scholarly journals Trajectory Planning for a Mobile Robot in a Dynamic Environment using an LSTM Neural Network

2021 ◽  
Vol 11 (22) ◽  
pp. 10689
Author(s):  
Alejandra Molina-Leal ◽  
Alfonso Gómez-Espinosa ◽  
Jesús Arturo Escobedo Cabello ◽  
Enrique Cuan-Urquizo ◽  
Sergio R Cruz-Ramírez
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Target Location ◽  
Short Term Memory ◽  
Dynamic Environment ◽  
Test Accuracy ◽  
Safe Navigation ◽  
Navigation Path ◽  
Sample Data ◽  
Dynamic Obstacle

Autonomous mobile robots are an important focus of current research due to the advantages they bring to the industry, such as performing dangerous tasks with greater precision than humans. An autonomous mobile robot must be able to generate a collision-free trajectory while avoiding static and dynamic obstacles from the specified start location to the target location. Machine learning, a sub-field of artificial intelligence, is applied to create a Long Short-Term Memory (LSTM) neural network that is implemented and executed to allow a mobile robot to find the trajectory between two points and navigate while avoiding a dynamic obstacle. The input of the network is the distance between the mobile robot and the obstacles thrown by the LiDAR sensor, the desired target location, and the mobile robot’s location with respect to the odometry reference frame. Using the model to learn the mapping between input and output in the sample data, the linear and angular velocity of the mobile robot are obtained. The mobile robot and its dynamic environment are simulated in Gazebo, which is an open-source 3D robotics simulator. Gazebo can be synchronized with ROS (Robot Operating System). The computational experiments show that the network model can plan a safe navigation path in a dynamic environment. The best test accuracy obtained was 99.24%, where the model can generalize other trajectories for which it was not specifically trained within a 15 cm radius of a trained destination position.

Local Path Planning of Mobile Robot Based on Long Short-Term Memory Neural Network

2021 ◽  
Vol 55 (1) ◽  
pp. 53-65
Author(s):  
Na Guo ◽  
Caihong Li ◽  
Di Wang ◽  
Yong Song ◽  
Guoming Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Mobile Robot ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Local Path Planning ◽  
Long Short Term Memory ◽  
Local Path

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.  

scholarly journals Enhanced Deep Learning Architectures for Face Liveness Detection for Static and Video Sequences

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1186
Author(s):  
Ranjana Koshy ◽  
Ausif Mahmood
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Anisotropic Diffusion ◽  
Short Term Memory ◽  
Total Error ◽  
Detection Accuracy ◽  
Test Accuracy ◽  
Replay Attack ◽  
Liveness Detection

Face liveness detection is a critical preprocessing step in face recognition for avoiding face spoofing attacks, where an impostor can impersonate a valid user for authentication. While considerable research has been recently done in improving the accuracy of face liveness detection, the best current approaches use a two-step process of first applying non-linear anisotropic diffusion to the incoming image and then using a deep network for final liveness decision. Such an approach is not viable for real-time face liveness detection. We develop two end-to-end real-time solutions where nonlinear anisotropic diffusion based on an additive operator splitting scheme is first applied to an incoming static image, which enhances the edges and surface texture, and preserves the boundary locations in the real image. The diffused image is then forwarded to a pre-trained Specialized Convolutional Neural Network (SCNN) and the Inception network version 4, which identify the complex and deep features for face liveness classification. We evaluate the performance of our integrated approach using the SCNN and Inception v4 on the Replay-Attack dataset and Replay-Mobile dataset. The entire architecture is created in such a manner that, once trained, the face liveness detection can be accomplished in real-time. We achieve promising results of 96.03% and 96.21% face liveness detection accuracy with the SCNN, and 94.77% and 95.53% accuracy with the Inception v4, on the Replay-Attack, and Replay-Mobile datasets, respectively. We also develop a novel deep architecture for face liveness detection on video frames that uses the diffusion of images followed by a deep Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to classify the video sequence as real or fake. Even though the use of CNN followed by LSTM is not new, combining it with diffusion (that has proven to be the best approach for single image liveness detection) is novel. Performance evaluation of our architecture on the REPLAY-ATTACK dataset gave 98.71% test accuracy and 2.77% Half Total Error Rate (HTER), and on the REPLAY-MOBILE dataset gave 95.41% accuracy and 5.28% HTER.

