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arXiv 2023年

作者： Zhang, Kai Zhou, Rong Adhikarla, Eashan Yan, Zhiling Liu, Yixin Yu, Jun Liu, Zhengliang Chen, Xun Davison, Brian D. Ren, Hui Huang, Jing Chen, Chen Zhou, Yuyin Fu, Sunyang Liu, Wei Liu, Tianming Li, Xiang Chen, Yong He, Lifang Zou, James Li, Quanzheng Liu, Hongfang Sun, Lichao Department of Computer Science and Engineering Lehigh University PA United States School of Computing University of Georgia GA United States Samsung Research America CA United States Department of Radiology Massachusetts General Hospital Harvard Medical School MA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PA United States PolicyLab Children’s Hospital of Philadelphia PA United States Center for Research in Computer Vision University of Central Florida FL United States Department of Computer Science and Engineering University of California Santa CruzCA United States McWilliams School of Biomedical Informatics UTHealth HoustonTX United States Department of Radiation Oncology Mayo Clinic AZ United States University of Pennsylvania PA United States PA United States Leonard Davis Institute of Health Economics PA United States Department of Biomedical Data Science Stanford University School of Medicine CA United States Department of Computer Science Stanford University CA United States

Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize holistic information. Generalist AI holds the potential to address these limitations due to its versatility in interpreting different data types and generating tailored outputs for diverse needs. However, existing biomedical generalist AI solutions are typically heavyweight and closed source to researchers, practitioners, and patients. Here, we propose BiomedGPT, the first open-source and lightweight vision-language foundation model, designed as a generalist capable of performing various biomedical tasks. BiomedGPT achieved state-of-the-art results in 16 out of 25 experiments while maintaining a computing-friendly model scale. We also conducted human evaluations to assess the capabilities of BiomedGPT in radiology visual question answering, report generation, and summarization. BiomedGPT exhibits robust prediction ability with a low error rate of 3.8% in question answering, satisfactory performance with an error rate of 8.3% in writing complex radiology reports, and competitive summarization ability with a nearly equivalent preference score to human experts. Our method demonstrates that effective training with diverse data can lead to more practical biomedical AI for improving diagnosis and workflow efficiency. © 2023, CC BY.

关键词： Visual languages

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Industrial informatics in optimizing software-defined vehicles for logistics

Industrial informatics in optimizing software-defined vehicl...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Bhavuk Samrat Thomas P K Santosh Kumar Benila A Rishabh Bhardwaj M. Vigenesh Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Adjunct Faculty School of Mangement PG Management JAIN (Deemed to be University) Bangalore Karnataka India AI & Ds Vishwakarma Institute of Technology Pune India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Integration of industrial informatics and software-defined vehicle optimization is revolutionizing logistics. This study discusses how this integration may improve logistics safety, sustainability, and efficiency. Logistics are the backbone of international companies; thus, they must run smoothly. Industrial informatics and optimization studies yielded new findings. This powerful technology combines advanced machine learning algorithms, data analytics, and real-time monitoring to change logistics. Data-driven decisions are the foundation of industrial informatics. This research determines the best data analytics methods for gas and labor savings. Real-time monitoring keeps logistical operations adaptive to changing circumstances, while predictive maintenance and energy-efficient driving boost system efficiency. Nearest Neighbor, Branch and Bound, Particle Swarm Optimization, Monte Carlo Simulation, and Genetic Algorithms reveal that the suggested technique is superior. This study shows the indicated approach's considerable effect. Its cheaper pricing, faster delivery, and lesser carbon emissions make it better than conventional solutions. Logistics operations are safer with the proposed method, which has a higher safety score and fewer accidents and incidents. The suggested method benefits from its flexibility, scalability, and adaptability, which are crucial in a fast-changing firm. Expense savings: Save $250,000 using the proposed technique. The new method results in a time savings of 15.79%. The suggested approach reduces carbon emissions by 3,000 kg (26.09%). The suggested method improves safety ratings by 10—11.36%.

