scholarly journals Air Entrainment in Drop Shafts: A Novel Approach Based on Machine Learning Algorithms and Hybrid Models

Fluids ◽  
2022 ◽  
Vol 7 (1) ◽  
pp. 20
Author(s):  
Francesco Granata ◽  
Fabio Di Nunno
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Air Entrainment ◽  
Hybrid Models ◽  
Operating Conditions ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Functional Relationships ◽  
Drop Shaft ◽  
Highly Nonlinear

Air entrainment phenomena have a strong influence on the hydraulic operation of a plunging drop shaft. An insufficient air intake from the outside can lead to poor operating conditions, with the onset of negative pressures inside the drop shaft, and the choking or backwater effects of the downstream and upstream flows, respectively. Air entrainment phenomena are very complex; moreover, it is impossible to define simple functional relationships between the airflow and the hydrodynamic and geometric variables on which it depends. However, this problem can be correctly addressed using prediction models based on machine learning (ML) algorithms, which can provide reliable tools to tackle highly nonlinear problems concerning experimental hydrodynamics. Furthermore, hybrid models can be developed by combining different machine learning algorithms. Hybridization may lead to an improvement in prediction accuracy. Two different models were built to predict the overall entrained airflow using data obtained during an extensive experimental campaign. The models were based on different combinations of predictors. For each model, four different hybrid variants were developed, starting from the three individual algorithms: KStar, random forest, and support vector regression. The best predictions were obtained with the model based on the largest number of predictors. Moreover, across all variants, the one based on all three algorithms proved to be the most accurate.

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.

scholarly journals Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Risks ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 4 ◽  
Author(s):  
Christopher Blier-Wong ◽  
Hélène Cossette ◽  
Luc Lamontagne ◽  
Etienne Marceau
Keyword(s):  
Machine Learning ◽  
Insurance Industry ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Actuarial Science ◽  
Science Field ◽  
The Past ◽  
Highly Nonlinear ◽  
Computer Scientists ◽  
Applications Of Machine Learning

In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these newer algorithms to tackle insurance-related tasks. In this work, we aim to review the applications of machine learning to the actuarial science field and present the current state of the art in ratemaking and reserving. We first give an overview of neural networks, then briefly outline applications of machine learning algorithms in actuarial science tasks. Finally, we summarize the future trends of machine learning for the insurance industry.

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

scholarly journals Comparative Performance of Machine Learning Algorithms for Cryptocurrency Forecasting

2018 ◽  
Vol 11 (3) ◽  
pp. 1121 ◽  
Author(s):  
Nor Azizah Hitam ◽  
Amelia Ritahani Ismail
Keyword(s):  
Machine Learning ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Support Vector ◽  
Small Range ◽  
Accuracy Rate ◽  
Comparative Performance ◽  
Vector Machines

Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done.

scholarly journals Techniques for Detecting Malware Traffic: A Comprehensive Approach to Feature Selection and Classification

2021 ◽  
Vol 9 (12) ◽  
pp. 1-10
Author(s):  
Harsha A K
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Steady Increase ◽  
Extreme Gradient Boosting

Abstract: Since the advent of encryption, there has been a steady increase in malware being transmitted over encrypted networks. Traditional approaches to detect malware like packet content analysis are inefficient in dealing with encrypted data. In the absence of actual packet contents, we can make use of other features like packet size, arrival time, source and destination addresses and other such metadata to detect malware. Such information can be used to train machine learning classifiers in order to classify malicious and benign packets. In this paper, we offer an efficient malware detection approach using classification algorithms in machine learning such as support vector machine, random forest and extreme gradient boosting. We employ an extensive feature selection process to reduce the dimensionality of the chosen dataset. The dataset is then split into training and testing sets. Machine learning algorithms are trained using the training set. These models are then evaluated against the testing set in order to assess their respective performances. We further attempt to tune the hyper parameters of the algorithms, in order to achieve better results. Random forest and extreme gradient boosting algorithms performed exceptionally well in our experiments, resulting in area under the curve values of 0.9928 and 0.9998 respectively. Our work demonstrates that malware traffic can be effectively classified using conventional machine learning algorithms and also shows the importance of dimensionality reduction in such classification problems. Keywords: Malware Detection, Extreme Gradient Boosting, Random Forest, Feature Selection.

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