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A dynamic platform for global pullorum disease and fowl typhoid

Animal Diseases 2025年第1期5卷 113-123页

作者： Xiamei Kang Canghong Jin Pengkun Gu Song Wang Zhengjie Gao Chenyi Dai Xiao Zhou Abubakar Siddique Haiyang Zhou Linlin Huang Yan Li Min Yue Department of Veterinary Medicine Zhejiang University College of Animal SciencesHangzhou 310058China School of Computer and Computing Science Hangzhou City UniversityHangzhou 310015China Hainan Institute of Zhejiang University Sanya 572000China School of Life Science Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China State Key Laboratory for Diagnosis and Treatment of Infectious Diseases National Clinical Research Center for Infectious DiseasesNational Medical Center for Infectious DiseasesThe First Affiliated HospitalCollege of MedicineZhejiang UniversityHangzhou 310003China Ningbo Academy of Agricultural Sciences Ningbo 315040China NewFit(Hangzhou)Information Technology Co. LtdHangzhou 310000China

Pullorum disease and fowl typhoid are caused by the Salmonella serovars Gallinarum biovars Pullorum and Gal-linarum,*** prevalence of these diseases varies across regions and is affected by different risk fac-tors that remain insufficiently *** fill this knowledge gap,we have compiled a global dataset for its prevalence,drawing upon a collection of literature from the last seven decades obtained from bilingual ***,a more interactive and dynamic platform is still needed for both academics and policymakers to improve biosecurity measures,limit disease transmission,and prevent future outbreaks at the global and local ***,we developed an advanced visualization platform to depict the prevalence of Salmonella Pullorum and Gallinarum,espe-cially in China,which is categorized by geographical region and temporal *** platform offers a user-friendly,efficient,and visually engaging tool to explore the prevalence of pullorum disease and fowl typhoid between 1945 and 2021 in different ***,this platform allows users to understand the influence of various fac-tors,such as breed,farm mode,economic usage and even the sex of the primary host,chickens,on the prevalence of this *** further provided a detailed overview of individual province within *** particular,by selecting two different provinces on the interface,users can quickly visualize and grasp the disparities in disease prevalence between the chosen *** interactive toolkit enables a dynamic exploration of the patterns and factors con-tributing to the prevalence of Salmonella Pullorum and *** interactive platform is freely available open source at http://139.9.85.208/.

关键词： Pullorum disease Fowl typhoid Interactive platform Prevalence prediction

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Quantum contrastive learning for human activity recognition

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Smart Health 2025年 36卷

作者： Ren, Yanhui Wang, Di An, Lingling Mao, Shiwen Wang, Xuyu Guangzhou Institute of Technology Xidian University Guangzhou China School of Computer Science and Technology Xidian University Xi'an China Department of Electrical and Computer Engineering Auburn University Auburn 36849 AL United States Knight Foundation School of Computing and Information Sciences Florida International University Miami 33199 FL United States

Deep learning techniques have been widely used for human activity recognition (HAR) applications. The major challenge lies in obtaining high-quality, large-scale labeled sensor datasets. However, unlike datasets such as images or text, HAR sensor datasets are non-intuitive and uninterpretable, making manual labeling extremely difficult. Self-supervised learning has emerged to address this problem, which can learn from large-scale unlabeled datasets that are easier to collect. Nevertheless, self-supervised learning has the increased computational cost and the demand for larger deep neural networks. Recently, quantum machine learning has attracted widespread attention due to its powerful computational capability and feature extraction ability. In this paper, we aim to address this classical hardware bottleneck using quantum machine learning techniques. We propose QCLHAR, a quantum contrastive learning framework for HAR, which combines quantum machine learning techniques with contrastive learning to learn better latent representations. We evaluate the feasibility of the proposed framework on six publicly available datasets for HAR. The experimental results demonstrate the effectiveness of the framework for HAR, which can surpass or match the precision of classical contrastive learning with fewer parameters. This validates the effectiveness of our approach and demonstrates the significant potential of quantum technology in addressing the challenges associated with the scarcity of labeled sensory data. © 2025

关键词： Contrastive learning Human activity recognition Quantum machine learning Quantum neural networks

