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检索条件"主题词=learning algorithms"

共 13093 条记录，以下是421-430 订阅

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Elucidating the physics of nanophotonic structures through explainable machine learning algorithms

Elucidating the physics of nanophotonic structures through e...

引用

2020 Frontiers in Optics Conference, FiO 2020

作者： Yeung, Christopher Tsai, Ju-Ming King, Brian Kawagoe, Yusaku Ho, David Raman, Aaswath Department of Materials Science and Engineering University of California Los AngelesCA90095 United States

Explainable machine learning algorithms were applied to convolutional neural networks to reveal deeper insights into the properties of metamaterials, demonstrating new avenues for physics discovery and device optimiza... 详细信息

ISBN: (纸本)9781943580804

关键词： learning algorithms

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Realistic evaluation of semi-supervised learning algorithms 6

Realistic evaluation of semi-supervised learning algorithms

引用

6th International Conference on learning Representations, ICLR 2018

作者： Oliver, Avital Odena, Augustus Raffel, Colin Cubuk, Ekin D. Goodfellow, Ian J. Google Brain

Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain. Approaches based on deep neural networks have recently proven successful on standard benchmark tasks. However, we argue that these benchmarks fail to address many issues that these algorithms would face in real-world applications. After creating a unified reimplementation of various widely-used SSL techniques, we test them in a suite of experiments designed to address these issues. We find that simple baselines which do not use unlabeled data can be competitive with the state-of-the-art, that SSL methods differ in sensitivity to the amount of labeled and unlabeled data, and that performance can degrade substantially when the unlabeled dataset contains out-of-class examples. © 6th International Conference on learning Representations, ICLR 2018 - Workshop Track Proceedings. All rights reserved.

关键词： learning algorithms

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A Comparison of Deep learning algorithms Dealing With Limited Samples in Hyperspectral Image Classification

A Comparison of Deep Learning Algorithms Dealing With Limite...

引用

2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Ranjan, Pallavi Girdhar, Ashish Delhi Technological University Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781665492942

Hyperspectral Imaging, also known as Spectroscopy, is used in various areas such as medicine, defense, submarine, remote sensing, and environmental monitoring. Several supervised or unsupervised deep learning algorithms have been developed to classify such hyperspectral images. A significant problem in HSI is insufficient data availability, as annotating the samples is time-consuming and labor-intensive. This study provides a comparison of deep learning algorithms that have been developed to deal with the limited data problem in the HSI domain. It compares the performance, classification accuracy and other relevant parameters that exist during the development of such algorithms. © 2023 IEEE.

关键词： learning algorithms

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Modeling Electro-Erosion Wear of Cryogenic Treated Electrodes of Mold Steels Using Machine learning algorithms 12th

Modeling Electro-Erosion Wear of Cryogenic Treated Electrode...

引用

12th International Symposium on Intelligent Manufacturing and Service Systems, IMSS 2023

作者： Cetin, Abdurrahman Atali, Gökhan Erden, Caner Ozkan, Sinan Serdar Vocational School of Sakarya Machinery and Metal Technology Sakarya University of Applied Sciences Sakarya Turkey Faculty of Technology Department of Mechatronics Engineering Sakarya University of Applied Sciences Sakarya Turkey Faculty of Applied Sciences Sakarya University of Applied Sciences Sakarya Turkey

ISBN: (纸本)9789819960613

Electro-erosion wear (EEW) is a significant problem in the mold steel industry, as it can greatly reduce the lifespan of electrodes. This study presents a machine-learning approach for predicting and modeling electrode and workpiece wear on an electrical discharge machining (EDM) machine. In the experimental design, EDM of CuCrZr and Cu electrodes of AISI P20 tool steel was carried out at different pulse currents and duration levels. In addition, CuCrZr and Cu electrodes used in the experiment were cryogenically treated at a predefined degree for multiple periods and then tempered. This study employed machine learning algorithms such as decision trees, random forests, and k-nearest neighbors to model the EEW of cryogenically treated electrodes made of mold steels. The results were compared according to the coefficient of determination (R2), adjusted R2, and root mean squared error. As a result, the decision trees outperformed the other algorithms with 0.99 R2 performance. This study provides valuable insights into the behavior of EEW in mold steel electrodes and could be used to optimize the manufacturing process and extend the lifespan of the electrodes. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： learning algorithms

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Applying one-class learning algorithms to predict phage-bacteria interactions 6

Applying one-class learning algorithms to predict phage-bact...

