Morphology-based prediction of cancer cell migration using an artificial neural network and a random decision forest

Vehicle-animal collisions represent a serious problem in roadway infrastructure. To avoid these roadway collisions, different mitigation systems have been applied in various regions of the world. In this article, a system for detecting animals on highways is presented using computer vision and machine learning algorithms. The models were trained to classify two groups of animals: capybaras and donkeys. Two variants of the convolutional neural network called Yolo (You only look once) were used, Yolov4 and Yolov4-tiny (a lighter version of the network). The training was carried out using pre-trained models. Detection tests were performed on 147 images. The accuracy results obtained were 84.87% and 79.87% for Yolov4 and Yolov4-tiny, respectively. The proposed system has the potential to improve road safety by reducing or preventing accidents with animals.

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Recognition and Digitization of Handwritten Text using Histogram of Gradients and Artificial Neural Network

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i6.5702 ◽

2021 ◽

Vol 12 (6) ◽

pp. 2555-2564

Author(s):

Dr.S.K.Nivetha Et al.

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Real Life ◽

Machine Learning Algorithms ◽

Postal Code ◽

Training Costs ◽

Handwritten Text ◽

Handwritten Text Recognition ◽

Artificial Neural

Handwriting recognition is one of the most persuasive and interesting projects as it is required in many real-life applications such as bank-check processing, postal-code recognition, handwritten notes or question paper digitization etc. Machine learning and deep learning methods are being used by developers to make computers more intelligent. A person learns how to execute a task by learning and repeating it over and over before it memorises the steps. The neurons in his brain will then be able to easily execute the task that he has mastered. This is also very close to machine learning. It employs a variety of architectures to solve various problems. Handwritten text recognition systems are models that capture and interpret handwritten numeric and character data from sources such as paper documents and photographs. For this application, a variety of machine learning algorithms were used. However, several limitations have been found, such as a large number of iterations, high training costs, and so on. Even though the other models have given impressive accuracy, it still has some drawbacks. In an unsupervised way, the Artificial Neural Network is used to learn effective data coding. For recognising real-world data, we built a model using Histogram of Oriented Gradients (HOG) and Artificial Neural Networks (ANN).

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An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine

Energies ◽

10.3390/en14216928 ◽

2021 ◽

Vol 14 (21) ◽

pp. 6928

Author(s):

Łukasz Wojtecki ◽

Sebastian Iwaszenko ◽

Derek B. Apel ◽

Tomasz Cichy

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Rock Mass ◽

Decision Tree ◽

Risk Prediction ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Hard Coal ◽

Underground Excavations

Rockburst is a dynamic rock mass failure occurring during underground mining under unfavorable stress conditions. The rockburst phenomenon concerns openings in different rocks and is generally correlated with high stress in the rock mass. As a result of rockburst, underground excavations lose their functionality, the infrastructure is damaged, and the working conditions become unsafe. Assessing rockburst hazards in underground excavations becomes particularly important with the increasing mining depth and the mining-induced stresses. Nowadays, rockburst risk prediction is based mainly on various indicators. However, some attempts have been made to apply machine learning algorithms for this purpose. For this article, we employed an extensive range of machine learning algorithms, e.g., an artificial neural network, decision tree, random forest, and gradient boosting, to estimate the rockburst risk in galleries in one of the deep hard coal mines in the Upper Silesian Coal Basin, Poland. With the use of these algorithms, we proposed rockburst risk prediction models. Neural network and decision tree models were most effective in assessing whether a rockburst occurred in an analyzed case, taking into account the average value of the recall parameter. In three randomly selected datasets, the artificial neural network models were able to identify all of the rockbursts.

