Deep and machine learning approaches for forecasting the residual value of heavy construction equipment: a management decision support model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Odey Alshboul ◽  
Ali Shehadeh ◽  
Maha Al-Kasasbeh ◽  
Rabia Emhamed Al Mamlook ◽  
Neda Halalsheh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Linear Regression ◽  
Performance Metrics ◽  
Construction Equipment ◽  
Percentage Error ◽  
Residual Value ◽  
Content Type ◽  
The Mean ◽  
Heavy Construction

PurposeHeavy equipment residual value forecasting is dynamic as it relies on the age, type, brand and model of the equipment, ranking condition, place of sale, operating hours and other macroeconomic gauges. The main objective of this study is to predict the residual value of the main types of heavy construction equipment. The residual value of heavy construction equipment is predicted via deep learning (DL) and machine learning (ML) approaches.Design/methodology/approachBased on deep and machine learning regression network integrated with data mining, random forest (RF), decision tree (DT), deep neural network (DNN) and linear regression (LR)-based modeling decision support models are developed. This research aims to forecast the residual value for different types of heavy construction equipment. A comprehensive investigation of publicly accessible auction data related to various types and categories of construction equipment was utilized to generate the model's training and testing datasets. In total, four performance metrics (i.e. the mean absolute error (MAE), mean squared error (MSE), the mean absolute percentage error (MAPE) and coefficient of determination (R2)) were used to measure and compare the developed algorithms' accuracy.FindingsThe developed algorithm's efficiency has been demonstrated by comparing the deep and machine learning predictions with real residual value. The accuracy of the results obtained by different proposed modeling techniques was comparable based on the performance evaluation metrics. DT shows the highest accuracy of 0.9111 versus RF with an accuracy of 0.8123, followed by DNN with an accuracy of 0.7755 and the linear regression with an accuracy of 0.5967.Originality/valueThe proposed novel model is designed as a supportive tool for construction project managers for equipment selling, purchasing, overhauling, repairing, disposing and replacing decisions.

scholarly journals The implication of machine learning for financial solvency prediction: an empirical analysis on public listed companies of Bangladesh

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Abdullah
Keyword(s):  
Machine Learning ◽  
Enterprise Resource Planning ◽  
Model Building ◽  
Performance Metrics ◽  
Financial Institution ◽  
Area Under The Curve ◽  
Resource Planning ◽  
Listed Companies ◽  
Content Type ◽  
Artificial Neural Network Classifier

PurposeFinancial health of a corporation is a great concern for every investor level and decision-makers. For many years, financial solvency prediction is a significant issue throughout academia, precisely in finance. This requirement leads this study to check whether machine learning can be implemented in financial solvency prediction.Design/methodology/approachThis study analyzed 244 Dhaka stock exchange public-listed companies over the 2015–2019 period, and two subsets of data are also developed as training and testing datasets. For machine learning model building, samples are classified as secure, healthy and insolvent by the Altman Z-score. R statistical software is used to make predictive models of five classifiers and all model performances are measured with different performance metrics such as logarithmic loss (logLoss), area under the curve (AUC), precision recall AUC (prAUC), accuracy, kappa, sensitivity and specificity.FindingsThis study found that the artificial neural network classifier has 88% accuracy and sensitivity rate; also, AUC for this model is 96%. However, the ensemble classifier outperforms all other models by considering logLoss and other metrics.Research limitations/implicationsThe major result of this study can be implicated to the financial institution for credit scoring, credit rating and loan classification, etc. And other companies can implement machine learning models to their enterprise resource planning software to trace their financial solvency.Practical implicationsFinally, a predictive application is developed through training a model with 1,200 observations and making it available for all rational and novice investors (Abdullah, 2020).Originality/valueThis study found that, with the best of author expertise, the author did not find any studies regarding machine learning research of financial solvency that examines a comparable number of a dataset, with all these models in Bangladesh.

A study of job involvement prediction using machine learning technique

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Youngkeun Choi ◽  
Jae Won Choi
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Predictive Performance ◽  
Job Involvement ◽  
Learning Technology ◽  
Machine Learning Technique ◽  
Data Set ◽  
Content Type ◽  
Hr Management ◽  
Learning Technique

Purpose Job involvement can be linked with important work outcomes. One way for organizations to increase job involvement is to use machine learning technology to predict employees’ job involvement, so that their leaders of human resource (HR) management can take proactive measures or plan succession for preservation. This paper aims to develop a reliable job involvement prediction model using machine learning technique. Design/methodology/approach This study used the data set, which is available at International Business Machines (IBM) Watson Analytics in IBM community and applied a generalized linear model (GLM) including linear regression and binomial classification. This study essentially had two primary approaches. First, this paper intends to understand the role of variables in job involvement prediction modeling better. Second, the study seeks to evaluate the predictive performance of GLM including linear regression and binomial classification. Findings In these results, first, employees’ job involvement with a lot of individual factors can be predicted. Second, for each model, this model showed the outstanding predictive performance. Practical implications The pre-access and modeling methodology used in this paper can be viewed as a roadmap for the reader to follow the steps taken in this study and to apply procedures to identify the causes of many other HR management problems. Originality/value This paper is the first one to attempt to come up with the best-performing model for predicting job involvement based on a limited set of features including employees’ demographics using machine learning technique.

