Search Results - (( based optimization based algorithm ) OR ( features extraction sensor algorithm ))
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1
Simulated kalman filter (SKF) based image template matching for distance measurement by using stereo vision system
Published 2018“…Stereo vision sensor can provide color and texture information for easy data and feature extraction. …”
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2
Kinect-based human gait recognition using locally linear embedded and support vector machine
Published 2018“…Next, the extracted gait features were then optimized using LLE known as DG-LLE and further classified by multi-class SVM with Error Correcting Output Code (ECOC) algorithm. …”
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3
Clarity-optimized wavelet with autoencoder-ReliefF ranking for enhanced UHF PD signal feature extraction
Published 2025“…To address the challenge of identifying the most discriminative features, this work integrates advanced feature ranking algorithms, namely, auto-encoder-based ranking and the ReliefF method. …”
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4
Class binarization with self-adaptive algorithm to improve human activity recognition
Published 2018“…To enhance the selection of most highly ranking features, irrelevant features are ‘pruned’ based on determined boundary threshold. …”
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5
Supervised deep learning algorithms for process fault detection and diagnosis under different temporal subsequence length of process data
Published 2025“…The findings clearly indicate that the IPO-Vitt, a variant of transformer-based models, exhibited the best FDD performance under 10% and 50% subsequence length of data on CSTR and TEP case studies, respectively, for optimal feature extraction, even with 10% of fully labelled input data.…”
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6
Wavelet based fault tolerant control of induction motor / Khalaf Salloum Gaeid
Published 2012“…There are two steps in the fault tolerant control process; in the first step the fault is identified with the feature extraction module, a fault decision module and a feature cluster module. …”
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7
Drowsiness Detection Using Ocular Indices from EEG Signal
Published 2022“…Different machine learning classification models, including the decision tree, the support vector machine (SVM), the K-nearest neighbor (KNN) method, and the bagged and boosted tree models, were trained based on the seven selected features. These models were further optimized to improve their performance. …”
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Ensemble Filter Based Feature Selection Technique for Classification of Human Activity Recognition
Published 2025“…An ensemble Random Forest (RF) was used as the base classifier to evaluate the performance of the hybrid algorithm. …”
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Square groove detection based on forstner with canny edge operator using laser vision sensor
Published 2023“…This research addresses 1he feature extraction algorithm of 1he square weld groove to make weld seam recognition more accurate. …”
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10
Feature extraction and supervised learning for volatile organic compounds gas recognition
Published 2023“…This research project aims to investigate effective feature extraction techniques that can be employed as discriminative features for machine learning algorithms. …”
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Wearable Sensor Feature Fusion for Human Activity Recognition (HAR) : A Proposed Classification Framework
Published 2022“…Due to the extensive feature engineering and human feature extraction required by traditional machine learning algorithms, they are time consuming to develop. …”
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Multi-sensor fusion based on multiple classifier systems for human activity identification
Published 2019“…To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. …”
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13
Feature extraction from oil palm in vitro shoot images
Published 2010“…The feature extraction techniques are discussed in this article. …”
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14
Internet of Things (IoT) based activity recognition strategies in smart homes: a review
Published 2022“…This technique is challenged by the nature of IoT technology and perceived data, as well as by human differences, which necessitated additional processing tasks to select significant features for the learning algorithms. In this work, we focus our review on activity recognition implementation strategies by examining various sensors and sensing technologies used to collect useful data from IoT devices, reviewing preprocessing and feature extraction techniques, as well as classification algorithms used to recognize human activities in smart homes. …”
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15
Multi-sensor fusion and deep learning framework for automatic human activity detection and health monitoring using motion sensor data / Henry Friday Nweke
Published 2019“…First, to investigate existing multi-sensor and automatic feature extraction methods for human activity detection and health monitoring using motion sensor. …”
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16
The noise reduction algorithm for star detection
Published 2025“…This improvement in denoising leads to more precise centroid extraction, contributing to more reliable star sensor performance for space navigation applications…”
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Designing an expressive virtual Kompang on mobile device with tri-axial accelerometer
Published 2018“…This paper is therefore implemented a simple extraction method by extracting percussive features from embedded sensors to map with the output sound with minimum delay. …”
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Evaluations of oil palm fresh fruit bunches maturity degree using multiband spectrometer
Published 2017“…The result illustrated that just one sensor features (sensor 4) are significantly enough to build a good, accurate classifier model that can predict, and classify the oil palm FFB ripeness, rather than the proposed 4 bands sensors with 32 feature attributes. …”
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19
Sensors Fusion based Online Mapping and Features Extraction of Mobile Robot in the Road Following and Roundabout
Published 2016“…A road feature extraction based mapping system using a sensor fusion technique for mobile robot navigation in road environments is presented in this paper. …”
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Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions
Published 2019“…These sensors are pre-processed and different feature sets such as time domain, frequency domain, wavelet transform are extracted and transform using machine learning algorithm for human activity classification and monitoring. …”
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