引用

6th IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019

作者： Lopez, Juan Fernando Sotelo, Jesus Alfonso Lopez Leite, DIogo Pena-Reyes, Carlos Universidad Autónoma de Occidente Department of Electrical and Automation Cali Colombia Computational Intelligence for Computational Biology Yverdon-les-Bains Switzerland

ISBN: (纸本)9781728156668

The need to predict phage-bacteria interactions is a nowadays concern to overcome bacterial resistance issue;public genome databases contain highly imbalanced datasets which have hindered this task. Throughout this paper we will investigate, implement and evaluate One-Class learning algorithms in order to predict phage-bacteria interactions using only positive samples. We will use the programming language Python aided by Scikit-Learn, Tensorflow and keras to develop the machine learning models and test them with real phage-bacteria interactions datasets. We trained the models using cross validation technique generating a gridsearch with all the datasets to find several combinations of hyperparameters available. Furthermore, we optimized those hyperparameters by using Pareto fronts based on seven different performance metrics, improving the efficiency of each algorithm for a given dataset. To refine each algorithm's performance separately we used the ensemble learning technique with an odd number of algorithms by simple voting. Finally, we managed to achieve an overall performance of 80% in predicting phage-bacteria interactions trained only with positive classes, this percentage in practice means that when a patient has an infection resistant to antibiotics, we have 80% of saving the life rather than maybe a 0% while finding the correct phage for the pathogenic host. © 2019 IEEE.

关键词： learning algorithms

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Development of Firm Security Plan for IoT with Continual learning algorithms 6

Development of Firm Security Plan for IoT with Continual Lea...

引用

6th IEEE Pune Section International Conference, PuneCon 2023

作者： Varma, Shubhangee Kishan Vyas, Vibha Agarwal, Vanita Cusrow Wadia Institute of Technology Department of Electronics and Telecommunication Pune India Technological University Department of Electronics and Telecommunication Coep Pune India

ISBN: (纸本)9798350324204

The Internet of Things (IoT) is a network of interconnected devices that may be used to remotely detect, identify, and operate physical objects. IoT's qualities allow for the incorporation of the real world into a digital system, which improves precision, productivity, and bottom-line results. The old internet is made up of powerful computers and servers with a wide range of resources, whereas IoT devices have less computing time and memory. The complicated security protocols and several layers used in conventional networks are incompatible with the real-time nature of the Internet of Things. The adoption of IoT devices in several contexts has raised serious concerns about users' privacy and safety, leading to a meteoric rise in the prevalence of cyber-attacks. The larger a network becomes, the more likely it is to be attacked. Therefore, the IoT network is far more susceptible to attacks than conventional networks. Since the number of potential attackers in an IoT setting is growing exponentially, so too are the security risks. To protect the IoT ecosystem from current threats, cutting-edge technology is essential. The suggested resampling approach, CLA-ST (Sampling Technique), is based on the Random Forest Synthetic Minority Over Sampling Technique. To identify risks in the IoT ecosystem, we use the CLA-ST, which is based on a network search calculation improvement approach. Proposed study conducts experiments on the N-BaIoT dataset and the NSL-KDD dataset to evaluate attack recognition in the IoT. There must be a reliable mechanism in place to detect and prevent threats in the IoT ecosystem. When compared to PPDT-ST, the suggested technique outperforms it in both attack detection and categorization. © 2023 IEEE.

关键词： learning algorithms

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Multi-task structured prediction for entity analysis: Search-based learning algorithms 9

Multi-task structured prediction for entity analysis: Search...

