Material Classification via Machine Learning Techniques: Construction Projects Progress Monitoring

Support Vector ◽

Ann Model ◽

Material Classification ◽

Nowadays, the construction industry is on a fast track to adopting digital processes under the Industrial Revolution (IR) 4.0. The desire to automate maximum construction processes with less human interference has led the industry and research community to inclined towards artificial intelligence. This chapter has been themed on automated construction monitoring practices by adopting material classification via machine learning (ML) techniques. The study has been conducted by following the structure review approach to gain an understanding of the applications of ML techniques for construction progress assessment. Data were collected from the Web of Science (WoS) and Scopus databases, concluding 14 relevant studies. The literature review depicted the support vector machine (SVM) and artificial neural network (ANN) techniques as more effective than other ML techniques for material classification. The last section of this chapter includes a python-based ANN model for material classification. This ANN model has been tested for construction items (brick, wood, concrete block, and asphalt) for training and prediction. Moreover, the predictive ANN model results have been shared for the readers, along with the resources and open-source web links.

Prediction of the optimal FSW process parameters for joints using machine learning techniques

Materials Testing ◽

10.1515/mt-2021-0058 ◽

2021 ◽

Vol 63 (12) ◽

pp. 1104-1111

Author(s):

Furkan Sarsilmaz ◽

Gürkan Kavuran

Keyword(s):

Mechanical Properties ◽

Tensile Strength ◽

Support Vector ◽

Ann Model ◽

Friction Stir ◽

Learning Techniques ◽

Svm Model ◽

Artificial Neural Network Ann ◽

Fsw Parameters

Abstract In this work, a couple of dissimilar AA2024/AA7075 plates were experimentally welded for the purpose of considering the effect of friction-stir welding (FSW) parameters on mechanical properties. First, the main mechanical properties such as ultimate tensile strength (UTS) and hardness of welded joints were determined experimentally. Secondly, these data were evaluated through modeling and the optimization of the FSW process as well as an optimal parametric combination to affirm tensile strength and hardness using a support vector machine (SVM) and an artificial neural network (ANN). In this study, a new ANN model, including the Nelder-Mead algorithm, was first used and compared with the SVM model in the FSW process. It was concluded that the ANN approach works better than SVM techniques. The validity and accuracy of the proposed method were proved by simulation studies.

Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index

Applied Sciences ◽

10.3390/app10051691 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1691 ◽

Cited By ~ 5

Author(s):

Deliang Sun ◽

Mahshid Lonbani ◽

Behnam Askarian ◽

Danial Jahed Armaghani ◽

Reza Tarinejad ◽

...

Keyword(s):

Machine Learning ◽

Point Load ◽

P Wave ◽

Brittleness Index ◽

Coefficient Of Determination ◽

Support Vector ◽

Ann Model ◽

Learning Techniques ◽

Rock Brittleness

Despite the vast usage of machine learning techniques to solve engineering problems, a very limited number of studies on the rock brittleness index (BI) have used these techniques to analyze issues in this field. The present study developed five well-known machine learning techniques and compared their performance to predict the brittleness index of the rock samples. The comparison of the models’ performance was conducted through a ranking system. These techniques included Chi-square automatic interaction detector (CHAID), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), and artificial neural network (ANN). This study used a dataset from a water transfer tunneling project in Malaysia. Results of simple rock index tests i.e., Schmidt hammer, p-wave velocity, point load, and density were considered as model inputs. The results of this study indicated that while the RF model had the best performance for training (ranking = 25), the ANN outperformed other models for testing (ranking = 22). However, the KNN model achieved the highest cumulative ranking, which was 37. The KNN model showed desirable stability for both training and testing. However, the results of validation stage indicated that RF model with coefficient of determination (R2) of 0.971 provides higher performance capacity for prediction of the rock BI compared to KNN model with R2 of 0.807 and ANN model with R2 of 0.860. The results of this study suggest a practical use of the machine learning models in solving problems related to rock mechanics specially rock brittleness index.

