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检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3137 条记录，以下是361-370 订阅

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Leveraging Synthetic data in Object Detection on Unmanned Aerial Vehicles 26

Leveraging Synthetic Data in Object Detection on Unmanned Ae...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Kiefer, Benjamin Ott, David Zell, Andreas Univ Tubingen Tubingen Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Acquiring data to train deep learning-based object detectors on Unmanned Aerial Vehicles (UAVs) is expensive, time-consuming and may even be prohibited by law in specific environments. On the other hand, synthetic data is fast and cheap to access. In this work, we explore the potential use of synthetic data in object detection from UAVs across various application environments. For that, we extend the open-source framework DeepGTAV to work for UAV scenarios. We capture various large-scale high-resolution synthetic data sets in several domains to demonstrate their use in real-world object detection from UAVs by analyzing multiple training strategies across several models. Furthermore, we analyze several different data generation and sampling parameters to provide actionable engineering advice for further scientific research. the DeepGTAV framework is available at https://***/Jyf5j.

关键词： Training Analytical models Object detection Detectors Autonomous aerial vehicles data models pattern recognition

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Non-invasive Technique for Detecting Glycosuria through Image Processing and Deep learning Approaches 5th

Non-invasive Technique for Detecting Glycosuria Through Imag...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Teja, Chitturi Siva Nitheesha, Gokaraju Kumar, Boyidi Ravi Krishna, Bandaru Radha Naik, Desavath Madhu Pareek, Prakash Singh, Lokendra Department of ECE Vishnu Institute of Technology Bhimavaram534202 India Dehradun248002 India

ISBN: (纸本)9783031770746

Glycosuria level monitoring is a fundamental aspect of diabetes management, enabling proactive and personalized care to prevent complications, improve overall health, and enhance the quality of life for individuals with diabetes. Conventional glycosuria monitoring methods often involve invasive procedures, potentially risking the immune system, especially with repeated injections. the research focuses on a non-invasive method utilizing urine samples for glycosuria testing to address these concerns. the proposed method integrates image processing techniques, such as Gaussian filtering and image resizing, to optimize input data with machine learning approaches. Deep learning, especially convolutional neural networks is employed for their ability in feature extraction and pattern recognition. the system aims to achieve high accuracy in categorizing glycosuria levels into groups such as diabetes, prediabetes, and normal, providing a reliable and non-invasive alternative to traditional glycosuria monitoring techniques. this approach shows promise in enhancing patient compliance and overall health monitoring for individuals requiring regular glucose assessment. the accuracy of the proposed method based on CNN achieves 98.54% where the existing method based on support vector machine (SVM) achieves only 88.46%. it is evident from simulation results, the proposed method attained 11.39% improved accuracy when compared to existing SVM. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Accuracy Classification Convolutional Neural Network Deep learning gaussian filter Glycosuria

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Understanding Autism Using machine learning: A Structural MRI Study 26

Understanding Autism Using Machine Learning: A Structural MR...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Ali, Mohamed T. ElNakieb, Yaser Shalaby, Ahmed Elnakib, Ahmed Mahmoud, Ali Zai, Huma Ghazal, Mohammed Barnes, Gregory El-Baz, Ayman Univ Louisville Bioengineering Dept Louisville KY 40292 USA Abu Dhabi Univ Abu Dhabi U Arab Emirates Univ Louisville Dept Neurol Louisville KY 40202 USA Univ Louisville Pediat Res Inst Louisville KY 40202 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In this study, we propose a Computer-Aided Diagnostic (CAD) system to diagnose and understand autism spectrum disorder (ASD) using structural MRI (sMRI). Starting with identifying morphological anomalies within the cortical regions of ASD subjects. Every cortical feature receives a score corresponding to their contribution in diagnosing a subject to be ASD or typically developed (TD). Scores are determined by hyper-optimized machine learning (ML) classifiers. An early personalized diagnosis of ASD becomes possible by the proposed CAD system. the proposed framework implements multiple stages including cerebral cortex extraction from structure MRI (sMRI). Moreover, the proposed framework identify the altered brain regions. We can summarize this framework in the following procedures: i) Cerebral cortex segmentation, ii) Parcellation of the cortex to Desikan-Killiany (DK) atlas;iii) Annotating brain regions which are associated with ASD;iv) Blocking for the confounding effect of both age and sex;v) Tailoring ASD neuroatlases;vi) Classifying ASD using neural networks (NN). We utilized Autism Brain Imaging data Exchange (ABIDE I) dataset to test the proposed framework. the proposed achieved a balanced accuracy score of 97% +/- 2%. In this study, we demonstrate the ability to describe specific developmental patterns of the brain in autism using tailored neuro-atlases, as well as, developing an objective CAD system using morphological features extracted from sMRI scans.

