Search Results - (( features extraction function algorithm ) OR ( using vectorization learning algorithm ))

Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition by Ali Adlan, Hanan Hassan

“…A novel feature extraction algorithm was developed to extract the feature vectors. …”
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Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA by Lee, Lok Hua, Ho, Cyrus Su Hui, Chan, Yee Ling, Tay, Gabrielle Wann Nii, Lu, Cheng-Kai, Tang, Tong Boon

“…The entire algorithm achieved a better performance through the radial basis function (RBF) support vector machine (SVM), with 82.70 accuracy, 78.44 sensitivity, 86.15 precision, and 91.02 specificity, respectively, when compared with conventional machine learning approaches that combine clinical, sociodemographic and genetic information as the predictor. …”
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Online sequential extreme learning machine for classification of Mycobacterium tuberculosis in Ziehl-Neelsen stained tissue by Muhammad Khusairi, Osman, Mohd Yusoff, Mashor, Prof. Dr., Hasnan, Jaafar, Assoc. Prof. Dr.

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Texture-based classification of workpiece surface images using the support vector machine by Ashour, Mohammed Waleed, Abdul Halin, Alfian, Khalid, Fatimah, Abdullah, Lili Nurliyana, Darwish, Samy H.

“…Machine vision can be used to semi- or fully automate this identification process by firstly extracting features from captured workpiece images, followed by analysis using machine learning algorithms. …”
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Noise eliminated ensemble empirical mode decomposition scalogram analysis for rotating machinery fault diagnosis by Atik, Faysal

“…The ability of CNN was compared with two traditional machine learning algorithms, k nearest neighbour (kNN) and the support vector machine (SVM), using statistical features from EEMD, CEEMD and NEEEMD. …”
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Forensic language of property theft genre based on mathematical formulae and machine learning algorithms / Hana' Abd Razak by Abd Razak, Hana'

“…Convolution Neural Network (CNN) using deep learning algorithm is chosen in identifying frequency of movement and execution time of housebreaking crime. …”
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The classification of wink-based eeg signals by means of transfer learning models by Jothi Letchumy, Mahendra Kumar

“…The implementation of pre-processing algorithms has been demonstrated to be able to mitigate the signal noises that arises from the winking signals without the need for the use signal filtering algorithms. …”
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Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network by Zafar, R., Kamel, N., Naufal, M., Malik, A.S., Dass, S.C., Ahmad, R.F., Abdullah, J.M., Reza, F.

“…MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. …”
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Smart fall detection by enhanced SVM with fuzzy logic membership function by Harum, Norharyati, Khalil, Mohamad Kchouri, Hazimeh, Hussein, Obeid, Ali

“…In addition, they use thresholds to identify falls based on artificial experiences or machine learning (ML) algorithms. …”
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Identification Of Flow Blockage Levels In Centrifugal Pump By Machine Learning by Ng, Woon Li

“…The purpose of this research is to develop an effective machine learning model for the classification of flow blockage levels in the centrifugal pump by using the statistically significant features from vibration and acoustic analysis. …”
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EEG-Based Person Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique by Liew, Siaw Hong

“…The electrodes, feature extraction, and feature selection analysis were tested using the benchmarking dataset from UCI repositories. …”
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Finger Movement Discrimination Of EMG Signals Towards Improved Prosthetic Control Using TFD by Shair, Ezreen Farina, Jamaluddin, Nur Asyiqin, Abdullah, Abdul Rahim

“…Three machine learning algorithms which are Support Vector Machine (SVM), k-Nearest Neighbor (KNN) and Ensemble Classifier are then used to classify the individuals and combine finger movement based on the extracted EMG feature from the spectrogram. …”
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An effective source number enumeration approach based on SEMD by Ge, Shengguo, Mohd Rum, Siti Nurulain, Ibrahim, Hamidah, Marsilah, Erzam, Perumal, Thinagaran

“…Then, the instantaneous phase feature is extracted to obtain a high-dimensional eigenvalue vector. …”
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Investigation of machine learning models in predicting compressive strength for ultra-high-performance geopolymer concrete: A comparative study by Abdellatief M., Hassan Y.M., Elnabwy M.T., Wong L.S., Chin R.J., Mo K.H.

“…Overall, the dataset of 128 CS results was used to develop the machine learning (ML) models. …”

Malware Detection using Deep Learning (DL) by Chowdhury Sajadul Islam, Madihah Mohd Saudi, Nur Hafiza Zakaria, Muji Setiyo

Comparative analysis of spatio/spectro-temporal data modelling techniques by Abdullah, Mohd Hafizul Afifi, Othman, Muhaini, Kasim, Shahreen

“…Other challenges include the dynamic pattern of spatial components features and inconsistency in the number of samples and feature-length used in the training and sampling datasets [2]. …”
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Estimating Face Emotion using Genetic Algorithm by Karthigayan, M., Mohd Rizon, Muhamed Juhari, Sazali, Yaacob, Nagarayan, R.

Subjects: “…Feature extraction…”
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Binary Coati Optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Pr... by Sammen S.S., Ehteram M., Sheikh Khozani Z., Sidek L.M.

“…For water level prediction, lagged rainfall and water level are used. In this study, we used extreme learning machine (ELM)-multi-kernel least square support vector machine (ELM-MKLSSVM), extreme learning machine (ELM)-LSSVM-polynomial kernel function (PKF) (ELM-LSSVM-PKF), ELM-LSSVM-radial basis kernel function (RBF) (ELM-LSSVM-RBF), ELM-LSSVM-Linear Kernel function (LKF), ELM, and MKLSSVM models to predict water level. …”

Development of a personified face emotion recognition technique using fitness function by Muthukaruppan, Karthigayan, Mohd Rizon, Mohamed Juhari, Ramachandran, Nagarajan, Masanori, Sugisaka, Sazali, Yaacob, Mohd Rozailan, Mamat, Hazry, Desa

Subjects: “…Feature extraction…”
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Support directional shifting vector: A direction based machine learning classifier by Kowsher, Md., Hossen, Imran, Tahabilder, Anik, Prottasha, Nusrat Jahan, Habib, Kaiser, Zafril Rizal, M Azmi

“…In this article, we have focused on developing a model of angular nature that performs supervised classification. Here, we have used two shifting vectors named Support Direction Vector (SDV) and Support Origin Vector (SOV) to form a linear function. …”
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