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arXiv 2024年

作者： Zhang, Yu-Xin Gui, Jie Yu, Baosheng Cong, Xiaofeng Gong, Xin Tao, Wenbing Tao, Dacheng School of Cyber Science and Engineering Southeast University Nanjing210000 China Purple Mountain Laboratories Nanjing210000 China Lee Kong Chian School of Medicine Nanyang Technological University 308232 Singapore School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Computing & Data Science Nanyang Technological University #32 Block N4 #02a-014 50 Nanyang Avenue Singapore639798 Singapore

Point cloud registration involves determining a rigid transformation to align a source point cloud with a target point cloud. This alignment is fundamental in applications such as autonomous driving, robotics, and medical imaging, where precise spatial correspondence is essential. Deep learning has greatly advanced point cloud registration by providing robust and efficient methods that address the limitations of traditional approaches, including sensitivity to noise, outliers, and initialization. However, a well-constructed taxonomy for these methods is still lacking, making it difficult to systematically classify and compare the various approaches. In this paper, we present a comprehensive survey and taxonomy on deep learning-based point cloud registration (DL-PCR). We begin with a formal description of the point cloud registration problem, followed by an overview of the datasets, evaluation metrics, and loss functions commonly used in DL-PCR. Next, we categorize existing DL-PCR methods into supervised and unsupervised approaches, as they focus on significantly different key aspects. For supervised DL-PCR methods, we organize the discussion based on key aspects, including the registration procedure, optimization strategy, learning paradigm, network enhancement, and integration with traditional methods;For unsupervised DL-PCR methods, we classify them into correspondence-based and correspondence-free approaches, depending on whether they require explicit identification of point-to-point correspondences. To facilitate a more comprehensive and fair comparison, we conduct quantitative evaluations of all recent state-of-the-art approaches, using a unified training setting and consistent data partitioning strategy. Lastly, we highlight the open challenges and discuss potential directions for future study. We hope this comprehensive survey will contribute to the advancement of DL-PCR by serving as a fundamental reference for researchers and practitioners in the field. A c

关键词： Taxonomies

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ODMGIS: An Outlier Detection Method Based on Multi-Granularity Information Sets

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IEEE Transactions on Fuzzy Systems 2025年

作者： Zhang, Pengfei He, Zhaoxuan Wang, Dexian Jiang, Tao Li, Baolin Liu, Jia Huang, Wei Li, Tianrui The School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu611137 China The Acupuncture and Tuina School Chengdu University of Traditional Chinese Medicine Chengdu611137 China The School of Logistics Chengdu University of Information Technology Chengdu60020 China The School of Computer and Software Engineering Xihua University Chengdu610039 China The College of Computer and Data Science Fuzhou University Fuzhou35010 China The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China

In the realm of data mining, outlier detection has emerged as a pivotal research focus, aimed at uncovering anomalies within datasets to extract meaningful and valuable insights. The objective is to leverage data mining methodologies to pinpoint anomalies within datasets, thereby revealing crucial and enlightening information. Herein, we introduce a groundbreaking outlier detection methodology, ODMGIS, that seamlessly integrates multi-granularity representation and information set concepts to devise the multi-granularity information set (MGIS) model. This model adeptly characterizes the distribution patterns of data points. Firstly, we employ entropy function and their complementary function as measurement tools to accurately quantify the inherent uncertainty in data with different distributions, and considers the sum of the two as a comprehensive representation of the overall uncertainty of the information source. Subsequently, an outlier score model is constructed based on MGIS, which can deeply characterize the degree of outlierness of samples, thereby effectively identifying abnormal points in the dataset. During validation, ODMGIS was rigorously tested on practical datasets from medicine and bioinformatics, and its performance was benchmarked against both traditional and the state-of-the-art algorithms, showcasing substantial benefits. This research not only contributes a fresh perspective to outlier detection but also sparks innovative avenues for exploring and advancing the granular computing theory. © 1993-2012 I IEEE. All rights reserved.

关键词： Granular computing

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Evaluating Machine Learning Classifiers for Breast Cancer Diagnosis: A Comparative Study Using Accuracy Metrics

Evaluating Machine Learning Classifiers for Breast Cancer Di...

