scholarly journals Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 81 ◽  
Author(s):  
Filippo Giammaria Praticò ◽  
Rosario Fedele ◽  
Vitalii Naumov ◽  
Tomas Sauer
Keyword(s):  
Machine Learning ◽  
Surface Defects ◽  
Great Accuracy ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Bottom Up ◽  
Road Pavement ◽  
Acoustic Signature ◽  
Machine Learning Approach ◽  
Set Up

The current methods that aim at monitoring the structural health status (SHS) of road pavements allow detecting surface defects and failures. This notwithstanding, there is a lack of methods and systems that are able to identify concealed cracks (particularly, bottom-up cracks) and monitor their growth over time. For this reason, the objective of this study is to set up a supervised machine learning (ML)-based method for the identification and classification of the SHS of a differently cracked road pavement based on its vibro-acoustic signature. The method aims at collecting these signatures (using acoustic-sensors, located at the roadside) and classifying the pavement’s SHS through ML models. Different ML classifiers (i.e., multilayer perceptron, MLP, convolutional neural network, CNN, random forest classifier, RFC, and support vector classifier, SVC) were used and compared. Results show the possibility of associating with great accuracy (i.e., MLP = 91.8%, CNN = 95.6%, RFC = 91.0%, and SVC = 99.1%) a specific vibro-acoustic signature to a differently cracked road pavement. These results are encouraging and represent the bases for the application of the proposed method in real contexts, such as monitoring roads and bridges using wireless sensor networks, which is the target of future studies.

scholarly journals Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal

2020 ◽  
Vol 10 (16) ◽  
pp. 5673 ◽  
Author(s):  
Daniela Cardone ◽  
David Perpetuini ◽  
Chiara Filippini ◽  
Edoardo Spadolini ◽  
Lorenza Mancini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stress State ◽  
Thermal Imaging ◽  
Driving Simulator ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Thermal Features ◽  
Driver Stress

Traffic accidents determine a large number of injuries, sometimes fatal, every year. Among other factors affecting a driver’s performance, an important role is played by stress which can decrease decision-making capabilities and situational awareness. In this perspective, it would be beneficial to develop a non-invasive driver stress monitoring system able to recognize the driver’s altered state. In this study, a contactless procedure for drivers’ stress state assessment by means of thermal infrared imaging was investigated. Thermal imaging was acquired during an experiment on a driving simulator, and thermal features of stress were investigated with comparison to a gold-standard metric (i.e., the stress index, SI) extracted from contact electrocardiography (ECG). A data-driven multivariate machine learning approach based on a non-linear support vector regression (SVR) was employed to estimate the SI through thermal features extracted from facial regions of interest (i.e., nose tip, nostrils, glabella). The predicted SI showed a good correlation with the real SI (r = 0.61, p = ~0). A two-level classification of the stress state (STRESS, SI ≥ 150, versus NO STRESS, SI < 150) was then performed based on the predicted SI. The ROC analysis showed a good classification performance with an AUC of 0.80, a sensitivity of 77%, and a specificity of 78%.

scholarly journals Twitter Sentiment Analysis Using Different Machine Learning and Feature Extraction Techniques

2021 ◽  
Vol 24 (3) ◽  
pp. 50-54
Author(s):  
Mohammad W.Habib ◽  
◽  
Zainab N. Sultani ◽  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Logistic Regression ◽  
Sentiment Analysis ◽  
Extraction Methods ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Extraction Techniques ◽  
Machine Learning Approach ◽  
Negative Sentiment

Twitter is considered a significant source of exchanging information and opinion in today's business. Analysis of this data is critical and complex due to the size of the dataset. Sentiment Analysis is adopted to understand and analyze the sentiment of such data. In this paper, a Machine learning approach is employed for analyzing the data into positive or negative sentiment (opinion). Different arrangements of preprocessing techniques are applied to clean the tweets, and various feature extraction methods are used to extract and reduce the dimension of the tweets' feature vector. Sentiment140 dataset is used, and it consists of sentiment labels and tweets, so supervised machine learning models are used, specifically Logistic Regression, Naive Bayes, and Support Vector Machine. According to the experimental results, Logistic Regression was the best amongst other models with all feature extraction techniques.