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9th Asian Conference on Machine learning, ACML 2017

作者： Ma, Chao Doppa, Janardhan Rao Tadepalli, Prasad Shahbazi, Hamed Fern, Xiaoli School of EECS Oregon State University CorvallisOR97331 United States School of EECS Washington State University PullmanWA99164 United States

Entity analysis in natural language processing involves solving multiple structured prediction problems such as mention detection, coreference resolution, and entity linking. We explore the space of search-based learning approaches to solve the problem of multi-task structured prediction (MTSP) in the context of entity analysis. In this paper, we study three different search architectures to solve MTSP problems that make different tradeoffs between speed and accuracy of training and inference. In all three architectures, we learn one or more scoring functions that employ both intra-task and inter-task features. In the "pipeline" architecture, which is the fastest, we solve different tasks one after another in a pipelined fashion. In the "joint" architecture, which is the most expensive, we formulate MTSP as a single-task structured prediction, and search the joint space of multi-task structured outputs. To improve the speed of joint architecture, we introduce two different pruning methods and associated learning techniques. In the intermediate "cyclic" architecture, we cycle through the tasks multiple times in sequence until there is no performance improvement. Results on two benchmark domains show that the joint architecture improves over the pipeline approach as well as the previous state-of-the-art approach based on graphical models. The cyclic architecture is faster than the joint approach and achieves competitive performance. © 2017 C. Ma, J.R. Doppa, P. Tadepalli, H. Shahbazi & X. Fern.

关键词： learning algorithms

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Federated learning algorithms: Towards Next Generation Communication Systems 3

Federated Learning Algorithms: Towards Next Generation Commu...

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3rd World Symposium on Communication Engineering, WSCE 2020

作者： Stergiou, Konstantinos D. Psannis, Konstantinos E. University of Macedonia Department of Applied Informatics Thessaloniki Greece

ISBN: (纸本)9781728185644

We provide a survey of four different categories of Federated learning algorithms and their limitations as these were unveiled through experiments using commonly accepted data sets. The level of data heterogeneity forms a potential benchmark to compare Federated Averaging, Gradient Descent, Evolutionary, and Differential Privacy methods and, among other criteria, identifies the gaps that need to be addressed from future approaches. © 2020 IEEE.

关键词： learning algorithms

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Comparison of Stock Market Prediction Using Deep learning algorithms

Comparison of Stock Market Prediction Using Deep Learning Al...

引用

International Conference on Big Data and Cloud Computing, ICBDCC 2021

作者： Revathi, S. Begam, Regina Radhika Akila, R. B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India

ISBN: (纸本)9789811921766

The stock market is the act of buying and selling the share of the companies and yield more profits. In order to provide the abnormal returns for the company by share market, the prediction of the stock market is necessary. Using deep learning algorithms, this analytics process predicts the daily return direction of the SPDR S&P 500 ETF (ticker symbol: SPY). The deep learning algorithms such as simple recurrent neural network and long short-term memory algorithm are applied to predict the daily direction of future price of S&P 500 based on the historical price. The performance of different procedures such as simple RNN and LSTM is compared. LSTM algorithm is found to be an accurate algorithm when compared to the other algorithms. The simple RNN and LSTM algorithm is implemented on another dataset such as Bombay Stock Exchange (BSE), and the performance of both algorithms is compared and simulated;the result of LSTM is a better algorithm, to predict a stock market daily return. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Accurate segmentation of grass-sward structures in RGB images using machine learning algorithms 12

Accurate segmentation of grass-sward structures in RGB image...

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12th European Conference on Precision Agriculture, ECPA 2019

作者： Rueda-Ayala, C. Rueda-Ayala, V. Höglind, M. Andujar, D. Spain Norwegian Institute of Bioeconomy Research NIBIO Særheim Postvegen 213 Klepp Stasjon 4353 Norway

ISBN: (纸本)9789086863372

This work aimed to develop an automatic color image processing tool for segmenting complex ley sward grass structures, i.e. green-leaves, straw and bare soil leaf area index (LAI), using artificial intelligence techniques. Good classification results were achieved with Gaussian mixture models (GMM) + nearest centroid classifier (NCC) trained with data from color scale transformations from all RGB channels to the color space achromatic luminance and blue and red chromatic channels (Y'CbCr), to the color model cyan, magenta, yellow and key (CMYK). Also vegetation indices were included. A good correspondence between computer-based estimations and the visual- and destructive-estimated field data, with R2 above 0.85 and even 0.95. This work provides a tool for rapid and accurate determination of parameters which otherwise would require a large amount of working hours. © Wageningen Academic Publishers 2019

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