关键词： Data analysis Machine learning algorithms Scalability Carbon dioxide Real-time systems Safety informatics Sustainable development Optimization Logistics

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A Novel Cluster Detection of COVID-19 Patients and Medical Disease Conditions Using Improved Evolutionary Clustering Algorithm Star

arXiv

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arXiv 2021年

作者： Hassan, Bryar A. Rashid, Tarik A. Hamarashid, Hozan K. Department of Computer Networks Technical College of Informatics Sulaimani Polytechnic University Sulaimani46001 Iraq Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani46001 Iraq Computer Science and Engineering Department University of Kurdistan Hewler Iraq Information Technology Department Computer Science Institute Sulaimani Polytechnic University Sulaimani46001 Iraq

With the increasing number of samples, the manual clustering of COVID-19 and medical disease data samples becomes time-consuming and requires highly skilled labour. Recently, several algorithms have been used for clustering medical datasets deterministically;however, these definitions have not been effective in grouping and analysing medical diseases. The use of evolutionary clustering algorithms may help to effectively cluster these diseases. On this presumption, we improved the current evolutionary clustering algorithm star (ECA*), called iECA*, in three manners: (i) utilising the elbow method to find the correct number of clusters;(ii) cleaning and processing data as part of iECA* to apply it to multivariate and domain-theory datasets;(iii) using iECA* for real-world applications in clustering COVID-19 and medical disease datasets. Experiments were conducted to examine the performance of iECA* against state-of-the-art algorithms using performance and validation measures (validation measures, statistical benchmarking, and performance ranking framework). The results demonstrate three primary findings. First, iECA* was more effective than other algorithms in grouping the chosen medical disease datasets according to the cluster validation criteria. Second, iECA* exhibited the lower execution time and memory consumption for clustering all the datasets, compared to the current clustering methods analysed. Third, an operational framework was proposed to rate the effectiveness of iECA* against other algorithms in the datasets analysed, and the results indicated that iECA* exhibited the best performance in clustering all medical datasets. Further research is required on real-world multi-dimensional data containing complex knowledge fields for experimental verification of iECA* compared to evolutionary algorithms. © 2021, CC BY-NC-ND.

关键词： Coronavirus

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Can researchers Assess the Suitability of Datasets to Answer Their research Questions, with Access to Metadata Only? 18

Can Researchers Assess the Suitability of Datasets to Answer...

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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： Tilston, George Williams, Richard Griffiths, Emily Al-Adely, Sarah Lawson-Tovey, Saskia Hulme, Will Short, Andrea Davies, Jim Welch, James Peek, Niels Centre for Health Informatics Division of Informatics Imaging and Data Sciences School of Health Sciences The University of Manchester ManchesterM13 9GB United Kingdom Nihr Manchester Biomedical Research Centre The University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Nihr Greater Manchester Patient Safety Translational Research Centre The University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Centre for Epidemiology Versus Arthritis The University of Manchester Manchester United Kingdom Centre for Genetics and Genomics Versus Arthritis Centre for Musculoskeletal Research The University of Manchester Manchester United Kingdom Division of Human Communication and Hearing School of Health Sciences The University of Manchester Manchester United Kingdom Nihr Oxford Biomedical Research Centre Oxford Big Data Institute Li Ka Shing Centre for Health Information and Discovery Old Road Campus Oxford United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom

ISBN: (纸本)9781643682648

Health research increasingly requires effective ways to identify existing datasets and assess their suitability for research. We sought to test whether researchers could use an existing metadata catalogue to assess the suitability of datasets for addressing specified research questions. Five datasets were described in the National institute for Health research Health informatics Collaborative metadata catalogue, and for each dataset five associated research questions were formulated, some of which were answerable with the dataset while others were not. Thirteen researchers each assessed whether the ten questions associated with two randomly selected datasets were answerable with the described datasets. After removing instances where participants misunderstood the question or lacked subject matter knowledge to make the assessment, we found that 87 out of 109 assessments (80%) were correct. Participants particularly struggled with one dataset which consisted of EHR data. The most common reason for incorrect assessments was the inability to find the relevant information in the metadata catalogue. © 2022 International Medical informatics Association (IMIA) and IOS Press.

关键词： Metadata

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Color-avoiding percolation in edge-colored Erdős–Rényi graphs

arXiv

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arXiv 2022年

作者： Ráth, Balázs Varga, Kitti Fekete, Panna Tímea Molontay, Roland Afréd Rényi Institute of Mathematics Hungary Department of Stochastics Institute of Mathematics Budapest University of Technology and Economics Hungary ELKH-BME Stochastics Research Group Chile Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Hungary ELKH-ELTE Egerváry Research Group Hungary Department of Applied Analysis and Computational Mathematics Eötvös Loránd University Hungary

We study a variant of the color-avoiding percolation model introduced by Krause et al., namely we investigate the color-avoiding bond percolation setup on (not necessarily properly) edge-colored Erd̋ os–Rényi random graphs. We say that two vertices are color-avoiding connected in an edge-colored graph if after the removal of the edges of any color, they are in the same component in the remaining graph. The color-avoiding connected components of an edge-colored graph are maximal sets of vertices such that any two of them are color-avoiding connected. We consider the fraction of vertices contained in color-avoiding connected components of a given size as well as the fraction of vertices contained in the giant color-avoiding connected component. Under some mild assumptions on the color-densities, we prove that these quantities converge and the limits can be expressed in terms of probabilities associated to edge-colored branching process trees. We provide explicit formulas for the limit of the normalized size of the giant color-avoiding connected component, and in the two-colored case we also provide explicit formulas for the limit of the fraction of vertices contained in color-avoiding connected components of a given size. Copyright © 2022, The Authors. All rights reserved.

