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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany Eth Zürich Switzerland Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China Bigo Technology Pte. Ltd. Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

ISBN: (纸本)9798350302493

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023 IEEE.

关键词： Image enhancement

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Survey on deep learning in multimodal medical imaging for cancer detection

arXiv

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arXiv 2023年

作者： Tian, Yan Xu, Zhaocheng Ma, Yujun Ding, Weiping Wang, Ruili Gao, Zhihong Cheng, Guohua He, Linyang Zhao, Xuran School of Computer Science and Technology Zhejiang Gongshang University Hangzhou310018 China School of Mathematical and Computational Sciences Massey University Auckland0632 New Zealand School of Information Science and Technology Nantong University Nantong226019 China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325000 China Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University Wenzhou325000 China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325000 China Jianpei Technology Co. Ltd Hangzhou311200 China

The task of multimodal cancer detection is to determine the locations and categories of lesions by using different imaging techniques, which is one of the key research methods for cancer diagnosis. Recently, deep learning-based object detection has made significant developments due to its strength in semantic feature extraction and nonlinear function fitting. However, multimodal cancer detection remains challenging due to morphological differences in lesions, interpatient variability, difficulty in annotation, and imaging artifacts. In this survey, we mainly investigate over 150 papers in recent years with respect to multimodal cancer detection using deep learning, with a focus on datasets and solutions to various challenges such as data annotation, variance between classes, small-scale lesions, and occlusion. We also provide an overview of the advantages and drawbacks of each approach. Finally, we discuss the current scope of work and provide directions for the future development of multimodal cancer detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Computer vision

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Proceedings of the Fortieth Conference on Uncertainty in artificial intelligence

作者： Zhiqiang Zhang Yongqiang Jiang Qian Gao Zhipeng Wang Key Laboratory of Computing Power Network and information Security Ministry of Eaucation Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Jinan China

Existing multimodal recommendation research still faces some challenges, such as not being able to fully mine the implicit relevance information of neighbor nodes, and the unreasonable weight allocation to imbalanced nodes. To address the aforementioned challenges, this paper introduces a new multimodal recommendation model called NS-MAR+. Specifically, the model firstly constructs a neighbor similarity graph convolutional network to capture the implicit relevance information and reasonably assigns the attention weights through the graph attention mechanism. Secondly, the model introduces a modal alignment and fusion mechanism by using a multilayer perceptron (MLP) to map image and text features into a shared space for comparison and fusion. In addition, the model constructs a user co-interaction graph and an item semantic graph based on the original information and performs graph convolution operations to enhance the preference information of users and items, and to better capture the interactions and internal features between users and items. Finally, MLP is employed to aggregate user and item representations and predict personalized recommendation rankings. To validate the experiment's efficacy, this paper compares with several leading multimodal recommendation models on public datasets with a performance improvement of 1% to 3%. The experimental outcomes indicate that the model in this paper has good superiority and accuracy in multimodal recommendation tasks.

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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Advancing Patient Care for Autonomous Saline Level Alert System with Cloud-Based Isolation Forest Algorithm

Advancing Patient Care for Autonomous Saline Level Alert Sys...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Nandhini R G. Sumathi S. Ramya Saravanan Thulasi Ishwarya M. V S. Murugan Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Information Technology Kongunadu College of Engineering and Technology (Autonomous) Trichy Tamil Nadu India Department of Cyber Security Paavai Engineering College Namakkal Tamil Nadu India Customer and Product Support Services John Deere India Pvt Ltd Pune Maharashtra India Artificial intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This work presents a novel method for improving patient care by creating a self-sufficient system to detect saline levels using an Isolation Forest Algorithm (IFA) hosted on the cloud. Standard practices for checking saline levels in IV infusion systems often include human intervention, which might cause important discrepancies to go undetected for longer periods of time. It presents a system that uses machine learning to identify aberrant saline levels in real-time, improving patient safety and relieving healthcare providers of some of their workload. The IFA, a powerful anomaly detection tool that works particularly well with high-dimensional data like that produced by intravenous infusion devices, is at the heart of our strategy. It gains scalability, flexibility, and real-time processing capabilities crucial for early alerting in clinical settings by implementing our algorithm on the cloud. Extensive testing and validation with both synthetic and real-world datasets prove that our suggested approach reliably and effectively detects abnormal saline levels with few false alarms. By providing healthcare practitioners with prompt alerts of possible infusion anomalies and allowing proactive action to avert adverse events, the autonomous saline level alarm system has major implications for patient care. The technology provides smooth interaction with current healthcare infrastructure by moving computing duties to the cloud, which eliminates the need for extra hardware and maintenance burdens.

