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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 2813 条记录，以下是421-430 订阅

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Convolutional Neural Network Segmentation for Satellite Imagery data to Identify Landforms Using U-Net Architecture 6th

Convolutional Neural Network Segmentation for Satellite Imag...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Goswami, Mitul Mohanty, Suneeta Dey, Sainath Mukherjee, Aniruddha Pattnaik, Prasant Kumar School of Computer Engineering KIIT Deemed To Be University Odisha Bhubaneswar India

ISBN: (纸本)9789819780891

this study demonstrates a novel use of the U-Net architecture in the field of semantic segmentation to detect landforms using preprocessed satellite imagery. the study applies the U-Net model for effective feature extraction by using convolutional neural network (CNN) segmentation techniques. Dropout is strategically used for regularization to improve the model’s perseverance, and the Adam optimizer is used for effective training. the study thoroughly assesses the performance of the U-Net architecture utilizing a large sample of preprocessed satellite topographical images. the model excels in semantic segmentation tasks, displaying high-resolution outputs, quick feature extraction, and flexibility to a wide range of applications. the findings highlight the U-Net architecture’s substantial contribution to the advancement of machine learning and image processing technologies. the U-Net approach, which emphasizes pixel-wise categorization and comprehensive segmentation map production, is helpful in practical applications such as autonomous driving, disaster management, and land use planning. this study not only investigates the complexities of U-Net architecture for semantic segmentation but also highlights its real-world applications in image classification, analysis, and landform identification. the study demonstrates the U-Net model’s key significance in influencing the environment of modern technology. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantic Segmentation

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Brain-like Combination of Feedforward and Recurrent Network Components Achieves Prototype Extraction and Robust pattern recognition 8th

Brain-like Combination of Feedforward and Recurrent Network ...

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8th Annual international conference on machine learning, Optimization and data science (LOD)

作者： Ravichandran, Naresh Balaji Lansner, Anders Herman, Pawel KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Computat Sci & Technol Stockholm Sweden Stockholm Univ Dept Math Stockholm Sweden KTH Royal Inst Technol Digital Futures Stockholm Sweden

ISBN: (纸本)9783031258909;9783031258916

Associative memory has been a prominent candidate for the computation performed by the massively recurrent neocortical networks. Attractor networks implementing associative memory have offered mechanistic explanation for many cognitive phenomena. However, attractor memory models are typically trained using orthogonal or random patterns to avoid interference between memories, which makes them unfeasible for naturally occurring complex correlated stimuli like images. We approach this problem by combining a recurrent attractor network with a feedforward network that learns distributed representations using an unsupervised Hebbian-Bayesian learning rule. the resulting network model incorporates many known biological properties: unsupervised learning, Hebbian plasticity, sparse distributed activations, sparse connectivity, columnar and laminar cortical architecture, etc. We evaluate the synergistic effects of the feedforward and recurrent network components in complex pattern recognition tasks on the MNIST handwritten digits dataset. We demonstrate that the recurrent attractor component implements associative memory when trained on the feedforward-driven internal (hidden) representations. the associative memory is also shown to perform prototype extraction from the training data and make the representations robust to severely distorted input. We argue that several aspects of the proposed integration of feedforward and recurrent computations are particularly attractive from a machine learning perspective.

关键词： Attractor Associative memory Unsupervised learning Hebbian learning Recurrent networks Feedforward networks Brain-like computing

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One-Shot Image learning Using Test-Time Augmentation 6th

One-Shot Image Learning Using Test-Time Augmentation

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6th Asian conference on pattern recognition (ACPR)

作者： Yamada, Keiichi Matsumi, Susumu Meijo Univ Dept Informat Engn Nagoya Aichi Japan

ISBN: (纸本)9783031023750;9783031023743

Modern image recognition systems require a large amount of training data. In contrast, humans can learn the concept of new classes from only one or a few image examples. A machine learning problem with only a few training samples is called few-shot learning and is a key challenge in the image recognition field. In this paper, we address one-shot learning, which is a type of few-shot learning in which there is one training sample per class. We propose a one-shot learning method based on metric learning that is characterized by data augmentation of a test target along with the training samples. Experimental results demonstrate that expanding both training samples and test target is effective in terms of improving accuracy. On a benchmark dataset, the accuracy improvement by the proposed method is 2.55% points, while the improvement by usual data augmentation which expands the training samples is 1.31% points. Although the proposed method is very simple, it achieves accuracy that is comparable or superior to some of existing methods.

