovee, Maziar Saeed, Nagham University of London City St. George's Department of Computer Science United Kingdom University of Sussex 6 G Lab School of Engineering and Informatics United Kingdom University of West London School of Computing and Engineering United Kingdom

ISBN: (纸本)9798350373011

The recent development in Internet-of-Things (IoT) networks utilizing Unmanned Aerial Vehicles (UAVs) have greatly enhanced system analysis and management. For example, an energy management system can exploit UAVs to intelligently predict output from renewable resources and estimate grid supply. However, UAVs are battery-operated and need to be charged regularly. It has become crucial to maintain supply and achieve net-zero with negligible emissions for high energy demanding IoT networks, such as those incorporating UAVs. A promising solution is to utilize backup batteries of cellular base stations (BSs) which often remain idle when grid supply is consistent. A P2P energy exchange between a BS seller and UAV buyer is possible in exchange of some incentives. However, BS operators may become selfish and not share their energy. Also, they can behave maliciously by selling energy at unreasonably high prices or sharing false information about the original source of energy. This paper proposes an energy trading system between 5G BSs and UAVs. A Reinforcement Learning (RL) algorithm is used by UAVs to select BS which maximizes incentives for both BSs and UAVs. Blockchain security increase energy exchange by 0.26 kWh and 4.9 kWh in presence of malicious and selfish BS seller respectively. © 2024 IEEE.

关键词： Sales

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A Post-structured Analysis of Chinese Thyroid Ultrasound Report Based on Natural Language Processing Technology and TI-RADS Model 24

A Post-structured Analysis of Chinese Thyroid Ultrasound Rep...

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8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Miao, Shumei Sheng, Rongrong Jing, Mang Wang, Zhongmin Yang, Guanyu School of Computer Science and Engineering Southeast University Jiangsu Nanjing China Department of Information the First Affiliated Hospital of Nanjing Medical University Jiangsu Nanjing China Department of Intelligent medical engineering School of Biomedical Engineering and Informatics Nanjing Medical University Jiangsu Nanjing China

ISBN: (纸本)9798400716874

Thyroid nodules are common and multiple diseases of the head and neck. Ultrasound examination is an important imaging method for the diagnosis of benign and malignant thyroid nodules. The extraction of TI -RADS standard-related entity information from ultrasonic reports has an important influence on the intelligent judgment of benign and malignant of thyroid nodule. Based on TI-RADS system model, and using CRFs model and support vector machine technology, this study carried out post-structured analysis of Chinese ultrasonic thyroid report and obtained high accuracy. High quality information extraction provides important information support for the follow-up intelligent decision support, and provides a research basis for the automatic pre-diagnosis of benign and malignant thyroid nodules. © 2024 ACM.

关键词： Endocrinology

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Self-checking Corner of Upper Acute Respiratory Infection Disease Using Certainty Factor Method

Self-checking Corner of Upper Acute Respiratory Infection Di...

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International Electronics Symposium (IES)

作者： Zulqhi Fahri Muda Arna Fariza Entin Martiana Kusumaningtyas Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

The Covid-19 pandemic has altered people’s behaviors, particularly those of students. Students prefer health consultations through telemedicine applications, which in 2020 recorded a surge of up to 600%. Telemedicine applications require costs incurred and inefficient time to conduct consultations. Seeing this, it is necessary to have a health-checking application at an institution, especially a campus, which is free of charge, without direct contact and has valid information, and is supported by artificial intelligence. The solution offered in this research provides a Self-Checking Corner application called Self-Heal as a pre-diagnosis of upper Acute Respiratory Infection (ARI) using the expert system method, Certainty Factor. This paper proposes the creation of an application based on the certainty factor method with valid data from experts. It optimizes the categorization and weighting of scalars, so it can be used for pre- diagnosis before the user consults a doctor. The use of data on symptoms, diseases, and rule tables is obtained from experts, namely doctors so that data validation is more accurate. The accuracy of the initial diagnosis of Acute Respiratory Infection (ARI) disease is close to 80% when the doctor diagnoses directly from 3 trials conducted. The application can be as an application for pre-diagnosis of upper respiratory tract diseases that save time and without cost.

关键词： COVID-19 Accuracy Costs Pandemics Telemedicine Medical services Security

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Recognition of human skin diseases using inception-V3 with transfer learning

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International Journal of Information Technology (Singapore) 2022年第6期14卷 3145-3154页

作者： Mamun, Md. Al Kabir, Md. Solaiman Akter, Morium Uddin, Mohammad Shorif Department of Public Health and Informatics Jahangirnagar University Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh

Skin disease is an irritable disease and may be the motive of deadly to human life. So, all of us ought to be aware of this alarming health problem. Recognition of skin diseases is a very challenging task because of its various characteristics. To avoid delay in treatment, in this paper, five most common skin diseases: Vascular lesion, Solar lentigo, Actinic keratosis, Squamous cell carcinoma, and Basal cell carcinoma have been investigated through the Inception-V3 with and without transfer learning. An extensive experiment is performed, and the model’s effectiveness is tested through standard metrics such as accuracy, F1 score, and AUC of the Receiver Operating Characteristics (ROC) curve. Inception-V3 with transfer learning has achieved the highest test accuracy of 98.16%. The obtained results are also compared with the state-of-the-art approaches. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Convolution neural network Inception V3 Skin diseases Transfer learning

