scholarly journals Surface Motion Prediction and Mapping for Road Infrastructures Management by PS-InSAR Measurements and Machine Learning Algorithms

2020 ◽  
Vol 12 (23) ◽  
pp. 3976
Author(s):  
Nicholas Fiorentini ◽  
Mehdi Maboudi ◽  
Pietro Leandri ◽  
Massimo Losa ◽  
Markus Gerke
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Central Italy ◽  
Machine Learning Algorithms ◽  
Boosted Regression Trees ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Surface Motion ◽  
Persistent Scatterers ◽  
Taylor Diagram

This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) measurements, geospatial analyses, and Machine Learning Algorithms (MLAs) are employed for achieving the purpose. Two single learners, i.e., Regression Tree (RT) and Support Vector Machine (SVM), and two ensemble learners, i.e., Boosted Regression Trees (BRT) and Random Forest (RF) are utilized for estimating the surface motion ratio in terms of mm/year over the Province of Pistoia (Tuscany Region, central Italy, 964 km2), in which strong subsidence phenomena have occurred. The interferometric process of 210 Sentinel-1 images from 2014 to 2019 allows exploiting the average displacements of 52,257 Persistent Scatterers as output targets to predict. A set of 29 environmental-related factors are preprocessed by SAGA-GIS, version 2.3.2, and ESRI ArcGIS, version 10.5, and employed as input features. Once the dataset has been prepared, three wrapper feature selection approaches (backward, forward, and bi-directional) are used for recognizing the set of most relevant features to be used in the modeling. A random splitting of the dataset in 70% and 30% is implemented to identify the training and test set. Through a Bayesian Optimization Algorithm (BOA) and a 10-Fold Cross-Validation (CV), the algorithms are trained and validated. Therefore, the Predictive Performance of MLAs is evaluated and compared by plotting the Taylor Diagram. Outcomes show that SVM and BRT are the most suitable algorithms; in the test phase, BRT has the highest Correlation Coefficient (0.96) and the lowest Root Mean Square Error (0.44 mm/year), while the SVM has the lowest difference between the standard deviation of its predictions (2.05 mm/year) and that of the reference samples (2.09 mm/year). Finally, algorithms are used for mapping surface motion over the study area. We propose three case studies on critical stretches of two-lane rural roads for evaluating the reliability of the procedure. Road authorities could consider the proposed methodology for their monitoring, management, and planning activities.

Land degradation risk mapping using novel machine learning algorithms

2020 ◽  
Author(s):  
Ali Torabi Haghighi ◽  
Hamid Darabi ◽  
Zahra Karimidastenaei ◽  
Ali Akbar Davudirad ◽  
Sajad Rouzbeh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Degradation ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Risk Mapping ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Kappa Index ◽  
Dragonfly Algorithm ◽  
Taylor Diagram

<p>Land degradation (LD) is a complex process affected by both anthropogenic and natural driving variables, and monitoring LD progression in areas under human‐induced stresses has become an essential task. In this study, we developed an approach for evaluating and mapping potential LD risks associated with human-induced and biophysical driving variables. We employed machine learning algorithms (Support Vector Machine (SVM), Multivariate Adaptive Regression Splines (MARS), Generalized Linear Model (GLM), and Dragonfly Algorithm (DA)) for LD risk mapping based on topographic (n=7), human-induced (n=5) and geo-environmental (n=6) variables and field measurements of degradation. The performance of different algorithms was assessed using receiver operating characteristic (ROC), Kappa index, and Taylor diagram. An urbanized watershed, Pole-doab in central Iran, was selected as the case study. The performance data indicated that DA (an novel optimized algorithm) was most accurate in LD risk mapping. In LD zone maps produced using SVM, GLM, MARS, and DA, 19.16%, 19.29%, 21.76%, and 22.40%, respectively, of total area in the Pole-doab watershed had a very high degradation risk. In all cases, the LD risk maps indicated that land in the southern part of the Pole-doab watershed is most exposed to degradation of different types.</p>

scholarly journals Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran

2020 ◽  
Vol 80 (1) ◽  
Author(s):  
Ali Torabi Haghighi ◽  
Hamid Darabi ◽  
Zahra Karimidastenaei ◽  
Ali Akbar Davudirad ◽  
Sajad Rouzbeh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Degradation ◽  
Environmental Variables ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Risk Mapping ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Taylor Diagram ◽  
Static Conditions

AbstractLand degradation (LD) is a complex process affected by both anthropogenic and natural driving variables, and its prevention has become an essential task globally. The aim of the present study was to develop a new quantitative LD mapping approach using machine learning techniques, benchmark models, and human-induced and socio-environmental variables. We employed four machine learning algorithms [Support Vector Machine (SVM), Multivariate Adaptive Regression Splines (MARS), Generalized Linear Model (GLM), and Dragonfly Algorithm (DA)] for LD risk mapping, based on topographic (n = 7), human-induced (n = 5), and geo-environmental (n = 6) variables, and field measurements of degradation in the Pole-Doab watershed, Iran. We assessed the performance of different algorithms using receiver operating characteristic, Kappa index, and Taylor diagram. The results revealed that the main topographic, geoenvironmental, and human-induced variable was slope, geology, and land use change, respectively. Assessments of model performance indicated that DA had the highest accuracy and efficiency, with the greatest learning and prediction power in LD risk mapping. In LD risk maps produced using SVM, GLM, MARS, and DA, 19.16%, 19.29%, 21.76%, and 22.40%, respectively, of total area in the Pole-Doab watershed had a very high degradation risk. The results of this study demonstrate that in LD risk mapping for a region, topographic, and geological factors (static conditions) and human activities (dynamic conditions, e.g., residential and industrial area expansion) should be considered together, for best protection at watershed scale. These findings can help policymakers prioritize land and water conservation efforts.