Estimation of structural response using convolutional neural network: application to the Suramadu bridge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arya Panji Pamuncak ◽  
Mohammad Reza Salami ◽  
Augusta Adha ◽  
Bambang Budiono ◽  
Irwanda Laory
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Structural Response ◽  
Force Sensor ◽  
Absolute Error ◽  
Percentage Error ◽  
Learning Approaches ◽  
Content Type ◽  
Cable Force

PurposeStructural health monitoring (SHM) has gained significant attention due to its capability in providing support for efficient and optimal bridge maintenance activities. However, despite the promising potential, the effectiveness of SHM system might be hindered by unprecedented factors that impact the continuity of data collection. This research presents a framework utilising convolutional neural network (CNN) for estimating structural response using environmental variations.Design/methodology/approachThe CNN framework is validated using monitoring data from the Suramadu bridge monitoring system. Pre-processing is performed to transform the data into data frames, each containing a sequence of data. The data frames are divided into training, validation and testing sets. Both the training and validation sets are employed to train the CNN models while the testing set is utilised for evaluation by calculating error metrics such as mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). Comparison with other machine learning approaches is performed to investigate the effectiveness of the CNN framework.FindingsThe CNN models are able to learn the trend of cable force sensor measurements with the ranges of MAE between 10.23 kN and 19.82 kN, MAPE between 0.434% and 0.536% and RMSE between 13.38 kN and 25.32 kN. In addition, the investigation discovers that the CNN-based model manages to outperform other machine learning models.Originality/valueThis work investigates, for the first time, how cable stress can be estimated using temperature variations. The study presents the first application of 1-D CNN regressor on data collected from a full-scale bridge. This work also evaluates the comparison between CNN regressor and other techniques, such as artificial neutral network (ANN) and linear regression, in estimating bridge cable stress, which has not been performed previously.

scholarly journals Predicting Runtime in HPC Environments for an Efficient Use of Computational Resources

2021 ◽  
Author(s):  
Mariza Ferro ◽  
Vinicius P. Klôh ◽  
Matheus Gritz ◽  
Vitor de Sá ◽  
Bruno Schulze
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Linear Regression ◽  
Decision Tree ◽  
High Performance ◽  
Performance Metrics ◽  
Scientific Applications ◽  
Computing Systems ◽  
Computational Resources ◽  
Performance Computing

Understanding the computational impact of scientific applications on computational architectures through runtime should guide the use of computational resources in high-performance computing systems. In this work, we propose an analysis of Machine Learning (ML) algorithms to gather knowledge about the performance of these applications through hardware events and derived performance metrics. Nine NAS benchmarks were executed and the hardware events were collected. These experimental results were used to train a Neural Network, a Decision Tree Regressor and a Linear Regression focusing on predicting the runtime of scientific applications according to the performance metrics.

scholarly journals Software Development Effort Duration and Cost Estimation using Linear Regression and K-Nearest Neighbors Machine Learning Algorithms

2019 ◽  
Vol 9 (2) ◽  
pp. 1043-1047
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Software Development ◽  
Cost Estimation ◽  
Performance Metrics ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Development Effort ◽  
Effort Estimation ◽  
K Nearest Neighbors

Effort estimation is a crucial step that leads to Duration estimation and cost estimation in software development. Estimations done in the initial stage of projects are based on requirements that may lead to success or failure of the project. Accurate estimations lead to success and inaccurate estimates lead to failure. There is no one particular method which cloud do accurate estimations. In this work, we propose Machine learning techniques linear regression and K-nearest Neighbors to predict Software Effort estimation using COCOMO81, COCOMONasa, and COCOMONasa2 datasets. The results obtained from these two methods have been compared. The 80% data in data sets used for training and remaining used as the test set. The correlation coefficient, Mean squared error (MSE) and Mean magnitude relative error (MMRE) are used as performance metrics. The experimental results show that these models forecast the software effort accurately.

A machine learning method to predict the technology adoption of blockchain in Palestinian firms

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ihab K.A. Hamdan ◽  
Wulamu Aziguli ◽  
Dezheng Zhang ◽  
Eli Sumarliah ◽  
Fauziyah Fauziyah
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Business Leaders ◽  
Ease Of Use ◽  
Machine Learning Algorithms ◽  
Content Type ◽  
Industrial Sectors ◽  
Technological Adoption ◽  
Perceived Benefit ◽  
Machine Learning Approach

PurposeThe study aims to deliver a decision support system for business leaders to estimate the potential for effective technological adoption of the blockchain (TAB) with a machine learning approach.Design/methodology/approachThis study uses a Bayesian network examination to develop an extrapolative system of decision support, highlighting the influential determinants that managers can employ to predict the TAB possibilities in their companies. Data were gathered from 167 SMEs in the largest industrial sectors in Palestine.FindingsThe results reveal perceived benefit and ease of use as the most influential determinants of the TAB.Originality/valueThis research is an initial effort to examine factors influencing TAB in the perspective of SMEs in Palestine using machine learning algorithms.

scholarly journals Machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic: a multisource Internet data approach

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dinda Thalia Andariesta ◽  
Meditya Wasesa
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Absolute Error ◽  
Percentage Error ◽  
Support Vector ◽  
Tourism Demand ◽  
Learning Models ◽  
Content Type ◽  
International Tourist ◽  
Machine Learning Models

PurposeThis research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.Design/methodology/approachTo develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).FindingsPrediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.Originality/valueFirst, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.

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