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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022"

共 265 条记录，以下是201-210 订阅

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6th international conference on Context-Aware Systems and Applications, ICCASA 2017 and 3rd international conference on Nature of Computation and Communication, ICTCC 2017

6th International Conference on Context-Aware Systems and Ap...

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6th international conference on Context-Aware Systems and Applications, ICCASA 2017 and 3rd international conference on Nature of Computation and Communication, ICTCC 2017

ISBN: (纸本)9783319778174

The proceedings contain 22 papers. The special focus in this conference is on . The topics include: The Context-Aware Calculating Method in Language Environment Based on Hedge Algebras Approach to Improve Result of Forecasting Time Series;algebraic Operations in Fuzzy Object-Oriented Databases Based on Hedge Algebras;context-Adaptive Values-Based Games for the Young: Responsible Decision Making for a Sustainable World;applying and Deploying Cyber Physical System in Monitoring and Managing Operations Under Mines and Underground Works;the Method of Maintaining Data Consistency in Allocating Resources for the P2P Network Model;Fragmentation in Distributed Database Design Based on KR Rough Clustering Technique;architectural Framework for Context Awareness and Health Conscious Applications on Mobile Devices;holistic Personas and the Five-Dimensional Framework to Assist Practitioners in Designing Context-Aware Accounting Information System e-learning Applications;abnormal Behavior Detection Based on Smartphone Sensors;A Context Adaptive Framework for IT Governance, Risk, Compliance and Security;An Effective of Data Organizing Method Combines with Naïve Bayes for Vietnamese Document Retrieval;an Effective Time Varying Delay Estimator Applied to Surface Electromyographic Signals;the Optimal Solution of Communication Resource Allocation in Distributed System Integrated on Cloud Computing;hybrid Classifier by Integrating Sentiment and Technical Indicator Classifiers;visualizing Space-Time Map for Bus;generation of Power State machine for Android Devices;modeling Self-adaptation - A Possible Endeavour?;enhancement of Wu-Manber Multi-pattern Matching Algorithm for Intrusion Detection System;goal-Capability-Commitment Based Context-Aware Collaborative Adaptive Diagnosis and Compensation;traffic Incident recognition Using Empirical Deep Convolutional Neural Networks Model.

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Research on short term stock selection strategy based on machine learning

Research on short term stock selection strategy based on mac...

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international conference on machine learning, Big Data and Business Intelligence (MLBDBI)

作者： Yanzheng Ren School of Mechatronics Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665417914

Based on theoretical analysis, this paper constructs a short-term stock selection strategy based on machine learning. The sample set is constructed based on the closing price trend of individual stocks in the last 20 trading days, and the machine learning algorithms GBDT and GBRank are used for training, and machine learning is used to automatically perform pattern recognition on the processing capabilities of high-dimensional nonlinear data. When forecasting, the former will rank stocks with higher rising probability in the next 3 trading days, and the latter will rank stocks with greater gains in the next 3 trading days. The stock selection strategy swaps positions every 3 trading days, and each time the top 10 stocks given by the equal-weight buying algorithm are used to construct an investment portfolio. The experimental results show that the short-term quantitative stock selection strategy based on the GBDT algorithm can outperform the market combination, namely the Shanghai and Shenzhen 300 Index, and has certain practicability.

关键词： Analytical models machine learning algorithms Computational modeling machine learning Mathematical models Data models pattern recognition

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Combining multiple image segmentations by maximizing expert agreement

Combining multiple image segmentations by maximizing expert ...

