Simulation of English translation text filtering based on machine learning and embedded system

2021 ◽  
Vol 83 ◽  
pp. 103982
Author(s):  
Sa Wang
Keyword(s):  
Machine Learning ◽  
Embedded System ◽  
English Translation ◽  
Text Filtering

Embedded System for Eye Blink Detection Using Machine Learning Technique

2021 ◽  
Author(s):  
Bishar R. Ibrahim ◽  
Farhad M. Khalifa ◽  
Subhi R. M. Zeebaree ◽  
Nashwan A. Othman ◽  
Ahmed Alkhayyat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Embedded System ◽  
Machine Learning Technique ◽  
Eye Blink ◽  
Learning Technique ◽  
Blink Detection

scholarly journals Controlling Embedded Systems Remotely via Internet-of-Things Based on Emotional Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Mohammad J. M. Zedan ◽  
Ali I. Abduljabbar ◽  
Fahad Layth Malallah ◽  
Mustafa Ghanem Saeed
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Remote Control ◽  
Embedded Systems ◽  
Embedded System ◽  
Electronic Devices ◽  
Recognition Rate ◽  
Disabled People ◽  
Research Attention ◽  
Image Preprocessing

Nowadays, much research attention is focused on human–computer interaction (HCI), specifically in terms of biosignal, which has been recently used for the remote controlling to offer benefits especially for disabled people or protecting against contagions, such as coronavirus. In this paper, a biosignal type, namely, facial emotional signal, is proposed to control electronic devices remotely via emotional vision recognition. The objective is converting only two facial emotions: a smiling or nonsmiling vision signal captured by the camera into a remote control signal. The methodology is achieved by combining machine learning (for smiling recognition) and embedded systems (for remote control IoT) fields. In terms of the smiling recognition, GENKl-4K database is exploited to train a model, which is built in the following sequenced steps: real-time video, snapshot image, preprocessing, face detection, feature extraction using HOG, and then finally SVM for the classification. The achieved recognition rate is up to 89% for the training and testing with 10-fold validation of SVM. In terms of IoT, the Arduino and MCU (Tx and Rx) nodes are exploited for transferring the resulting biosignal remotely as a server and client via the HTTP protocol. Promising experimental results are achieved by conducting experiments on 40 individuals who participated in controlling their emotional biosignals on several devices such as closing and opening a door and also turning the alarm on or off through Wi-Fi. The system implementing this research is developed in Matlab. It connects a webcam to Arduino and a MCU node as an embedded system.

scholarly journals An Evaluation and Dissemination Model for the Machine Learning Embedded System Lifecycle in Clinical Practice Settings

2021 ◽  
Author(s):  
melanie besculides ◽  
Ksenia Gorbenko ◽  
Cardinale Smith ◽  
Robert Freeman ◽  
David Reich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Practice ◽  
Embedded System ◽  
Quantitative Methods ◽  
Clinical Decision Making ◽  
Comprehensive Evaluation ◽  
Healthcare Setting ◽  
Clinical Decision ◽  
Qualitative And Quantitative Methods ◽  
Better Than

Machine learning (ML) algorithms are gaining popularity in clinical practice settings due to their ability to process information in ways that augment human reasoning. While tools that rely on output from ML algorithms in the healthcare setting are appealing for their ability to aid in clinical decision making and streamline workflows, their implementation and effectiveness are not well documented. There is an abundance of published ML literature that focuses on whether algorithms can predict an outcome or predict it better than previous algorithms, but a dearth of effort evaluating their implementation or impact on patient outcomes. While developing and validating algorithms is an important first step in research, comprehensive evaluation is needed before application of ML algorithms in new settings is considered. Evaluation should examine both the process of implementation and the outcomes using a mix of qualitative and quantitative methods. This commentary describes a model we developed to guide our institutional ML evaluation efforts.

scholarly journals Analysis of comparative performance of deep learning models for sentiment analysis

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Mirza Murtaza
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Neural Nets ◽  
Test Accuracy ◽  
Comparative Performance ◽  
Text Filtering ◽  
Percent Accuracy

Abstract Sentiment analysis of text can be performed using machine learning and natural language processing methods. However, there is no single tool or method that is effective in all cases. The objective of this research project is to determine the effectiveness of neural network-based architecture to perform sentiment analysis of customer comments and reviews, such as the ones on Amazon site. A typical sentiment analysis process involves text preparation (of acquired content), sentiment detection, sentiment classification and analysis of results. In this research, the objective is to a) identify the best approach for text preparation in a given application (text filtering approach to remove errors in data), and, most importantly, b) what is the best machine learning (feed forward neural nets, convolutional neural nets, Long Short-Term Memory networks) approach that provides best classification accuracy. In this research, a set of three thousand two hundred reviews of food related products were used to train and experiment with a neural network-based sentiment analysis system. The neural network implementation of six different models provided close to one-hundred percent accuracy of test data, and a decent test accuracy in mid-80%. The results of the research would be useful to businesses in evaluating customer preferences for products or services.  

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