scholarly journals LSTM DSS Automatism and Dataset Optimization for Diabetes Prediction

2019 ◽  
Vol 9 (17) ◽  
pp. 3532 ◽  
Author(s):  
Alessandro Massaro ◽  
Vincenzo Maritati ◽  
Daniele Giannone ◽  
Daniele Convertini ◽  
Angelo Galiano
Keyword(s):  
Neural Network ◽  
Enterprise Resource Planning ◽  
Short Term Memory ◽  
Resource Planning ◽  
Training Dataset ◽  
Test Accuracy ◽  
Type I ◽  
Patient Health ◽  
Diabetes Prediction ◽  
Few Data

The paper is focused on the application of Long Short-Term Memory (LSTM) neural network enabling patient health status prediction focusing the attention on diabetes. The proposed topic is an upgrade of a Multi-Layer Perceptron (MLP) algorithm that can be fully embedded into an Enterprise Resource Planning (ERP) platform. The LSTM approach is applied for multi-attribute data processing and it is integrated into an information system based on patient management. To validate the proposed model, we have adopted a typical dataset used in the literature for data mining model testing. The study is focused on the procedure to follow for a correct LSTM data analysis by using artificial records (LSTM-AR-), improving the training dataset stability and test accuracy if compared with traditional MLP and LSTM approaches. The increase of the artificial data is important for all cases where only a few data of the training dataset are available, as for more practical cases. The paper represents a practical application about the LSTM approach into the decision support systems (DSSs) suitable for homecare assistance and for de-hospitalization processes. The paper goal is mainly to provide guidelines for the application of LSTM neural network in type I and II diabetes prediction adopting automatic procedures. A percentage improvement of test set accuracy of 6.5% has been observed by applying the LSTM-AR- approach, comparing results with up-to-date MLP works. The LSTM-AR- neural network can be applied as an alternative approach for all homecare platforms where not enough training sequential dataset is available.

scholarly journals Real-time Respiratory Tumor Motion Prediction based on Temporal Convolutional Neural Network (Preprint)

2021 ◽  
Author(s):  
Panchun Chang ◽  
Jun Dang ◽  
Jianrong Dai ◽  
Wenzheng Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Target Location ◽  
Short Term Memory ◽  
Radiation Beam ◽  
Dynamic Tracking ◽  
Time Latency ◽  
Beam Delivery ◽  
Deep Learning Model

BACKGROUND Dynamic tracking of tumor with radiation beam in radiation therapy requires prediction of real-time target location ahead of beam delivery as the treatment with beam or gating tracking brings in time latency. OBJECTIVE A deep learning model based on a temporal convolutional neural network (TCN) using multiple external makers was developed to predict internal target location through multiple external markers in this study. METHODS The respiratory signals from 69 treatment fractions of 21 cancer patients treated with the Cyberknife Synchrony device were used to train and test the model. The reported model’s performance was evaluated through comparing with a long short term memory model in terms of root-mean-square-error (RMSE) between real and predicted respiratory signals. Besides, the effect of external marker number was also investigated. RESULTS The average RMSEs (mm) for 480-ms ahead of prediction using TCN model in the superior–inferior (SI), anterior–posterior (AP) and left–right (LR) and radial directions were 0.49, 0.28, 0.25 and 0.67, respectively. CONCLUSIONS The experiment results demonstrated that the TCN respiratory prediction model could predict the respiratory signals with sub-millimeter accuracy.

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