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 Sensory Descriptor Analysis of Whisky Lexicons through the Use of Deep Learning

Foods ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1633
Author(s):  
Chreston Miller ◽  
Leah Hamilton ◽  
Jacob Lahne
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Descriptive Analysis ◽  
Food Products ◽  
Training Dataset ◽  
Test Accuracy ◽  
Language Structure ◽  
Deep Learning Model ◽  
Descriptor Analysis ◽  
Descriptive Language

This paper is concerned with extracting relevant terms from a text corpus on whisk(e)y. “Relevant” terms are usually contextually defined in their domain of use. Arguably, every domain has a specialized vocabulary used for describing things. For example, the field of Sensory Science, a sub-field of Food Science, investigates human responses to food products and differentiates “descriptive” terms for flavors from “ordinary”, non-descriptive language. Within the field, descriptors are generated through Descriptive Analysis, a method wherein a human panel of experts tastes multiple food products and defines descriptors. This process is both time-consuming and expensive. However, one could leverage existing data to identify and build a flavor language automatically. For example, there are thousands of professional and semi-professional reviews of whisk(e)y published on the internet, providing abundant descriptors interspersed with non-descriptive language. The aim, then, is to be able to automatically identify descriptive terms in unstructured reviews for later use in product flavor characterization. We created two systems to perform this task. The first is an interactive visual tool that can be used to tag examples of descriptive terms from thousands of whisky reviews. This creates a training dataset that we use to perform transfer learning using GloVe word embeddings and a Long Short-Term Memory deep learning model architecture. The result is a model that can accurately identify descriptors within a corpus of whisky review texts with a train/test accuracy of 99% and precision, recall, and F1-scores of 0.99. We tested for overfitting by comparing the training and validation loss for divergence. Our results show that the language structure for descriptive terms can be programmatically learned.

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 Fast Wearable Sensor–Based Foot–Ground Contact Phase Classification Using a Convolutional Neural Network with Sliding-Window Label Overlapping

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4996 ◽  
Author(s):  
Haneul Jeon ◽  
Sang Lae Kim ◽  
Soyeon Kim ◽  
Donghun Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Lower Limb ◽  
Sliding Window ◽  
Swing Phase ◽  
Training Dataset ◽  
Test Accuracy ◽  
Ground Contact ◽  
Contact Phase ◽  
Walking Gait

Classification of foot–ground contact phases, as well as the swing phase is essential in biomechanics domains where lower-limb motion analysis is required; this analysis is used for lower-limb rehabilitation, walking gait analysis and improvement, and exoskeleton motion capture. In this study, sliding-window label overlapping of time-series wearable motion data in training dataset acquisition is proposed to accurately detect foot–ground contact phases, which are composed of 3 sub-phases as well as the swing phase, at a frequency of 100 Hz with a convolutional neural network (CNN) architecture. We not only succeeded in developing a real-time CNN model for learning and obtaining a test accuracy of 99.8% or higher, but also confirmed that its validation accuracy was close to 85%.

An Expert System for Predicting ERP Post-Implementation Benefits Using Artificial Neural Network

2018 ◽  
pp. 760-784
Author(s):  
Ahad Zare Ravasan ◽  
Saeed Rouhani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Enterprise Resource Planning ◽  
Resource Planning ◽  
Good Prediction ◽  
Industry Type ◽  
Artificial Neural ◽  
Expected Benefits ◽  
Artificial Neural Network Ann

Implementing Enterprise Resource Planning systems (ERPs) is a complex and costly project which usually delivers only a few of expected benefits. Obtaining the expected benefits of ERPs is impressed by a variety of factors and variables which is related to an organization or project environment. In this paper, the idea of predicting ERP post-implementation benefits based on the organizational profiles and factors has been discussed. Regarding the need to form the expectations of organizations about ERP, an expert system is developed by using Artificial Neural Network (ANN) method to articulate the relationships between some organizational factors and ERP's achieved benefits. The expert system's role is in the preparation to capture the data from the new enterprises wishes to implement ERP and predict likely benefits might be achieved from the system. For this end, factors of organizational profiles (such as industry type, size, structure, and so on) are recognized and a feed-forward architecture and Levenberg-Marquardt (trainlm) neural network model is designed, trained and validated with 171 surveyed data of Middle-East located enterprises experienced ERP. The trained ANN embedded in developed expert system predicts with the average correlation coefficients of 0.745, which is respectively high and proves the idea of dependency of ERP post-implementation benefits on the organizational profiles. Besides, total correct classification rate of 0.701 shows good prediction power which can help firms in predicting ERP benefits before system implementation.

