检索结果-内蒙古大学图书馆

Unleashing WSN Potential: Bowerbird Optimized Energy-Conscious Multipath Clustering with Causal Dilated Cosine for Enhanced Load Balancing

学校读者我要写书评

暂无评论

Unleashing WSN Potential: Bowerbird Optimized Energy-Conscio...

Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Rakesh Kumar Joon Anitha R Saravana Selvan P. S. Latha Mageshwari S. Gayathri Priya K. Malathi Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Kablana Haryana India Department of Artificial Intelligence and Data Science S.A. Engineering College Chennai Tamil Nadu India School of Professional Engineering Manukau Institute of Technology Auckland New Zealand Department of Science & Humanities (Physics) R. M. K. Engineering College Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering R.M.D. Engineering College Kavaraipettai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Wireless Sensor Networks (WSNs) are essential in the collection of real time data across different fields, including environmental monitoring and process control. However, due to the bounded amount of energy in sensor nodes, network lifetime becomes an issue, which hinders performance and reliability. Thus, optimization of WSN performance in an attempt to minimize energy consumption is essential for reliable data transmission. In conventional routing and clustering models, network load distribution is not achieved adequately, resulting in a more significant loading of nodes and faster exhaustion of energy. To solve these problems, it is necessary to carefully design load distribution and path finding algorithms to support long-term WSN operation. The objective of this research is to design a framework-Ski-CDiCo-BO-that integrates Skill Optimization for clustering, and ensuring an energy-efficient paths while reducing redundant transmissions on nodes and conserving node energies, and uses the Causal Dilated Cosine Architecture for sophisticated load balancing, and Bowerbird Optimization for improving the parameterization of neural networks. Analysis reveals that the presented Ski-CDiCo-BO framework provides improved performance of various parameters including data delivery success rate of above 99.8% and network lifetime gain by up to 99.5%. Also, load distribution accuracy remains above 99.3%, which indicates that the framework is capable of avoiding node overload and end-to-end delay improved upon by 99.6%, indicating that the solution maintains high parameters even in dynamically changing conditions. In conclusion, Ski-CDiCo-BO maximizes WSN efficiency by implementing cutting-edge clustering, load balancing, and optimization techniques, setting new standards for energy-conscious and reliable sensor network performance across diverse applications.

关键词： Wireless sensor networks Energy consumption Scalability Heuristic algorithms Load management Routing Energy efficiency Reliability Environmental monitoring Optimization

Automated Pipeline for Regional Epicardial Adipose Tissue Distribution Analysis in the Left Atrium 15th

学校读者我要写书评

暂无评论

Automated Pipeline for Regional Epicardial Adipose Tissue Di...

15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024

作者： Lluch, Èric Castañeda, Eduardo Weiss, Maximilian E. R. Vizitiu, Anamaria Jacob, Athira Jami, Jitin Audigier, Chloé Meister, Felix Mihalef, Viorel Passerini, Tiziano Siemens Healthineers Digital Technologies and Innovation Erlangen Germany Pattern Recognition Lab Department of Computer Science Friedrich-Alexander University Erlangen-Nürnberg Erlangen Germany Siemens SRL Brasov Romania Siemens Healthineers Digital Technology and Innovation Princeton United States Machine-Learning and Data Analytics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Advanced Clinical Imaging Technology Siemens Healthineers AG Lausanne Switzerland

ISBN: (纸本)9783031877551

Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting approximately 3% of the global population and rising to 11% in individuals over 80 years old. The distribution of epicardial adipose tissue (EAT) around the atria plays a significant role in the pathophysiology of AF, yet its regional analysis remains underexplored. This paper introduces an automated pipeline capable of segmenting 14 atrial regions from patient CT angiograms, a notable advancement over previous attempts to automate the creation of synthetic anatomical or physics-based twins. Our fully automatic pipeline requires no human supervision and provides the regional EAT quantification successfully on 91.9% of the analyzed patients for which the LA and its structures were automatically segmented (1797 out of 1954 patients), including the automatic segmentation of the aorta and pericardium to assess EAT volume around the left atrium. By applying principal component analysis of the regional EAT to analyze a separate dataset of 84 individuals who were clinically labeled, we identified a distinct group that contained all the patients with AF. This finding suggests that there is a significant link between the pattern of fat distribution and AF. This novel approach has the potential to enhance our understanding of AF and improve patient outcomes by enabling large-scale, regional fat analysis in an automated manner on all patients with accurate segmentation of left atrial structures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Principal component analysis

Secure Device on boarding in IoT Networks

学校读者我要写书评

暂无评论

Secure Device on boarding in IoT Networks

International Conference on Science technology Engineering and Management (ICONSTEM)