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International Carnahan Conference on Security technology

作者： Mohanaprakash T A R. Kalaiyarasi Ramya. M Vyshnavi K Madhura Deventhiran I. Jaichitra Department of CSE SOET CMR University Bangaluru India Department of Artificial intelligence and Data Science Rajalakshmi Institute of Technology Chennai India Department of CSE St.Joseph's Institute of Technology OMR Chennai Department of IT Dr.MG.R.Educational and Research Institute Chennai India Department of CSE Panimalar Engineering College Chennai India

ISBN: (数字)9798350315875

ISBN: (纸本)9798350315882

Globally, breast cancer ranks among the top causes of death for women, and the number of cases is increasing yearly. For better treatment outcomes and patient survival, accurate diagnosis and prediction are essential. Developments in the early detection and diagnosis of cancer are essential for encouraging a long and healthy life. Reaching a high level of accuracy in cancer prediction improves patient care by allowing for prompt interventions. Enhancing survival rates requires machine learning-based breast cancer prediction. On the Breast Cancer Wisconsin Diagnostic dataset, this study tested all machine learning algorithm. Random Forest outperformed other classifiers and obtained the greatest accuracy (98.4%). The study emphasizes how crucial machine learning is to the detection and prognosis of breast cancer. The Python programming language and Scikit-Iearn library were used in the work, which was carried out under the Anaconda environment.

关键词： Measurement Support vector machines Accuracy Machine learning algorithms Breast cancer Security Reliability Prognostics and health management Random forests Python

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Solving water scarcity challenges in arid regions: A novel approach employing human-based meta-heuristics and machine learning algorithm for groundwater potential mapping

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Chemosphere 2024年 363卷 142859页

作者： Razavi-Termeh, Seyed Vahid Sadeghi-Niaraki, Abolghasem Farhangi, Farbod Khiadani, Mehdi Pirasteh, Saied Choi, Soo-Mi Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone XR Research Center Sejong University Seoul Korea Republic of Geoinformation Tech. Center of Excellence Faculty of Geodesy and Geomatics Engineering K.N. Toosi University of Technology Tehran19697 Iran School of Engineering Edith Cowan University 270 Joondalup Drive JoondalupWA6027 Australia Institute of Artificial Intelligence School of Mechanical and Electrical Engineering Shaoxing University China Department of Geotechnics and Geomatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

Addressing water scarcity challenges in arid regions is a pressing concern and demands innovative solutions for accurate groundwater potential mapping (GPM). This study presents a comprehensive evaluation of advanced modeling techniques to enhance the precision of GPM. This study, conducted in the Zayandeh Rood watershed, Iran, employed a spatial database comprising 16 influential factors on groundwater potential and data from 175 wells. This study introduced an innovative approach to GPM by enhancing the Random Forest (RF) algorithm. This enhancement involved integrating three metaheuristic algorithms inspired by human behavior: ICA (Imperialist Competitive Algorithm), TLBO (Teaching-Learning-Based Optimization), and SBO (Student Psychology Based Optimization). The modeling process used 70% training data and 30% evaluation data. data preprocessing was performed using the multicollinearity test method and frequency ratio (FR) technique to refine the dataset. Subsequently, the GPM was generated using four distinct models, demonstrating the combined power of machine learning and human-inspired metaheuristic algorithms. The performance of the models was systematically assessed through extensive statistical analyses, including root mean squared error (RMSE), mean absolute error (MAE), area under the curve (AUC) for the receiver operating characteristic curve (ROC), Friedman tests, chi-squared tests, and Wilcoxon signed-rank tests. RF-ICA and RF-SPBO emerged as frontrunners, displaying statistically comparable accuracy and significantly outperforming RF-TLBO and the non-optimized RF model. The results of the GPM revealed the exceptional accuracy of RF-ICA, which exhibited a commanding AUC score of 0.865, underscoring its superiority in discriminating between different groundwater potential classes. RF-SPBO also displayed strong performance with an AUC of 0.842, highlighting its effectiveness in inaccurate classification. RF-TLBO and the non-optimized RF model achieved AU

关键词： Geographic information systems

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Als: Active Learning-Based Image Segmentation Model for Skin Lesion

SSRN

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SSRN 2022年

作者： Shu, Xiu Yang, Yunyun Xie, Ruicheng Liu, Jun Chang, Xiaojun Wu, Boying School of Science Harbin Institute of Technology Shenzhen China Information Systems Technology and Design Singapore University of Technology and Design Singapore Australian Artificial Intelligence Institute Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia School of Mathematics Harbin Institute of Technology Harbin China

Medical image segmentation requires experienced doctors to provide accurate segmentation results as ground truth, since it involves plentiful medical knowledge. The deep learning-based model requires a large number of medical images and ground truth, during the training stage, which brings a heavy workload to the doctor. Therefore, based on the differences between medical images, we designed an active learning model to select more representative images for doctors to obtain the ground truth, which saves their time in segmenting similar medical images. Eventually, these representative images will serve as training sets and test sets, training a more accurate segmentation model. In this paper, we propose the active learning-based segmentation (ALS) model for medical images, in which we use the mean structure similarity index measure to measure how similar the two images are. Then we select some highly similar images from the entire training set. To our knowledge, we are the first to apply the active learning strategy based on the mean structure similarity index measure to medical image segmentation. Finally, we reduce the amount of data to 75%, 50%, 25%, and 12.5% to validate our ALS model. We use the selected images after active learning training and the medical semantic segmentation model to segment medical images, and the active learning model can reduce the cost of labeling and obtain accurate segmentation results through experiments, and even better than the segmentation results with a large number of highly similar data. © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Neural Network-Based Wind Field Reconstruction Method with Limited Measurement Points for Urban Blocks

