Classification of Vowels from Imagined Speech with Convolutional Neural Networks

Imagined speech is a relatively new electroencephalography (EEG) neuro-paradigm, which has seen little use in Brain-Computer Interface (BCI) applications. Imagined speech can be used to allow physically impaired patients to communicate and to use smart devices by imagining desired commands and then detecting and executing those commands in a smart device. The goal of this research is to verify previous classification attempts made and then design a new, more efficient neural network that is noticeably less complex (fewer number of layers) that still achieves a comparable classification accuracy. The classifiers are designed to distinguish between EEG signal patterns corresponding to imagined speech of different vowels and words. This research uses a dataset that consists of 15 subjects imagining saying the five main vowels (a, e, i, o, u) and six different words. Two previous studies on imagined speech classifications are verified as those studies used the same dataset used here. The replicated results are compared. The main goal of this study is to take the proposed convolutional neural network (CNN) model from one of the replicated studies and make it much more simpler and less complex, while attempting to retain a similar accuracy. The pre-processing of data is described and a new CNN classifier with three different transfer learning methods is described and used to classify EEG signals. Classification accuracy is used as the performance metric. The new proposed CNN, which uses half as many layers and less complex pre-processing methods, achieved a considerably lower accuracy, but still managed to outperform the initial model proposed by the authors of the dataset by a considerable margin. It is recommended that further studies investigating classifying imagined speech should use more data and more powerful machine learning techniques. Transfer learning proved beneficial and should be used to improve the effectiveness of neural networks.

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Corn Disease Classification Using Transfer Learning and Convolutional Neural Network

JUITA Jurnal Informatika ◽

10.30595/juita.v9i2.11686 ◽

2021 ◽

Vol 9 (2) ◽

pp. 211

Author(s):

Faisal Dharma Adhinata ◽

Gita Fadila Fitriana ◽

Aditya Wijayanto ◽

Muhammad Pajar Kharisma Putra

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Disease Classification ◽

Machine Learning Techniques ◽

Early Detection Of Disease ◽

Global Average ◽

Learning Techniques ◽

Corn Plants

Indonesia is an agricultural country with abundant agricultural products. One of the crops used as a staple food for Indonesians is corn. This corn plant must be protected from diseases so that the quality of corn harvest can be optimal. Early detection of disease in corn plants is needed so that farmers can provide treatment quickly and precisely. Previous research used machine learning techniques to solve this problem. The results of the previous research were not optimal because the amount of data used was slightly and less varied. Therefore, we propose a technique that can process lots and varied data, hoping that the resulting system is more accurate than the previous research. This research uses transfer learning techniques as feature extraction combined with Convolutional Neural Network as a classification. We analysed the combination of DenseNet201 with a Flatten or Global Average Pooling layer. The experimental results show that the accuracy produced by the combination of DenseNet201 with the Global Average Pooling layer is better than DenseNet201 with Flatten layer. The accuracy obtained is 93% which proves the proposed system is more accurate than previous studies.

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Hyperspectral Image Classification Based on a Shuffled Group Convolutional Neural Network with Transfer Learning

Remote Sensing ◽

10.3390/rs12111780 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1780 ◽

Cited By ~ 2

Author(s):

Yao Liu ◽

Lianru Gao ◽

Chenchao Xiao ◽

Ying Qu ◽

Ke Zheng ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Classification Accuracy ◽

Hyperspectral Image ◽

Classification Performance ◽

Training Dataset ◽

Hyperspectral Image Classification ◽

Different Levels

Convolutional neural networks (CNNs) have been widely applied in hyperspectral imagery (HSI) classification. However, their classification performance might be limited by the scarcity of labeled data to be used for training and validation. In this paper, we propose a novel lightweight shuffled group convolutional neural network (abbreviated as SG-CNN) to achieve efficient training with a limited training dataset in HSI classification. SG-CNN consists of SG conv units that employ conventional and atrous convolution in different groups, followed by channel shuffle operation and shortcut connection. In this way, SG-CNNs have less trainable parameters, whilst they can still be accurately and efficiently trained with fewer labeled samples. Transfer learning between different HSI datasets is also applied on the SG-CNN to further improve the classification accuracy. To evaluate the effectiveness of SG-CNNs for HSI classification, experiments have been conducted on three public HSI datasets pretrained on HSIs from different sensors. SG-CNNs with different levels of complexity were tested, and their classification results were compared with fine-tuned ShuffleNet2, ResNeXt, and their original counterparts. The experimental results demonstrate that SG-CNNs can achieve competitive classification performance when the amount of labeled data for training is poor, as well as efficiently providing satisfying classification results.

