![]() Shashank Karrthikeyaa, Vineeth Vijayaraghavan).Pages 84-97 A Classical-Quantum Hybrid Approach for Unsupervised Probabilistic Machine Learning (Prasanna Date, Catherine Schuman, Robert Patton, Thomas Potok).Pages 98-117 Comparing TensorFlow Deep Learning Performance and Experiences Using CPUs via Local PCs and Cloud Solutions (Robert Nardelli, Zachary Dall, Sotiris Skevoulis).Pages 118-130 An Efficient Segmentation Technique for Urdu Optical Character Recognizer (OCR) (Saud Ahmed Malik, Muazzam Maqsood, Farhan Aadil, Muhammad Fahad Khan).Pages 131-141 Adaptive Packet Routing on Communication Networks Based on Reinforcement Learning (Tanyaluk Deeka, Boriboon Deeka, Surajate On-rit).Pages 142-151 ScaffoldNet: Detecting and Classifying Biomedical Polymer-Based Scaffolds via a Convolutional Neural Network (Darlington Ahiale Akogo, Xavier-Lewis Palmer).Pages 152-161 Transfer Learning for Cross-Domain Sequence Tagging Tasks (Meng Cao, Chaohe Zhang, Dancheng Li, Qingping Zheng, Ling Luo).Pages 162-173 Ensemble Models for Enhancement of an Arabic Speech Emotion Recognition System (Rached Zantout, Samira Klaylat, Lama Hamandi, Ziad Osman).Pages 174-187 Automating Vehicles by Deep Reinforcement Learning Using Task Separation with Hill Climbing (Mogens Graf Plessen).Pages 188-210 Information Augmentation, Reduction and Compression for Interpreting Multi-layered Neural Networks (Ryotaro Kamimura).Pages 211-223 Enhance Rating Algorithm for Restaurants (Jeshreen Balraj, Cassim Farook).Pages 224-234 Reverse Engineering Creativity into Interpretable Neural Networks (Marilena Oita).Pages 235-247 Developing a Deep Learning Model to Implement Rosenblatt’s Experiential Memory Brain Model (Abu Kamruzzaman, Yousef Alhwaiti, Charles C. Chen).Pages 1-10 Intelligent Signal Classifier for Brain Epileptic EEG Based on Decision Tree, Multilayer Perceptron and Over-Sampling Approach (Jimmy Ming-Tai Wu, Meng-Hsiun Tsai, Chia-Te Hsu, Hsien-Chung Huang, Hsiang-Chun Chen).Pages 11-24 Korean-Optimized Word Representations for Out-of-Vocabulary Problems Caused by Misspelling Using Sub-character Information (Seonhghyun Kim, Jai-Eun Kim, Seokhyun Hawang, Berlocher Ivan, Seung-Won Yang).Pages 25-32 A Regressive Convolution Neural Network and Support Vector Regression Model for Electricity Consumption Forecasting (Youshan Zhang, Qi Li).Pages 33-45 Facial Expression Recognition and Analysis of Interclass False Positives Using CNN (Junaid Baber, Maheen Bakhtyar, Kafil Uddin Ahmed, Waheed Noor, Varsha Devi, Abdul Sammad).Pages 46-54 CASCADENET: An LSTM Based Deep Learning Model for Automated ICD-10 Coding (Sheikh Shams Azam, Manoj Raju, Venkatesh Pagidimarri, Vamsi Chandra Kasivajjala).Pages 55-74 Automated Gland Segmentation Leading to Cancer Detection for Colorectal Biopsy Images (Syed Fawad Hussain Naqvi, Salahuddin Ayubi, Ammara Nasim, Zeeshan Zafar).Pages 75-83 A Two-Fold Machine Learning Approach for Efficient Day-Ahead Load Prediction at Hourly Granularity for NYC (Syed Shahbaaz Ahmed, Raghavendran Thiruvengadam, A. Pages i-xiv Contextual Binding: A Deductive Apparatus in Artificial Neural Networks (Jim Q. As a result, SVM, Logistic Regression and Naïve Bayes classifier achieved the highest accuracy with 89.09% in AUC measurement.Table of contents : Front Matter. The performance was tested by using Dataset 1 from BCI Competition IV which consists of imaginary hand and foot movement EEG data. Five classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree and Logistic Regression are used in the study. Due to the high dimensionality of data resulting from the feature extraction stage, the Linear Discriminant Analysis (LDA) is used to minimize the number of dimension by finding the feature subspace that optimizes class separability. The Fast Fourier Transform (FFT) features is extracted from the electroencephalography (EEG) signals to transform the signals into frequency domain. ![]() The BCI system consists of two main steps which are feature extraction and classification. This paper focuses on classification of motor imagery in Brain Computer Interface (BCI) by using classifiers from machine learning technique.
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