An Expert System for Predicting ERP Post-Implementation Benefits Using Artificial Neural Network

2014 ◽  
Vol 10 (3) ◽  
pp. 24-45 ◽  
Author(s):  
Ahad Zare Ravasan ◽  
Saeed Rouhani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Enterprise Resource Planning ◽  
Size Structure ◽  
Organizational Factors ◽  
Resource Planning ◽  
Good Prediction ◽  
Artificial Neural ◽  
Expected Benefits

Implementing Enterprise Resource Planning systems (ERPs) is a complex and costly project which usually delivers only a few of expected benefits. Obtaining the expected benefits of ERPs is impressed by a variety of factors and variables which is related to an organization or project environment. In this paper, the idea of predicting ERP post-implementation benefits based on the organizational profiles and factors has been discussed. Regarding the need to form the expectations of organizations about ERP, an expert system is developed by using Artificial Neural Network (ANN) method to articulate the relationships between some organizational factors and ERP's achieved benefits. The expert system's role is in the preparation to capture the data from the new enterprises wishes to implement ERP and predict likely benefits might be achieved from the system. For this end, factors of organizational profiles (such as industry type, size, structure, and so on) are recognized and a feed-forward architecture and Levenberg-Marquardt (trainlm) neural network model is designed, trained and validated with 171 surveyed data of Middle-East located enterprises experienced ERP. The trained ANN embedded in developed expert system predicts with the average correlation coefficients of 0.745, which is respectively high and proves the idea of dependency of ERP post-implementation benefits on the organizational profiles. Besides, total correct classification rate of 0.701 shows good prediction power which can help firms in predicting ERP benefits before system implementation.

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.

scholarly journals ChemPix: Automated Recognition of Hand-drawn Hydrocarbon Structures Using Deep Learning

2021 ◽  
Author(s):  
Hayley Weir ◽  
Keiran Thompson ◽  
Ben Choi ◽  
Amelia Woodward ◽  
Augustin Braun ◽  
...  
Keyword(s):  
Neural Network ◽  
Web Application ◽  
Short Term Memory ◽  
Recognition Accuracy ◽  
Training Dataset ◽  
Automated Recognition ◽  
Chemical Structures ◽  
Promising Tool ◽  
Domain Expertise ◽  
Molecule Recognition

<p>Inputting molecules into chemistry software, such as quantum chemistry packages, currently requires domain expertise, expensive software and/or cumbersome procedures. Leveraging recent breakthroughs in machine learning, we develop ChemPix: an offline, hand-drawn hydrocarbon structure recognition tool designed to remove these barriers. A neural image captioning approach consisting of a convolutional neural network (CNN) encoder and a long short-term memory (LSTM) decoder learned a mapping from photographs of hand-drawn hydrocarbon structures to machine-readable SMILES representations. We generated a large auxiliary training dataset, based on RDKit molecular images, by combining image augmentation, image degradation and background addition. Additionally, a small dataset of ~600 hand-drawn hydrocarbon chemical structures was crowd-sourced using a phone web application. These datasets were used to train the image-to-SMILES neural network with the goal of maximizing the hand-drawn hydrocarbon recognition accuracy. By forming a committee of the trained neural networks, we achieved a nearly 10 percentage point improvement of the molecule recognition accuracy and were able to assign a confidence value for the prediction based on the number of agreeing votes. The top ensemble model achieved a hand-drawn hydrocarbon recognition accuracy of 77% for the first prediction and 86% if the top 3 predictions were considered; in over 50% of cases, the model was at least 97% confident in the prediction, making it a promising tool for real-world use cases.</p>

scholarly journals ChemPix: Automated Recognition of Hand-drawn Hydrocarbon Structures Using Deep Learning

2021 ◽  
Author(s):  
Hayley Weir ◽  
Keiran Thompson ◽  
Ben Choi ◽  
Amelia Woodward ◽  
Augustin Braun ◽  
...  
Keyword(s):  
Neural Network ◽  
Web Application ◽  
Short Term Memory ◽  
Recognition Accuracy ◽  
Training Dataset ◽  
Automated Recognition ◽  
Chemical Structures ◽  
Promising Tool ◽  
Domain Expertise ◽  
Molecule Recognition

<p>Inputting molecules into chemistry software, such as quantum chemistry packages, currently requires domain expertise, expensive software and/or cumbersome procedures. Leveraging recent breakthroughs in machine learning, we develop ChemPix: an offline, hand-drawn hydrocarbon structure recognition tool designed to remove these barriers. A neural image captioning approach consisting of a convolutional neural network (CNN) encoder and a long short-term memory (LSTM) decoder learned a mapping from photographs of hand-drawn hydrocarbon structures to machine-readable SMILES representations. We generated a large auxiliary training dataset, based on RDKit molecular images, by combining image augmentation, image degradation and background addition. Additionally, a small dataset of ~600 hand-drawn hydrocarbon chemical structures was crowd-sourced using a phone web application. These datasets were used to train the image-to-SMILES neural network with the goal of maximizing the hand-drawn hydrocarbon recognition accuracy. By forming a committee of the trained neural networks, we achieved a nearly 10 percentage point improvement of the molecule recognition accuracy and were able to assign a confidence value for the prediction based on the number of agreeing votes. The top ensemble model achieved a hand-drawn hydrocarbon recognition accuracy of 77% for the first prediction and 86% if the top 3 predictions were considered; in over 50% of cases, the model was at least 97% confident in the prediction, making it a promising tool for real-world use cases.</p>

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