Search Results - (( using classification using algorithm ) OR ( automatic identification using algorithm ))
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Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs
Published 2021“…Finally, the k-nearest neighbors ( kNN) classification technique was used for automatic gender identification of an emotional-based EEG dataset. …”
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Feature extraction using active appearance model algorithm with Bayesian classification approach
Published 2013“…This paper addressed some of these issues to bring face recognition more closely to being useful for real-life applications. It directed towards the illumination-invariant automatic recognition of faces and analysis to improve face verification and identification performance.To compare with other feature extraction at the end of the study, an evaluation has been done with an existing face recognition system using AAM algorithm. …”
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Feature extraction using spectral centroid and mel frequency cepstral coefficient for Quranic accent automatic identification
Published 2014“…This paper presents the process of Quranic Accent Automatic Identification. Recent feature extraction technique that is used for Quranic verse rule identification/Tajweed include Mel Frequency Cepstral Coefficients (MFCC) which prone to additive noise and may reduce the classification result. …”
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The efficacy of deep learning algorithm in classifying chilli plant growth stages
Published 2021“…The demand in this field has created various opportunities, especially for automatic classification using deep learning methods. …”
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A multi-color based features from facial images for automatic ethnicity identification model
Published 2019“…Finally, the proposed ethnicity identification was tested using several classification algorithms. …”
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Automatic identification of epileptic seizures from EEG signals using sparse representation-based classification
Published 2020“…This study is based on sparse representation-based classification (SRC) theory and the proposed dictionary learning using electroencephalogram (EEG) signals. …”
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Plant leaf recognition algorithm using ant colony-based feature extraction technique
Published 2013“…To do this, at first, based on the proposed algorithm,the physiological dimensions of leaves are automatically measured and with regard to these parameters, specified features such as shape, morph, texture and colour are extracted from the image of the plant leaf through image processing to create a reserved feature database to be used for different species of plants. …”
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Modelling of the anti-collision algorithm in RFID system: article / Shafinaz Ismail
Published 2014“…To avoid these collisions, there are several anti-collision algorithms used in the RFID system. The major classifications of the algorithms are Aloha based protocols and tree based protocols. …”
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Modelling of the anti-collision algorithm in RFID system / Shafinaz Ismail
Published 2014“…To avoid these collisions, there are several anti-collision algorithms used in the RFID system. The major classifications of the algorithms are Aloha based protocols and tree based protocols. …”
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High impedance fault detection and identification based on pattern recognition of phase displacement computation
Published 2018“…Subsequently, an automatic HIF classification algorithm based on predefined indices is proposed to perform event identification and HIF detection. …”
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Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks
Published 2014“…Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems.Classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect to predefined set of classes.In this paper, we conduct a performance evaluation based on the noiseless and noisy EEG-based epileptic seizure data using various classification algorithms including BayesNet, DecisionTable, IBK, J48/C4.5, and VFI.The reconstructed and noisy EEG data are decomposed with discrete cosine transform into several sub-bands.In addition, some of statistical features are extracted from the wavelet coefficients to represent the whole EEG data inputs into the classifiers.Benchmark on widely used dataset is utilized for automatic epileptic seizure detection including both normal and epileptic EEG datasets.The classification accuracy results confirm that the selected classifiers have greater potentiality to identify the noisy epileptic disorders.…”
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Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network
Published 2016“…First proposed algorithm is the combination of momentum algorithm with adaptive learning rate (ALR) algorithm, and second proposed algorithm is the combination of momentum algorithm with automatic learning rate selection (ALRS) algorithm. …”
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Texture-based feature using multi-blocks gray level co-occurrence matrix for ethnicity identification
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Advances in automatic insect classification
Published 2014“…This paper details advances in insect recognition, discussing representative works from different types of method and classifiers algorithm. Among the method used in the previous research includes color histogram, edge detection and feature extraction (SIFT vector). …”
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Fingerprint feature extraction based discrete cosine transformation (DCT)
Published 2009“…The extracted nCT data is used as input for the backpropagation neural network training for personal identification.…”
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The Contribution of Feature Selection and Morphological Operation For On-Line Business System’s Image Classification
Published 2015“…Classification process is part of the important phase in automatic image annotation (AIA). …”
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Object-based imagery analysis for automatic urban tree species detection using high resolution satellite image
Published 2016“…The method of maximum likelihood classification and support vector machines leads to the lowest classification accuracy since these algorithms extract only the spectral information of each pixel and consequently fail to utilize spatial, color and textural information.…”
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