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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3244 条记录，以下是491-500 订阅

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An Improved ICP Algorithm for 3D Point Cloud Registration 3

An Improved ICP Algorithm for 3D Point Cloud Registration

引用

3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Chen, Anguo Zhuang, Jinlei Han, Xiuqin Harbin Institute of Technology School of Mechatronics Engineering Harbin China Hit Wuhu Robot Technology Research Institute Wuhu China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

The rigid registration of two point clouds is a fundamental task in many areas, such as 3D reconstruction and robot navigation. The Iterative Closest Point (ICP) algorithm has been widely for this task. The basic principle of ICP algorithm is to match corresponding points between the two point clouds and compute an optimal transformation matrix that minimizes the Euclidean distance between corresponding points. A major drawback of the ICP algorithm is the sensitivity to partial overlaps often observed in 3D scans. In this regard, we propose a new rigid registration algorithm based on ICP. Firstly, the Super 4PCS algorithm is used to implement the initial alignment of point clouds to reduce the probability of ICP algorithm falling into a local optimal solution. Then, we propose a local refinement registration method by adaptively eliminating the boundary points of the overlap region of two point clouds. Our algorithm effectively solves the problem that ICP algorithm achieves poor registration accuracy or even produces an incorrect result when dealing with partial overlap data. According to the experimental results, our algorithm achieves better registration accuracy than the classical ICP algorithm. © 2022 IEEE.

关键词： Iterative methods

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Research on big data women's portrait platform based on knowledge graph analysis 3

Research on big data women's portrait platform based on know...

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3rd international conference on Electronic Information Engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Dong, Qingshuang Ding, Bing Xia, Minghui School of Communication Qufu Normal University Rizhao China Faculty of Education Languages and Psychology Segi University Selangor Malaysia School of Educational Sciences Hunan Normal University Changsha China

ISBN: (纸本)9781510680449

This study adopts the scientific knowledge graph research methodology, utilizing Cite Space information visualization software and China National Knowledge Infrastructure (CNKI) for visual analysis. It focuses on 483 journal papers from the CNKI database about personalized courses based on women profiling in China, especially revolutionary women. The research visually analyzes the studies on personalized platforms based on women's portrait in China. The study reveals that establishing student profiles involves using data science and educational technology methodologies such as machine learning, data mining, and artificial intelligence to process and analyze student information. This investigation aims to assist educational professionals in swiftly and accurately detecting the developmental trends of platforms based on women's portrait from a vast literature corpus. Furthermore, it aids in tracking dynamic hotspots in personalized platforms based on women's portrait and understanding the developmental directions of such platforms. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Students

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Swarm Intelligence and Its Impact on data mining and Knowledge Discovery 3rd

Swarm Intelligence and Its Impact on Data Mining and Knowled...

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3rd international conference on data Science, machine learning and Applications, ICDSMLA 2021

作者： Murthy, Mantripragada Yaswanth Bhanu Prasanthi, Katragadda Kilaru, Mrudula Kakarla, Sandhya Rani Guntur522508 India

ISBN: (纸本)9789811959356

The swarm intelligence algorithms are established as comprehensive approaches for resolving the complicated problems in optimization through the simulation of "behaviors of the biological swarms." Recently, data mining areas seek more attention that requires analysis of huge data and quick management. Most of the traditional techniques have not provided effective performance, and they are only applicable for differentiable and continuous functions. Further, the group of population-based techniques is proved to be effective in certain researches, where the swarm intelligence algorithms provide the significant potential for relevant data mining tasks. Hence, this paper demonstrates the biological inspiration and some of the swarm intelligence concepts, in which the main focus is given to "Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms." The primary data mining strategies are also described along with certain existing and present works based on swarm intelligence approaches. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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3rd international conference on Innovations in Computer Science and Software Engineering, ICONICS 2022

3rd International Conference on Innovations in Computer Scie...

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3rd international conference on Innovations in Computer Science and Software Engineering, ICONICS 2022

ISBN: (纸本)9781665461412

The proceedings contain 28 papers. The topics discussed include: experimentation for decentralized resource-based multi-pool mining in Ethereum blockchain;performance enhancement in agriculture sector based on image processing;frameworks, applications and challenges in streaming big data analytics: a review;prevention and safety strategies for road accidents by using evolutionary algorithms;intrusion detection using deep learning techniques;analyzing applicant’s pre-admission data and predicting applicant’s performance in pre-admission test using data mining techniques;quantum computing and machine learning algorithms - a review;real-time violence detection using deep learning techniques;a study on the e-commerce trends using data analysis;and a systematic review on the motivations of cyber-criminals and their attacking policies.

