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Survey of feature selection and extraction techniques for stock market prediction
Financial Innovation, 2023 In stock market forecasting, the identification of critical features that affect the performance of machine learning (ML) models is crucial to achieve accurate stock price predictions.
Htet Htet Htun, Michael Biehl, N. Petkov
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Feature extraction for epileptic seizure detection using machine learning
Current Medicine Research and Practice, 2020 Background: Epilepsy is a common neurological disorder and affects approximately 70 million people worldwide. The traditional approach used by neurologists for the detection of seizure is time consuming.
Renuka Mohan Khati, Rajesh Ingle
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Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion
Italian National Conference on Sensors, 2021 Bearing is one of the most important parts of rotating machinery with high failure rate, and its working state directly affects the performance of the entire equipment.
Huibin Zhu +4 more
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A dual quantum image feature extraction method: PSQIFE
IET Image Processing, 2022 In digital image processing, feature extraction occupies a very important position, which is related to the effect of image classification or recognition. At present, effective quantum feature extraction methods are relatively lacking.
Jie Su, Shuhan Lu, Lin Li
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Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020 We propose a novel decentralized feature extraction approach in federated learning to address privacy-preservation issues for speech recognition. It is built upon a quantum convolutional neural network (QCNN) composed of a quantum circuit encoder for ...
Chao-Han Huck Yang +6 more
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UNLABELED SELECTED SAMPLES IN FEATURE EXTRACTION FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 Feature extraction plays a key role in hyperspectral images classification. Using unlabeled samples, often unlimitedly available, unsupervised and semisupervised feature extraction methods show better performance when limited number of training samples ...
A. Kianisarkaleh +2 more
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Linear feature extraction for ranking [PDF]
Information Retrieval Journal, 2018 We address the feature extraction problem for document ranking in information retrieval. We then propose LifeRank, a Linear feature extraction algorithm for Ranking. In LifeRank, we regard each document collection for ranking as a matrix, referred to as the original matrix.
Gaurav Pandey +4 more
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IEEE Transactions on Geoscience and Remote Sensing, 2016 Due to the advantages of deep learning, in this paper, a regularized deep feature extraction (FE) method is presented for hyperspectral image (HSI) classification using a convolutional neural network (CNN).
Yushi Chen +4 more
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Feature Extraction and Classification of Automatically Segmented Lung Lesion Using Improved Toboggan Algorithm [PDF]
, 2017 The accurate detection of lung lesions from computed tomography (CT) scans is essential for clinical diagnosis. It provides valuable information for treatment of lung cancer.
Bavya, K. (K), Julian, M. P. (Mr)
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UAT: Universal Attention Transformer for Video Captioning
Sensors, 2022 Video captioning via encoder–decoder structures is a successful sentence generation method. In addition, using various feature extraction networks for extracting multiple features to obtain multiple kinds of visual features in the encoding process is a ...
Heeju Im, Yong-Suk Choi
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