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An Assessment of Machine Learning Techniques for Predicting Turbine Airfoil Component Temperatures, Using FEA Simulations for Training Data

Volume 5A: Heat Transfer ◽

10.1115/gt2019-91004 ◽

2019 ◽

Author(s):

James A. Tallman ◽

Michal Osusky ◽

Nick Magina ◽

Evan Sewall

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Surrogate Model ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Artificial Neural

Abstract This paper provides an assessment of three different machine learning techniques for accurately reproducing a distributed temperature prediction of a high-pressure turbine airfoil. A three-dimensional Finite Element Analysis thermal model of a cooled turbine airfoil was solved repeatedly (200 instances) for various operating point settings of the corresponding gas turbine engine. The response surface created by the repeated solutions was fed into three machine learning algorithms and surrogate model representations of the FEA model’s response were generated. The machine learning algorithms investigated were a Gaussian Process, a Boosted Decision Tree, and an Artificial Neural Network. Additionally, a simple Linear Regression surrogate model was created for comparative purposes. The Artificial Neural Network model proved to be the most successful at reproducing the FEA model over the range of operating points. The mean and standard deviation differences between the FEA and the Neural Network models were 15% and 14% of a desired accuracy threshold, respectively. The Digital Thread for Design (DT4D) was used to expedite all model execution and machine learning training. A description of DT4D is also provided.

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A Comparative Study on Supervised Machine Learning Algorithms for Copper Recovery Quality Prediction in a Leaching Process

Sensors ◽

10.3390/s21062119 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2119

Author(s):

Victor Flores ◽

Claudio Leiva

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Random Forest ◽

Mining Industry ◽

Machine Learning Algorithms ◽

Copper Recovery ◽

Support Vector ◽

Copper Mining

The copper mining industry is increasingly using artificial intelligence methods to improve copper production processes. Recent studies reveal the use of algorithms, such as Artificial Neural Network, Support Vector Machine, and Random Forest, among others, to develop models for predicting product quality. Other studies compare the predictive models developed with these machine learning algorithms in the mining industry as a whole. However, not many copper mining studies published compare the results of machine learning techniques for copper recovery prediction. This study makes a detailed comparison between three models for predicting copper recovery by leaching, using four datasets resulting from mining operations in Northern Chile. The algorithms used for developing the models were Random Forest, Support Vector Machine, and Artificial Neural Network. To validate these models, four indicators or values of merit were used: accuracy (acc), precision (p), recall (r), and Matthew’s correlation coefficient (mcc). This paper describes the dataset preparation and the refinement of the threshold values used for the predictive variable most influential on the class (the copper recovery). Results show both a precision over 98.50% and also the model with the best behavior between the predicted and the real values. Finally, the obtained models have the following mean values: acc = 0.943, p = 88.47, r = 0.995, and mcc = 0.232. These values are highly competitive when compared with those obtained in similar studies using other approaches in the context.

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A new ML-based approach to enhance student engagement in online environment

PLoS ONE ◽

10.1371/journal.pone.0258788 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0258788

Author(s):

Sarra Ayouni ◽

Fahima Hajjej ◽

Mohamed Maddeh ◽

Shaha Al-Otaibi

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Student Engagement ◽

Decision Tree ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Online Environment

The educational research is increasingly emphasizing the potential of student engagement and its impact on performance, retention and persistence. This construct has emerged as an important paradigm in the higher education field for many decades. However, evaluating and predicting the student’s engagement level in an online environment remains a challenge. The purpose of this study is to suggest an intelligent predictive system that predicts the student’s engagement level and then provides the students with feedback to enhance their motivation and dedication. Three categories of students are defined depending on their engagement level (Not Engaged, Passively Engaged, and Actively Engaged). We applied three different machine-learning algorithms, namely Decision Tree, Support Vector Machine and Artificial Neural Network, to students’ activities recorded in Learning Management System reports. The results demonstrate that machine learning algorithms could predict the student’s engagement level. In addition, according to the performance metrics of the different algorithms, the Artificial Neural Network has a greater accuracy rate (85%) compared to the Support Vector Machine (80%) and Decision Tree (75%) classification techniques. Based on these results, the intelligent predictive system sends feedback to the students and alerts the instructor once a student’s engagement level decreases. The instructor can identify the students’ difficulties during the course and motivate them through e-mail reminders, course messages, or scheduling an online meeting.

