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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Mingshuo Jin, Keyan Bao, Wenzhuo Chen, Zhenghan Macao Polytechnic University Faculty of Applied Sciences China Xinjiang University College of Software Shayibak District China Jilin University College of Computer Science and Technology Chaoyang District China Microsoft Danling District China

ISBN: (纸本)9798350386226

The accurate recognition and comprehensive understanding of medical images depicting human tissue represent a central focus in computer vision research. Many tasks within medical imaging rely on deep neural networks, particularly those with U-shaped architectures and skip connections. The advancement of computer vision technologies demands the application of convolutional neural networks (CNNs). Despite progress, two major challenges remain in medical image processing: (1) developing a model framework with low computational complexity that allows for efficient inference without compromising accuracy, and (2) designing a model with strong generalization capabilities across various datasets derived from patients with differing pathologies, thereby mitigating domain shift challenges. In response to the first issue, we propose a novel unsupervised domain adaptation method utilizing Interoperable Batch Normalization (IBN) to integrate multiple channels within deep neural networks, enhancing adversarial domain adaptation. Our experimental evaluation on the Hubmap and Synapse multiorgan segmentation datasets reveals that the proposed RRUNet model achieves superior performance compared to existing methods, setting a new standard in the domain. © 2024 IEEE.

关键词： Convolutional neural networks

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Development of Deepfake Detection Techniques for Protecting Multimedia Information using Deep Learning 3

Development of Deepfake Detection Techniques for Protecting ...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Siva Rama Lingham, N. Devakanth, J Jude Moses Anto Raj, Gowtham Gayathri, K. Janani, R. Dhanapal, R. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Acharya Institute of Technology Department of Computer Science and Engineering Bangalore India Bannari Amman Institute of Technolgy Department of Information Technology Erode India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

The development of proposed deepfake detection techniques plays a prominent role in the safeguarding the multimedia information. The various challenges are addressed using the aid of deep learning techniques. This involves hybrid optimization techniques such as particle swarm optimization and genetic algorithms. This helps to improve the accuracy and efficiency in the detection of deepfake. PSO helps in optimizing the weights and parameters of the neural network to obtain faster convergence. GA helps in obtaining potential solutions to obtain the robust deepfake detection model. The integration of deep learning with hybrid optimization involves the collection and preprocessing of the multimedia information and proceeds to an optimization algorithm. The manipulated content in the online platform are detected during the training process. The hybrid optimization tcehniques helps in obtaining model generability and achieving resilience in various attacks occurring in the deepfake generation. The integration of PSO and GA helps in the accurate detection of deepfake content through continuous training and evaluation. Thus the proposed system forms a safeguarding tool for multimedia content and helps to eliminate various risks. © 2024 IEEE.

关键词： Genetic algorithms

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A biased edge enhancement method for truss-based community search

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Frontiers of computer science 2024年第3期18卷 251-253页

作者： Yuqi LI Tao MENG Zhixiong HE Haiyan LIU Keqin LI School of Computer and Information Engineering Central South University of Forestry and TechnologyChangsha 410082China School of Business Central South University of Forestry and TechnologyChangsha 410082China College of Information Engineering Changsha Medical UniversityChangsha 410219China Department of Computer Science State University of New YorkNew York 12561USA

1 Introduction A related study called community search,whose target is to find dense subgraphs containing the given node,has drawn a growing amount of attention recently[1].To explore the higher-order structure of complex networks,truss-based community search methods[2]have been ***,the truss-based hypergraph constructed from the original graph is frequently fragmented and consists of numerous subgraphs and isolated nodes[3],which boils down to the fact that these methods often pay only attention to the truss connections but ignore the lower-order connectivity of the original graph.

关键词： graph. truss connectivity

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IoT-Enabled Advanced Foam Firefighting E-Vehicle 1

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Ravi, R. Kannadhasan, S. Mangaleswaran, M. Bharathi, R. Kabilan, R. Mallika Pandeeswari, R. Department of Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India Department of Electronics and Communication Engineering Study World College of Engineering Coimbatore India Department of AI & DS S.A. Engineering College Chennai India Department of Information Technology M. Kumarasamy College of Engineering Karur India Department of Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli India

ISBN: (数字)9789819981359

ISBN: (纸本)9789819981342

A natural or man-made calamity may nevertheless happen periodically, despite the many fire safety measures in place. In the case of a fire, we must deploy hazardous human resources to put out flames and save lives. Robots may very well replace people in the firefighting process due to advancements in technology, notably in robotics. Because they would not have to take the possibility of endangering people's lives, firemen would be more effective as a result. The foam and water systems will automatically detect fires thanks to an Arduino-built firefighting robot. In this proposed method, the Arduino is utilized to build a basic robot that can move in response to instructions. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Global system for mobile communications