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Gated Self-supervised Learning for Improving Supervised Learning

Gated Self-supervised Learning for Improving Supervised Lear...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Erland Hilman Fuadi Aristo Renaldo Ruslim Putu Wahyu Kusuma Wardhana Novanto Yudistira Departement of Informatics Engineering Faculty of Computer Science Brawijaya University Malang Indonesia

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

In past research on self-supervised learning for image classification, the use of rotation as an augmentation has been common. However, relying solely on rotation as a self-supervised transformation can limit the ability of the model to learn rich features from the data. In this paper, we propose a novel approach to self-supervised learning for image classification using several localizable augmentations with the combination of the gating method. Our approach uses flip and shuffle channel augmentations in addition to the rotation, allowing the model to learn rich features from the data. Furthermore, the gated mixture network is used to weigh the effects of each self-supervised learning on the loss function, allowing the model to focus on the most relevant transformations for classification.

关键词： Deep learning Training Accuracy Supervised learning Self-supervised learning Logic gates Data models

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Multi-Normalization Residual Graph Convolutional Network for 3D Human Pose Estimation

Multi-Normalization Residual Graph Convolutional Network for...

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2025 International Workshop on Advanced Imaging Technology, IWAIT 2025

作者： Pramono, Andy Chang, I-Cheng Puspasari, Betty Dewi Animation Study Program Universitas Negeri Malang Jawa Timur Malang Indonesia Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Informatics Engineering Institut Teknologi Nasional Malang Jawa Timur Malang Indonesia

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

3D human pose estimation (HPE) has improved significantly through Graph Convolutional Networks (GCNs), which effectively model body part ***, GCNs have limitations, including uniform feature transformations across nodes and reliance on skeleton-based graphs that may miss complex motion *** address these issues, we introduce a Multi-Normalization Residual Graph Convolutional Network that fine-tunes the graph structure through affinity multi-normalization and activation, allowing the representation of additional connections beyond the *** extensive ablation study shows that this approach enhances performance with minimal overhead while maintaining the same model size, consistently outperforming state-of-the-art techniques on two benchmark datasets. © 2024 SPIE.

关键词： Musculoskeletal system

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3D-EX: A Unified Dataset of Definitions and Dictionary Examples

3D-EX: A Unified Dataset of Definitions and Dictionary Examp...

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2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023

作者： Almeman, Fatemah Sheikhi, Hadi Espinosa-Anke, Luis CardiffNLP School of Computer Science and Informatics Cardiff University United Kingdom College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia School of Computer Engineering Iran University of Science and Technology Iran AMPLYFI United Kingdom

ISBN: (纸本)9789544520922

Definitions are a fundamental building block in lexicography, linguistics and computational semantics. In NLP, they have been used for retrofitting word embeddings or augmenting contextual representations in language models. However, lexical resources containing definitions exhibit a wide range of properties, which has implications in the behaviour of models trained and evaluated on them. In this paper, we introduce 3D-EX, a dataset that aims to fill this gap by combining well-known English resources into one centralized knowledge repository in the form of triples. 3D-EX is a unified evaluation framework with carefully pre-computed train/validation/test splits to prevent memorization. We report experimental results that suggest that this dataset could be effectively leveraged in downstream NLP tasks. Code and data are available at https://***/F-Almeman/3D-EX. © 2023 Incoma Ltd. All rights reserved.

关键词： Natural language processing systems

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Facial Palsy Image Classification Using Landmarks Extraction and Extreme Learning Machine

Facial Palsy Image Classification Using Landmarks Extraction...

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IEEE EMBS Conference on Biomedical engineering and Sciences (IECBES)

作者： Frisca P. A. Febriyanti Fitra Abdurrachman Bachtiar Agus W. Widodo Informatics Engineering Department Faculty of Computer Science University of Brawijaya Malang Indonesia

ISBN: (数字)9798350383409

ISBN: (纸本)9798350383416

Facial palsy is a condition characterized by facial paralysis, affecting the patient's motor function. Detection is typically done through a clinical expert's direct observation of facial muscles. However, manual examinations are less effective as patients tend to go for health checks when symptoms indicate severe dysfunction. Due to these challenges, a classification system is built as early screening for facial palsy. This research aims to propose the detection of facial palsy using Extreme Learning Machine algorithm. In this research, we use three datasets: CK+, JAFFE, and Youtube Facial Palsy (YFP). The dataset is divided into training and testing data in an 80:20 ratio. Applied preprocessing techniques of landmark facial feature extraction, selection, and Euclidean distance calculation at specific face points. We employ k-fold cross-validation to find the optimal parameter during the training process. We also implemented oversampling and undersampling techniques to handle imbalanced data. The system classifies facial palsy into normal, mild, moderate, and severe dysfunction. The ELM model achieved Accuracy values of 0.9949, Precision of 0.9888, Recall of 0.9887, and F1-score of 0.9888 on balanced data using an oversampling technique. The current research indicates that the implemented techniques can handle imbalanced data, and contribute to better model performance.

关键词： Training Accuracy Video on demand Extreme learning machines Biological system modeling Data models Robustness Data mining Web sites Testing

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