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3rd international Workshop on machine learning in Medical Imaging, MLMI 2012, Held in conjunction with the 15th international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012

作者： Kamarainen, Joni-Kristian Lensu, Lasse Kauppi, Tomi Machine Vision and Pattern Recognition Laboratory Department of Information Technology Lappeenranta University of Technology P.O. Box 20 FI-53851 Lappeenranta Finland

ISBN: (纸本)9783642354274

A common characteristic of collecting the ground truth for medical images is that multiple experts provide only partially coherent manual segmentations, and in some cases, with varying confidence. As the result, there is considerable spatial variation between the expert segmentations, and for training and testing, the "true" ground truth is estimated by disambiguating (combining) the provided segments. STAPLE and its derivatives are the state-of-the-art approach for disambiguating multiple spatial segments provided by clinicians. In this work, we propose a simple yet effective procedure based on maximizing the joint agreement of experts. Our algorithm produces the optimal disambiguation by maximizing the agreement and no priors are used. In the experimental part, we generate a new ground truth for the popular diabetic retinopathy benchmark, DiaRetDB1, for which the original expert markings are publicly available. We demonstrate performance superior to the original and also STAPLE generated ground truth. In addition, the DiaRetDB1 baseline method performs better with the new ground truth. © 2012 Springer-Verlag.

关键词： Image segmentation

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Exploration of pattern recognition Methods for Motor Imagery EEG Signal with Convolutional Neural Network Approach 3

Exploration of Pattern Recognition Methods for Motor Imagery...

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3rd international conference on Emerging Electrical Energy, Electronics and Computing Technologies 2021, ICE4CT 2021

作者： Zahra, Hanina N Zakaria, Hasballah Hermanto, Beni R School of Electrical Engineering and Informatics Bandung Institute of Technology West Java Bandung40132 Indonesia

As an application of EEG, Motor Imagery based Brain-Computer Interface (MI BCI) plays a significant role in assisting patients with disability to communicate with their environment. MI BCI could now be realized through various methods such as machine learning. Many attempts using different machine learning approaches as MI BCI applications have been done with every one of them yielding various results. While some attempts managed to achieve agreeable results, some still failed. This failure may be caused by the separation of the feature extraction and classification steps, as this may lead to the loss of information which in turn causes lower classification accuracy. This problem can be solved by integrating feature extraction and classification by harnessing a classification algorithm that processed the input data as a whole until it produces the prediction, hence the use of convolutional neural network (CNN) approach which is known for its versatility in processing and classifying data all in one go. In this study, the CNN exploration involved a task to classify 5 different classes of fingers' imaginary movement (thumb, index, middle, ring, and pinky) based on the processed raw signal provided. The CNN performance was observed for both non-augmented and augmented data with the data augmentation techniques used include sliding window, noise addition, and the combination of those two methods. From these experiments, the results show that the CNN model managed to achieve an averaged accuracy of 47%, meanwhile with the help of augmentation techniques of sliding window, noise addition, and the combined methods, the model achieved even higher averaged accuracy of 57,1%, 47,2%, and 57,5% respectively. © Published under licence by IOP Publishing Ltd.

关键词： Brain computer interface

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A New Deep Fuzzy Based MSER Model for Multiple Document Images Classification 3rd

A New Deep Fuzzy Based MSER Model for Multiple Document Imag...

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3rd international conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Biswas, Kunal Shivakumara, Palaiahnakote Sivanthi, Sittravell Pal, Umapada Lu, Yue Liu, Cheng-Lin Ayub, Mohamad Nizam Bin Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy Beijing China

ISBN: (纸本)9783031090363

Understanding document images uploaded on social media is challenging because of multiple types like handwritten, printed and scene text images. This study presents a new model called Deep Fuzzy based MSER for classification of multiple document images (like handwritten, printed and scene text). The proposed model detects candidate components that represent dominant information irrespective of the type of document images by combining fuzzy and MSER in a novel way. For every candidate component, the proposed model extracts distance-based features which result in proximity matrix (feature matrix). Further, the deep learning model is proposed for classification by feeding input images and feature matrix as input. To evaluate the proposed model, we create our own dataset and to show effectiveness, the proposed model is tested on standard datasets. The results show that the proposed approach outperforms the existing methods in terms of average classification rate. © 2022, Springer Nature Switzerland AG.

