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arXiv 2024年

作者： Wang, Zhisong Ye, Yiwen Chen, Ziyang Shu, Minglei Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University China Shandong Artificial Intelligence Institute Qilu University of Technology Shandong Academy of Sciences China Ningbo Institute of Northwestern Polytechnical University Ningbo315048 China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

Scribble-based weakly supervised segmentation techniques offer comparable performance to fully supervised methods while significantly reducing annotation costs, making them an appealing alternative. Existing methods often rely on auxiliary tasks to enforce semantic consistency and use hard pseudo labels for supervision. However, these methods often overlook the unique requirements of models trained with sparse annotations. Since the model must predict pixel-wise segmentation maps with limited annotations, the ability to handle varying levels of annotation richness is critical. In this paper, we adopt the principle of ‘from few to more’ and propose MaCo, a weakly supervised framework designed for medical image segmentation. MaCo employs masked context modeling (MCM) and continuous pseudo labels (CPL). MCM uses an attention-based masking strategy to disrupt the input image, compelling the model’s predictions to remain consistent with those of the original image. CPL converts scribble annotations into continuous pixel-wise labels by applying an exponential decay function to distance maps, resulting in continuous maps that represent the confidence of each pixel belonging to a specific category, rather than using hard pseudo labels. We evaluate MaCo against other weakly supervised methods using three public datasets. The results indicate that MaCo outperforms competing methods across all datasets, setting a new record in weakly supervised medical image segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Pixels

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Impact of E-Waste on Pregnant Lady, Newborn Baby, and Fetus

Impact of E-Waste on Pregnant Lady, Newborn Baby, and Fetus

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Ankita Tiwari N. Ramesh Babu Nitin Rakesh Mandeep Kumar Jagjit Singh Dhattarwal Abhishek Bhola Department of Engineering Mathematics College of Engineering Koneru Lakshmaih Education Foundation Guntur India Department of Mathematucs School of Advnced Sciences Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India School of Law Sharda University Greater Noida India Department of Artificial Intelligence & Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Chaudhary Charan Singh Haryana Agricultural University College of Agriculture Bawal

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Hazardous materials included in electronic trash (e-waste) can affect fetuses, neonates, and expectant mothers. Preterm birth, birth irregularities, and miscarriage are among the risks associated with pregnant women who are exposed to e-waste chemicals. Exposure to e-waste puts newborns at risk for respiratory and neurodevelopmental disorders. Developmental irregularities and growth restrictions are possible in fetuses. To reduce these risks and protect mother and child health, effective methods including appropriate e-waste discarding and public health programmes are important.

关键词： Pregnancy Pediatrics Osteoporosis Toxic chemicals Anemia Electronic waste Nutrients Safety Public healthcare Phthalates

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Advancing Telestroke Services Technology with Cloud and AI Solutions for Enhanced Care

Advancing Telestroke Services Technology with Cloud and AI S...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： Mothiram Rajasekaran R. Meenakshi P. K. Dhal R. Aiyshwariya Devi S. Sujatha R. Kamalakannan Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tami Nadu India Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and 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 Department of Electrical and Electronics engineering S.A. Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Telestroke services have become an essential component of stroke therapy, especially in areas with few specialists' medical facilities. To further improve telestroke services, this paper investigates the possibility of integrating cloud computing with artificial intelligence (AI) technologies. With the scalability, accessibility, and cost-effectiveness of cloud infrastructure, healthcare professionals worldwide may communicate and share data easily. Machine learning algorithms, image analysis tools, and other forms of AI are enhancing telestroke services by giving doctors access to sophisticated decision-making capabilities. Better patient outcomes result from these systems' capacity to facilitate quicker and more precise diagnosis and treatment planning. Predictive analytics powered by AI can identify patients at risk early on and provide individualized treatment plans that can better meet each patient's requirements. By combining cloud and AI technology, telestroke services can improve patient care by analyzing large datasets, allocating resources more efficiently, and fostering better teamwork among specialists. It may perform beyond geographical restrictions and ensure that stroke patients all around the globe have equal access to high-quality treatment. The potential is to transform stroke care delivery using data-driven insights and improved decision-making skills. It simplifies processes and improves efficiency.