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RIS-Assisted Passive Localization (RAPL): An Efficient Zero-Overhead Framework Using Conditional Sample Mean

arXiv

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arXiv 2025年

作者： Yao, Jiawei Mao, Yijie Chen, Mingzhe Hu, Ye School of Information Science and Technology ShanghaiTech University Shanghai201210 China Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States

Reconfigurable Intelligent Surface (RIS) has been recognized as a promising solution for enhancing localization accuracy. Traditional RIS-based localization methods typically rely on prior channel knowledge, beam scanning, and pilot-based assistance. These approaches often result in substantial energy and computational overhead, and require real-time coordination between the base station (BS) and the RIS. To address these challenges, in this work, we move beyond conventional methods and introduce a novel data-driven, multiple RISs-assisted passive localization approach (RAPL). The proposed method includes two stages, the angle-of-directions (AoDs) between the RISs and the user is estimated by using the conditional sample mean in the first stage, and then the user’s position is determined based on the estimated multiple AoD pairs in the second stage. This approach only utilizes the existing communication signals between the user and the BS, relying solely on the measurement of received signal power at each BS antenna for a set of randomly generated phase shifts across all RISs. Moreover, by obviating the need for real-time RIS phase shift optimization or user-to-BS pilot transmissions, the method introduces no additional communication overhead, making it highly suitable for deployment in real-world networks. The proposed scheme is then extended to multi-RIS scenarios considering both parallel and cascaded RIS topologies. Numerical results show that the proposed RAPL improves localization accuracy while significantly reducing energy and signaling overhead compared to conventional methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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Incomplete Multi-view Clustering Based on Joint Concept Decomposition and Anchor Graph Learning 11th

Incomplete Multi-view Clustering Based on Joint Concept Dec...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Li, Zhuowen Chen, Hongmei Xiang, Biao Yuan, Zhong Luo, Chuan Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China College of Computer Science Sichuan University Chengdu610065 China

ISBN: (纸本)9783031927430

The main objective of incomplete multi-view clustering is to effectively utilize the existing view information to fill in the missing data and to mine the complementary information and potential associations between multiple views to effectively group samples. The existing primary means for recovering missing information are classified into two parts: one uses matrix decomposition or low-rank constraints to fill in the missing views, and the other constructs multiple graph structures and uses graph regularization to recover the corresponding missing parts. However, the two methods are relatively independent and are not used jointly to improve the model performance. This paper proposes an incomplete multi-view clustering method with complete clustering metrics and complete anchor graphs (CFAG). Specifically, each view’s missing samples are recovered by a matrix of consistent clustering metrics and represented by an anchor graph. The recovered complete data performs anchor graph construction and anchor learning and utilizes the orthogonal variation principle to learn the consistency structure of multiple anchor graphs to mine the complementary information among multiple views. The clustering metrics are obtained based on the consistency structure, and the consistency of the clustering metrics is also considered to obtain the final clustering results. The effectiveness of the proposed method is verified on multiple datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graphic methods

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Halftone Image Steganography Based on Minimizing Distortion with Pixel Density Transition 7th

Halftone Image Steganography Based on Minimizing Distortion...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Yu, Mujian Luo, Junwei Xu, Bozhi Chen, Guoliang Lu, Wei School of Computer Science and Engineering Guangdong Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China Guangdong Science and Technology Innovation Monitoring and Research Center Guangzhou510033 China

ISBN: (纸本)9783030786113

Many advanced halftone steganographic schemes focus only on the distortion of human visual perception or the distortion according to statistics. In this paper, a halftone image steganography based on minimizing distortion with density transition is proposed which aims at utilizing the entropy model and pixel density to resist potential steganalysis. First, the entropy model is established on the image database to describe the texture content and transformed to the preliminary distortion score map. Because the form of texture presentation is distinct between halftone images and ordinary binary images, the feature of pixel density is introduced to represent the local intensity in images. Then the pixel density transition adjustment based on the entropy model is presented, which makes the distortion score more reliable. The final additive distortion map is generated by combining the entropy model and the strategy of density transition. To play the advantage of distortion measurement, syndrome-trellis code (STC) is applied with the distortion map to minimize the embedding distortions. Experimental results demonstrate that compared with other halftone steganographic schemes, the proposed method achieves high statistical security and great visual quality with considerable embedding capacity. © 2021, Springer Nature Switzerland AG.

关键词： Pixels

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Towards Automated Screening of Literature on Artificial Intelligence in Nursing 18

Towards Automated Screening of Literature on Artificial Inte...

