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A review on computer vision systems in monitoring of poultry: A welfare perspective

artificial intelligence in Agriculture 2020年第1期4卷 184-208页

作者： Cedric Okinda Innocent Nyalala Tchalla Korohou Celestine Okinda Jintao Wang Tracy Achieng Patrick Wamalwa Tai Mang Mingxia Shen College of Artificial Intelligence Laboratory ofModern Facility Agriculture Technology and Equipment Engineering of Jiangsu ProvinceNanjing Agricultural UniversityJiangsu 210031PR China College of Veterinary Science and Agriculture University of NairobiLower KabeteNairobiKenya Faculty of Bioscience and Engineering Ghent UniversityGhentBelgium Faculty of Engineering Department of Agricultural EngineeringEgerton UniversityNjoroKenya

Monitoring of poultry welfare-related bio-processes and bio-responses is vital in welfare assessment and management of welfare-related *** the current development in information technologies,computer vision has become a promising tool in the real-time automation of poultry monitoring systems due to its non-intrusive and non-invasive properties,and its ability to present a wide range of ***,it can be applied to monitor several bio-processes and *** review summarizes the current advances in poultrymonitoring techniques based on computer vision systems,i.e.,conventional machine learning-based and deep learning-based systems.A detailed presentation on the machine learning-based system was presented,i.e.,pre-processing,segmentation,feature extraction,feature selection,and dimension reduction,and ***,deep learning approaches in poultry monitoring were also ***,the challenges and possible solutions presented by researches in poultry monitoring,such as variable illumination conditions,occlusion problems,and lack of augmented and labeled poultry datasets,were discussed.

关键词： Computer vision Deep learning Machine learning Monitoring Poultry Welfare

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Incremental Feature Selection for Dynamic Incomplete data Using Sub-Tolerance Relation

SSRN

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SSRN 2023年

作者： Zhao, Jie Ling, Yun Huang, Faliang Wang, Jiahai See-To, Eric Wing kuen Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

TRS theory is widely used for feature selection with incomplete data. However, the traditional positive region-based measure ignores uncertainty, resulting in redundant features and poor classification accuracy. In addition, the computation of the positive region incurs significant time and space costs. To address these limitations, we extend the tolerance class to a Subrelation Tolerance Class (STC) by decomposing the tolerance relation into two subrelations. STC transforms feature selection into a two-stage certainty measurement by partitioning the universe into certainty and uncertainty regions. By progressively filtering out certain regions, the computational space is reduced. Furthermore, we propose a new significance measure that incorporates both certain and uncertain information. This measure effectively reduces redundant features, resulting in improved computational efficiency and classification accuracy. Using STC and our measure, we develop an incremental feature selection algorithm for dealing with incomplete streaming data, which is validated through extensive experiments and comparisons. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Secure IoHT data Sharing in the Cloud: Machine Learning with Logistic Regression for Privacy Protection

Secure IoHT Data Sharing in the Cloud: Machine Learning with...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Ramakrishnan Raman K. Saravanan Atul A Gokhale K. Anitha Ishwarya M.V S. Sujatha Symbiosis Institute of Business Management Pune Symbiosis International (Deemed University) Pune Maharashtra India Department of Information Technology R.M.D. Engineering College Chennai Tamil Nadu India Symbiosis School of Culinary Arts Symbiosis International (Deemed University) Pune Maharashtra India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Healthcare delivery and patient monitoring have been transformed by the fast rise of the Internet of Healthcare Things (IoHT). Cloud storage of sensitive health data creates privacy issues. Logistic Regression (LR) is used to ensure IoHT data sharing in this investigation. The cloud architecture allows efficient data exchange while protecting health information. LR is used to predict data sharing risk and make privacy choices. A varied collection of health measures and demographic data trains the model to generalize well to numerous contexts. The suggested system is tested extensively with Logistic Regression, which reliably identifies and mitigates privacy threats. data accessibility and privacy are balanced by the model's high sensitivity and specificity. It also examines the model's interpretability to provide privacy prediction factors. This openness improves user trust and regulatory compliance. This technique is scalable for large-scale IoHT installations. The cloud-based IoHT data sharing challenge has a unique approach. Machine learning, especially Logistic Regression, protects privacy without reducing health data usefulness. This research increases healthcare data security in the connected device age.

