Search Results - parallel extraction ((((learning algorithm) OR (sensor algorithm))) OR (bat algorithm))
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Simulated kalman filter (SKF) based image template matching for distance measurement by using stereo vision system
Published 2018“…One of the important issues for sensors is the accuracy of distance measurement. This thesis explains the development of new algorithm for distance measurement using stereo vision sensor. …”
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Thesis -
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A Parallel-Model Speech Emotion Recognition Network Based on Feature Clustering
Published 2023“…To address this issue, we proposed a novel algorithm called F-Emotion to select speech emotion features and established a parallel deep learning model to recognize different types of emotions. …”
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A parallel-model speech emotion recognition network based on feature clustering
Published 2023“…To address this issue, we proposed a novel algorithm called F-Emotion to select speech emotion features and established a parallel deep learning model to recognize different types of emotions. …”
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Combining deep and handcrafted image features for MRI brain scan classification
Published 2019“…In this paper, a deep learning feature extraction algorithm is proposed to extract the relevant features from MRI brain scans. …”
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Mapreduce algorithm for weather dataset
Published 2017“…The temperature, humidity and visibility attributes from the dataset has been extracted by the MapReduce Algorithm into structure data. …”
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Real-time Rotation Invariant Hand Tracking Using 3D Data
Published 2014“…The propose hand tracking algorithm is rotation invariant, since it can detects and tracks various rotations of hand and it is also can remove unwanted object (noise) that also moving parallel with hand's position.…”
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MapReduce algorithm for weather dataset
Published 2018“…The temperature, humidity and visibility attributes from the dataset has been extracted by the MapReduce Algorithm into structure data. …”
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A novel neuroscience-inspired architecture: for computer vision applications
Published 2016“…The theory behind deep learning, the human visual system was investigated and general principles of how it functions are extracted. …”
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Proceeding Paper -
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Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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Electromygraphy (EMG) signal based hand gesture recognition using Artificial Neural Network (ANN)
Published 2011“…ANNs are particularly useful for complex pattern recognition and classification tasks. The capability of learning from examples, the ability to reproduce arbitrary non-linear functions of input, and the highly parallel and regular structure of ANNs make them especially suitable for pattern recognition tasks. …”
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Proceeding Paper -
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An integrated priority-based cell attenuation model for dynamic cell sizing
Published 2012“…A new, robust integrated priority-based cell attenuation model for dynamic cell sizing is proposed and simulated using real mobile traffic data.The proposed model is an integration of two main components; the modified virtual community – parallel genetic algorithm (VC-PGA) cell priority selection module and the evolving fuzzy neural network (EFuNN) mobile traffic prediction module.The VC-PGA module controls the number of cell attenuations by ordering the priority for the attenuation of all cells based on the level of mobile level of mobile traffic within each cell.The EFuNN module predicts the traffic volume of a particular cell by extracting and inserting meaningful rules through incremental, supervised real-time learning.The EFuNN module is placed in each cell and the output, the predicted mobile traffic volume of the particular cell, is sent to local and virtual community servers in the VC-PGA module.The VC-PGA module then assigns priorities for the size attenuation of all cells within the network, based on the predicted mobile traffic levels from the EFuNN module at each cell.The performance of the proposed module was evaluated on five adjacent cells in Selangor, Malaysia. …”
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WiFi-based human activity recognition through wall using deep learning
Published 2023“…Furthermore, a deep learning algorithm based on RNN with an LSTM algorithm is used to classify the activity instances indoors, achieving up to 97.5% accuracy in classifying seven activities. …”
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