关键词： Cloud computing Technological innovation Decision making Medical treatment Transforms Stroke (medical condition) Prediction algorithms Teamwork artificial intelligence Predictive analytics

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Improving Mall Traffic Flow with IoT and Reinforcement Learning Techniques in Footfall Analytics

Improving Mall Traffic Flow with IoT and Reinforcement Learn...

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Visual Analytics and data Visualization (ICVADV), International Conference on

作者： S. Muthumarilakshmi A. Sairam N. Mohankumar D. Hemalatha S. Velmurugan T. R. GaneshBabu Department of CSE Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Artificial Intelligence & Data Science Chennai Institute of Technology Chennai India Department of Electronics and Communication Engineering T.J.S. Engineering College Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

An innovative method for improving mall traffic flow utilizing IoT devices and reinforcement learning (RL) methods in footfall analytics is presented. Inefficiencies, congestion, and less-than-ideal consumer experiences are common outcomes of the conventional wisdom about managing mall foot traffic. Our proposed system uses Internet of Things (IoT) sensors to track shoppers' flow and density in real time. This data is then input into an RL model to optimize and predict traffic flow patterns. Aiming to enhance the shopping experience and distribute foot traffic more equitably, the system automatically adjusts parameters, including digital signage, escalator functioning, and store promotions. Simulation and pilot deployment results show substantial improvement in lowering congestion and boosting mall visitors' time spent there. By integrating IoT with powerful machine learning (ML) algorithms, this integrated technique demonstrates how smarter and more responsive retail environments may be created. IoT and RL improves mall traffic flow by analyzing footfall trends, optimizing routing, mitigating congestion, and enhancing customer experience.

关键词： Technological innovation Smart cities Visual analytics Scalability Reinforcement learning Routing Real-time systems Sensor systems Internet of Things Intelligent sensors

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Unsupervised Social Bot Detection via Structural Information Theory

arXiv

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arXiv 2024年

作者： Peng, Hao Zhang, Jingyun Huang, Xiang Hao, Zhifeng Li, Angsheng Yu, Zhengtao Yu, Philip S. School of Cyber Science and Technology Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China State Key Laboratory of Public Big Data Guizhou University No. 2708 South Section of Huaxi Avenue Huaxi District Guizhou Province Guiyang City550025 China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China College of Science University of Shantou No. 243 University Road Shantou515063 China School of Computer Science and Engineering Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Research on social bot detection plays a crucial role in maintaining the order and reliability of information dissemination while increasing trust in social interactions. The current mainstream social bot detection models rely on black-box neural network technology, e.g., Graph Neural Network, Transformer, etc., which lacks interpretability. In this work, we present UnDBot, a novel unsupervised, interpretable, yet effective and practical framework for detecting social bots. This framework is built upon structural information theory. We begin by designing three social relationship metrics that capture various aspects of social bot behaviors: Posting Type Distribution, Posting Influence, and Follow-to-follower Ratio. Three new relationships are utilized to construct a new, unified, and weighted social multi-relational graph, aiming to model the relevance of social user behaviors and discover long-distance correlations between users. Second, we introduce a novel method for optimizing heterogeneous structural entropy. This method involves the personalized aggregation of edge information from the social multi-relational graph to generate a two-dimensional encoding tree. The heterogeneous structural entropy facilitates decoding of the substantial structure of the social bots network and enables hierarchical clustering of social bots. Thirdly, a new community labeling method is presented to distinguish social bot communities by computing the user's stationary distribution, measuring user contributions to network structure, and counting the intensity of user aggregation within the community. Compared with ten representative social bot detection approaches, comprehensive experiments demonstrate the advantages of effectiveness and interpretability of UnDBot on four real social network datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Information theory