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3rd international conference on Image Processing and Vision Engineering, IMPROVE 2023

3rd International Conference on Image Processing and Vision ...

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3rd international conference on Image Processing and Vision Engineering, IMPROVE 2023

ISBN: (纸本)9789897586422

The proceedings contain 23 papers. The special focus in this conference is on Image Processing and Vision Engineering. The topics include: Multi-Scale Surface Normal Estimation from Depth Maps;intrinsic Image Decomposition: Challenges and New Perspectives;vegetation Coverage and Urban Amenity Mapping Using Computer Vision and machine learning;a Deep learning Approach for Estimating the Rind Thickness of Trentingrana Cheese from Images;application of Particle Detection Methods to Solve Particle Overlapping Problems;an Anisotropic and Asymmetric Causal Filtering Based Corner Detection Method;layer-wise External Attention for Efficient Deep Anomaly Detection;emotion Based Music Visualization with Fractal Arts;handling data Heterogeneity in Federated learning with Global data Distribution;climbing with Virtual Mentor by Means of Video-Based Motion Analysis;normalised Color Distances;fuzzy Inference System in a Local Eigenvector Based Color Image Smoothing Framework;3D Reference-Based Skeletal Movement Evaluation;FUB-Clustering: Fully Unsupervised Batch Clustering;from Depth Sensing to Deep Depth Estimation for 3D Reconstruction: Open Challenges;deep learning and Medical Image Analysis: Epistemology and Ethical Issues;an Integrated Mobile Vision System for Enhancing the Interaction of Blind and Low Vision Users with Their Surroundings;automatic Defect Detection in Sewer Network Using Deep learning Based Object Detector;facial Expression recognition with Quarantine Face Masks Using a Synthetic dataset Generator;A Global Multi-Temporal dataset with STGAN Baseline for Cloud and Cloud Shadow Removal.

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Research on Medical Image Classification for Oral Cancer 3rd

Research on Medical Image Classification for Oral Cancer

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Wang, Kunkun Shi, Linjun Gu, Chaochen Mo, Fan Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Oral Mucosal Diseases Ninth People’s Hospital Shanghai Jiaotong University School of Medicine Shanghai China Shanghai Key Laboratory of Stomatology and Shanghai Research Institution of Stomotology National Clinical Research Center of Stomatology Shanghai China

ISBN: (纸本)9789819624676

In recent years, deep learning has made significant progress in medical imaging, deepening the crossover between the medical and industrial fields. However, not all medical images are suitable for deep learning neural networks;simultaneously, some medical images are difficult to collect and preprocess. In this paper, we reconstruct the image information collected by dental clinicians over many years, normalizing it with diagnostic results as the gold standard. On this basis, we explore the applicability and potential of deep learning on complex medical images. Further, we compare the performance of multiple models on this dataset and visualize the learning outcomes, demonstrating that deep learning networks exhibit good learning capabilities on certain images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Facial recognition and Animal recognition from Live Stream Video Using machine learning 3

Facial Recognition and Animal Recognition from Live Stream V...

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3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Dua, Deepika Chauhan, Rahul Kiran Kumar, C. Radhika, S. Subramanian, Balambigai Bhatt, Chandradeep Uttaranchal University Computing Science Department Uttarakhand Dehradun India Graphic Era Hill University Cse Department Uttarakhand Dehradun India Codecraft Technologies Data Science Karnataka Bangalore India R.M.K. Engineering College Information Technology Department Tamil Nadu Kavaraipettai India Kongu Engineering College Ece Department Tamil Nadu Perundurai India