关键词： Scalability Medical services Forestry Real-time systems Safety Streams Standards Research and development Optimization Testing

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Quantum Computing based Channel and Signal Modeling for 6G Wireless System

TechRxiv

引用

TechRxiv 2023年

作者： Farouk, Ahmed AbuAli, Najah Abed Mumtaz, Shahid The Department of Computer Science Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt United Arab Emirates Department of Applied Informatics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Nottingham Trent University Engineering Department United Kingdom

Sixth-generation wireless technology (6G) introduces a paradigm shift and fundamental transformation of digital wireless connectivity by converging pillars of softwarization, virtualization, and wireless networks. Terahertz (THz) communication technologies are predicted to become more significant in 6G applications as the requirement for bandwidth grows and wireless cell sizes shrink. As a result, 6G will be able to deal with and manage numerous devices and services requiring enhanced spectral throughput and efficiently work with high interference levels. This convergence highlights the increased threat surface of 6G networks and the potentially severe impacts of sophisticated cyber incidents. Furthermore, the heterogeneity of connected devices and provided services will generate a huge amount of data to be processed and managed efficiently. Quantum computing (QC) can efficiently solve several 6G computationally hard problems with a quadratic speedup and provide adaptive techniques for controlling the current and future significant security threats of the 6G network. This article will discuss the role of various QC components on 6G and explore the opportunities and challenges to achieving such transformation. © 2023, CC BY.

关键词： Quantum cryptography

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A High Quality Secure Medical Image Steganography Method

A High Quality Secure Medical Image Steganography Method

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Computing and Information technology (ICCIT-1441), 2020 International Conference on

作者： Mohamed Abdel Hameed M. Hassaballah Saddam Bekhet Narmatha C Mourad A. Kenk Department of Computer Science Faculty of Computers and Information Luxor University Luxor Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj Saudi Arabia Faculty of Commerce South Valley University Qena Egypt Faculty of Computers and Information Technology University of Tabuk Tabuk City Saudi Arabia Department of Computer Science Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt

The exchange of medical images over the Internet has prompted substantial claims on the last years due to the presentation of cloud-based medical data systems according to various web services in the area of telemedicine applications. The preservation of confidential information has ever been a key indicator of the effectiveness of these systems. In this paper, an efficient data hiding technique is proposed for Digital Imaging and Communications in Medicine (DICOM) images hence security can be specified for the highly invaluable and private sensitive data of the patient. The proposed method is developed for DICOM images to embed Patient Record Information (PRI) for maintaining the fidelity of both the critical components of the image and private patient data. The proposed method is very effective in telemedicine applications as it can increase the security of the PRI during its transmission over the Internet from different remote places.