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A systematic review of text classification research based on deep learning models in Arabic language

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i6.pp6629-6643 ◽

2020 ◽

Vol 10 (6) ◽

pp. 6629

Author(s):

Ahlam Wahdan ◽

Sendeyah AL Hantoobi ◽

Said A. Salloum ◽

Khaled Shaalan

Keyword(s):

Neural Network ◽

Systematic Review ◽

Neural Networks ◽

Deep Learning ◽

Text Classification ◽

Arabic Language ◽

Machine Learning Techniques ◽

Learning Models ◽

Learning Techniques

Classifying or categorizing texts is the process by which documents are classified into groups by subject, title, author, etc. This paper undertakes a systematic review of the latest research in the field of the classification of Arabic texts. Several machine learning techniques can be used for text classification, but we have focused only on the recent trend of neural network algorithms. In this paper, the concept of classifying texts and classification processes are reviewed. Deep learning techniques in classification and its type are discussed in this paper as well. Neural networks of various types, namely, RNN, CNN, FFNN, and LSTM, are identified as the subject of study. Through systematic study, 12 research papers related to the field of the classification of Arabic texts using neural networks are obtained: for each paper the methodology for each type of neural network and the accuracy ration for each type is determined. The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. Our results provide some findings regarding how deep learning models can be used to improve text classification research in Arabic language.

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Electrocardiogram Classification Based on Deep Convolutional Neural Networks: A Review

10.54216/fpa.030103 ◽

2021 ◽

pp. 43-53

Author(s):

admin admin ◽

◽

Adnan Mohsin Abdulazeez

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Classification Accuracy ◽

Machine Learning Techniques ◽

Deep Convolutional Neural Networks ◽

Learning Techniques ◽

Good Classification ◽

Learning Architectures ◽

Ecg Data

Due to many new medical uses, the value of ECG classification is very demanding. There are some Machine Learning (ML) algorithms currently available that can be used for ECG data processing and classification. The key limitations of these ML studies, however, are the use of heuristic hand-crafted or engineered characteristics of shallow learning architectures. The difficulty lies in the probability of not having the most suitable functionality that will provide this ECG problem with good classification accuracy. One choice suggested is to use deep learning algorithms in which the first layer of CNN acts as a feature. This paper summarizes some of the key approaches of ECG classification in machine learning, assessing them in terms of the characteristics they use, the precision of classification important physiological keys ECG biomarkers derived from machine learning techniques, and statistical modeling and supported simulation.

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Fundus Image Classification Using Convolutional Neural Network

10.3233/apc210277 ◽

2021 ◽

Author(s):

U. Savitha ◽

Kodali Lahari Chandana ◽

A. Cathrin Sagayam ◽

S. Bhuvaneswari

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Learning System ◽

Machine Learning Techniques ◽

Eye Disease ◽

Clinical Use ◽

Fundus Image ◽

Learning Techniques ◽

Eye Pattern

Different eye disease has clinical use in defining of the actual status of eye, in the outcome of the medication and other alternatives in the curative phase. Mainly simplicity, clinical nature are the most important requirements for any classification system. In the existing they used different machine learning techniques to detect only single disease. Whereas deep learning system, which is named as Convolutional neural networks (CNNs) can show hierarchical representing of images between disease eye and normal eye pattern.

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Prediction of Motor Failure Time Using An Artificial Neural Network

Sensors ◽

10.3390/s19194342 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4342 ◽

Cited By ~ 6

Author(s):

Gustavo Scalabrini Sampaio ◽

Arnaldo Rabello de Aguiar Vallim Filho ◽

Leilton Santos da Silva ◽

Leandro Augusto da Silva

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Failure Time ◽

Machine Learning Techniques ◽

Network Training ◽

Model Generalization ◽

The Neural Network ◽

Learning Techniques ◽

Artificial Neural

Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this work were to propose a methodology to treat and transform the collected data from a vibration system that simulated a motor and to build a dataset to train and test an Artificial Neural Network capable of predicting the future condition of the equipment, pointing out when a failure can happen. To achieve this goal, a device model was built to simulate typical motor vibrations, consisting of a computer cooler fan and several magnets. Measurements were made using an accelerometer, and the data were collected and processed to produce a structured dataset. The neural network training with this dataset converged quickly and stably, while the tests performed, k-fold cross-validation and model generalization, presented excellent performance. The same tests were performed with other machine learning techniques, to demonstrate the effectiveness of neural networks mainly in their generalizability. The results of the work confirm that it is possible to use neural networks to perform predictive tasks in relation to the conditions of industrial equipment. This is an important area of study that helps to support the growth of smart industries.