作者： P. Sathyaraj Shankar Nayak Bhukya S. Rukmani Devi Chetan Umadi A. Ajina Rajendiran M Department of Electronics and Communication Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Computer Science Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Electronics& Telecommunication Engineering Dayananda Sagar College of Engineering Bengaluru Karnataka India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The fast spread of Internet of Things (IoT) gadgets has led to unprecedented number of interconnected systems offering many applications and services. Diverse elements in IoT networks increase security vulnerabilities, especially when adding new devices. IoT device onboarding secures device integration, authentication, and network connection authorization. This research shows the hazards and limitations of secure IoT device connections. Research examines onboarding protocols' merits and downsides. Bootstrapping, mutual authentication, and key establishment protect the IoT device-network connection in the proposed architecture. Overcoming scalability and resource constraints makes onboarding several IoT devices straightforward. We also analyze IoT network security with PKI and certificate-based authentication. Safe communication pathways and ongoing observation and system adjustments to address new security issues are discussed in this essay. Researchers test the technology in IoT environments. The framework handles onboarding in this case. The method safeguards onboarding and IoT ecosystem data, research demonstrate. A detailed device onboarding approach increases IoT security talks. The framework fixes onboarding issues and vulnerabilities for secure IoT networks.

关键词： Protocols Scalability Ecosystems Authentication Interconnected systems Network security Mathematics

Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node data: Whole City Traffic and ETA from Stationary Vehicle Detectors 36

学校读者我要写书评

暂无评论

Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph...

36th Annual Conference on Neural Information Processing Systems - Competition Track, NeurIPS 2022

作者： Neun, Moritz Eichenberger, Christian Martin, Henry Spanring, Markus Siripurapu, Rahul Springer, Daniel Deng, Leyan Wu, Chenwang Lian, Defu Zhou, Min Lumiste, Martin Ilie, Andrei Wu, Xinhua Lyu, Cheng Lu, Qing-Long Mahajan, Vishal Lu, Yichao Li, Jiezhang Li, Junjun Gong, Yue-Jiao Grötschla, Florian Mathys, Joël Wei, Ye Haitao, He Fang, Hui Malm, Kevin Tang, Fei Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland School of Data Science University of Science and Technology of China China Huawei Noah’s Ark Lab. China Bolt Technology Tallinn Estonia University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering Northeastern University BostonMA United States Technical University of Munich Germany Layer 6 AI Toronto Canada School of Coumpute Science and Engineering South China University of Technology Guangzhou China ETH Zurich Switzerland Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom HERE Technologies ChicagoIL United States Kaiko Zurich Switzerland Machine Learning Institute Johannes Kepler University Linz Austria

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future – super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data. © 2023 M. Neun et al.

关键词： Travel time

Topologically Faithful Multi-class Segmentation in Medical Images

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Berger, Alexander H. Lux, Laurin Stucki, Nico Bürgin, Vincent Shit, Suprosanna Banaszak, Anna Rueckert, Daniel Bauer, Ulrich Paetzold, Johannes C. School of Computation Information and Technology Technical University of Munich Munich Germany Munich Data Science Institute Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany School of Medicine and Health Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Computing Imperial College London London United Kingdom Department of Quantitative Biomedicine University of Zurich Switzerland

Topological accuracy in medical image segmentation is a highly important property for downstream applications such as network analysis and flow modeling in vessels or cell counting. Recently, significant methodological advancements have brought well-founded concepts from algebraic topology to binary segmentation. However, these approaches have been underexplored in multi-class segmentation scenarios, where topological errors are common. We propose a general loss function for topologically faithful multi-class segmentation extending the recent Betti matching concept, which is based on induced matchings of persistence barcodes. We project the N-class segmentation problem to N single-class segmentation tasks, which allows us to use 1-parameter persistent homology, making training of neural networks computationally feasible. We validate our method on a comprehensive set of four medical datasets with highly variant topological characteristics. Our loss formulation significantly enhances topological correctness in cardiac, cell, artery-vein, and Circle of Willis segmentation.7 © 2024, CC BY-NC-SA.

关键词： Image segmentation

Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Weber, Leander Lapuschkin, Sebastian Binder, Alexander Samek, Wojciech Department of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Berlin10587 Germany ICT Cluster Singapore Institute of Technology 138683 Singapore BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin Germany Department of Informatics University of Oslo Oslo0373 Norway

Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex and opaque machine learning (ML) models. Despite the development of a multitude of methods to explain the decisions of black-box classifiers in recent years, these tools are seldomly used beyond visualization purposes. Only recently, researchers have started to employ explanations in practice to actually improve models. This paper offers a comprehensive overview over techniques that apply XAI practically for improving various properties of ML models, and systematically categorizes these approaches, comparing their respective strengths and weaknesses. We provide a theoretical perspective on these methods, and show empirically through experiments on toy and realistic settings how explanations can help improve properties such as model generalization ability or reasoning, among others. We further discuss potential caveats and drawbacks of these methods. We conclude that while model improvement based on XAI can have significant beneficial effects even on complex and not easily quantifyable model properties, these methods need to be applied carefully, since their success can vary depending on a multitude of factors, such as the model and dataset used, or the employed explanation method. © 2022, CC BY-NC-SA.

