Classification model with feature generation guided by cluster scoring

2014 ◽  
Author(s):  
Wen Fan ◽  
Ping Wang ◽  
Hongyue Sun
Keyword(s):  
Classification Model ◽  
Feature Generation

scholarly journals Epilepsy attacks recognition based on 1D octal pattern, wavelet transform and EEG signals

2021 ◽  
Author(s):  
Türker Tuncer ◽  
Sengul Dogan ◽  
Ganesh R. Naik ◽  
Paweł Pławiak
Keyword(s):  
Wavelet Transform ◽  
Classification Model ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Diagnostic Systems ◽  
Feature Generation ◽  
Eeg Signals ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Eeg Classification

AbstractElectroencephalogram (EEG) signals have been generally utilized for diagnostic systems. Nowadays artificial intelligence-based systems have been proposed to classify EEG signals to ease diagnosis process. However, machine learning models have generally been used deep learning based classification model to reach high classification accuracies. This work focuses classification epilepsy attacks using EEG signals with a lightweight and simple classification model. Hence, an automated EEG classification model is presented. The used phases of the presented automated EEG classification model are (i) multileveled feature generation using one-dimensional (1D) octal-pattern (OP) and discrete wavelet transform (DWT). Here, main feature generation function is the presented octal-pattern. DWT is employed for level creation. By employing DWT frequency coefficients of the EEG signal is obtained and octal-pattern generates texture features from raw EEG signal and wavelet coefficients. This DWT and octal-pattern based feature generator extracts 128 × 8 = 1024 (Octal-pattern generates 128 features from a signal, 8 signal are used in the feature generation 1 raw EEG and 7 wavelet low-pass filter coefficients). (ii) To select the most useful features, neighborhood component analysis (NCA) is deployed and 128 features are selected. (iii) The selected features are feed to k nearest neighborhood classifier. To test this model, an epilepsy seizure dataset is used and 96.0% accuracy is attained for five categories. The results clearly denoted the success of the presented octal-pattern based epilepsy classification model.

A glance on dystonias, how to recognize and handle them

2019 ◽  
pp. 22-29
Author(s):  
F. N. Mercan ◽  
E. Bayram ◽  
M. C. Akbostanci
Keyword(s):  
Differential Diagnosis ◽  
Movement Disorder ◽  
Age Of Onset ◽  
Clinical Manifestations ◽  
Body Part ◽  
Classification Model ◽  
Treatment Choices ◽  
Muscle Groups

Dystonia refers to an involuntary, repetitive, sustained, painful and twisting movements of the affected body part. This movement disorder was first described in 1911 by Hermain Oppenheim, and many studies have been conducted to understand the mechanism, the diagnosis and the treatment of dystonia ever since. However, there are still many unexplained aspects of this phenomenon. Dystonia is diagnosed by clinical manifestations, and various classifications are recommended for the diagnosis and the treatment. Anatomic classification, which is based on the muscle groups involved, is the most helpful classification model to plan the course of the treatment. Dystonias can also be classified based on the age of onset and the cause. These dystonic syndromes can be present without an identified etiology or they can be clinical manifestations of a neurodegenerative or neurometabolic disease. In this review we summarized the differential diagnosis, definition, classifications, possible mechanisms and treatment choices of dystonia.

Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

2020 ◽  
Vol 4 (2) ◽  
pp. 329-335
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Purwono
Keyword(s):  
Social Media ◽  
Gradient Descent ◽  
Classification Model ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Performance Patterns ◽  
A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

Faktor-Faktor yang Berpengaruh dalam Mendapatkan Pekerjaan bagi Lulusan Statistika IPB dengan Menggunakan Metode CHAID

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Aulia Dwi Oktavia ◽  
Aam Alamudi ◽  
Budi Susetyo
Keyword(s):  
College Graduates ◽  
Influential Factors ◽  
Classification Model ◽  
Statistical Software ◽  
Waiting Period ◽  
Level Of Education ◽  
Economic Problems ◽  
Factors Influencing

Unemployment is one of the economic problems in Indonesia. Judging from the level of education that was completed there were unemployment from the level of college graduates. This encourages the level of competition in getting jobs to be more stringent, so that college graduates (bachelor of Statistics in IPB) must have the preparation of various factors to maintain the quality of their graduates. The quality of college graduates can be seen from the length of time waiting to get a job. This study aims to determine the influential factors in getting a job for graduates of the IPB Statistics degree, so that the CHAID method can be used in this study. The results of CHAID's analysis in this study in the form of tree diagrams using α = 10% explained that the factors influencing the waiting period variables were sex, internship, and the ability to master statistical software, where the accuracy value generated by the classification model was 79.3 %.

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

2020 ◽  
Vol 17 (4) ◽  
pp. 497-506
Author(s):  
Sunil Patel ◽  
Ramji Makwana
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Classification Model ◽  
Hand Gesture ◽  
Flow Data ◽  
Dynamic Hand Gesture Recognition ◽  
Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

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