关键词： Image classification One-shot learning Test-time augmentation

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Proceedings - 2023 international conference on Blockchain Technology and Applications, ICBTA 2023

Proceedings - 2023 International Conference on Blockchain Te...

引用

6th international conference on Blockchain Technology and Applications, ICBTA 2023

ISBN: (纸本)9798350317404

the proceedings contain 26 papers. the topics discussed include: forecasting model of fishery import and export trade data using deep learning method;assessing word difficulty: a mapping method;three-state recognition of corporate turnaround using support vector machine;time series classification based on intuitionistic fuzzy clustering similarity measure;modeling and prediction of building carbon emission in Jiangsu Province;digital asset valuation method for online games based on machine learning algorithms;multi-view clustering of dual graph non-negative matrices factorization with diversity constraints;design of physical education teaching system based on computer network technology;prediction of equipment consumption based on partial least squares regression and Markov chain models;and research on media communication based on graph neural network.

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data mining Approach in Predicting House Price for Automated Property Appraiser Systems 6th

Data Mining Approach in Predicting House Price for Automated...

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6th international conference on Innovative Computing and Communication, ICICC 2023

作者： Yousir, Naeem th. Abdulameer, Shaymaa Mohammed Mostafa, Salama A. College of Information Engineering Al-Nahrain University Baghdad10072 Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor86400 Malaysia

ISBN: (纸本)9789819930098

the house price prediction model can be a logistic tool in assisting individuals and companies to determine the cost of a property or a house on sale and the best time to acquire a house. With the ever-increasing in house price rates, data mining approaches have been used as a property appraiser system to predict a house price based on house features before making a deal. Existing research in house prediction involves collecting a number of house price estimation attributes into a dataset of house price features. this study is aimed at building a model for predicting house price based on the house price features dataset. then dataset is run through machine learning (ML) algorithms to learn the pattern and predict new house price cases. the cross-industry standard process for data mining (CRISP-DM) method is used for this research. Decision tree regression (DTR), linear regression (LR), multiple linear regression (MLR), and random forest regression (RFR) are the ML algorithms applied in this research. the results have shown that RFR is the most suitable algorithm for this dataset, with an average coefficient of determination (R square) of 82.09%, followed by the DTR with an R square score of 73.32% when using different training and learning splitting ratios. An application of the RFR model can be used in real estate companies for estimating the prices of properties. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multiple linear regression

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Detecting Toxic Comments in Bengali Language 6th

Detecting Toxic Comments in Bengali Language

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Mondal, Abir Roy, Kingshuk Das, Susmita Dutta, Arpita Techno Main Salt Lake Kolkata India School of Computer Science Engineering and Technology Bennett University Greater Noida Uttar Pradesh India

ISBN: (纸本)9789819780891

An increasing number of users utilize public platforms to communicate. the languages used by general public are diverse and varied. Detection of the offensive words utilized by people on these online platforms is challenging. Unstructured texts, inappropriate models combined with low-resource datasets, cause the research on less prevalent languages quite arduous. In this chapter, we have attempted to address the problem of offensive comments detection in Bengali, which is a low-resource language with limited approaches. Our approach is a binary classification model that successfully classifies toxic comments from nontoxic comments. Natural Language Processing techniques have been implemented for preprocessing of the unstructured data. Deep learning model with a modified version of BERT architecture has been applied. this model has been pretrained on a large corpus of Bengali text and efficiently detects the offensive words in the social media Bengali comments. the proposed approach is performing effectively when compared with different baselines. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors 6

Know Your Space: Inlier and Outlier Construction for Calibra...

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6th international conference on Medical Imaging with Deep learning (MIDL)

作者： Narayanaswamy, Vivek Mubarka, Yamen Anirudh, Rushil Rajan, Deepta Spanias, Andreas thiagarajan, Jayaraman J. Arizona State Univ Tempe AZ 85287 USA Lawrence Livermore Natl Lab Livermore CA USA Microsoft Redmond WA USA

We focus on the problem of producing well-calibrated out-of-distribution (OOD) detectors, in order to enable safe deployment of medical image classifiers. Motivated by the difficulty of curating suitable calibration datasets, synthetic augmentations have become highly prevalent for inlier/outlier specification. While there have been rapid advances in data augmentation techniques, this paper makes a striking finding that the space in which the inliers and outliers are synthesized, in addition to the type of augmentation, plays a critical role in calibrating OOD detectors. Using the popular energy-based OOD detection framework, we find that the optimal protocol is to synthesize latent-space inliers along with diverse pixel-space outliers. Based on empirical studies with multiple medical imaging benchmarks, we demonstrate that our approach consistently leads to superior OOD detection (15% - 35% in AUROC) over the state-of-the-art in a variety of open-set recognition settings.