Designing a Compact Microstrip Antenna Using the Machine Learning Approach

Journal of Telecommunications and Information Technology ◽

10.26636/jtit.2020.143520 ◽

2020 ◽

Vol 4 ◽

pp. 44-52

Author(s):

Kanhaiya Sharma ◽

Ganga Prasad Pandey

Keyword(s):

Machine Learning ◽

Microstrip Antenna ◽

Dual Band ◽

Ann Model ◽

Shape Prediction ◽

Learning Techniques ◽

Notch Length ◽

Artificial Neural Network Ann ◽

Rectangular Microstrip Antenna

This paper presents how machine learning techniques may be applied in the process of designing a compact dual-band H-shaped rectangular microstrip antenna (RMSA) operating in 0.75–2.20 GHz and 3.0–3.44 GHz frequency ranges. In the design process, the same dimensions of upper and lower notches are incorporated, with the centered position right in the middle. Notch length and width are verified for investigating the antenna. An artificial neural network (ANN) model is developed from the simulated dataset, and is used for shape prediction. The same dataset is used to create a mathematical model as well. The predicted outcome is compared and it is determined that the model relying on ANN offers better results

Evaluation of Machine Learning Approaches for Automated Diagnosis of COVID-19 using X-Ray images (Preprint)

10.2196/preprints.18947 ◽

2020 ◽

Author(s):

Mazin Mohammed ◽

Karrar Hameed Abdulkareem ◽

Mashael S. Maashi ◽

Salama A. Mostafa A. Mostafa ◽

Abdullah Baz ◽

...

Keyword(s):

Machine Learning ◽

Computational Method ◽

Learning Performance ◽

Support Vector ◽

Learning Approaches ◽

Data Set ◽

X Ray ◽

Wide Range ◽

BACKGROUND In most recent times, global concern has been caused by a coronavirus (COVID19), which is considered a global health threat due to its rapid spread across the globe. Machine learning (ML) is a computational method that can be used to automatically learn from experience and improve the accuracy of predictions. OBJECTIVE In this study, the use of machine learning has been applied to Coronavirus dataset of 50 X-ray images to enable the development of directions and detection modalities with risk causes.The dataset contains a wide range of samples of COVID-19 cases alongside SARS, MERS, and ARDS. The experiment was carried out using a total of 50 X-ray images, out of which 25 images were that of positive COVIDE-19 cases, while the other 25 were normal cases. METHODS An orange tool has been used for data manipulation. To be able to classify patients as carriers of Coronavirus and non-Coronavirus carriers, this tool has been employed in developing and analysing seven types of predictive models. Models such as , artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbour (k-NN), Decision Tree (DT), and CN2 rule inducer were used in this study.Furthermore, the standard InceptionV3 model has been used for feature extraction target. RESULTS The various machine learning techniques that have been trained on coronavirus disease 2019 (COVID-19) dataset with improved ML techniques parameters. The data set was divided into two parts, which are training and testing. The model was trained using 70% of the dataset, while the remaining 30% was used to test the model. The results show that the improved SVM achieved a F1 of 97% and an accuracy of 98%. CONCLUSIONS :. In this study, seven models have been developed to aid the detection of coronavirus. In such cases, the learning performance can be improved through knowledge transfer, whereby time-consuming data labelling efforts are not required.the evaluations of all the models are done in terms of different parameters. it can be concluded that all the models performed well, but the SVM demonstrated the best result for accuracy metric. Future work will compare classical approaches with deep learning ones and try to obtain better results. CLINICALTRIAL None

Machine Learning Methods to Classify Mushrooms for Edibility-A Review

International Journal for Modern Trends in Science and Technology - RTT2020 ◽

10.46501/ijmtst060909 ◽

2020 ◽

Vol 06 (09) ◽

pp. 54-58

Author(s):

Rakesh Kumar Y and Dr. V. Chandrasekhar

Keyword(s):

Neural Network ◽

Machine Learning ◽

Literature Review ◽

Edible Mushroom ◽

Support Vector ◽

Applied Machine Learning ◽

Learning Techniques ◽

There are thousands of species of Mushrooms in the world; they are edible and non-edible being poisonous. It is difficult for non-expertise person to Identify poisonous and edible mushroom of all the species manually. So a computer aided system with software or algorithm is required to classify poisonous and nonpoisonous mushrooms. In this paper a literature review is presented on classification of poisonous and nonpoisonous mushrooms. Most of the research works to classify the type of mushroom have applied, machine learning techniques like Naïve Bayes, K-Neural Network, Support vector Machine(SVM), Artificial Neural Network(ANN), Decision Tree techniques. In this literature review, a summary and comparisons of all different techniques of mushroom classification in terms of its performance parameters, merits and demerits faced during the classification of mushrooms using machine learning techniques.