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HQFS: High-Quality Feature Selection for Accurate Change Detection 12th

HQFS: High-Quality Feature Selection for Accurate Change De...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Xing, Yan Xu, Qi’ao Zhao, Qingyi Huang, Rui Zhang, Yuxiang College of Safety Science and Engineering Civil Aviation University of China Tianjin China College of Computer Science and Technology Civil Aviation University of China Tianjin China

ISBN: (纸本)9783031463044

High-quality features play a crucial role in achieving accurate change detection (CD). However, current CD methods often rely on simple combinations of features or the computations of feature differences between bi-temporal images, which cannot fully explore the representative capacity of features. In this paper, we design a high-quality feature selection module, called HQFS, to generate high-quality change-related features for accurate CD. Firstly, we analyze the pros and cons of the features utilized in recent deep learning-based CD methods. Based on this analysis, we construct a feature pool containing various change-related features. The proposed HQFS module is then employed to learn the importance of these features and assign higher weights to the high-quality features to amplify their contributions in computing the change maps. To further enhance the performance, we propose two additional components: the fully attention enhanced feature extraction network and the attention pyramid fusion & change mask prediction module. The former focuses on extracting features from images, while the latter leverages attention mechanisms and pyramid fusion techniques to generate accurate change predictions. Comprehensive experiments on three benchmark datasets demonstrate the superiority of our method over seven state-of-the-art change detectors. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Change detection

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Dynamic Resource Allocation for IoT-Fog Architectures Leveraging Differential Services and Adaptive QoS Models 5

Dynamic Resource Allocation for IoT-Fog Architectures Levera...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Mahavaishnavi, V. Padmavathi, V. Saminathan, R. Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai600123 India A.V.C. Engineering College Department of Information Technology Mayiladuthurai609305 India Annamalai University Department of Computer Science and Engineering Annamalainagar608002 India

ISBN: (纸本)9798331518097

The constant changes in IoT-Fog architectures, allocating resources plays a vital role in overall resource management, which includes several different requests for applications. This paper proposes a dynamic resource allocation framework based on adaptive QoS and differential service models for IoT-Fog networks. Consequently, the proposed model sorts IoT applications according to the necessary QoS for their implementation, thus improving resource use and satisfying end-users. In addition, the framework complements machine learning algorithms to forecast the resources' needs and dynamically allocate the resources in response to the changing requirements, thereby increasing responsivity and decreasing resource rivalry. In this paper, to conduct several simulations and real-world evaluations of this adaptive QoS-based approach, showing that it improves service delivery, especially for latency-sensitive and high-priority applications. Thus, this research presents a method for large-scale optimization of resource allocation in the IoT-fog environment and supports reliable and efficient service deployment in distributed and often limited-resource settings. © 2024 IEEE.

关键词： Quality of service

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Simultaneous Wireless Information and Power Transfer for STAR-RIS-Assisted AAV Networks

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IEEE Internet of Things Journal 2025年第12期12卷 19915-19928页

作者： Xie, Wenwu Qin, Lijuan Wang, Ji Wu, Weimin Li, Xingwang Xu, Hongbo Yang, Liang Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Central China Normal University Department of Electronics and Information Engineering Wuhan430079 China Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

This article explores the benefits of deploying simultaneously transmitting and reflecting reconfigurable intelligence surfaces (STAR-RIS) in autonomous aerial vehicle (AAV) networks with simultaneous wireless information and power transfer. In the proposed system, the AAV utilizes STAR-RIS to radiate energy-carrying information signals (ECISs) to multiple outdoor energy receivers and multiple indoor information receivers without flying over indoor no-fly zone. Based on the AAV propulsion power formula, we successively introduce fly-hover-broadcast (FHB) and path discretization (PD) protocols to minimize the total AAV energy consumption by jointly using the extended penalty function and optimization algorithm based on the expected value of channel status information, where the AAV flight constraints, no-fly constraint, minimized energy or information threshold constraints, and STAR-RIS phase-shift constraints are met. The FHB protocol, which requires the AAV to radiate the ECISs to the users at only a few hovering positions, provides a lower bound performance of AAV energy consumption, while the PD protocol is used to discuss the general situation of the ECISs during AAV flight. Simulation results demonstrate that the utilization of the STAR-RIS in AAV networks outperforms the traditional RIS in improving energy efficiency and extending AAV flight time. © 2014 IEEE.