SSRN

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SSRN 2024年

作者： Wang, Ranpeng Liu, Yi Li, Congjian Gao, Hang Wang, Gangqiao Institute of Public Safety Research Department of Engineering Physics Tsinghua University Beijng100084 China School of Safety Science Tsinghua University Beijing100084 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China College of Artificial Intelligence Tianjin University of Science and Technology Tianjin300450 China Institute of Big Data intelligent Management and Decision College of Management Shenzhen University Shenzhen518055 China

The rapid reconstruction of wind fields in urban blocks plays a crucial role in retrofitting zero-energy buildings, guiding the selection and placement of wind turbines. Although numerical methods and the Proper Orthogonal Decomposition (POD) method can provide relatively accurate results, their high computational cost and associated discontinuities pose challenges for acquiring high-spatial-resolution data affected by buildings. In this study, we develop a deep learning approach that can reconstruct wind fields affected by complex building layouts using a limited number of measuring points. The model is meticulously crafted with considerations given to model structure, loss function, and point selection method. Extensive analysis and comparisons with conventional designs are conducted to validate the proposed model. The results demonstrate that our model outperforms the conventional methods, achieving a mean absolute error in velocity of only 0.12 m/s over the inlet velocity range of 1 to 20 m/s, with only ten measurement points. Furthermore, a comparison with the POD method reveals that our model excels in extracting and preserving the crucial features of wind fields affected by complex building layouts, enabling higher migration capability. Overall, the proposed method delivers the potential for accurately reconstructing wind fields in urban blocks. © 2024, The Authors. All rights reserved.

关键词： Numerical methods

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qing Liu Qian Gao Jun Fan Zhiqiang Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Ltd Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN— a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model’s effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets.

关键词： Measurement Bridges Contrastive learning Signal processing data processing Vectors Space exploration Resource management Speech processing Mutual information

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

arXiv

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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Revolutionizing Urban Drainage: A Smart IoT Approach to Stormwater Management Using AdaBoosting Algorithm

Revolutionizing Urban Drainage: A Smart IoT Approach to Stor...

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Advances in Modern Age Technologies for Health and engineering science (AMATHE), International Conference on

作者： P S Thanigaivelu A. Sairam P. Vijayan A. Suresh Kumar N. Mohankumar T. R. GaneshBabu Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur 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 Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamil Nadu India Department of Management Studies Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

The management of stormwater in urban areas across the globe is becoming an increasingly difficult problem because of the increased risk of floods caused by climate change. Typical drainage methods often turn out to be insufficient, which calls for the development of novel approaches. This study suggests a smart stormwater management system powered by the Internet of Things and aims to boost urban resilience and reduce floods. The system continually monitors critical drainage characteristics, such as water levels, flow rates, and water quality, thanks to a network of Internet of Things sensors. To accurately predict probable flooding occurrences, advanced predictive algorithms examine this data together with incoming meteorological information. The technology performs real-time dynamic management of the drainage network, improving the efficiency of pumps and valves to reduce the likelihood of flooding. When potential flood hazards are identified, those who need to be informed, such as local authorities and citizens, are notified through an early warning system. The findings of this study lead to a more effective allocation of resources, an improvement in water quality, and an increase in the resilience of metropolitan areas to severe weather events. This approach, powered by the Internet of Things, promises a route toward sustainable and flood-resilient communities by transforming urban drainage.

关键词： Ethics Urban areas Water quality Prediction algorithms Real-time systems Stormwater Floods

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Design and fabrication of library management bot

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AIP Conference Proceedings 2025年第1期3204卷

作者： T. Vignesh Senthil Kumar S. Arul Murugan D. Helan Jemima A. Dheeraj R. Saran Sarvesh Department of Mechatronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India. Department of Artificial Intelligence & Data Science Erode Sengunthar Engineering College Erode India.

The primary goal of the paper is to design and construct a Library Bot that assists people to easily get through the pathway, the books, etc. These robots navigate the people based on their needs, and help them in shelf management, finding their books, assist them. It also finds and does the stocking of books. It comes with an inbuilt user interface that ensures better communication with the users or readers. It has a touchscreen to help the users effectively. Speech recognition is done to assist the users by interacting effectively with them. The navigation system is also incorporated to guide the users to their respective needs. It also further analyses the requirements of the end users and helps them identify the books based on their interests.

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