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Validation Optimisation using Machine Learning Techniques

10.21467/preprints.100 ◽

2020 ◽

Author(s):

R Akshay Dharmapuri

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Search Engine ◽

Machine Learning Techniques ◽

Test Cases ◽

Text Search ◽

Learning Techniques ◽

Scoring Algorithms ◽

Multiple Stages

Integration and validation is the most vital part before releasing products to customers in Intel. The validation team qualifies the release based on multiple stages of validation on hardware and software stack. Bugs are raised after execution of test cases on each platform and so similar bugs arise which are filed by the user. There is a immediate concern on this and hence, many issues are closed as duplicates.The main objective is to find these similar bugs for each bug filed and thereby,debug efforts can be reused.Similar bugs are found by term based search using ElasticSearch ,a text search engine and neural network based search where context is considered.Using elasticsearch,scoring algorithms based on driver versions and platform hierarchy are applied to rank the similar bugs. LSTM neural networks are also incorporated to predict duplicate bugs by considering context of the sentence and thereby, increasing accuracy.

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Crop Quality Prediction Using Ml And Neural Networks

International Journal on Cybernetics & Informatics ◽

10.5121/ijci.2021.100202 ◽

2021 ◽

Vol 10 (02) ◽

pp. 07-11

Author(s):

Kanakaveti Narasimha Dheeraj ◽

Goutham. R. J ◽

Arthi. L

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Random Forest ◽

Machine Learning Techniques ◽

Quality Prediction ◽

Random Forest Regression ◽

Power Efficient ◽

Learning Techniques ◽

Better Than

Agriculture is said to be the backbone of the economy. Farmers toil hard with different kinds of crops to make good and healthy food for the country. There are more existing systems but uses outdated machine-learning techniques based on RNN( Recurrent neural network) which makes the process slower and more time-consuming. Here We are proposing a new CNN(Convolutional neural network ) based system which is fast and gives accurate results within seconds. CNN is power-efficient and is more suitable for real-time implementation. In this project, we use CNN algorithms which is very much better than the RNN algorithms used in the existing system.More parameters will be taken for the consideration of prediction in the proposed system. And we use Random Forest Regression, Multiple Linear Regression

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Neural Network-Based Architecture for Sentiment Analysis in Indian Languages

Journal of Intelligent Systems ◽

10.1515/jisys-2017-0398 ◽

2019 ◽

Vol 28 (3) ◽

pp. 361-375 ◽

Cited By ~ 2

Author(s):

Rupal Bhargava ◽

Shivangi Arora ◽

Yashvardhan Sharma

Keyword(s):

Neural Network ◽

Neural Networks ◽

Sentiment Analysis ◽

Short Term Memory ◽

Machine Learning Techniques ◽

Indian Languages ◽

Network Layers ◽

Learning Techniques ◽

Error Accumulation ◽

Sequential Models

Abstract Sentiment analysis refers to determining the polarity of the opinions represented by text. The paper proposes an approach to determine the sentiments of tweets in one of the Indian languages (Hindi, Bengali, and Tamil). Thirty-nine sequential models have been created using three different neural network layers [recurrent neural networks (RNNs), long short-term memory (LSTM), convolutional neural network (CNN)] with optimum parameter settings (to avoid over-fitting and error accumulation). These sequential models have been investigated for each of the three languages. The proposed sequential models are experimented to identify how the hidden layers affect the overall performance of the approach. A comparison has also been performed with existing approaches to find out if neural networks have an added advantage over traditional machine learning techniques.

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Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽

10.3390/s21010194 ◽

2020 ◽

Vol 21 (1) ◽

pp. 194

Author(s):

Sarah Gonzalez ◽

Paul Stegall ◽

Harvey Edwards ◽

Leia Stirling ◽

Ho Chit Siu

Keyword(s):

Activity Recognition ◽

Principal Components ◽

Classification Accuracy ◽

Wearable Sensors ◽

Sensor Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Techniques ◽

Measurement Units ◽

The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

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