关键词： Color

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Dynamic cat swarm optimization algorithm for backboard wiring problem

arXiv

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arXiv 2021年

作者： Ahmed, Aram Rashid, Tarik Saeed, Soran Department of Information Technology Technical College of Informatics Sulaimani Polytechnic University Sulaimanyah Iraq International academic office Kurdistan Institution for Strategic Studies and Scientific Research Sulaimanyah Iraq Department of Computer Science and Engineering School of Science and Engineering University of Kurdistan Hewler Erbil Iraq

This paper presents a powerful swarm intelligence meta-heuristic optimization algorithm called Dynamic Cat Swarm Optimization. The formulation is through modifying the existing Cat Swarm Optimization. The original Cat Swarm Optimization suffers from the shortcoming of "premature convergence", which is the possibility of entrapment in local optima which usually happens due to the off-balance between exploration and exploitation phases. Therefore, the proposed algorithm suggests a new method to provide a proper balance between these phases by modifying the selection scheme and the seeking mode of the algorithm. To evaluate the performance of the proposed algorithm, 23 classical test functions, 10 modern test functions (CEC 2019) and a real world scenario are used. In addition, the Dimension-wise diversity metric is used to measure the percentage of the exploration and exploitation phases. The optimization results show the effectiveness of the proposed algorithm, which ranks first compared to several well-known algorithms available in the literature. Furthermore, statistical methods and graphs are also used to further confirm the outperformance of the algorithm. Finally, the conclusion as well as future directions to further improve the algorithm are discussed. © 2021, CC BY-NC-ND.

关键词： Heuristic algorithms

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Machine Learning and computer Vision Techniques in Continuous Beehive Monitoring Applications: A Survey

arXiv

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arXiv 2022年

作者： Bilik, Simon Zemcik, Tomas Kratochvila, Lukas Ricanek, Dominik Richter, Miloslav Zambanini, Sebastian Horak, Karel Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 Brno61600 Czech Republic Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Yliopistonkatu 34 Lappeenranta53850 Finland Computer Vision Lab Institute of Visual Computing & Human-Centered Technology Faculty of Informatics TU Wien Favoritenstr. 9/193-1 ViennaA-1040 Austria

Wide use and availability of machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new applications appears also in biology and agriculture, where they may be used to detect infections, parasites and weeds, but also for automated monitoring and early warning systems. This goes in concordance with the introduction of the easily accessible hardware and development kits such as the Arduino, or RaspberryPi families. In this paper, we survey 50 papers focusing on the methods of automated beehive monitoring using computer vision techniques. Particularly on the pollen and Varroa mite detection together with the bee traffic monitoring. Such systems could also be used for monitoring of honeybee colonies and for the inspection of their health state, which could potentially identify dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Further on, we also include analysis of the research trends in this application field and we outline the possible directions of new development. Our paper is also aimed at veterinary and apidology professionals and experts, who may not be familiar with machine learning to introduce them to its capabilities, hence each family of techniques is prefaced by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring. © 2022, CC BY.

关键词： computer vision

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The Development of the Multi-Layer Security Network Authentication System using Blockchain technology

The Development of the Multi-Layer Security Network Authenti...

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International Conference on System Modeling & Advancement in research Trends (SMART)

作者： S. K. UmaMaheswaran Arti Rana Kavita Rani Yashpal Singh Suhas Harbola Kumud Pant Department of Mathematics Sri Sai Ram Engineering College Chennai Tamil Nadu India Uttaranchal Institute of Technology Uttaranchal University Department of CSE Lloyd Institute of Engineering and Technology Greater Noida Uttar Pradesh Department of Computer Science & Engineering Jain (Deemed-to-be University) Bengaluru Karnataka India National Informatics Centre (NIC) New Delhi Delhi India Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (纸本)9781665487351

