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.

Hypertension Prediction Using Machine Learning Technique

2020 ◽  
Vol 11 (3) ◽  
pp. 52-62
Author(s):  
Youngkeun Choi ◽  
Jae Choi
Keyword(s):  
Machine Learning ◽  
Predictive Performance ◽  
Learning Technology ◽  
Full Model ◽  
Accuracy Rate ◽  
Medical Problems ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Average Glucose

Machine learning technology is used in advanced data analysis and optimization approaches for different kinds of medical problems. Hypertension is complicated, and every year it causes a lot of many severe illnesses such as stroke and heart disease. This study essentially had two primary goals. Firstly, this paper intends to understand the role of variables in hypertension modeling better. Secondly, the study seeks to evaluate the predictive performance of the decision trees. Based on these results, first, age, BMI, and average glucose level influence hypertension significantly, while other variables have an influence. Second, for the full model, the accuracy rate is 0.905, which implies that the error rate is 0.095. Among the patients who were predicted not to have hypertension, the accuracy that would not have hypertension was 90.51%, and the accuracy that had strike was 30.77% among the patients who were predicted to have hypertension.

SOM approach for clustering customers using credit card transactions

2019 ◽  
Vol 12 (3) ◽  
pp. 372-388
Author(s):  
Seda Yanık ◽  
Abdelrahman Elmorsy
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Credit Card ◽  
Consumption Patterns ◽  
Self Organizing Map ◽  
Machine Learning Technique ◽  
Data Set ◽  
Number Of Clusters ◽  
Content Type ◽  
Learning Technique

Purpose The purpose of this paper is to generate customer clusters using self-organizing map (SOM) approach, a machine learning technique with a big data set of credit card consumptions. The authors aim to use the consumption patterns of the customers in a period of three months deducted from the credit card transactions, specifically the consumption categories (e.g. food, entertainment, etc.). Design/methodology/approach The authors use a big data set of almost 40,000 credit card transactions to cluster customers. To deal with the size of the data set and the eliminated the required parametric assumptions the authors use a machine learning technique, SOMs. The variables used are grouped into three as demographical variables, categorical consumption variables and summary consumption variables. The variables are first converted to factors using principal component analysis. Then, the number of clusters is specified by k-means clustering trials. Then, clustering with SOM is conducted by only including the demographical variables and all variables. Then, a comparison is made and the significance of the variables is examined by analysis of variance. Findings The appropriate number of clusters is found to be 8 using k-means clusters. Then, the differences in categorical consumption levels are investigated between the clusters. However, they have been found to be insignificant, whereas the summary consumption variables are found to be significant between the clusters, as well as the demographical variables. Originality/value The originality of the study is to incorporate the credit card consumption variables of customers to cluster the bank customers. The authors use a big data set and dealt with it with a machine learning technique to deduct the consumption patterns to generate the clusters. Credit card transactions generate a vast amount of data to deduce valuable information. It is mainly used to detect fraud in the literature. To the best of the authors’ knowledge, consumption patterns obtained from credit card transaction are first used for clustering the customers in this study.

scholarly journals Efficient CNN Approach for Facial Expression Recognition

2021 ◽  
Vol 2129 (1) ◽  
pp. 012083
Author(s):  
Gheyath Mustafa Zebari ◽  
Dilovan Asaad Zebari ◽  
Diyar Qader Zeebaree ◽  
Habibollah Haron ◽  
Adnan Mohsin Abdulazeez ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Deep Neural Network ◽  
Learning Technology ◽  
Expression Recognition ◽  
Machine Learning Technique ◽  
Learning Technique

Abstract In the last decade, the Facial Expression Recognition field has been studied widely and become the base for many researchers, and still challenging in computer vision. Machine learning technique used in facial expression recognition facing many problems, since human emotions expressed differently from one to another. Nevertheless, Deep learning that represents a novel area of research within machine learning technology has the ability for classifying people’s faces into different emotion classes by using a Deep Neural Network (DNN). The Convolution Neural Network (CNN) method has been used widely and proved as very efficient in the facial expression recognition field. In this study, a CNN technique for facial expression recognition has been presented. The performance of this study has been evaluated using the fer2013 dataset, the total number of images has been used. The accuracy of each epoch has been tested which is trained on 29068 samples, validate on 3589 samples. The overall accuracy of 69.85% has been obtained for the proposed method.

scholarly journals The extraction of beautiful sound patterns from Sunthorn Phu’s poem using machine learning technique and internal rhyme rule

2021 ◽  
Vol 7 (2) ◽  
pp. 198
Author(s):  
Pattarakorn Suksanguan ◽  
Sajjaporn Waijanya ◽  
Nuttachot Promrit
Keyword(s):  
Machine Learning ◽  
Role Model ◽  
Learning Technology ◽  
Accuracy Score ◽  
Machine Learning Technique ◽  
Distinctive Features ◽  
Average Accuracy ◽  
Learning Technique ◽  
Great Poet ◽  
Sound Patterns