A minimally-occlusive cuff method utilizing ultrasound vascular imaging for stress-free blood pressure measurement

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
K Uemura ◽  
T Nishikawa ◽  
T Kawada ◽  
M Sugimachi
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Approach ◽  
Funding Source ◽  
Cuff Inflation ◽  
Time Calibration ◽  
Machine Learning Approach ◽  
Occlusive Cuff

Abstract Objective Occlusive cuff inflation in ambulatory blood pressure (BP) monitoring disturbs the daily life of the user, and affects efficacy of monitoring. To overcome this limitation, we have developed a novel minimally-occlusive cuff method for stress-free measurement of BP. This study aimed to experimentally evaluate the reliability of this method, and improve the precision of this method by implementing a machine learning algorithm. Methods In this method, a thin-plate-type ultrasound probe (Size: 5.6mm-thickness × 28mm × 26mm; weight: 10g) is placed between the cuff and the skin, and used to measure the ultrasonic dimension of the artery (Figure 1). The cuff pressure (Pc), arterial dimension at systole (Ds) and diastole (Dd), systolic BP (SBP) and diastolic BP (DBP) during cuff inflation are theoretically related by the following equations, SBP-Pc = P0·Exp[α·Ds] DBP-Pc = P0·Exp[α·Dd] Where P0 and α are constants, and α indicates arterial stiffness. Since multiple sets of the two equations can be defined over multiple cardiac beats while measuring Pc, Ds and Dd during mild cuff inflation (Pc is controlled less than 50 mmHg, Figure 1), it is possible to estimate SBP (SBPe) and DBP (DBPe) as solutions of the equations. In 6 anesthetized dogs, we attached the cuff and the probe to the right thigh to get SBPe and DBPe, which were one-time calibrated in each animal against reference SBP and DBP measured by using an intra-arterial catheter. We also determined the pulse arrival time (PAT), which is a commonly employed parameter in cuff-less BP monitoring. In all the dogs, BP was changed extensively by infusing noradrenaline or sodium nitroprusside. Results DBPe correlated tightly with DBP with a coefficient of determination (R2) of 0.85±0.08, and predicted DBP with error of 3.9±7.9 mmHg after one-time calibration (Figure 2). PAT correlated poorly with DBP (R2=0.49±0.17), and predicted DBP less accurately than this method. SBPe correlated well with SBP (R2=0.78±0.08) (Figure 3). However, even after one-time calibration, difference between SBPe and SBP was 2.6±18.9 mmHg, which was not acceptable. To improve the precision in SBP prediction, we used supervised machine learning approach with use of a support vector algorithm (Python, Scikit-learn), which regressed feature variables (SBPe, DBPe, Ds, Dd heart rate, and PAT) against teacher signal (reference SBP). The support vector algorithm, once trained, predicted SBP with acceptable accuracy with error of 0.7±6.9 mmHg (Figure 3). Conclusions This method reliably tracks BP changes without occlusive cuff inflation. Once calibrated, this method measures DBP accurately. With the aid of machine learning, precision in SBP prediction was greatly improved to an acceptable level. This method with machine learning approach has potential for stress-free BP measurement in ambulatory BP monitoring. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Japan Society for the Promotion of Science

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

scholarly journals Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3827
Author(s):  
Gemma Urbanos ◽  
Alberto Martín ◽  
Guillermo Vázquez ◽  
Marta Villanueva ◽  
Manuel Villa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Vessel ◽  
Hyperspectral Imaging ◽  
Imaging Techniques ◽  
Venous Blood ◽  
Healthy Tissue ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Arterial Blood

Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.

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