Due to the fact that crypto is a novel security strategy. This section outlines a multi-layer safe Internet of Everything networks theory based on blockchains, we will now explain the construction of a layered network security authenticator using blockchain technologies. By grouping Connected technologies into a pass connectivity and implementing the blockchain based platform at every level of a network, the strategy reduces the challenges of actually deploying blockchain technologies while maintaining the mankind's strict security and validity guarantee. It offers a nationwide connectivity option for the Web of Things. A stone chain is a collection of blocks that holds data. The kind of public blockchain affects the information stored inside a piece. For instance, a lot coin block contains data about the Presenter, the Reception, and the amount of bit coins that will be communicated. The brand's first block is called the Node. The goal of the block chain is to make data available to everyone who uses a program to access it. Entry to all this account for reading may be unrestricted (‘permit less’) or restricted (‘approval’). Block chain today makes it feasible to send, receive, and store digital data in a secure manner. Additionally, it uses mathematical encryption to protect every transaction. By adopting this, banks might boost their current security and disclosures to unprecedented heights.

关键词： Authorization Steganography Privacy Authentication Quality control Blockchains Encryption

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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IEEE International Conference on Industrial informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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Towards deployment-centric multimodal AI beyond vision and language

arXiv

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arXiv 2025年

作者： Liu, Xianyuan Zhang, Jiayang Zhou, Shuo van der Plas, Thijs L. Vijayaraghavan, Avish Grishina, Anastasiia Zhuang, Mengdie Schofield, Daniel Tomlinson, Christopher Wang, Yuhan Li, Ruizhe van Zeeland, Louisa Tabakhi, Sina Demeocq, Cyndie Li, Xiang Das, Arunav Timmerman, Orlando Baldwin-McDonald, Thomas Wu, Jinge Bai, Peizhen Al Sahili, Zahraa Alwazzan, Omnia Do, Thao N. Suvon, Mohammod N.I. Wang, Angeline Cipolina-Kun, Lucia Moretti, Luigi A. Farndale, Lucas Jain, Nitisha Efremova, Natalia Ge, Yan Varela, Marta Lam, Hak-Keung Celiktutan, Oya Evans, Ben R. Coca-Castro, Alejandro Wu, Honghan Abdallah, Zahraa S. Chen, Chen Danchev, Valentin Tkachenko, Nataliya Lu, Lei Zhu, Tingting Slabaugh, Gregory G. Moore, Roger K. Cheung, William K. Charlton, Peter H. Lu, Haiping Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom School of Computer Science University of Sheffield Sheffield United Kingdom The Alan Turing Institute London United Kingdom Department of Metabolism Digestion and Reproduction Imperial College London London United Kingdom Department of Applied AI Simula Research Laboratory Oslo Norway Information School University of Sheffield Sheffield United Kingdom NHS England Leeds United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Engineering King’s College London London United Kingdom Department of Computing Science University of Aberdeen Aberdeen United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Department of Informatics King’s College London London United Kingdom Department of Earth Sciences University of Cambridge Cambridge United Kingdom Department of Computer Science University of Manchester Manchester United Kingdom Department of Computer Science Queen Mary University of London London United Kingdom Digital Environment Research Institute Queen Mary University of London London United Kingdom Department of Computer Science University of Bath Bath United Kingdom Department of Classics King’s College London London United Kingdom School of Electrical Electronic and Mechanical Engineering University of Bristol Bristol United Kingdom School of Engineering University of the West of England Bristol United Kingdom Cancer Research UK Scotland Institute Glasgow United Kingdom School of Business and Management Queen Mary University of London London United Kingdom City St George’s University of London London United Kingdom British Antarctic Survey Cambridge United Kingdom School of Health and Wellbeing University of Glasgow Glasgow United Kingdom Chief Data & AI Office Lloyds Banking Group Lo

Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most multimodal AI advances focus on models for vision and language data, while their deployability remains a key challenge. We advocate a deployment-centric workflow that incorporates deployment constraints early to reduce the likelihood of undeployable solutions, complementing data-centric and model-centric approaches. We also emphasise deeper integration across multiple levels of multimodality and multidisciplinary collaboration to significantly broaden the research scope beyond vision and language. To facilitate this approach, we identify common multimodal-AI-specific challenges shared across disciplines and examine three real-world use cases: pandemic response, self-driving car design, and climate change adaptation, drawing expertise from healthcare, social science, engineering, science, sustainability, and finance. By fostering multidisciplinary dialogue and open research practices, our community can accelerate deployment-centric development for broad societal impact. © 2025, CC BY.

关键词： Decision making

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