scholarly journals Performance Evaluation of Machine Learning Frameworks for Aphasia Assessment

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2582
Author(s):  
Seedahmed S. Mahmoud ◽  
Akshay Kumar ◽  
Youcun Li ◽  
Yiting Tang ◽  
Qiang Fang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Healthy Individuals ◽  
Comparative Performance ◽  
Time Frequency ◽  
Assessment Tasks ◽  
Decision Logic ◽  
Performance Results ◽  
Learning Frameworks ◽  
Speech Assessment

Speech assessment is an essential part of the rehabilitation procedure for patients with aphasia (PWA). It is a comprehensive and time-consuming process that aims to discriminate between healthy individuals and aphasic patients, determine the type of aphasia syndrome, and determine the patients’ impairment severity levels (these are referred to here as aphasia assessment tasks). Hence, the automation of aphasia assessment tasks is essential. In this study, the performance of three automatic speech assessment models based on the speech dataset-type was investigated. Three types of datasets were used: healthy subjects’ dataset, aphasic patients’ dataset, and a combination of healthy and aphasic datasets. Two machine learning (ML)-based frameworks, classical machine learning (CML) and deep neural network (DNN), were considered in the design of the proposed speech assessment models. In this paper, the DNN-based framework was based on a convolutional neural network (CNN). Direct or indirect transformation of these models to achieve the aphasia assessment tasks was investigated. Comparative performance results for each of the speech assessment models showed that quadrature-based high-resolution time-frequency images with a CNN framework outperformed all the CML frameworks over the three dataset-types. The CNN-based framework reported an accuracy of 99.23 ± 0.003% with the healthy individuals’ dataset and 67.78 ± 0.047% with the aphasic patients’ dataset. Moreover, direct or transformed relationships between the proposed speech assessment models and the aphasia assessment tasks are attainable, given a suitable dataset-type, a reasonably sized dataset, and appropriate decision logic in the ML framework.

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 A Framework for Medical Data Analysis using Deep Learning based on Conventional Neural Network (CNN) and Variable Auto-Encoder

2019 ◽  
Vol 8 (3) ◽  
pp. 852-857
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Data Classification ◽  
Medical Data ◽  
Complex Task ◽  
Medical Data Classification ◽  
High Level ◽  
Performance Results

Medical data classification is an important and complex task. Due to the nature of data, the data is in different forms like text, numeric, images and sometimes combination of all. The goal of this paper is to provide a high-level introduction into practical machine learning for purposes of medical data classification. In this paper we use CNN-Auto encoder to extract data from the medical repository and made the classification of heterogeneous medical data. Here Auto encoder uses to get the prime features and CNN is there to extract detailed features. Combination of these two mechanisms are more suitable for medical data classification. Hybrid AE-CNN (auto encoder based Convolutional neural network). Here the performance of proposed mechanism with respect to baseline methods will be assessed. The performance results showed that the proposed mechanism performed well.

Neural Network Based Classifier Ensembles

2012 ◽  
pp. 229-239
Author(s):  
B. Verma
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Classifier Ensemble ◽  
Ensemble Classifiers ◽  
Classifier Ensembles ◽  
Comparative Performance ◽  
Rotation Forest ◽  
The Third ◽  
Benchmark Datasets ◽  
Neural Classifier

This chapter presents the state of the art in classifier ensembles and their comparative performance analysis. The main aim and focus of this chapter is to present and compare the author’s recently developed neural network based classifier ensembles. The three types of neural classifier ensembles are considered and discussed. The first type is a classifier ensemble that uses a neural network for all its base classifiers. The second type is a classifier ensemble that uses a neural network as one of the classifiers among many of its base classifiers. The third and final type is a classifier ensemble that uses a neural network as a fusion classifier. The chapter reviews recent neural network based ensemble classifiers and compares their performances with other machine learning based classifier ensembles such as bagging, boosting, and rotation forest. The comparison is conducted on selected benchmark datasets from UCI machine learning repository.