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RAKG:Document-level Retrieval Augmented Knowledge Graph Construction

arXiv

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arXiv 2025年

作者： Zhang, Hairong Si, Jiaheng Yan, Guohang Qi, Boyuan Cai, Pinlong Mao, Song Wang, Ding Shi, Botian Shanghai Artificial Intelligence Laboratory China School of Statistics and Data Science Nankai University Tianjin China Faculty of Computing Harbin Institute of Technology China Computer School Beijing Information Science and Technology University China

With the rise of knowledge graph based retrieval-augmented generation (RAG) techniques such as GraphRAG and Pike-RAG, the role of knowledge graphs in enhancing the reasoning capabilities of large language models (LLMs) has become increasingly prominent. However, traditional Knowledge Graph Construction (KGC) methods face challenges like complex entity disambiguation, rigid schema definition, and insufficient cross-document knowledge integration. This paper focuses on the task of automatic document-level knowledge graph construction. It proposes the Document-level Retrieval Augmented Knowledge Graph Construction (RAKG) framework. RAKG extracts pre-entities from text chunks and utilizes these pre-entities as queries for RAG, effectively addressing the issue of long-context forgetting in LLMs and reducing the complexity of Coreference Resolution. In contrast to conventional KGC methods, RAKG more effectively captures global information and the interconnections among disparate nodes, thereby enhancing the overall performance of the model. Additionally, we transfer the RAG evaluation framework to the KGC field and filter and evaluate the generated knowledge graphs, thereby avoiding incorrectly generated entities and relationships caused by hallucinations in LLMs. We further developed the MINE dataset by constructing standard knowledge graphs for each article and experimentally validated the performance of RAKG. The results show that RAKG achieves an accuracy of 95.91% on the MINE dataset, a 6.2 % point improvement over the current best baseline, GraphRAG (89.71%). The code is available at https://***/LMMApplication/RAKG. Copyright © 2025, The Authors. All rights reserved.

关键词： Graphic methods

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Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentification

arXiv

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arXiv 2023年

作者： Gao, Zan Wei, Shengxun Guan, Weili Zhu, Lei Wang, Meng Chen, Shengyong The Shandong Artificial Intelligence Institute Qilu University of Technology Shandong Academy of Sciences Jinan250014 China The Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China The Faculty of Information Technology Monash University Clayton Campus Australia The School of Electronic and Information Engineering Tongji University Shanghai200092 China The school of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

Cloth-changing person reidentification (ReID) is a newly emerging research topic aimed at addressing the issues of large feature variations due to cloth-changing and pedestrian view/pose changes. Although significant progress has been achieved by introducing extra information (e.g., human contour sketching information, human body keypoints, and 3D human information), cloth-changing person ReID remains challenging because pedestrian appearance representations can change at any time. Moreover, human semantic information and pedestrian identity information are not fully explored. To solve these issues, we propose a novel identity-guided collaborative learning scheme (IGCL) for cloth-changing person ReID, where the human semantic is effectively utilized and the identity is unchangeable to guide collaborative learning. First, we design a novel clothing attention degradation stream to reasonably reduce the interference caused by clothing information where clothing attention and mid-level collaborative learning are employed. Second, we propose a human semantic attention and body jigsaw stream to highlight the human semantic information and simulate different poses of the same identity. In this way, the extraction features not only focus on human semantic information that is unrelated to the background but are also suitable for pedestrian pose variations. Moreover, a pedestrian identity enhancement stream is proposed to enhance the identity importance and extract more favorable identity robust features. Most importantly, all these streams are jointly explored in an end-to-end unified framework, and the identity is utilized to guide the optimization. Extensive experiments on six public clothing person ReID datasets (LaST, LTCC, PRCC, NKUP, Celeb-reID-light, and VC-Clothes) demonstrate the superiority of the IGCL method. It outperforms existing methods on multiple datasets, and the extracted features have stronger representation and discrimination ability and are weakly corre

关键词： Semantics

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Domain-Specific Versus General-Purpose Word Representations in Sentiment Analysis for Deep Learning Models 7th

Domain-Specific Versus General-Purpose Word Representations ...

