Search Results - (( data normalization _ algorithm ) OR ( pattern classification using algorithm ))
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1
Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier
Published 2018“…There are two stages in the proposed classification system. Firstly, the 1D-LBP algorithm is used to extract the features of the normalized iris images and save the data in a text file according to the subject and the combinations to evaluate for the next stage. …”
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2
Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak
Published 2019“…It involves development of Max-Min Rule-Based Classification Algorithm. The fourth stage is to design evaluation methodology of Max-Min Rule-Based Classification Algorithm using classifier model. …”
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3
Swarm negative selection algorithm for electroencephalogram signals classification
Published 2009“…The SNS classification model use negative selection and PSO algorithms to form a set of memory Artificial Lymphocytes (ALCs) that have the ability to distinguish between normal and epileptic EEG patterns. …”
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4
EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter
Published 2019“…This is taking advantage on the discriminative feature provided by both methods, statistical and CSP filter, which is expected to increase the accuracy of the eye state classification algorithm. The process of developing the EEG eye state classification algorithm, includes data extraction, pre-processing, data normalization, feature extraction, feature selection and classification are detailed out in this paper. …”
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5
EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter
Published 2019“…This is taking advantage on the discriminative feature provided by both methods, statistical and CSP filter, which is expected to increase the accuracy of the eye state classification algorithm. The process of developing the EEG eye state classification algorithm, includes data extraction, pre-processing, data normalization, feature extraction, feature selection and classification are detailed out in this paper. …”
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6
Artificial immune system based on real valued negative selection algorithms for anomaly detection
Published 2015“…Self data are regarded as the normal behavioral pattern of the monitored system. …”
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7
Converged Classification Network For Matching Cost Computation
Published 2020“…The stereo matching algorithm capable of producing the disparity or depth map in computer. …”
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8
Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification
Published 2017“…Meanwhile, the improved GA-MLP classification performance has been evaluated using datasets that vary in input features and output sizes. …”
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9
VGG16-based deep learning architectures for classification of lung sounds into normal, crackles, and wheezes using Gammatonegrams
Published 2023“…The classification results were obtained using the Google Collaboratory platform.…”
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10
Handgrip strength evaluation using neuro fuzzy approach
Published 2010“…The purpose of this study is to collect handgrip strength of patients and distinguish them from the normal persons. Multilevel Perception neural network utilizes the back-propagation learning algorithm is suitable to discover relationships and patterns in the dataset. …”
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11
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Electing the best set of features will help to improve the classifier predictions in terms of the normal and abnormal pattern. The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. …”
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12
Pemetaan Pm10 Dan Aot Menggunakan Teknik Penderiaan Jauh Di Semenanjung Malaysia
Published 2006“…The proposed algorithms were also validated using the multidate data. …”
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13
Neurons to heartbeats: spiking neural networks for electrocardiogram pattern recognition
Published 2024“…An extensive study is conducted to analyse the learning parameters that affect SNN performance, significantly influencing result accuracy. Through a two-classification process, the differentiation between normal and abnormal ECG patterns can be achieved in this study. …”
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14
The effect of pre-processing techniques and optimal parameters on BPNN for data classification
Published 2015“…The Min-Max, Z-Score, and Decimal Scaling Normalization pre-processing techniques were analyzed. …”
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15
Ultrasound-based tissue characterization and classification of fatty liver disease: A screening and diagnostic paradigm
Published 2015“…These classification algorithms are trained using the features extracted from the patient data in order for them to learn the relationship between the features and the end-result (FLD present or absent). …”
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16
Rule-base wearable embedded platform for seizure detection from real EEG data in ambulatory state
Published 2014“…The system distinguishes between 'Normal' and 'Seizure' state using on-the-fly calculated features representing the statistical measures for specifically filtered signals from the raw data. …”
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17
Intelligent Fuzzy Classifier for Pre-Seizure Detection from Real Epileptic Data
Published 2014“…In this paper, a classification method is presented using an Fuzzy Inference Engine to detect the incidences of preseizures in real/raw Epilepsy data. …”
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18
Intelligent Fuzzy Classifier for pre-seizure detection from real epileptic data
Published 2014“…In this paper, a classification method is presented using an Fuzzy Inference Engine to detect the incidences of pre-seizures in real/raw Epilepsy data. …”
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19
Fuzzy Platform for Embedded Wearable EEG Seizure Detection in Ambulatory State
Published 2014“…The system distinguishes between 'Normal' and 'Seizure' state using on-the-fly calculated features representing the statistical measures for specifically filtered signals from the raw data. …”
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20
Embedded wearable EEG seizure detection in ambulatory state
Published 2014“…The system distinguishes between 'Normal' and 'Seizure' state using on-the-fly calculated features representing the statistical measures for specifically filtered signals from the raw data. …”
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