关键词： Neuroimaging Autism Image segmentation Cerebral cortex Design automation Magnetic resonance imaging machine learning

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Exploring the Potential of machine learning for Early Cattle Disease Diagnosis 5

Exploring the Potential of Machine Learning for Early Cattle...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Gokul Krishna, R. Periyasamy, S.V. Roshan Khan, S.B. Mohan Raj, T. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350321425

Dairy farming has long been a common occupation in regions where industrial agriculture is not prevalent, such as India. the dairy industry has seen a rise in productivity as a result of dairy farming modernization, but it is still afflicted by various ailments that can affect cow product output, and poor-quality dairy products not only hamper long-term national economic success. Due to the large number of dairy cattle housed in various dairies, dairy owners and local governments struggle to maintain and monitor their health. A health management method must include continuous monitoring of each cow's health as well as rapid identification and treatment of sick animals. To do this, sensor technology is employed to measure essential animal properties such as heart rate and temperature. this data is then gathered and put into a data mining system, which forecasts any disease-related events. As a result, even routine veterinary exams and animal care may become more expensive. As a result, in this study, the costs of livestock monitoring are avoided. © 2023 IEEE.

关键词： data mining

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Empowering Healthcare with Deep learning: An Application for Early Detection of Skin Cancer 4th

Empowering Healthcare with Deep Learning: An Application for...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Oluwatobi, Komolafe Joseph Asghar, Ikram Diyan, Muhammad Nawaz, Rab Saleem, Sidra Ullah, Rahmat Ahmad, Jawad Univ Teesside Sch Comp Engn & Digital Technol Middlesbrough Cleveland England Univ Agr Faisalabad Faisalabad Pakistan Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England Edinburgh Napier Univ Edinburgh Midlothian Scotland

ISBN: (纸本)9783031821523;9783031821530

Skin cancer is a hazardous ailment and a leading contributor to mortality. Early diagnosis of skin cancer can significantly minimize or prevent these fatalities. the diagnostic process can be both lengthy and costly. this study aims to establish a basis for creating an exact and effective convolutional neural network (CNN) model for detecting skin cancer. this model aims to enhance the early and accurate diagnosis of skin cancers, potentially leading to a decrease in the number of associated fatalities. the model utilizes a convolutional neural network with Keras Tensorflow as the backend to categorize seven distinct categories of skin cancer. Subsequently, the results are analyzed to determine the practical applications of the model. the MobileNet optimizer is used for classification. A web application has been developed that offers dermatologists the three most likely diagnostics for a specific blister. It will aid in swiftly recognizing patients with high priorities and accelerating their workflow. the application generates a result within a time frame of 5-10 seconds.

关键词： Skin Cancer CNN machine learning image classification medical data security

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A Joint-BERT Method for Knowledge Base Question Answering 5

A Joint-BERT Method for Knowledge Base Question Answering

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5th international conference on machine learning and Natural Language Processing (MLNLP)

作者： Zhang, Tianyu Chen, Zhiyun East China Normal Univ Sch Data Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781450399067

this paper proposes a Joint-BERT method to solve the knowledge base question answering (KBQA) task involved a single fact. It divides it into two subtasks, topic entity recognition and relation detection. For the entity recognition subtask, instead of treating it as a sequence labeling task, a simpler approach of applying a pointer network on the BERT encoder is used to predict the start and end positions of the topic entity. the subtask of relation detection, sharing the BERT encoder, computes candidate predicates rankings from both local and global matching perspectives, and finally trains the two tasks jointly in a multi-task learning framework, so that the two tasks benefit from each other. Experiments show that: Joint-BERT model achieves competitive results on the SimpleQuestions benchmark.

关键词： BERT KBQA Multi-task learning Pointer Network

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2023 5th international conference on Control and Robotics, ICCR 2023

2023 5th International Conference on Control and Robotics, I...

