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2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021

作者： Javokhir, Musaev Nguyen, Ngoc Thanh Hwang, Dosam Yeungnam University Department of Computer Engineering Gyeongsan Korea Republic of Wroclaw University of Science and Technology Department of Applied Informatics Wroclaw50-570 Poland Faculty of Information Technology Nguyen Tat Thanh University Ho Chi Minh City70000 Viet Nam

ISBN: (纸本)9781665436038

The application of computer vision (CV) in many fields is becoming an important factor in development, and the demand for high accuracy CV products is increasing. Therefore, a new method that ensembles three trained models with the same architecture but different inputs is proposed. Each of the different channels of the images were used as input. The first channel of the image was used in the dataset for the first model, the second channel was used for the second model, and the third channel was used for the third model. The classification probabilities were achieved after training three different models with three channels. The model with the highest accuracy was selected as the main model, and it was added to the classification probabilities of certain classes of the main model. Applying this method to the classification task resulted in 75.45% accuracy, while using the entire image for the model resulted in 70.9% accuracy. The advantage of using this ensemble method is that it can be used simultaneously with other ensemble methods and achieves better results. Dividing an image into three different channels than simply using the entire image helps the model learn the image better. © 2021 IEEE.

关键词： Probability

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STAR-RIS-NOMA Assisted Power Constrained Covert Communication Systems

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IEEE Transactions on Vehicular Technology 2025年

作者： Lei, Yuxin Huang, Gaojian Li, Xingwang Tian, Zhifa Mumtaz, Shahid Asif, Muhammad Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China City University of Macau Faculty of Data Science 999078 China Henan Polytechnic University School of Surveying and Land Information Engineering Jiaozuo454003 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454099 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Silesian University of Technology Department of Applied Informatics Gliwice Poland Department of Engineering Nottingham Trent University United Kingdom Jiangsu University School of Computer Science and Communication Engineering Zhenjiang China

In this paper, a covert communication system assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in non-orthogonal multiple access (NOMA) networks is proposed. When the maximum channel power gain from the transmitter in the main network to the users in adjacent networks is smaller than the threshold, the transmitter sends the NOMA signals to the users with the assistance of STAR-RIS. Moreover, channel estimation errors and imperfect successive interference cancellation (SIC) are considered in the system analysis. To characterize the covert performance, the detection error probability (DEP) and the average minimum detection error probability (AMDEP) are first derived. Then, the closed-form solutions of the outage probability (OP) for the users are obtained. Finally, the maximum effective covert rate (ECR) subject to the covertness constraint and non-interruption constraint is studied. The numerical simulation results show that: 1) the detection performance of Willie (warden) deteriorates as the channel estimation errors increase;2) when the transmitter power exceeds the threshold, communication in the main network is interrupted;3) under a certain transmit power, the maximum ECR increases with the increasing of the number of STAR-RIS reflection elements. © 2025 IEEE.

关键词： Channel estimation

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Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis

arXiv

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arXiv 2023年

作者： Bera, Asish Nasipuri, Mita Krejcar, Ondrej Bhattacharjee, Debotosh The Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Campus Rajasthan Pilani333031 India The Department of Computer Science and Engineering Jadavpur University Kolkata32 India The Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic

Human body-pose estimation is a complex problem in computer vision. Recent research interests have been widened specifically on the Sports, Yoga, and Dance (SYD) postures for maintaining health conditions. The SYD pose categories are regarded as a fine-grained image classification task due to the complex movement of body parts. Deep Convolutional Neural Networks (CNNs) have attained significantly improved performance in solving various human body-pose estimation problems. Though decent progress has been achieved in yoga postures recognition using deep learning techniques, fine-grained sports, and dance recognition necessitates ample research attention. However, no benchmark public image dataset with sufficient inter-class and intra-class variations is available yet to address sports and dance postures classification. To solve this limitation, we have proposed two image datasets, one for 102 sport categories and another for 12 dance styles. Two public datasets, Yoga-82 which contains 82 classes and Yoga-107 represents 107 classes are collected for yoga postures. These four SYD datasets are experimented with the proposed deep model, SYD-Net, which integrates a patch-based attention (PbA) mechanism on top of standard backbone CNNs. The PbA module leverages the self-attention mechanism that learns contextual information from a set of uniform and multi-scale patches and emphasizes discriminative features to understand the semantic correlation among patches. Moreover, random erasing data augmentation is applied to improve performance. The proposed SYD-Net has achieved state-of-the-art accuracy on Yoga-82 using five base CNNs. SYD-Net’s accuracy on other datasets is remarkable, implying its efficiency. Our Sports-102 and Dance-12 datasets are publicly available at https://***/view/syd-net/home. © 2023, CC BY.