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18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021

作者： Moen, Hans Alhuwail, Dari Björne, Jari Block, Lori Celin, Sven Jeon, Eunjoo Kreiner, Karl Mitchell, James Ozegović, Gabriela Ronquillo, Charlene Esteban Sequeira, Lydia Tayaben, Jude Topaz, Maxim Veeranki, Sai Pavan Kumar Peltonen, Laura-Maria Department of Computing University of Turku Turku Finland Information Science Department Kuwait University Sabah AlSalem University City Kuwait School of Nursing University of British Columbia Okanagan Kelowna Canada Ait Austrian Institute of Technology Graz GmbH Austria Technology Research Samsung Sds Seoul Korea Republic of School of Computing and Mathematics Keele University United Kingdom Institute of Health Policy Management and Evaluation University of Toronto Ontario Canada College of Nursing Benguet State University Philippines Columbia University New York United States Institute of Neural Engineering Graz University of Technology Graz Austria Department of Nursing Science University of Turku Turku Finland

ISBN: (纸本)9781643682648

We evaluate the performance of multiple text classification methods used to automate the screening of article abstracts in terms of their relevance to a topic of interest. The aim is to develop a system that can be first trained on a set of manually screened article abstracts before using it to identify additional articles on the same topic. Here the focus is on articles related to the topic 'artificial intelligence in nursing'. Eight text classification methods are tested, as well as two simple ensemble systems. The results indicate that it is feasible to use text classification technology to support the manual screening process of article abstracts when conducting a literature review. The best results are achieved by an ensemble system, which achieves a F1-score of 0.41, with a sensitivity of 0.54 and a specificity of 0.96. Future work directions are discussed. © 2022 International Medical Informatics Association (IMIA) and IOS Press.

关键词： Nursing

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Skin Cancer Classification using Machine Learning Techniques

Skin Cancer Classification using Machine Learning Techniques

引用

BIT Conference (BITCON), International

作者： I. Sapthami Ch. Srilakshmi Batta Saranya N. Kumareshan Ramesh Kumar Ayyasamy P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Information Technology VNR VJIET Bachupally Hyderabad India AIDS Department CBITGandipet Hyderabad India Department of ECE Professor/Centre for Advances in Signal Processing and Sensor Networks Sri Eshwar College of Engineering Coimbatore Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputtur TamilNadu

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

The death rate from skin cancer has radically grown due to a lack of understanding about the indications and prevention measures. It is one of the most lethal types of cancers, and the mortality rate due to skin cancer has risen sharply. Cancer must be diagnosed as early as possible to halt its growth and prevent metastasis. Even though there are numerous varieties of skin cancer, melanoma, basal cell carcinoma, and squamous cell carcinoma are considered the three most dangerous types of skin cancer. This research, using the CNN image-processing technology and machine learning, tries to detect and classify into the many kinds of skin cancer. Artificial intelligence (AI) based algorithms for automated skin cancer classification have consistently outperformed human professionals in several clinical studies, laying the groundwork for a future in which AI will be widely used.

关键词： Squamous cell carcinoma Prevention and mitigation Mortality Melanoma Machine learning Data augmentation Skin Classification algorithms Convolutional neural networks Skin cancer

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Evaluation of Chinese electronic enterprise from business and customers perspectives 2

Evaluation of Chinese electronic enterprise from business an...

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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Bamansoor, Samer El-Ebiary, Yousef A. Baker Yusoff, Fakhrul Hazman Pathmanathan, P. Ravindran Saany, Syarilla Iryani Ahmad Bakar, Zainab Binti Abu Aseh, Khairi Pande, Bishwajeet Al Moaiad, Yazeed MEDIU Faculty of Computer and Information Technology Malaysia University of Aden Aden Yemen UniSZA Faculty of Informatics and Computing Malaysia Universiti Teknologi MARA Malaysia International Institute of Applied Science Swiss School of Management Netherlands UniSZA Faculty of Informatics and Computing Malaysia MEDIU Faculty of Computer and Information Technology Malaysia Gyancity Research Consultancy Pvt Ltd Motihari India