A machine learning–based approach for adaptive sparse time–frequency analysis used in structural health monitoring

2020 ◽  
Vol 19 (6) ◽  
pp. 1963-1975 ◽  
Author(s):  
Yuequan Bao ◽  
Yibing Guo ◽  
Hui Li
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Structural Health Monitoring ◽  
Frequency Analysis ◽  
Health Monitoring ◽  
Instantaneous Frequency ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Structural Health ◽  
Reconstructed Signal

Time–frequency analysis is an essential subject in nonlinear and non-stationary signal processing in structural health monitoring, which can give a clear illustration of the variation trend of time-varying parameters. Thus, it plays a significant role in structural health monitoring, such as data analysis, and nonlinear damage detection. Adaptive sparse time–frequency analysis is a recently developed method used to estimate an instantaneous frequency, which can achieve high-resolution adaptivity by looking for the sparsest time–frequency representation of the signal within the largest possible time–frequency dictionary. However, in adaptive sparse time–frequency analysis, non-convex least-square optimization is the most important and difficult part of the algorithm; therefore, in this research the powerful optimization capabilities of machine learning were employed to solve the non-convex least-square optimization and achieve the accurate estimation of the instantaneous frequency. First, the adaptive sparse time–frequency analysis was formalized into a machine-learning task. Then, a four-layer neural network was designed, the first layer of which was used for training the coefficients of the envelope of each basic functions in a linear space. The next two merge layers were used to solve the complex calculation in a neural network. Finally, the real and imaginary parts of the reconstructed signal were the outputs of the output layer. The optimal weights in this designed neural network were trained and optimized by comparing the output reconstructed signal with the target signal, and a stochastic gradient descent optimizer was used to update the weights of the network. Finally, the numerical examples and experimental examples of a cable model were employed to illustrate the ability of the proposed method. The results show that the proposed method which is called neural network–adaptive sparse time–frequency analysis can give accurate identification of the instantaneous frequency, and it has a better robustness to initial values when compared with adaptive sparse time–frequency analysis.

scholarly journals Machine Learning Model for IRIS Flower Classification using Tensor Flow and PyTorch

2021 ◽  
pp. 15-23
Author(s):  
Dr. Kalaivazhi Vijayaragavan ◽  
S. Prakathi ◽  
S. Rajalakshmi ◽  
M Sandhiya
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Classification Accuracy ◽  
Learning Model ◽  
Training Data ◽  
Data Set ◽  
Machine Learning Model ◽  
Learning Frameworks ◽  
Learning Concept ◽  
Iris Data

Machine learning is a subfield of artificial intelligence, which is learning algorithms to make decision-based on data and try to behave like a human being. Classification is one of the most fundamental concepts in machine learning. It is a process of recognizing, understanding, and grouping ideas and objects into pre-set categories or sub-populations. Using precategorized training datasets, machine learning concept use variety of algorithms to classify the future datasets into categories. Classification algorithms use input training data in machine learning to predict the subsequent data that fall into one of the predetermined categories. To improve the classification accuracy design of neural network is regarded as effective model to obtain better accuracy. However, design of neural network is usually consider scaling layer, perceptron layers and probabilistic layer. In this paper, an enhanced model selection can be evaluated with training and testing strategy. Further, the classification accuracy can be predicted. Finally by using two popular machine learning frameworks: PyTorch and Tensor Flow the prediction of classification accuracy is compared. Results demonstrate that the proposed method can predict with more accuracy. After the deployment of our machine learning model the performance of the model has been evaluated with the help of iris data set.