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The Impact of Advanced Neural Network Architectures for Extracting Elusive Information

The Impact of Advanced Neural Network Architectures for Extr...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Renu Kachhoria Love Mittal Prakash Divakaran Vinay Singh N. Thangarasu S. Rajarajeswari Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science Mangalayatan University Aligarh India Department of Bussiness & Management Himalayan University Itanagar India Department of Coputing & Information Technology Usha Martin University Ranchi India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai

Advanced Neural community Architectures, consisting of Convolution Neural Networks (CNNs), have emerged as powerful solutions to demanding situations in extracting complicated and elusive information from large, high-dimensional datasets. CNNs have effective feature extraction abilities that may be used to identify subtle patterns, traits, and relationships in those datasets. but, effective architectures must be tailored to the particular nature of the hassle that allows you to maximize the potential of these fashions. CNNs were used effectively in some of domain names to discover styles in facts that could otherwise remain hidden. As an example, CNNs have been used inside the medical domain to quickly and accurately become aware of cardiologic patterns in electrocardiograms. Similarly, they have been used within the security domain to perceive capability threats earlier than they occur. In each domain names, the capability to extract cognitive facts from numerous datasets speedy and appropriately has saved lives and avoided failures. The exercise of the use of superior neural architectures to permit statistics extraction is complex and requires thoughtful layout. The structure must recall the traits of the project, which include the records kind, the range of dimensions concerned, and the amount of noise or outliers, to determine which strategies are maximum suitable for every specific utility.

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A Novel Dark and Light Primary Color Prior Theory for Road Visibility Detection

IJLAI Transactions on Science and Engineering

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IJLAI Transactions on science and engineering 2025年第1期3卷 72-77页

作者： Yuankun Du Fengping Liu School of Big Data and Artificial Intelligence Zhengzhou University of Science and Technology Zhengzhou 450064China School of Information Engineering Zhengzhou University of Science and Technology Zhengzhou 450064China

Aiming at the problems of high complexity and low accuracy of existing visibility detection methods,a road visibility detection method based on monitoring images is ***,the transmittance of dark and bright primary colors is obtained by the theory of dark and bright primary color ***,the atmospheric light value and atmospheric transmittance are optimized by using adaptive fog removal weight and adaptive filtering window,and the transmittance of the first and last end points of the lane line is one-toone corresponding to the optimized dark and bright primary ***,the atmospheric extinction coefficient and visibility are calculated by combining the distance between the end and end of the lane *** experimental results show that this method can achieve high precision detection within 100-600m,and the relative error is less than 10%.Compared with other methods,the detection efficiency of this method is faster,the accuracy is higher and the realization is easier.

关键词： Road visibility detection Atmospheric light value Atmospheric extinc

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IoT-Enabled Table Turnover for Dining Experiences with AdaBoost Algorithm

IoT-Enabled Table Turnover for Dining Experiences with AdaBo...

引用

Innovation in technology (APCIT), Asia Pacific Conference on

作者： Chitra Sabapathy Ranganathan N. Mohankumar G. Sumathi A. Nithya P. Sudha Juliet M. Muthulekshmi Mphasis Corporation Chennai Tamil Nadu India Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Artificial Intelligence & Data Science Kongunadu College of Engineering and Technology (Autonomous) Trichy Tamil Nadu India Department of Information Technology St. Joseph’s Institute of Technology Chennai Tamil Nadu India Department of Electronics and Instrumentation Engineering Meenakshi College of Engineering Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

Traditional dining experiences are undergoing a period of transition because of the growing use of Internet of Things (IoT) technologies and machine learning in the restaurant business. This article presents revolutionary IoT-driven technology, intended to maximize the table turnover rate and improve client happiness. The system can gather real-time data on the occupancy status of each table since it places occupancy sensors at each table. This allows for the effective distribution of tables as they become available. model is software that automates table cleaning alerts, staff coordination, and predictive analytics for resource planning. It is designed to connect seamlessly with queue management and point-of-sale (POS) systems. This novel strategy improves the restaurant's efficiency, reduces customers' wait times, and maximizes earning potential during peak hours. The suggested solution offers a chance to change dining venues by combining the capabilities of the IoT with dynamic table management. This will result in a better customer dining experience and improved operational results for companies.

关键词： Technological innovation Machine learning Solids Real-time systems Software Sensors Internet of Things

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