scholarly journals Voice Disorder Detection via an m-Health System: Design and Results of a Clinical Study to Evaluate Vox4Health

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Ugo Cesari ◽  
Giuseppe De Pietro ◽  
Elio Marciano ◽  
Ciro Niri ◽  
Giovanna Sannino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Practice ◽  
Clinical Study ◽  
Health System ◽  
Acoustic Analysis ◽  
Machine Learning Techniques ◽  
Voice Disorder ◽  
Rule Based ◽  
Learning Techniques ◽  
The Voice

Objectives. The current study presents a clinical evaluation of Vox4Health, an m-health system able to estimate the possible presence of a voice disorder by calculating and analyzing the main acoustic measures required for the acoustic analysis, namely, the Fundamental Frequency, jitter, shimmer, and Harmonic to Noise Ratio. The acoustic analysis is an objective, effective, and noninvasive tool used in clinical practice to perform a quantitative evaluation of voice quality. Materials and Methods. A clinical study was carried out in collaboration with medical staff of the University of Naples Federico II. 208 volunteers were recruited (mean age, 44.2 ± 13.9 years), 58 healthy subjects (mean age, 36.7 ± 13.3 years) and 150 pathological ones (mean age, 47 ± 13.1 years). The evaluation of Vox4Health was made in terms of classification performance, i.e., sensitivity, specificity, and accuracy, by using a rule-based algorithm that considers the most characteristic acoustic parameters to classify if the voice is healthy or pathological. The performance has been compared with that achieved by using Praat, one of the most commonly used tools in clinical practice. Results. Using a rule-based algorithm, the best accuracy in the detection of voice disorders, 72.6%, was obtained by using the jitter or shimmer value. Moreover, the best sensitivity is about 96% and it was always obtained by using jitter. Finally, the best specificity was achieved by using the Fundamental Frequency and it is equal to 56.9%. Additionally, in order to improve the classification accuracy of the next version of the Vox4Health app, an evaluation by using machine learning techniques was conducted. We performed some preliminary tests adopting different machine learning techniques able to classify the voice as healthy or pathological. The best accuracy (77.4%) was obtained by the Logistic Model Tree algorithm, while the best sensitivity (99.3%) was achieved using the Support Vector Machine. Finally, Instance-based Learning performed the best specificity (36.2%). Conclusions. Considering the achieved accuracy, Vox4Health has been considered by the medical experts as a “good screening tool” for the detection of voice disorders in its current version. However, this accuracy is improved when machine learning classifiers are considered rather than the rule-based algorithm.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

scholarly journals Applying Artificial Intelligence for Operating System Fingerprinting

2021 ◽  
Vol 7 (1) ◽  
pp. 51
Author(s):  
Rubén Pérez-Jove ◽  
Cristian R. Munteanu ◽  
Alejandro Pazos Sierra ◽  
José M. Vázquez-Naya
Keyword(s):  
Machine Learning ◽  
Operating System ◽  
Computer Security ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Reference Database ◽  
Penetration Test ◽  
Rule Based ◽  
Learning Techniques ◽  
Rule Based Approach

In the field of computer security, the possibility of knowing which specific version of an operating system is running behind a machine can be useful, to assist in a penetration test or monitor the devices connected to a specific network. One of the most widespread tools that better provides this functionality is Nmap, which follows a rule-based approach for this process. In this context, applying machine learning techniques seems to be a good option for addressing this task. The present work explores the strengths of different machine learning algorithms to perform operating system fingerprinting, using for that, the Nmap reference database. Moreover, some optimizations were applied to the method which brought the best results, random forest, obtaining an accuracy higher than 96%.

Corporate Sector Fraud

2021 ◽  
pp. 16-31
Author(s):  
Jay Prakash Maurya ◽  
Deepak Rathore ◽  
Sunil Joshi ◽  
Manish Manoria ◽  
Vivek Richhariya
Keyword(s):  
Machine Learning ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Corporate Fraud ◽  
Rule Based ◽  
Detection Systems ◽  
User Behaviors ◽  
Learning Techniques ◽  
Modern Era

This chapter aims to possess a review of machine learning techniques for detection of corporate fraud in modern era. Detecting company frauds using traditional procedures is time costly as immense volume of information must be analysed. Thus, further analytical procedures should be used. Machine learning techniques are most emerging topic with great importance in field of information learning and prediction. The machine learning (ML) approach to fraud detection has received a lot of promotion in recent years and shifted business interest from rule-based fraud detection systems to ML-based solutions. Machine learning permits for making algorithms that process giant data-sets with several variables and facilitate realize these hidden correlations between user behaviors and also the probability of fallacious actions. Strength of machine learning systems compared to rule-based ones is quicker processing and less manual work. The chapter aims at machine-driven analysis of knowledge reports exploitation machine learning paradigm to spot fraudulent companies.

scholarly journals Hammering Acoustic Analysis Using Machine Learning Techniques for Piping Inspection

2020 ◽  
Vol 32 (4) ◽  
pp. 789-797
Author(s):  
Kou Ikeda ◽  
◽  
Akiya Kamimura
Keyword(s):  
Machine Learning ◽  
Acoustic Analysis ◽  
Low Cost ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Industrial Plants ◽  
Inspection Robot ◽  
Learning Techniques ◽  
Variational Autoencoder ◽  
Corrosion Under Insulation

In Japan, the deterioration of industrial plants built during the period of high economic growth in the middle of the 20th century has recently become a social concern. Corrosion under insulation (CUI) of piping in such plants is a pressing problem. X-ray and ultrasound inspections are conventional methods for detecting CUI; however, these methods are time-consuming and expensive. Therefore, rapid and low-cost screening techniques for CUI are required. We develop a hammering-type inspection robot system that moves inside the piping and records hammering sounds. Furthermore, we propose an acoustic analysis method to identify anomalous parts from the hammering sound using machine learning techniques. Using three testing pipes, we can successfully identify anomalous parts through acoustic analysis using a deep neural network as a supervised learning method. However, in practical piping inspections, the detection of anomalies without training data is required for further applications. Therefore, we investigate unsupervised learning anomaly detection using an autoencoder and a variational autoencoder and report the results.

scholarly journals Voice Disorder Identification by Using Machine Learning Techniques

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 16246-16255 ◽  
Author(s):  
Laura Verde ◽  
Giuseppe De Pietro ◽  
Giovanna Sannino
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Voice Disorder ◽  
Learning Techniques ◽  
Disorder Identification
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