scholarly journals Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis

Informatics ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 79
Author(s):  
Enas Elgeldawi ◽  
Awny Sayed ◽  
Ahmed R. Galal ◽  
Alaa M. Zaki
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Learning Models ◽  
Before And After ◽  
Tuning Process ◽  
Machine Learning Models

Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a comprehensive comparative analysis of various hyperparameter tuning techniques is performed; these are Grid Search, Random Search, Bayesian Optimization, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). They are used to optimize the accuracy of six machine learning algorithms, namely, Logistic Regression (LR), Ridge Classifier (RC), Support Vector Machine Classifier (SVC), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB) classifiers. To test the performance of each hyperparameter tuning technique, the machine learning models are used to solve an Arabic sentiment classification problem. Sentiment analysis is the process of detecting whether a text carries a positive, negative, or neutral sentiment. However, extracting such sentiment from a complex derivational morphology language such as Arabic has been always very challenging. The performance of all classifiers is tested using our constructed dataset both before and after the hyperparameter tuning process. A detailed analysis is described, along with the strengths and limitations of each hyperparameter tuning technique. The results show that the highest accuracy was given by SVC both before and after the hyperparameter tuning process, with a score of 95.6208 obtained when using Bayesian Optimization.

scholarly journals Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4324
Author(s):  
Moaed A. Abd ◽  
Rudy Paul ◽  
Aparna Aravelli ◽  
Ou Bai ◽  
Leonel Lagos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sliding Contact ◽  
Tactile Sensation ◽  
Support Vector ◽  
Textured Surfaces ◽  
K Nearest Neighbor ◽  
Tactile Sensors ◽  
Time Frequency

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.

Machine Learning Models for Finger Bend Evaluation using Implemented Low cost Flex Sensor

2021 ◽  
Vol 9 (VI) ◽  
pp. 3605-3611
Author(s):  
Pratyush Kaware
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Low Cost ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

In this paper a cost-effective sensor has been implemented to read finger bend signals, by attaching the sensor to a finger, so as to classify them based on the degree of bent as well as the joint about which the finger was being bent. This was done by testing with various machine learning algorithms to get the most accurate and consistent classifier. Finally, we found that Support Vector Machine was the best algorithm suited to classify our data, using we were able predict live state of a finger, i.e., the degree of bent and the joints involved. The live voltage values from the sensor were transmitted using a NodeMCU micro-controller which were converted to digital and uploaded on a database for analysis.

scholarly journals Heart disease prediction using machine learning techniques : a survey

2018 ◽  
Vol 7 (2.8) ◽  
pp. 684 ◽  
Author(s):  
V V. Ramalingam ◽  
Ayantan Dandapath ◽  
M Karthik Raja
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Learning Techniques ◽  
Vector Machines ◽  
Supervised Learning Algorithms ◽  
Life Threatening

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

scholarly journals Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3532 ◽  
Author(s):  
Nicola Mansbridge ◽  
Jurgen Mitsch ◽  
Nicola Bollard ◽  
Keith Ellis ◽  
Giuliana Miguel-Pacheco ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Time Budget ◽  
Learning Algorithms ◽  
Eating Behaviour ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Optimum Number ◽  
Eating Behaviours ◽  
Adaptive Boosting

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

scholarly journals A Comparative Analysis of Machine Learning Algorithms Modeled from Machine Vision-Based Lettuce Growth Stage Classification in Smart Aquaponics

2020 ◽  
Vol 11 (9) ◽  
pp. 442-449 ◽  
Author(s):  
Sandy C. Lauguico ◽  
◽  
Ronnie S. Concepcion II ◽  
Jonnel D. Alejandrino ◽  
Rogelio Ruzcko Tobias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Machine Vision ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Urban Farming ◽  
K Nearest Neighbor ◽  
Lettuce Growth

The arising problem on food scarcity drives the innovation of urban farming. One of the methods in urban farming is the smart aquaponics. However, for a smart aquaponics to yield crops successfully, it needs intensive monitoring, control, and automation. An efficient way of implementing this is the utilization of vision systems and machine learning algorithms to optimize the capabilities of the farming technique. To realize this, a comparative analysis of three machine learning estimators: Logistic Regression (LR), K-Nearest Neighbor (KNN), and Linear Support Vector Machine (L-SVM) was conducted. This was done by modeling each algorithm from the machine vision-feature extracted images of lettuce which were raised in a smart aquaponics setup. Each of the model was optimized to increase cross and hold-out validations. The results showed that KNN having the tuned hyperparameters of n_neighbors=24, weights='distance', algorithm='auto', leaf_size = 10 was the most effective model for the given dataset, yielding a cross-validation mean accuracy of 87.06% and a classification accuracy of 91.67%.

scholarly journals Cyber Bullying Detection for Twitter Using ML Classification Algorithms

2021 ◽  
Vol 9 (11) ◽  
pp. 24-29
Author(s):  
Muskan Patidar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Natural Language ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Cyber Bullying ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms

Abstract: Social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. Cyberbullying refers to the use of technology to humiliate and slander other people. It takes form of hate messages sent through social media and emails. With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. We have tried to propose a possible solution for the above problem, our project aims to detect cyberbullying in tweets using ML Classification algorithms like Naïve Bayes, KNN, Decision Tree, Random Forest, Support Vector etc. and also we will apply the NLTK (Natural language toolkit) which consist of bigram, trigram, n-gram and unigram on Naïve Bayes to check its accuracy. Finally, we will compare the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection. Keywords: Cyber bullying, Machine Learning Algorithms, Twitter, Natural Language Toolkit

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