关键词： Sports

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Enhancing Production Planning in the Internet of Vehicles: A Transformer-based Federated Reinforcement Learning Approach

Enhancing Production Planning in the Internet of Vehicles: A...

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IEEE Conference on Vehicular Technology (VTC)

作者： Jinhua Chen Zihan Zhao Keping Yu Shahid Mumtaz Joel J. P. C. Rodrigues Mohsen Guizani Takuro Sato Graduate School of Science and Engineering Hosei University Tokyo Japan Department of Applied Informatics Silesian University of Technology Gliwice Poland Departement of Computer Sciences Nottingham Trent University Nottingham UK Amazonas State University Manaus AM Brazil Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE Research Institute for Science and Engineering Waseda University Tokyo Japan

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

The Internet of Vehicles (10V) brings significant economic benefits to countries. However, large-scale smart vehicle production planning remains challenging in the 10V. Currently, heuristic algorithms and solvers commonly used for these problems often lack scalability and fall into local optima. Moreover, security concerns about wireless data transfer arising from multi-factory manufacturing processes are garnering attention. To address these issues, this paper introduces an algorithm, TRL, which is a Transformer-based Reinforcement Learning for vehicle production planning problems. Furthermore, we propose a Transformer-based Federated Reinforcement Learning algorithm, named TFRL, tailored for large-scale manufacturing and secure wireless communication. Experimental results showcase the high performance and security of TFRL. It schedules 1000 orders in about 14 seconds and avoids exchanging plaintext during the interaction. Compared to Non-dominated Sorting Genetic Algorithm II(NSGA-II), the TFRL enhances computational speed by 95.12% and reduces constraint violation scores by 93.18%.

关键词： Wireless communication Vehicular and wireless technologies Schedules Production planning Reinforcement learning Transformers Manufacturing

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AI4FIDS: Multimodal Federated Intrusion Detection

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IEEE Transactions on Emerging Topics in Computing 2025年

作者： Radoglou-Grammatikis, Panagiotis Bouzinis, Pavlos S. Makris, Ioannis Lagkas, Thomas Argyriou, Vasileios Papadopoulos, Georgios Th Fouliras, Panagiotis Seritan, George Sarigiannidis, Panagiotis University of Western Macedonia Department of Electrical and Computer Engineering Campus ZEP Kozani Kozani50100 Greece K3Y Ltd William Gladstone 31 Sofia1000 Bulgaria MetaMind Innovation P.C. Kila Kozani Kozani50100 Greece Democritus University of Thrace Department of Computer Science Kavala Campus Kavala65404 Greece Kingston University London Department of Networks and Digital Media Penrhyn Road Kingston upon Thames Surrey KT1 2EE London United Kingdom Harokopio University of Athens Department of Informatics and Telematics Omirou 9 Tavros AthensGR17778 Greece University of Macedonia Department of Applied Informatics 156 Egnatia Street ThessalonikiGR54-636 Greece Politehnica University of Bucharest Electrical Engineering Faculty Bucharest Romania

The rapid progression of smart technologies creates several advantages like enhanced connectivity, personalisation solutions and environmental sustainability. However, this revolution creates also several cyber risks. In particular, the attackers have the ability to synthesise and automate advanced attack scenarios over time, while it is evident that Artificial Intelligence (AI) allows the composition of intelligent attack vectors that can adapt in real-time to conventional countermeasures. Despite the fact that AI can also benefit defensive mechanisms, there are still functional and privacy issues that need to be resolved. First, AI requires appropriate datasets that can differ from environment to environment. In addition, these datasets usually are not available due to privacy issues. Finally, adversarial attacks have the ability to target and affect the AI-based decision-making process. Therefore, in light of the previous remarks, we provide AI4FIDS, a multimodal Intrusion Detection System (IDS) for critical infrastructures. AI4FIDS leverages Federated learning (FL) and combines multiple data sources, thus allowing cooperative intelligence across multiple domains in a private manner and minimising the impact of potential adversarial attacks. In this paper, we present in detail the architectural design and specifications of AI4FIDS, while the evaluation results demonstrate their detection performance, taking into account several datasets and aggregation strategies. Finally, based on the evaluation results, we discuss how the overall reliability and detection capabilities (in terms of detecting multi-step attack scenarios) of AI4FIDS can be improved by combining the detection outcomes of the components behind AI4FIDS. © 2013 IEEE.