Physical Data Auditing for Attack Detection in Cyber-Manufacturing Systems: Blockchain for Machine Learning Process

2019 ◽  
Author(s):  
Jinwoo Song ◽  
Diksha Shukla ◽  
Mingtao Wu ◽  
Vir V. Phoha ◽  
Young B. Moon
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Learning Process ◽  
Manufacturing Systems ◽  
Detection System ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Comparative Performance ◽  
Physical Data

Abstract Auditing physical data using machine learning can enhance the security in Cyber-Manufacturing System (CMS). However, the physical data processing itself is prone to cyber-attacks. Connections based on the internet in CMS opens the route for adversaries to compromise the attack detection system itself. To prevent data from malicious data injection in CMS, this paper proposes an enhanced Simple Convolutional Neural Network (SCNN) based attack detection system employing a blockchain. There are three contributions of this paper: (i) introducing a secure attack detection system using blockchain, (ii) optimizing the cost and time for CMS by training on the simulated images, and (iii) presenting a real-time attack detection system for CMS by simplifying the convolutional neural network. The paper demonstrates the effectiveness of the blockchain implementation by presenting the comparative performance analysis of the proposed attack detection system with and without blockchain implementation using an example of a simulated attack on the machine learning process.

scholarly journals A New Method of Lithology Classification Based on Convolutional Neural Network Algorithm by Utilizing Drilling String Vibration Data

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 888 ◽  
Author(s):  
Gang Chen ◽  
Mian Chen ◽  
Guobin Hong ◽  
Yunhu Lu ◽  
Bo Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Well Logging ◽  
Low Latency ◽  
Time Frequency ◽  
Vibration Data ◽  
String Vibration ◽  
Lithology Identification ◽  
Drilling String

Formation lithology identification is of great importance for reservoir characterization and petroleum exploration. Previous methods are based on cutting logging and well-logging data and have a significant time lag. In recent years, many machine learning methods have been applied to lithology identification by utilizing well-logging data, which may be affected by drilling fluid. Drilling string vibration data is a high-density ancillary data, and it has the advantages of low-latency, which can be acquired in real-time. Drilling string vibration data is more accessible and available compared to well-logging data in ultra-deep well drilling. Machine learning algorithms enable us to develop new lithology identification models based on these vibration data. In this study, a vibration dataset is used as the signal source, and the original vibration signal is filtered by Butterworth (BHPF). Vibration time–frequency characteristics were extracted into time–frequency images with the application of short-time Fourier transform (STFT). This paper develops lithology classification models using new data sources based on a convolutional neural network (CNN) combined with Mobilenet and ResNet. This model is used for complex formation lithology, including fine gravel sandstone, fine sandstone, and mudstone. This study also carries out related model accuracy verification and model prediction results interpretation. In order to improve the trustworthiness of decision-making results, the gradient-weighted class-activated thermal localization map is applied to interpret the results of the model. The final verification test shows that the single-sample decision time of the model is 10 ms, the test macro precision rate is 90.0%, and the macro recall rate is 89.3%. The lithology identification model based on vibration data is more efficient and accessible than others. In conclusion, the CNN model using drill string vibration supplies a superior method of lithology identification. This study provides low-latency lithology classification methods to ensure safe and fast drilling.

scholarly journals Semantic Image Segmentation in Duckietown

2021 ◽  
Vol 19 (3) ◽  
pp. 26-39
Author(s):  
D. E. Shabalina ◽  
K. S. Lanchukovskaya ◽  
T. V. Liakh ◽  
K. V. Chaika
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Image Segmentation ◽  
Semantic Segmentation ◽  
Network Algorithms ◽  
Segmentation Algorithms ◽  
Classical Semantic ◽  
Learning Frameworks ◽  
Segmentation Problem

The article is devoted to evaluation of the applicability of existing semantic segmentation algorithms for the “Duckietown” simulator. The article explores classical semantic segmentation algorithms as well as ones based on neural networks. We also examined machine learning frameworks, taking into account all the limitations of the “Duckietown” simulator. According to the research results, we selected neural network algorithms based on U-Net, SegNet, DeepLab-v3, FC-DenceNet and PSPNet networks to solve the segmentation problem in the “Duckietown” project. U-Net and SegNet have been tested on the “Duckietown” simulator.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

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