ISBN: (纸本)9798350330724

The use of facial recognition and object detection in a surveillance system is being studied in this study. Closed-circuit television (CCTV) is typically used in video surveillance systems to record footage for security purposes. It is used to manually determine a person's identity based on how they seem on the recorded video. A facial recognition system is typically not included in today's video surveillance camera systems. With a little adjustment, a security camera system can be used for real-time face detection and recognition. The suggested method takes use of a security camera system that uses face recognition and vehicle detection technology from the 'Haar cascade classifier' to automatically determine a person's identification. The data collection, recognizer training, and face recognition and vehicle detection process were the three key stages of this project's development. Using Python programming and the OpenCV package, all three phases were carried out using a video operating system. According to the findings, the suggested method correctly projects the output of human face identification at a distance of 0.4 to 1.2 metres, in medium and normal light conditions, and with a facial angle within 40°. Targeted images may use facial accessories so long as they don't obscure the facial features. The system achieves an accuracy ranging from 90.27 % to 98.2% in detection of objects like animals, car, building etc. © 2023 IEEE.

关键词： Python

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No-reference image quality assessment based on deep learning 3

No-reference image quality assessment based on deep learning

引用

3rd international conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Wang, Yifan Zhang, Fan Chang, Sheng Zhang, Xinhong School of Computer and Information Engineering Henan University Kaifeng475004 China School of Software Henan University Kaifeng475004 China

ISBN: (纸本)9781510668522

Images form the basis of human vision and are an important source of information for both human perception and machine pattern recognition. Since the development of the image quality evaluation field, a large number of image quality evaluation algorithms have emerged. In recent years, deep learning has become a hot area and has also been applied to the image domain. This paper focuses on reference-free image quality evaluation and reviews the no-reference image quality assessment based on deep learning. Firstly, the classification of IQA, technical indexes for IQA algorithm evaluation, and several public IQA databases available online are introduced. Then, several deep learning models applied to NR-IQA are discussed, compared, and evaluated in detail. Finally, an outlook on future research is provided. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Classification (of information)

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MQTT equipped Intrusion detection system for IoT devices based machine learning Algorithms 3

MQTT equipped Intrusion detection system for IoT devices bas...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Sharma, Priya Sharma, Sanjay Kumar Dani, Diksha University School of Information and Communication Technology Gautam Buddha University Greater Noida India Indraprastha Engineering College Ghaziabad India

ISBN: (纸本)9781665462006

In the era of COVID19, the world has shifted to an online presence and is now forced to embrace the usage of digital technology in their daily lives. With the meteoric rise of internet-based devices, there is a requirement for a protocol for secure communication between these devices. Message Queueing Telemetry Transport (MQTT) is the standard protocol for IoT devices. The MQTT implementation with IDS have very prominent usability and has huge potential for increasing efficiency. Therefore, in this paper, an IDS has been proposed with MQTT as a protocol for IoT devices, using machine learning to improve the pattern recognition of the IDS. The proposed system has been tested with three machine learning algorithms, namely, and the results show that they are adequate for the MQTT protocol © 2022 IEEE.

关键词： Intrusion detection

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An Explainable AI Framework for Treatment Failure Model for Oncology Patients 3rd

An Explainable AI Framework for Treatment Failure Model for ...

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21st Artificial Intelligence in Medicine (AIME) conference / 3rd international Workshop on Explainable Artificial Intelligence in Healthcare (XAI-Healthcare) / 1st international Workshop on Process mining Applications for Healthcare (PM4H)

作者： Zaidi, Syed Hamail Hussain Hashmat, Bilal Farooq, Muddassar CureMD Res 80 Pine St 21St Floor New York NY 10005 USA

ISBN: (纸本)9783031543029;9783031543036

The black box nature of current AI models has raised serious concerns about accountability, bias and trust in the models that might undermine their relevance and usefulness in the field of medicine where human lives are at risk. AI in medicine has the ability to derive meaningful inferences from real world data - an emerging school of thought namely Real World Evidence (RWE) studies - that can assist medical practitioners to improve evidence based quality of care. In the field of oncology, the accuracy and performance of inference models are as important as clinically relevant and sound explanations of the inference. In this paper, we present an Explainable AI (XAI) framework for our AI model that predicts the suitability of a chemotherapy treatment at the time of its prescription based on RWE. The framework provides explanations both for a specific patient and also for the model. It provides explanations like feature analysis, counterfactual, and top risk factors that contribute to a treatment failure. As a result, the framework adds an explainability layer between treatment failure predictive model and oncologists, thereby enabling evidence based assistance to oncologists in designing chemotherapy plans.

关键词： Explainability machine learning Model Agnostic Oncology

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