ISBN: (纸本)9781665432221

The evaluation of companies in the era of modern technologies is due to how and to what extent these companies use such technologies. Provided that modern technologies are dealt with in all aspects of the company, internally and externally. Also, the first company to provide market data and information for free to all shoppers and visitors to its online platform around the world is the Alibaba Group. Alibaba also has its own mission of making the process of buying or marketing everything in the world uncomplicated for everyone, but specifically to introduce Chinese business to the people of the world serving China's vision of conquering the world with its products. Alibaba Group revenues are rising as it reaches more demand than its opponent in the global market. Besides that, the Alibaba Group communication system and how the business connects with the retailer is very interesting. Their business approach has made them famous and distinguished in Asia. Internationally, it is important for Alibaba to gain a higher connection to join well-known western brands, especially luxury and luxury brands, due to the unfulfilled requirements among Chinese customers for the brand that cannot be reached in China. So, it is also possible for Alibaba to be a lucrative brand for retailers around the world to sell their products and as the world grows, that could also be for the Business to Business (B2B) or Customer to Customer (C2C) marketplace. © 2021 IEEE.

关键词： Electronic commerce

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Medicine recommendation system for diabetes using prior medical knowledge 4

Medicine recommendation system for diabetes using prior medi...

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4th International Conference on Vision, Image and Signal Processing, ICVISP 2020

作者： Wedagu, Mulubrhan Ayalew Chen, Dehua Hussain, Muhammad Ather Iqbal Gebremeskel, Tsegay Orlando, Mayugi Tanguy Manzoor, Arslan School of Computer Science and Technology Donghua University Shanghai China College of Information Science and Technology Donghua University Shanghai China School of Computing Technology Aksum University Axum Ethiopia

ISBN: (纸本)9781450389532

Medicine recommendation system could assist doctors in making an accurate diagnosis and predict medicine. According to CDC reports, more than 250,000 people die by medication error;for that matter, many deep learning models were previously proposed to solve the high order correlations of diabetic medicine for diabetic patients. However, they are failed to address the benefit of prior medical knowledge from experienced doctors. This study proposes a recommendation method called (Diabetes Medicine Recommendation System) DIMERS model, which combines a prior medical knowledge of doctors with bidirectional Long Short-Term Memory (BiLSTM). In this study, DIMERS, first, preprocesses general tests of diabetic patient test results and medicines with a complex data achievement strategy. After that, we use a weighted block with prior medical knowledge to enhance the learning and explainable abilities of deep neural networks (DNN). Our study's overall performance was excellent. We train and validate using the real world Ruijin hospital and drug bank datasets that contain 25,280 patients within three months gaps and 63 types of diabetes medicines. © 2020 ACM.

关键词： Recommender systems

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Unsupervised Multivariate Time Series Anomaly Detection by Feature Decoupling in Federated Learning Scenarios

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： He, Yifan Ding, Xi Tang, Yateng Guan, Jihong Zhou, Shuigeng Fudan University School of Computer Science Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Tencent Weixin Group Guangzhou510308 China Tongji University Department of Computer Science and Technology Shanghai201804 China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai200092 China

Anomalies are usually regarded as data errors or novel patterns previously unseen, which are quite different from most observed data. Accurate detection of anomalies is crucial in various application scenarios. This paper focuses on unsupervised anomaly detection from Multivariate Time Series (MTS), as real-world data collected from sources such as wearable devices, medical equipment and industrial machines typically manifest as MTS and are often unlabeled. Anomaly detection in MTS represents a data-driven challenge that traditionally requires substantial centralized data for training models. However, in practice, data are frequently distributed among multiple institutions, with privacy concerns restricting unrestricted access. To address these issues, we introduce Feature Decoupling Federated Learning (FDFL), an approach designed to collaboratively train a representation learning network over multiple clients for unsupervised anomaly detection in MTS. Unlike previous methods that simply integrate MTS anomaly detection algorithms with federated learning strategies, FDFL specifically addresses heterogeneity among clients by decoupling the representation network into shared and private branches through a contrastive learning mechanism. This method aggregates shared parameters during each federated round while maintaining client-specific private parameters locally. Additionally, we develop a self-attention block that integrates the representations derived from both shared and private parameters to reconstruct MTS and identify anomalies based on reconstruction errors. Extensive experiments conducted on three publicly available datasets demonstrate that FDFL outperforms existing algorithms in most cases, highlighting the effectiveness and superiority of our proposed method in MTS anomaly detection. © 2020 IEEE.

关键词： Federated learning

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