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Machine learning model development for predicting road transport GHG emissions in Canada

WSB Journal of Business and Finance ◽

10.2478/wsbjbf-2019-0022 ◽

2019 ◽

Vol 53 (2) ◽

pp. 55-72

Author(s):

Mohd Jawad Ur Rehman Khan ◽

Anjali Awasthi

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Multilayer Perceptron ◽

Ghg Emissions ◽

Predictive Performance ◽

Road Transport ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Artificial Neural

Abstract Prediction of greenhouse gas (GHG) emissions is important to minimise their negative impact on climate change and global warming. In this article, we propose new models based on data mining and supervised machine learning algorithms (regression and classification) for predicting GHG emissions arising from passenger and freight road transport in Canada. Four models are investigated, namely, artificial neural network multilayer perceptron, multiple linear regression, multinomial logistic regression and decision tree models. From the results, it was found that artificial neural network multilayer perceptron model showed better predictive performance over other models. Ensemble technique (Bagging & Boosting) was applied on the developed multilayer perceptron model, which significantly improved the model’s predictive performance.

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Machine learning and artificial neural network prediction of interfacial thermal resistance between graphene and hexagonal boron nitride

Nanoscale ◽

10.1039/c8nr05703f ◽

2018 ◽

Vol 10 (40) ◽

pp. 19092-19099 ◽

Cited By ~ 21

Author(s):

Hong Yang ◽

Zhongtao Zhang ◽

Jingchao Zhang ◽

Xiao Cheng Zeng

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Boron Nitride ◽

Thermal Resistance ◽

Hexagonal Boron Nitride ◽

Machine Learning Algorithms ◽

Single Layer Graphene ◽

Interfacial Thermal Resistance ◽

Artificial Neural

Several machine learning algorithms and artificial neural network structures are used to predict the interfacial thermal resistance between single layer graphene and hexagonal boron nitride with only the knowledge of the system temperature, inter-layer coupling strength, and in-plane tensile strain.

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Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia

Scientific Reports ◽

10.1038/s41598-021-96872-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Marwah Sattar Hanoon ◽

Ali Najah Ahmed ◽

Nur’atiah Zaini ◽

Arif Razzaq ◽

Pavitra Kumar ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Air Temperature ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Unseen Data ◽

Temperature And Humidity ◽

Artificial Neural ◽

Artificial Neural Network Ann

AbstractAccurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh). Daily data over 24 years for Kula Terengganu station were obtained from the Malaysia Meteorological Department. Results showed that MLP-NN performs well among the others in predicting daily T and Rh with R of 0.7132 and 0.633, respectively. However, in monthly prediction T also MLP-NN model provided closer standards deviation to actual value and can be used to predict monthly T with R 0.8462. Whereas in prediction monthly Rh, the RBF-NN model's efficiency was higher than other models with R of 0.7113. To validate the performance of the trained both artificial neural network (ANN) architectures MLP-NN and RBF-NN, both were applied to an unseen data set from observation data in the region. The results indicated that on either architecture of ANN, there is good potential to predict daily and monthly T and Rh values with an acceptable range of accuracy.

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Crop Recommendation System by Artificial Neural Network

10.21203/rs.3.rs-874525/v1 ◽

2021 ◽

Author(s):

Bangaru Kamatchi S ◽

R. Parvathi

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Recommendation System ◽

Climatic Factors ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Classification Model ◽

Crop Type ◽

Artificial Neural

Abstract The agriculture yield mostly depends on climate factors. Any information associated with climatic factors will help farmers in foreordained farming. Choosing a right crop at right time is most important to get proper yield. To help the farmers in decision making process a classification model is built by considering the agro climatic parameters of a crop like temperature, relative humidity, type of soil, soil pH and crop duration and a recommendation system is built based on three factors namely crop, type of crop and the districts. Predicting the districts is the novel approach in which crop pattern of 33 districts of Tamilnadu is marked and based on that classification model is built. Thorough analysis of machine learning algorithms incorporating pre-processing, data augmentation and comparison of optimizers and activation function of ANN. Log loss metric is used to validate the models. The results shows that artificial neural network is the best predictive model for classification of crops crop type and district based on agrometeorological climatic condition. The accuracy of artificial neural network model is compared with five different machine learning algorithms to analyse the performance.

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