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5th international conference on Control and Robotics, ICCR 2023

ISBN: (纸本)9798350307627

the proceedings contain 53 papers. the topics discussed include: enhancing robot self-localization accuracy and robustness: a variational autoencoder-based approach;predicting prostate cancer risk stratification based on PSA and functional subsets of peripheral lymphocyte;pipeline leak diagnosis based on SDAE;fiducial marker and camera-based passive high-accuracy 6-DoF tracking for mobile objects;RoboCon: a modular robotic containerization, orchestration, and load balancing technique for mixed hierarchy task handling across computing platforms;facial expression recognition based on dual scale hybrid attention mechanism;data selection using genetic algorithm to improve transfer learning efficiency in brain-machine-interface systems;and multi-missile cooperative guidance law under communication delay and topological transformation in 3-D.

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A Framework for Cybercrime Prediction on Twitter Tweets Using Text-Based machine learning Algorithm 5

A Framework for Cybercrime Prediction on Twitter Tweets Usin...

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5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Matias, Sheila Marie M. Costales, Jefferson A. De Los Santos, Christian M. Eulogio "Amang" Rodriguez Institute of Science and Technology Manila Philippines

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

With the rise in popularity, social media platforms such as Twitter are more recognized for calling tweets to construct user networks that can communicate. Because of the increasing number of Twitter users and the expansion in cybercrime rates. Cybercrime is a crime perpetrated using technology. It is feasible that the social networking platform might be utilized to commit a crime. the researchers focused on cyberbullying and cyberthreat, given the severity of cybercrime's effects on victims and given the devastating effects of cybercrime on victims, it is critical to devise effective methods for predicting and preventing it. Made advantage of the Twitter API and the tweepy python module to extract a real-time tweet from Twitter. In order to improve their ability to detect cybercrimes and sentiments, through employed an online source dataset to build a model. the goal of this study is to create a machine learning strategy for anticipating cybercrimes. Find the approach that best fits the data by comparing it to other methods. To evaluate the accuracy and dependability of the trained model, through gathered data and information from dependable sources. the significance of the research is shown by the analysis of the data. © 2022 IEEE.

关键词： Crime

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5th IEEE international conference on Advanced Information and Communication Technologies, AICT 2023 - Proceedings

5th IEEE International Conference on Advanced Information an...

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5th IEEE international conference on Advanced Information and Communication Technologies, AICT 2023

ISBN: (纸本)9798350372571

the proceedings contain 49 papers. the topics discussed include: disaster recovery solution for on-premises infrastructure using Proxmox backup server;a method of encoding video information to increase its reliability in an information and telecommunications network;prediction of states of information and communication systems using machine learning;Wi-Fi repeater influence on wireless access;method of selective video segment processing for intelligent video image quality enhancement technologies;investigating the efficiency of tournament selection operator in genetic algorithm for solving TSP;an adaptive spectrum time assignment in OFDM datacenter optical networks;big data analysis for startup of supporting Ukraine internet tourism;and AR intelligent real-time method for cultural heritage object recognition.

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Minimal Solvers for Point Cloud Matching with Statistical Deformations 26

Minimal Solvers for Point Cloud Matching with Statistical De...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Flood, Gabrielle Tegler, Erik Gillsjö, David Heyden, Anders Aström, Kalle Lund Univ Ctr Math Sci Lund Sweden

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

An important issue in simultaneous localisation and mapping is how to match and merge individual local maps into one global map. this is addressed within the field of robotics and is crucial for multi-robot SLAM. there are a number of different ways to solve this task depending on the representation of the map. To take advantage of matching and merging methods that allow for deformations of the local maps it is important to find feature matches that capture such deformations. In this paper we present minimal solvers for point cloud matching using statistical deformations. the solvers use either three or four point matches. these solve for either rigid or similarity transformation as well as shape deformation in the direction of the most important modes of variation. Given an initial set of tentative matches based on, for example, feature descriptors or machine learning we use these solvers in a RANSAC loop to remove outliers among the tentative matches. We evaluate the methods on both synthetic and real data and compare them to RANSAC methods based on Procrustes and demonstrate that the proposed methods improve on the current state-of-the-art.

关键词： Point cloud compression three-dimensional displays Simultaneous localization and mapping Shape Merging machine learning Task analysis

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