引用

7th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2018

作者： Hung, Bui Thanh Data Analytics & Artificial Intelligence Laboratory Engineering---Technology Faculty Thu Dau Mot University 6 Tran Van On Phu Hoa Thu Dau MotBinh Duong Viet Nam

ISBN: (纸本)9789813291850

Sentiment analysis, also known as opinion mining or emotion artificial intelligence, has been gradually becoming a hot-trend topic in the recent years due to its wide applications and also the challenges in handling the dataset efficiently. With the popular deep learning-based approach, using vectors of word as an input for the models is a good way for the machine to learn the relation between words in a document. In this paper, vector representations of word are created based on two different ways: learning from its own internal dataset, or deriving from a famous pre-trained library known as fastText. The two representations will then be used in documents (we classify sentiment on document level) to generate input to 4 deep learning models: Long-Short Term Memory Network (LSTM), Bi-directional Long-Short Term Memory Network (BiLSTM), Convolutional Neural Network (CNN) and Convolutional Neural Network combined with Long-Short Term Memory Network (CNN-LSTM). We do experiment in Vietnamese review dataset and explorer words in domain-specific and general–purpose before turning into vectors in two different ways: tokenized and not tokenized. The result shows the best result of each of the approaches in four deep learning models: LSTM, Bi-LSTM, CNN and CNN-LSTM. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Consistency Approximation: Incremental Feature Selection Based on Fuzzy Rough Set Theory

SSRN

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SSRN 2023年

作者： Zhao, Jie Wu, Daiyang Wu, JiaXin Ye, Wenhao Huang, Faliang Wang, Jiahai See-To, Wing-Kuen Eric Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

Fuzzy Rough Set Theory (FRST)-based feature selection has been widely used as a preprocessing step to handle dynamic and large datasets. However, large-scale or high-dimensional datasets remain intractable for FRST-based feature selection approaches due to high space complexity and unsatisfactory classification performance. To overcome these challenges, we propose a Consistency Approximation (CA)-based framework for incremental feature selection. By exploring CA, we introduce a novel significance measure and a tri-accelerator. The CA-based significance measure provides a mechanism for each sample in the universe to keep members with different class labels within its fuzzy neighbourhood as far as possible, while keeping members with the same label as close as possible. Furthermore, our tri-accelerator reduces the search space and decreases the computational space with a theoretical lower bound. The experimental results demonstrate the superiority of our proposed algorithm compared to state-of-the-art methods on efficiency and classification accuracy, especially for large-scale and high-dimensional datasets. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Leveraging Digital Twin technology to Combat Cardiovascular Disease: A Comprehensive Review

Leveraging Digital Twin Technology to Combat Cardiovascular ...

引用

artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Induni Nayodhara Weerarathna Praveen Kumar Prateek Verma David Raymond Anurag Luharia Gaurav Mishra Department of Biomedical Sciences Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Radiotherapy Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Radiodiagnosis Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The innovative Digital Twin (DT) technology, which constructs a copy of objects, has emerged as a groundbreaking approach in various sectors, including the healthcare industry. This thorough examination delves into the potential of utilizing DT technology to address disease (CVD), a prominent cause of illness and death globally. By combining real-time data, sophisticated analytics and machine learning algorithms, DTs provide a platform for monitoring, diagnosing and enhancing health. The document explores the background and evolution of DT technology, its role in precision medicine, and its transformative impact on managing CVD. It also sheds light on how digital twins replicate, forecast and analyze the functions and performance of systems to offer insights into prevention, therapy options and personalized care. Furthermore, it discusses the obstacles and constraints of incorporating DT technology in healthcare settings, such as data security concerns, interoperability challenges, and ethical considerations. The paper showcases case studies that highlight the applications of DTs in research and treatment to underscore the potential of this technology in transforming patient outcomes. The evaluation concludes with prospects stressing the importance of research efforts and collaborative initiatives to surmount existing hurdles and fully leverage the advantages of DT technology in addressing cardiovascular disease.