关键词： Federated learning

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Mqtt Routing Optimizing Based Intrusion Detection for Internet of Things Using Hybrid Machine Learning

Mqtt Routing Optimizing Based Intrusion Detection for Intern...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Haider K. Hoomod Mohammad Alaa Al-Hamami Aqeel Kamil Kadhim Qutaiba Humadi Mohammed Jolan Rokan Niaf College of Education Mustansiriyah University Baghdad Iraq Department of Management Information Systems Applied Science University Manama Kingdom of Bahrain Computer Techniques Engineering Department Imam Al-Kadhim College Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq Informatics Institute for Postgraduate Studies University of Information Technology and Communications Baghdad Iraq

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

With the advent of the Internet of Things, a world was born in which everything could be uniquely identified and monitored, tracked, and managed by computer programs. Items can self-configure using a predefined communication protocol. Other software programs can be linked to operate together using Message Queue Telemetry Transport Protocol (MQTT) or MQSeries components. Business integration software and middleware are common terms for this category of applications. With its suggested lightweight, easy-to-implement, and lowbandwidth system for billions of smart items, the MQTT protocol offers a wide range of potential applications, including home automation. In order to enhance the power savings in MQservice routing, we presented an intelligent assistant for optimizing routing model. For Internet of Things (IoT) purposes, it introduces an optimal routing model based on optimized hybrid machine learning-based anomaly detection (OHMLA). In order to extend the reach of a network, the offered approach allows Internet of Things devices to make efficient use of energy. To further enhance Intrusion Detection identification and get optimum network selection routes, the moth Salp Swarm technique (SSA) technique is used for the optimized hyperparameters. The model is capable of accurately detecting attacks that result in network congestion, causing delays in connection response and data transfer in IoT network.

关键词： Transport protocols Network intrusion detection Machine learning Routing Data transfer Data models Delays Classification algorithms Internet of Things Intelligent sensors

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KNOWLEDGE-BASED DATASET FOR TRAINING PE MALWARE DETECTION MODELS

arXiv

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arXiv 2022年

作者： Švec, Peter Balogh, Štefan Homola, Martin Kl’uka, Ján Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovičova 3 Bratislava Slovakia Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University Mlynská dolina Bratislava Slovakia

Ontologies are a standard for semantic schemata in many knowledge-intensive domains of human interest. They are now becoming increasingly important also in areas until very recently dominated by subsymbolic representations and machine-learning-based data processing. One such area is information security, and more specifically malware detection. We propose PE Malware Ontology that offers a reusable semantic schema for Portable Executable (PE, Windows binary format) malware files. The ontology was inspired by the structure of the data in the EMBER dataset and it currently covers the data intended for static malware analysis. With this proposal, we hope to achieve: a) a unified semantic representation for PE malware datasets that are available or will be published in the future;(b) applicability of symbolic, neural-symbolic, or otherwise explainable approaches in the PE Malware domain that may lead to improved interpretability of results which may now be characterized by the terms defined in the ontology;and (c) by joint publishing of semantically treated EMBER data, including fractional datasets, also improved reproducibility of experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Ontology

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Shun-Cheng Chang Hsin-Pei Yu Yi-Hsien Hsieh Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan Institute of Applied Mathematical Sciences NTU Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag.

关键词： Visualization Accuracy Three-dimensional displays Liver Transformers Planning Convolutional neural networks Context modeling Biomedical imaging Tumors

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Employing Deep Neural Networks for Real-Time Anomaly Detection and Mitigation in IoT-Based Smart Grid Cybersecurity Systems

Employing Deep Neural Networks for Real-Time Anomaly Detecti...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Aadam Quraishi Maher Ali Rusho Anurag Prasad Ismail Keshta Richard Rivera Mohammed Wasim Bhatt M.D. Research Intervention Treatment Institute Houston Texas US Department of Lockheed Martin Engineering Management University of Colorado Boulder Boulder Colorado IIT Kharagpur (Rajiv Gandhi School Of Intellectual Property Law) Gurugram India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador Model Institute of Engineering and Technology Jammu J&K India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

This research introduces a novel anomaly detection framework for IoT -based Smart Grid Cybersecurity Systems. Leveraging autoencoders, LSTM networks, GANs, SOMs, and transfer learning, our approach achieves superior precision, recall, and execution time compared to existing methods. Visualizations and an ablation study further validate the method's efficiency, emphasizing the critical roles of attention mechanisms and transfer learning. This comprehensive solution addresses the dynamic challenges of smart grid cybersecurity, offering a versatile and adaptive anomaly detection mechanism for real-world applications. This indicates the real-time efficacy of our anomaly detection method. Through our study of ablation and all aspects of computing, we discovered that attention processes and transfer learning facilitate faster problem solving in a dynamic smart grid. Our method is distinct and adaptable enough to address every problem arising from the discovery of anomalies in IoT-driven Smart Grid Cybersecurity Systems.

关键词： Visualization Transfer learning Circuits Real-time systems Smart grids Problem-solving computer security

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Shun-Cheng Chang Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan NTU Institute of Applied Mathematical Sciences Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan NTU Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages.

关键词： Liver cancer Accuracy Computed tomography Biological system modeling Transformers Planning Biomedical imaging

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