The melodious poems have been written from the distinctive features of poetry or based on each country's typical style. Especially, Thai poems which composed by the use of specific forming, such as Internal Rhyme to develop melodiousness. The most attractive and well-known poems were composed by a genius Thai poet named Sunthorn Phu. He is a role model for Thai poets. UNESCO honored him as the world’s great poet and the best role model in poetry works. In this article, we proposed extracting 15,796 sentences (Waks) of the beautiful sound patterns of Phra Aphai Mani’s tales by machine learning technology in conjunction with the rules of internal Rhyme Klon-Suphap by using the Apriori Algorithm. The extraction of vowel rhymes separated by a group of Waks including 1) Poem Wak No. 1; 2) Poem Wak No. 2; 3) Poem Wak No. 3; and 4) Poem Wak No. 4. In this article, “Wak” means sentence. The created tool can extract the internal rhyme patterns and the 25 popular pattern vowels. The popular pattern illustrates the melodiousness of the Poem and sets up a standard of how to melodiously compose a poem. Then, the evaluation of the experiments was done by using 144 Waks selected from the extraction of the beautiful patterns and evaluated by the consistency score from 3 experts. The average accuracy score resulted in 95.30%.

Aerial vehicle guidance based on passive machine learning technique

2016 ◽  
Vol 9 (3) ◽  
pp. 255-273 ◽  
Author(s):  
Chethan Upendra Chithapuram ◽  
Aswani Kumar Cherukuri ◽  
Yogananda V. Jeppu
Keyword(s):  
Machine Learning ◽  
Proportional Navigation ◽  
Machine Learning Technique ◽  
Content Type ◽  
Q Learning ◽  
Aerial Vehicles ◽  
Learning Technique ◽  
Aerial Vehicle ◽  
Proportional Navigation Guidance ◽  
Better Than

Purpose The purpose of this paper is to develop a new guidance scheme for aerial vehicles based on artificial intelligence. The new guidance scheme must be able to intercept maneuvering targets with higher probability and precision compared to existing algorithms. Design/methodology/approach A simulation setup of the aerial vehicle guidance problem is developed. A model-based machine learning technique known as Q-learning is used to develop a new guidance scheme. Several simulation experiments are conducted to train the new guidance scheme. Orthogonal arrays are used to define the training experiments to achieve faster convergence. A well-known guidance scheme known as proportional navigation guidance (PNG) is used as a base model for training. The new guidance scheme is compared for performance against standard guidance schemes like PNG and augmented proportional navigation guidance schemes in presence of sensor noise and computational delays. Findings A new guidance scheme for aerial vehicles is developed using Q-learning technique. This new guidance scheme has better miss distances and probability of intercept compared to standard guidance schemes. Research limitations/implications The research uses simulation models to develop the new guidance scheme. The new guidance scheme is also evaluated in the simulation environment. The new guidance scheme performs better than standard existing guidance schemes. Practical implications The new guidance scheme can be used in various aerial guidance applications to reach a dynamically moving target in three-dimensional space. Originality/value The research paper proposes a completely new guidance scheme based on Q-learning whose performance is better than standard guidance schemes.

scholarly journals Detecting Predominance of on-Street Parking Payment Schemes by Means of Linear Regression

2020 ◽  
Vol 8 (6) ◽  
pp. 59-64
Keyword(s):  
Linear Regression ◽  
Traffic Congestion ◽  
Linear Regression Analysis ◽  
Open Data ◽  
Urban Traffic ◽  
Parking Space ◽  
Machine Learning Technique ◽  
Data Set ◽  
Learning Technique ◽  
Payment Schemes

—On street parking is one of the important and crucial components of urban traffic and transportation system. Allocation of parking space on street is major reason for traffic congestion. Optimizing traffic congestion and facilitating on street parking is a long stand issue. According to urban environment it is expected that car drivers prefers parking space based on road conditions, speed limit and surrounding activities and availability of parking space. The other major components to be ponder while searching parking space is payment method used while parking the car. This paper investigates car driver’s behaviors in selecting parking payment schemas, visualized data as well predicted via machine learning technique of linear regression analysis on the open data set of On-street Car Parking Meters with Location of City of Melbourne's in the Australian.

Improving the performance of the intrusion detection systems by the machine learning explainability

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Quang-Vinh Dang
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
State Of The Art ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Data Set ◽  
Content Type ◽  
Detection Systems ◽  
Experimental Works ◽  
The Relationship

Purpose This study aims to explain the state-of-the-art machine learning models that are used in the intrusion detection problem for human-being understandable and study the relationship between the explainability and the performance of the models. Design/methodology/approach The authors study a recent intrusion data set collected from real-world scenarios and use state-of-the-art machine learning algorithms to detect the intrusion. The authors apply several novel techniques to explain the models, then evaluate manually the explanation. The authors then compare the performance of model post- and prior-explainability-based feature selection. Findings The authors confirm our hypothesis above and claim that by forcing the explainability, the model becomes more robust, requires less computational power but achieves a better predictive performance. Originality/value The authors draw our conclusions based on their own research and experimental works.

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

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