关键词： Image classification

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Policy Generation from Latent Embeddings for Reinforcement learning 3rd

Policy Generation from Latent Embeddings for Reinforcement...

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

作者： Artaud, Corentin Pina, Rafael Shi, Xiyu De-Silva, Varuna Institute for Digital Technologies Loughborough University London The Broadcast Centre Here East 3 Lesney Ave E20 3BS London United Kingdom

ISBN: (纸本)9783031463372

The human brain endows us with extraordinary capabilities that enable us to create, imagine, and generate anything we desire. Specifically, we have fascinating imaginative skills allowing us to generate fundamental knowledge from abstract concepts. Motivated by these traits, numerous areas of machine learning, notably unsupervised learning and reinforcement learning, have started using such ideas at their core. Nevertheless, these methods do not come without fault. A fundamental issue with reinforcement learning especially now when used with neural networks as function approximators is their limited achievable optimality compared to its uses from tabula rasa. Due to the nature of learning with neural networks, the behaviours achievable for each task are inconsistent and providing a unified approach that enables such optimal policies to exist within a parameter space would facilitate both the learning procedure and the behaviour outcomes. Consequently, we are interested in discovering whether reinforcement learning can be facilitated with unsupervised learning methods in a manner to alleviate this downfall. This work aims to provide an analysis of the feasibility of using generative models to extract learnt reinforcement learning policies (i.e. model parameters) with the intention of conditionally sampling the learnt policy-latent space to generate new policies. We demonstrate that under the current proposed architecture, these models are able to recreate policies on simple tasks whereas fail on more complex ones. We therefore provide a critical analysis of these failures and discuss further improvements which would aid the proliferation of this work. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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3rd international Symposium on Automation, Mechanical and Design Engineering, SAMDE 2022

3rd International Symposium on Automation, Mechanical and De...

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3rd international Symposium on Automation, Mechanical and Design Engineering, SAMDE 2022

ISBN: (纸本)9783031400698

The proceedings contain 38 papers. The special focus in this conference is on international Symposium on Automation, Mechanical and Design Engineering. The topics include: UWB Localization Based on Improved SO-Robust KF Fusion Algorithm;class-Imbalanced and Fine-Grained Object Detection in Video Surveillance for Production Safety;NLFA: A Non Local Fusion Alignment Module for Multi-Scale Feature in Object Detection;Image Reconstruction for Rain Removal in Both Wavelet and Spatial Frequency Sub-bands Using W-CycleGANs;Pedestrian Multi-object Tracking Based on ResNeXt and FairMOT;recognition of Leftover Food Based on Deep learning;Task Offloading for UAV-Assisted of Collaborative Mobile Edge Computing Network Based on Deep Reinforcement learning;Multi-scale Rain Removal Across Multiple Frequencies Sub-bands Using MS-CycleGANs;research on the Optimization of the Assembly Sequence of Continuous Steel Arch Bridges with Multi-span Deck;different Structural Parameters Impacted on the Rough Surfaces Contact Force and Area Between the Base and the Magnetic Film Cantilever;performance Prediction Method of Laser Device Energy Component Based on Neural Network;Preliminary Design of 2D Ultrasonic Vibration Assisted Micro-milling (UVAMM) Using Piezoelectric Stack Actuator;self-Fitting Hearing Aid Algorithm Based on Improved Interactive Genetic Algorithm;bending Process Parameters Optimization of the 316L Tube with Small R/D Ratio: A Physical Prior Knowledge Based Bayesian Optimization Approach;Modified Johnson–Cook Model of AA5005 Alloy for Thermal Tensile Tests;vibration Prediction Method for New Buildings in Existing Metro Operation Area with Soft Soil;mechanical Structural Improvement Design and Vibration Test of Coupling Guard for Pump System;effect of Horizontal Plates on the Sliding of Breakwater Caissons.

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Support logic for feature representation, pattern recognition and machine learning

Support logic for feature representation, pattern recognitio...