关键词： Toys

Analyzing the Effects of Different Urban and Rural Characteristics on Surface Urban Heat Island Intensity Using the Spatial Autoregressive Model

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Wongsi, Noppachai Wongsai, Sangdao Wanishsakpong, Wandee Suwanprasit, Chanida Department of Mathematics and Statistics Faculty of Science and Technology Thammasat University Pathumthani12121 Thailand Thammasat University Research Unit in Data Learning Thammasat University Pathumthani12121 Thailand Department of Statistics Faculty of Science Kasetsart University Bangkok10900 Thailand Department of Geography Faculty of Social Sciences Chiang Mai University Chiang Mai50200 Thailand College of Arts Media and Technology Chiang Mai University Chiang Mai50200 Thailand

This study introduces an approach for quantifying the SUHII using the generalized mixed spatial autoregressive (SAR) model, in the Eastern Economic Corridor of Thailand. A comparative analysis was performed using the mean differences and simple linear regression (SLR), SLR with kernel density estimation (KDE), and spatial autoregressive regression (SAR) models. A year of MODIS LST was the dependent variable. The independent variables were the ISA percentage (%ISA), derived from the Sentinel-2 image using the RF classifier, and the geographical locations. Our findings indicated that using the mean differences on pixels without water surface and altitude led to the underestimated SUHII. By fitting the SLR with KDE on the smoothed %ISA, the LST was overfitted. The SLR applied to the original dataset mitigated the overestimation issue, but could not address the spatial dependency. The SAR model revealed a significant reduction in the spatial dependency and enhanced the model accuracy without overfitting. The 95% confidence intervals for LST from the SAR were found to be twice as narrow as those from other models. To evaluate the spatial assumption, we suggested making a map of how the spatial residuals are spread out, showing if there are clusters or dispersed. © 2023, The Authors. All rights reserved.

关键词： Atmospheric temperature

OPTIMIZING EXPLANATIONS BY NETWORK CANONIZATION AND HYPERPARAMETER SEARCH

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Pahde, Frederik Yolcu, Galip Ümit Binder, Alexander Samek, Wojciech Lapuschkin, Sebastian Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Germany Technische Universität Berlin Germany ICT Cluster Singapore Institute of Technology Singapore University of Oslo Norway BIFOLD – Berlin Institute for the Foundations of Learning and Data Germany

Explainable AI (XAI) is slowly becoming a key component for many AI applications. Rule-based and modified backpropagation XAI approaches however often face challenges when being applied to modern model architectures including innovative layer building blocks, which is caused by two reasons. Firstly, the high flexibility of rule-based XAI methods leads to numerous potential parameterizations. Secondly, many XAI methods break the implementation-invariance axiom because they struggle with certain model components, e.g., BatchNorm layers. The latter can be addressed with model canonization, which is the process of re-structuring the model to disregard problematic components without changing the underlying function. While model canonization is straightforward for simple architectures (e.g., VGG, ResNet), it can be challenging for more complex and highly interconnected models (e.g., DenseNet). Moreover, there is only little quantifiable evidence that model canonization is beneficial for XAI. In this work, we propose canonizations for currently relevant model blocks applicable to popular deep neural network architectures, including VGG, ResNet, EfficientNet, DenseNets, as well as Relation Networks. We further suggest a XAI evaluation framework with which we quantify and compare the effects of model canonization for various XAI methods in image classification tasks on the Pascal VOC and ILSVRC2017 datasets, as well as for Visual Question Answering using CLEVR-XAI. Moreover, addressing the former issue outlined above, we demonstrate how our evaluation framework can be applied to perform hyperparameter search for XAI methods to optimize the quality of explanations. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

Legal perspective on possible fairness measures - A legal discussion using the example of hiring decisions

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Hauer, Marc P. Kevekordes, Johannes Haeri, Maryam Amir TU Kaiserslautern Germany WWU Münster Germany TU Kaiserslautern Germany Learning Data-Analytics and Technology Department University of Twente Netherlands

With the increasing use of AI in algorithmic decision making (e.g. based on neural networks), the question arises how bias can be excluded or mitigated. There are some promising approaches, but many of them are based on a"fair" ground truth, others are based on a subjective goal to be reached, which leads to the usual problem of how to define and compute"fairness". The different functioning of algorithmic decision making in contrast to human decision making leads to a shift from a process-oriented to a result-oriented discrimination assessment. We argue that with such a shift society needs to determine which kind of fairness is the right one to choose for which certain scenario. To understand the implications of such a determination we explain the different kinds of fairness concepts that might be applicable for the specific application of hiring decisions, analyze their pros and cons with regard to the respective fairness interpretation and evaluate them from a legal perspective (based on EU law). © 2021, CC BY-NC-ND.

关键词： Decision making