关键词： Industries data privacy Precision medicine data integration Medical services Real-time systems Digital twins Cardiovascular diseases Biomedical monitoring Monitoring

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A Stochastic Flight Problem Simulation to Minimize Cost of Refuelling

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Computers, Materials & Continua 2021年第10期69卷 849-871页

作者： Said Ali Hassan Khalid Alnowibet Miral H.Khodeir Prachi Agrawal Adel F.Alrasheedi Ali Wagdy Mohamed Department of Operations Research and Decision Support Faculty of Computers and Artificial IntelligenceCairo UniversityGiza12613Egypt Department of Statistics and Operations Research College of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia Department of Mathematics and Scientific Computing National Institute of Technology HamirpurHimachal Pradesh177005India Department of Operations Research Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt

Commercial airline companies are continuously seeking to implement strategies for minimizing costs of fuel for their flight routes as acquiring jet fuel represents a significant part of operating and managing expenses for airline activities.A nonlinear mixed binary mathematical programming model for the airline fuel task is presented to minimize the total cost of refueling in an entire flight route *** model is enhanced to include possible discounts in fuel prices,which are performed by adding dummy variables and some restrictive constraints,or by fitting a suitable distribution function that relates prices to purchased *** obtained fuel plan explains exactly the amounts of fuel in gallons to be purchased from each airport considering tankering strategy while minimizing the pertinent cost of the whole flight *** relation between the amount of extra burnt fuel taken through tinkering strategy and the total flight time is also considered.A case study is introduced for a certain flight rotation in domestic US air transport *** mathematical model including stepped discounted fuel prices is *** problem has a stochastic nature as the total flight time is a random variable,the stochastic nature of the problem is realistic and more appropriate than the deterministic *** stochastic style of the problem is simulated by introducing a suitable probability distribution for the flight time duration and generating enough number of runs to mimic the probabilistic real *** similar real application problems are modelled as nonlinear mixed binary ones that are difficult to handle by exact ***,metaheuristic approaches are widely used in treating such different optimization *** this paper,a gaining sharing knowledge-based procedure is used to handle the mathematical *** algorithm basically based on the process of gaining and sharing knowledge throughout the human *** generated simulation runs of

关键词： Stochastic flight problem cost of refuelling ferrying strategy tankering discounted prices simulation procedure nonlinear mixed binary model metaheuristic algorithm gaining-sharing knowledge-based algorithm

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Human Pose Estimation Based on GAN and DCGAN Models

Human Pose Estimation Based on GAN and DCGAN Models

引用

International Conference on Machine Learning, Big data and Business intelligence (MLBDBI)

作者： Jiaming Ma Jiaqi Li Shenglu Feng School of Computer Science and Technology Donghua University Shanghai China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China School of Electrical Engineering Wuhan University Wuhan China

ISBN: (纸本)9781665417914

As an important application based on the field of computer vision, the various human movements and gestures can be reconstructed by detecting the key articulation points of the human body. It is mainly used in human behavior recognition, human-computer interaction, and attitude tracking. However, the current human pose estimation models have many challenges, such as difficulty detecting the non-typical articulation points of the human body and inaccurate locating of the extremities. They are prone to error or lack of information in complex situations. This paper proposed GAN and DCGAN models to tackle this issue, which can improve the accuracy of human posture prediction. This paper mainly focuses on the contribution of Generative Adversarial Network to the detection of human key articulation points, revising the original model posture and obtaining a model that is closer to the real posture of the human body. The experimental results demonstrated that the model's accuracy is improved to a certain extent after using the DCGAN model. Furthermore, we note that in most cases, the performance of the proposed model is superior to others.

关键词： Training Analytical models Convolution Tracking Biological system modeling Pose estimation Neural networks

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