关键词： Energy utilization

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Multi-Source Medical Data Integration and Mining for Healthcare Services 7

Multi-Source Medical Data Integration and Mining for Healthc...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Jeyaranjani, J. Prakash, Kodali Gnana Cheemakurthi, Sai Pavan Reddy, Banda Anoop Aashray, Lagishetty SRM Madurai College of Engineering and Technology Pottapalayam Computer Science and Engineering Sivagangai District India Kalasalingam Academy of Research and Education Anandnagar Computer Science and Engineering Krishnankoil India

ISBN: (纸本)9798350397253

As the Internet of Health (IoH) era has come into being, health services are progressively moving online and have been creating a sizable amount of health-related data related to patients, physicians, medical infrastructure, and other related topics. The effective integration and analysis of this IoH data have good implications for the provision of medical treatment and scientific disaster diagnosis. IoH data, however, are frequently dispersed throughout several departments and only partially protect user privacy. Therefore, efficiently integrating or mining the critical IoH data, while maintaining user privacy, is sometimes a difficult issue. Thus, this study provides IoH as a solution to this problem, allowing the IoH to submit medications to a specific patient without disclosing patient information and the admin to access the medications upon IoH's approval. Patients can watch physicians and consult them only once admin and the doctor have given their approval. © 2023 IEEE.

关键词： Data integration

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Advanced Insights in Nursing science: Applying ARIMA-CLSTM for Concept Analysis 3

Advanced Insights in Nursing Science: Applying ARIMA-CLSTM f...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Thirumalaikumari, T. Kumar, Vinay Somasundaram, K. Josephson, P. Joel Mavliya, Anil Kumar Anwer Basha, H. Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India B.N. College of Engineering and Technology Department of Computer Science and Engineering Lucknow India Bannari Amman Institute of Technology Department of Information Science and Engineering Tamil Nadu Sathyamangalam India Department of Ece Telangana Hyderabad India Coer College of Nursing and Parmedical Sciences Rookee UK Haridawar India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India

ISBN: (纸本)9798350381931

The phrase "nursing science"extends much beyond the theoretical underpinnings of topics like asepsis and body mechanics. This lecture takes a classical Aristotelian stance in its examination of scientific concepts, natural phenomena, scientific knowledge, and the procedures of inquiry and proof. After that, it will show how the science is useful by applying traditional approaches to nursing. Preprocessing, feature extraction, and model training must proceed without pauses. A part of preparing data is making sure it is normalized and removing stop words. The GLCM is utilized for feature extraction;however, while it provides a large amount of data, it is not optimal for picture feature extraction, image recognition, or classification. It is necessary to extract features before training an ARIMA-CLSTM model. The suggested method outperforms the state-of-the-art algorithms using the current data. A considerable improvement was noted in terms of accuracy, with the findings reaching 97.33%. © 2024 IEEE.

关键词： Nursing

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Blockchain based Secure Erlang Server for Request based Group Communication over XMPP

Blockchain based Secure Erlang Server for Request based Grou...

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2023 International Conference on Advances in Intelligent Computing and Applications, AICAPS 2023

作者： Johnpaul, C.I. Vivekanandan, Manojkumar Premkamal, Praveen Kumar Ramya, R. St. Thomas College Department of Computer Science and Applications Kerala Thrissur India Srm University-AP School of Engineering and Applied Sciences Department of Computer Science and Engineering Andhra Pradesh Amaravati India Kamaraj College of Engineering and Technology Department of Computer Science and Engineering Virudhunagar India

ISBN: (纸本)9798350333817

Many real world activities in computer science scenarios are linked with concurrency and security related issues and have to handle large number of processes to be executed in parallel with false safe security solutions. There are many traditional methods in programming languages to handle concurrency. Concurrency is one of the major issues that need to be addressed by most of the servers when dealing with the group communication operations. Security of the data as well as the credibility of the users are the other aspects when a group of users involve in real-time communication. Many light-weighted servers are designed to carryout elementary operations of request handling, file sharing etc. In design of such servers having large number of clients, the request service handling will be based on the individual server programs. Keeping track of individual credibility and establishing concurrency solutions in server design is challenging. The whole work describes the significance and implementation of an Erlang based XMPP server in comparison with a Python based XMPP server with a view to service the client request handling operations for sending messages, group chatting, buddy-list creation, presence identification integrated with XML messaging pattern as per the XMPP protocol. We also accomplish the security and credibility of the users using a blockchain based interface that keep track of user activities during group communication. The security analysis is also performed for blockchain based interface. © 2023 IEEE.

关键词： Python

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