Volume 3: 21st International Conference on Advanced Vehicle Technologies; 16th International Conference on Design Education ◽

Human Computer Interface Using Electroencephalography for Driver Behavior Classification

10.1115/detc2019-97540 ◽

2019 ◽

Author(s):

Vamsi K. Manchala ◽

Alvaro V. Clara ◽

Susheelkumar C. Subramanian ◽

Sangram Redkar ◽

Thomas Sugar

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Driver Behavior ◽

Driving Behavior ◽

Support Vector ◽

Affective States ◽

K Nearest Neighbor ◽

Learning Techniques ◽

Abstract It is important to know and be able to classify the drivers’ behavior as good, bad, keen or aggressive, which would aid in driver assist systems to avoid vehicle crashes. This research attempts to develop, test, and compare the performance of machine learning methods for classifying human driving behavior. It also proposes to correlate driver affective states with the driving behavior. The major contributions of this work are to classify the driver behavior using Electroencephalograph (EEG) while driving simulated vehicle and compare them with the behavior classified using vehicle parameters and affective states. The study involved both classical machine learning techniques such as k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN) and latest “unsupervised” Hybrid Deep Learning techniques, and compared the accuracy of classification across subjects, various driving scenarios and affective states.

Stock Prediction Using Machine Learning

WSEAS TRANSACTIONS ON COMPUTER RESEARCH ◽

10.37394/232018.2021.9.17 ◽

2021 ◽

Vol 9 ◽

pp. 152-158

Author(s):

Shubha Singh ◽

Sreedevi Gutta ◽

Ahmad Hadaegh

Keyword(s):

Neural Network ◽

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Support Vector ◽

Stock Price Prediction ◽

Price Prediction ◽

Learning Techniques ◽

The Trend of stock price prediction is becoming more popular than ever. Share market is difficult to predict due to its volatile nature. There are no rules to follow to predict what will happen with the stock in the future. To predict accurately is a huge challenge since the market trend always keep changing depending on many factors. The objective is to apply machine learning techniques to predict stocks and maximize the profit. In this work, we have shown that with the help of artificial intelligence and machine learning, the process of prediction can be improved. While doing the literature review, we realized that the most effective machine learning tool for this research include: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Genetic Algorithms (GA). All categories have common and unique findings and limitations. We collected data for about 10 years and used Long Short-Term Memory (LSTM) Neural Network-based machine learning models to analyze and predict the stock price. The Recurrent Neural Network (RNN) is useful to preserve the time-series features for improving profits. The financial data High and Close are used as input for the model.

Feature selection for DDoS detection using classification machine learning techniques

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v9.i1.pp137-145 ◽

2020 ◽

Vol 9 (1) ◽

pp. 137

Author(s):

Andi Maslan ◽

Kamaruddin Malik Bin Mohamad ◽

Feresa Binti Mohd Foozy

Keyword(s):

Machine Learning ◽

Computer System ◽

Industrial Revolution ◽

Developed Countries ◽

Support Vector ◽

Ddos Attacks ◽

Learning Techniques ◽

Ddos Detection ◽

A Site

Computer system security is a factor that needs to be considered in the era of industrial revolution 4.0, namely by preventing various threats to the system, as well as being able to detect and repair any damage that occurs to the computer system. DDoS attacks are a threat to the company at this time because this attack is carried out by making very large requests for a site or website server so that the system becomes stuck and cannot function at all. DDoS attacks in Indonesia and developed countries always increase every year to 6% from only 3%. To minimize the attack, we conducted a study using Machine Learning techniques. The dataset is obtained from the results of DDoS attacks that have been collected by the researchers. From the datasets there is a training and testing of data using five techniques classification: Neural Network, Naïve Bayes and Random Forest, KNN, and Support Vector Machine (SVM), datasets processed have different percentages, with the aim of facilitating in classifying. From this study it can be concluded that from the five classification techniques used, the Forest random classification technique achieved the highest level of accuracy (98.70%) with a Weighted Avg 98.4%. This means that the technique can detect DDoS attacks accurately on the application that will be developed.

Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

International Journal of Advanced Research in Computer Science and Software Engineering ◽

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

10.23956/ijarcsse/v7i7/0177 ◽

2017 ◽

Vol 7 (7) ◽

pp. 172

Author(s):

Anantvir Singh Romana

Keyword(s):

Machine Learning ◽

Subsequent Treatment ◽

Machine Learning Algorithms ◽

Support Vector ◽

Disease Prediction ◽

Classification Problems ◽

Learning Techniques ◽

Neural Network Classifiers ◽

Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.