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Residual VGGNet for Unveiling Optimal Process Parameters of Oil Rig Welding Through Mathematical Modeling and Optimization

Residual VGGNet for Unveiling Optimal Process Parameters of ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： John T Mesia Dhas Utpal Saikia K K Arun C.Vimala Josphine M.Theodore Kingslin N Padmavathi Department of Computer Science and Engineering School of Computing Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology (deemed to be university) Chennai Tamilnadu India Department of Mathematics Silapathar College Silapathar Dhemaji Assam India Department of Mechanical Engineering Kumaraguru College of Technology Coimbatore Tamilnadu India Artificial Intelligence and Data Science R.M.K Engineering College Kavaraipettai Department of Electronics & Communication Engineering R.M.K College of Engineering & Technology Puduvoyal Tamilnadu India Department of Electronics and Instrumentation Engineering R.M.D. Engineering College Tamilnadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The optimization of welding techniques for oil rig components is crucial to upholding the structural integrity and safety of offshore drilling activities. In this study, we utilize a residual VGGNet architecture, which is a modified version of the well-known VGGNet, for the purpose of extracting crucial information from images of welding processes. The methodology employed in this study involves the collection of a complete dataset comprising welding process images obtained from diverse oil rig welding activities. The provided images are accompanied by annotations that provide the associated welding parameters, including current, voltage, and welding speed. The residual VGGNet is subsequently trained to acquire knowledge about the complex associations between picture features and welding parameters. In order to develop a reliable mathematical model, regression techniques are utilized to precisely establish the relationship between the extracted features and welding parameters. The performance of the model is evaluated using cross-validation and compared to traditional regression models to emphasize its greater ability to capture intricate patterns within the data.

关键词： Accuracy Welding Oils Voltage Feature extraction Mathematical models data models Safety Reliability Optimization

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Specific-Modal Spatial Guidance and Feature Enhancement for Multi-modal Brain Tumor Segmentation

Specific-Modal Spatial Guidance and Feature Enhancement for ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Junyang Han Cheng Xue Hongzhi Liu Shiyu Zhu Jiacheng Nie Weixuan Wan Chunfeng Yang Wenxue Yu Xiaoying Tang Yang Chen Shijie Wang Pinzheng Zhang Jean-Louis Coatrieux School of Computer Science and Engineering Southeast University Nanjing China College of Engineering Southern University of Science and Technology Shenzhen China Ministry of Education The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) Nanjing China Centre de Recherche en Information Biomedicale Sino-Francais Inserm University of Rennes 1 Rennes France

Multi-modal information plays a pivotal role in the segmentation of brain tumors. However, previous studies have largely overlooked the distinctive characteristics of individual modalities, which are correlated with the target tumor region due to distinct imaging principles. In this paper, we harness the distinctive traits of individual modalities and introduce a brain tumor segmentation model called specific modality guided brain tumor segmentation model (SMG-BTS). Our SMG-BTS adopts a three-branch encoder-decoder architecture. The main branch utilizes full modalities fused at input-level, while the two affiliated branches operate in parallel to provide guidance to the main branch in acquiring a robust representation. We propose a specific modality spatial guidance (SMSG) module to guide the process of feature extraction. Spatial information is obtained from selected modalities and utilized to enhance features extracted from the main branch. A shared-specific feature enhancement(SSFE) module is proposed to enhance the shared features across modalities and utilizes modality-specific features to further supplement specific information of modalities. Experimental results on the BraTS2021 benchmark dataset demonstrate the effectiveness of our proposed SMG-BTS over state-of-the-art brain tumor segmentation methods.

关键词：

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MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTERING 12

MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTER...