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE)

作者： J.F. Baldwin R.M. Gooch T.P. Martin Department of Engineering Mathematics and Advanced Computing Research Centre University of Bristol Bristol UK

The formalism of support logic provides a framework for deductive inference, with mathematically sound and consistent treatment of uncertainty and evidence which is aggregated through the reasoning process. The authors apply support logic programming to pattern recognition. Initially, a pattern classifier is constructed by encoding expert knowledge of the problem domain into rules of support logic. Fuzzy sets allow the general properties of features to be described precisely. Semantic unification provides an alternative to the usual metric-based similarity criteria. The validity of the approach is established by cross-validating the support logic classifier against models from alternative paradigms. The authors then attempt to circumvent the requirement for a domain expert, and assess the extent to which data-driven learning processes can be used to automatically derive components of the support logic classifier.< >

关键词： pattern recognition machine learning Logic programming Tin Uncertainty Fuzzy sets Extraterrestrial measurements Prototypes Acoustical engineering Mathematics

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Milky WAI: Unlocking the Secrets of Raw Cow Milk Through Speckle pattern and AI 3

Milky WAI: Unlocking the Secrets of Raw Cow Milk Through Spe...

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Bello, Valentina Bassi, Irene Fiocchi, Matteo Nuzzi, Cristina Bellassi, Paolo Cattaneo, Luca Pasinetti, Simone University of Pavia Diii Pavia Italy University of Brescia Dimi Brescia Italy Università Cattolica del Sacro Cuore DiSTAS Cremona Italy Universita Cattolica del Sacro Cuore Diana Piacenza Italy

ISBN: (纸本)9798350378009

This paper introduces a novel method for raw cow milk classification combining speckle pattern (SP) imaging and AI-based processing of statistical parameters. Raw cow milk has been classified considering (i) 6 statistical features extracted from raw SP images, and (ii) 4 features extracted from the Gray-Level Co-occurrence Matrix (GLCM) computed on each SP image. We conducted 4 experimental campaigns, resulting in 24, 000 SP images retrieved from 20 milks produced by 20 cows. We then considered two different datasets: (i) a Complete dataset (made of all 24,000 frames), and (ii) a Reduced dataset, built by excluding data from one acquisition campaign. The two datasets were then split into training and testing sub-datasets. We trained three Wide Neural Networks (WNN) using different strategies. WNN1 and WNN2 have been trained and tested using the Complete and the Reduced datasets respectively, while WNN3 has been trained on the Reduced tested on the Complete dataset. WNN3 only attained 72% accuracy (revealing sensitivity to environmental conditions), while WNN1 and WNN2 achieved a test accuracy higher than 90%, demonstrating effectiveness without relying on computationally expensive models such as CNNs. This approach could be considered as a promising initial step for cow milk classification using SP imaging. © 2024 IEEE.

关键词： Data analysis machine learning measurement science milk analysis speckle pattern

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A Review of Recent Advances in Deep learning for Object Detection

A Review of Recent Advances in Deep Learning for Object Dete...

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Innovations in Computer Science & Software Engineering (ICONICS), international conference on

作者： Muhammad Fahad Safdar Hanan Sharif Faisal Rehman Zahid Bilal Saqlain Ahmad Hadia Maqsood Department of Computer Science & Information Technology Lahore Leads University Lahore Pakistan

Computer Vision has given a way for computer systems to see by way of deciphering the surrounding objects, which has been considered a crucial problem over the years. There are quite a number of techniques devised for object detection and deep learning, but most of the research has been focused on deep learning in recent years. Visual object detection covers numerous recognition pattern tasks like image classification. This article aims to review a visual analytics approach for better understanding, identifying, and cleansing object detection frameworks and the effects of some factors such as sampling strategies, feature learning, detector architectures, proposal generation, etc., on visual object detection with special reference to detection components, learning strategies, and their applications along with benchmarks.

关键词： Deep learning Representation learning Image recognition Visual analytics Object detection pattern recognition Proposals

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