关键词： Deep neural networks out-of-distribution detection data augmentation energy medical imaging open-set recognition

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A Survey on Exploratory Mineral data Analysis on Geological Location Using Deep learning 6th

A Survey on Exploratory Mineral Data Analysis on Geological ...

引用

6th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Santhi, P. Angelin Pricila, S.A. Devisha, T. Madhumitha, C. tharani, S. Department of Computer Science and Engineering M.Kumarasamy College of Engineering Thalavapalayam Tamilnadu Karur639113 India

ISBN: (纸本)9789811935701

the increase in demand of metals which is due to the rise in metal prices. As a result, seismic methods will become a more important tool for mine design and exploration to help unravel buildings containing mineral reserves at vast depth. Seismic methods can be used to pick out the mineral deposits which are located at depth with higher achievements. the cost of production would reduce, and the return on invested capital infrastructure would increase, if it was feasible to see clearly beneath a mining site and chart the position or extent of the resources there. machine learning and deep learning have many numbers of classification techniques. User is needed to pre-select the characteristics of input in machine learning, deep learning for supervised learning tasks uses raw data to determine categorization features (or attributes). Deep learning possesses easier methods to analyze the uninterpreted images. the efficiency is greater in deep learning when compared with machine learning. Compared to existing machine learning techniques, deep learning techniques come up with more efficiency in mineral classification. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Distributed Photovoltaic Load Probability Forecasting Model Based on Clustering Characteristic 6

Distributed Photovoltaic Load Probability Forecasting Model ...

引用

6th international conference on machine learning and machine Intelligence, MLMI 2023

作者： Hu, Hailin Yan, Li Data Center State Grid Shandong Electric Power Company Information and Telecommunication Company China

ISBN: (纸本)9798400709456

Accurate distributed photovoltaic load forecasting can effectively assist the overall power dispatch and then reduce the risk of grid operation. Different from the existing research, we deeply mine the clustering characteristics of load in the time dimension based on the characteristics of similar days, in order to solve the problem of missing spatial fine-grained weather information. Moreover, because the load clustering label of the day to be predicted is unknown, we propose a clustering label prediction model of distributed photovoltaic load in substation area based on historical load and weather information. Based on the analysis of clustering characteristics, we use the combination model of deep residual network and quantile regression, and proposes a probability forecasting model corresponding to different cluster categories. Compared with the existing models, the proposed model has better performance in both point prediction and probability prediction. © 2023 ACM.

关键词： Forecasting

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Litchi Fruit Instance Segmentation from UAV Sensed Images Using Spatial Attention-Based Deep learning Model 10th

Litchi Fruit Instance Segmentation from UAV Sensed Images Us...

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10th Biennial international conference on pattern recognition and machine Intelligence (PReMI)

作者： Chakraborty, Debarun Deka, Bhabesh Tezpur Univ Dept ECE Tezpur 784028 Assam India

ISBN: (纸本)9783031451690;9783031451706

this paper highlights the importance of deep learning-based litchi segmentation in precision agriculture using machine vision. the proposed method involves preparing a mixed UAV litchi and MinneApple database, consisting of 2000 images of the same size 256 x 256. this paper introduces a modified Mask-RCNN-based instance segmentation model;incorporating a spatial attention block in the backbone network ResNet101, to mitigate one of the significant challenges in litchi counting, i.e., occlusion. the results demonstrate that the proposed model achieves a mean Average Precision (mAP), recall, and F1-score of 90.81%, 89.00%, and 90.35%, respectively, for separated and unoccluded litchis, and an mAP, recall, and F1-score of 81.41%, 82.42%, and 81.91%, respectively, for occluded litchis. the proposed model provides better detection accuracy while minimizing computational burden, showing its potential for efficient and accurate litchi detection and counting in precision agriculture.

关键词： Unmanned Aerial Vehicle (UAV) Deep learning Instance Segmentation Litchi Precision Agriculture Spatial Attention Module (SAM)

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