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12th International Conference on Learning Representations, ICLR 2024

作者： Burg, Max F. Zenkel, Thomas Vystrčilová, Michaela Oesterle, Jonathan Höfling, Larissa Willeke, Konstantin F. Lause, Jan Müller, Sarah Fahey, Paul G. Ding, Zhiwei Restivo, Kelli Sridhar, Shashwat Gollisch, Tim Berens, Philipp Tolias, Andreas S. Euler, Thomas Bethge, Matthias Ecker, Alexander S. International Max Planck Research School for Intelligent Systems Tübingen Germany Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Tübingen AI Center University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany Centre for Integrative Neuroscience University of Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany Hertie Institute for AI in Brain Health University of Tübingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States University Medical Center Göttingen Department of Ophthalmology Germany Bernstein Center for Computational Neuroscience Göttingen Germany University of Göttingen Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Maximum principle

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A PCA-based fuzzy tensor evaluation model for multiple-criteria group decision making

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Applied Soft Computing 2023年 132卷

作者： Singh, Meenu Pant, Millie Kong, Lingping Alijani, Zahra Snášel, Václav Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Uttarakhand247667 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Uttarakhand247667 India Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Czech Republic Institute for Research and Applications of Fuzzy Modeling University of Ostrava 30. dubna 22 701 03 Ostrava Czech Republic

Multiple-criteria group decision-making (MCGDM) problems mainly consist of multiple factors and multiple Decision Makers (DMs) or Users, for which dimension extension is necessary when considering all the entries of DMs together. Tensor, a generalized form of a matrix, displays a multi-way array item, which is the most suitable and practical way to represent high-dimensional data without losing any information. In this paper, we first reduce the dimension through Principal Component Analysis (PCA), which helps consider the most-informative criteria. Then, we reintroduced the tensor as a fuzzy-form tensor for the MCGDM problem because of user information uncertainty. We choose Interval-valued Neutrosophic Fuzzy numbers (IVNFNs) as the basis for the tensor form because of their ability to distinguish between truth, indeterminacy, and falsity in the data. Lastly, a Generalized Interval-valued Neutrosophic Fuzzy Weighted Geometric (GIVNFWG) operator is defined. Moreover, a generalized framework for fuzzy-form tensors for high-dimensional MCGDM problems is proposed. The feasibility and efficiency of this proposed process is illustrated for a real-world MCGDM problem of ranking the most efficient Third Party Reverse Logistics Partners (3PRLPs), i.e., recycled fiber-based paper mills for the packaging industry. The obtained results are according to the experts and are validated using sensitivity analysis. This analysis facilitates in assessing the impact on the overall ranking performance of 3PRLPs by considering various combinations of environment and technological sub-criteria. © 2022 Elsevier B.V.

关键词： Principal component analysis

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Multi-view clustering guided by unconstrained non-negative matrix factorization

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Knowledge-Based Systems 2023年 266卷

作者： Deng, Ping Li, Tianrui Wang, Dexian Wang, Hongjun Peng, Hong Horng, Shi-Jinn School of Computer and Software Engineering Xihua University Chengdu610039 China 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 Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Chengdu611756 China Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Multi-view clustering based on non-negative matrix factorization (NMFMvC) is a well-known method for handling high-dimensional multi-view data. To satisfy the non-negativity constraint of the matrix, NMFMvC is usually solved using the Karush–Kuhn–Tucker (KKT) conditions. However, this optimization method is poorly scalable. To this end, we propose an unconstrained non-negative matrix factorization multi-view clustering (uNMFMvC) model. First, the objective function was constructed by decoupling the elements of the matrix and combining the elements with a non-linear mapping function in a non-negative value domain. The objective function was then optimized using the stochastic gradient descent (SGD) algorithm. Subsequently, three uNMFMvC methods were constructed based on different mapping functions and detailed reasoning was provided. Finally, experiments were conducted on eight public datasets and compared with cutting-edge multi-view clustering methods. The experimental results demonstrate that the proposed model has significant advantages. © 2023 Elsevier B.V.

关键词： Non-negative matrix factorization

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