scholarly journals Foreseeing Employee Attritions using Diverse Data Mining Strategies

2019 ◽  
Vol 8 (3) ◽  
pp. 620-626
Keyword(s):  
Machine Learning ◽  
Best Practices ◽  
Employee Turnover ◽  
Employee Retention ◽  
Forecast Model ◽  
Learning Systems ◽  
Knowledge Based ◽  
Machine Learning Model ◽  
Employee Attrition ◽  
Diverse Data

“Employee turnover is a noteworthy matter in knowledge-based companies.” On the off chance that employee leaves, they carry with them tacit information, often a source of competitive benefit to the other firms. Keeping in mind the end goal, to stay in the market and retain its employees, an organization requires minimizing employee attrition. This article discusses the employee churn/attrition forecast model using various methods of Machine Learning. Model yields are then scrutinized to outline and experiment the best practices on employee withholding at different stages of the employee’s association with an organization. This work has the potential for outlining better employee retention designs and enhancing employee contentment. This paper incorporates and condenses the capacity to gain from information and give information-driven experiences, choice, and forecasts and thinks about significant machine learning systems that have been utilized to create predictive churn models.

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

scholarly journals Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen

2019 ◽  
Vol 14 (3) ◽  
pp. 302-307
Author(s):  
Benjamin Q. Huynh ◽  
Sanjay Basu
Keyword(s):  
Machine Learning ◽  
Food Prices ◽  
Internally Displaced ◽  
Fuel Prices ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Using Data ◽  
Diverse Data ◽  
Internally Displaced Population ◽  
Persistence Model

ABSTRACTObjectives:Armed conflict has contributed to an unprecedented number of internally displaced persons (IDPs), individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when IDPs will migrate to an area remains a major challenge for aid delivery organizations. We sought to develop an IDP migration forecasting framework that could empower humanitarian aid groups to more effectively allocate resources during conflicts.Methods:We modeled monthly IDP migration between provinces within Syria and within Yemen using data on food prices, fuel prices, wages, location, time, and conflict reports. We compared machine learning methods with baseline persistence methods of forecasting.Results:We found a machine learning approach that more accurately forecast migration trends than baseline persistence methods. A random forest model outperformed the best persistence model in terms of root mean square error of log migration by 26% and 17% for the Syria and Yemen datasets, respectively.Conclusions:Integrating diverse data sources into a machine learning model appears to improve IDP migration prediction. Further work should examine whether implementation of such models can enable proactive aid allocation for IDPs in anticipation of forecast arrivals.

scholarly journals Classifying Multilingual Text in Quality Assurance

2021 ◽  
Vol 12 (3) ◽  
pp. 1987-1992
Author(s):  
Sze Pei Tan Et.al
Keyword(s):  
Machine Learning ◽  
Life Science ◽  
Real Life ◽  
Learning Systems ◽  
Daily Activities ◽  
Multinational Company ◽  
Failure Investigation ◽  
Machine Learning Model ◽  
Multilingual Text ◽  
Life Challenge

Machine learning systems play an important role in helping and assisting engineers in their daily activities. Many jobs can now be automated, and one of them is in handling and processing customers’ complaints before they could proceed with failure investigation. In this paper, we discuss a real-life challenge faced by the manufacturing engineers in a life science multinational company. This paper presents a step by step methodology of multilingual translation and multiclassification of Repair Codes. This solution will allow manufacturing engineers to take advantage of machine learning model to reduce the time taken to manually translate row by row and verify the Repair Codes in the file.

Predicting Job Satisfaction and Employee Turnover Using Machine Learning

2020 ◽  
Vol 17 (9) ◽  
pp. 4092-4097
Author(s):  
Inchara Yogesh ◽  
K. R. Suresh Kumar ◽  
Niveditha Candrashekaran ◽  
Dhrithi Reddy ◽  
Harshitha Sampath
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Succession Planning ◽  
Employee Turnover ◽  
Employee Retention ◽  
Random Forest Classifier ◽  
Machine Learning Techniques ◽  
Positive Feedbacks ◽  
Learning Techniques ◽  
New Employee

Employee turn_over inflicts costs on the company. The employee must be supplanted, and the new employee trained. These quits may likewise make critical and exorbitant interruptions the production process. This gives lucid motivation to the firm to forestall stops or, in any event, to have the option to anticipate when and where stops can be anticipated. On the off chance that employees are approached to assess their superiors and the appropriate responses will be made accessible to the superior, it is most obvious that only positive feedbacks will be provided. Along these lines, the point is to utilize Machine Learning techniques to foresee employee turn_over. Appropriate predictions cause companies to take necessary decisions on employee retention or succession planning. Algorithms: One-Sample T-Test (T-Test), Decision Tree (DT), AdaBoost (AB), Logistic Regression (LR), Random Forest Classifier (RFC).

Study on Data Transfer in Meteorological Forecast of Small and Medium-Sized Cities and Its Application in Zhaoqing City

2019 ◽  
Vol 63 (7) ◽  
pp. 1076-1083
Author(s):  
Tianwen Huang ◽  
Fei Jiao
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Data Transfer ◽  
Moving Average ◽  
Least Square Method ◽  
Forecast Model ◽  
Least Square ◽  
Temperature And Precipitation ◽  
Machine Learning Model ◽  
Accurate Forecast

Abstract Using historical data, a machine learning model is usually built to forecast the future meteorological elements such as temperature, precipitation, etc. However, for numerous small and medium-sized cities, it is a challenging task because the maintained data of these cities are usually very limited due to historical or infrastructural reasons. So it is difficult to build an accurate forecast model in small and medium-sized cities. Aiming at this problem, a forecast method based on transfer learning method is proposed. Using instance-based transfer learning, this method extends the data of the target city by transferring the data from related cities and then builds a forecast model based on the extended dataset, so that the problem of insufficient samples in machine learning is solved. As a case study, the proposed technique is applied in Zhaoqing City, China. In the experiments, the data of temperature sequence and the precipitation sequence of Gaoyao weather station in Zhaoqing district are extended according to the data of related cities. The transferred temperature data and precipitation data are collected from 1884 to 1997 in Hong Kong and 1908 to 2016 in Guangzhou, respectively. Then temperature and precipitation forecasting models are built based on least square method and autoregressive integrated moving average. The experimental results have been verified by the actual situation. The results justify the effectiveness of the proposed method in building accurate meteorological forecasting model with limited data, and the superiority over existing techniques.

scholarly journals Machine intelligence today: applications, methodology, and technology

2021 ◽  
Vol 44 (2) ◽  
pp. 104-114
Author(s):  
Bernhard G. Humm ◽  
Hermann Bense ◽  
Michael Fuchs ◽  
Benjamin Gernhardt ◽  
Matthias Hemmje ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Best Practices ◽  
Machine Intelligence ◽  
Strong Impact ◽  
Industrial Manufacturing ◽  
Knowledge Based ◽  
Current Trends ◽  
Culture Education

AbstractMachine intelligence, a.k.a. artificial intelligence (AI) is one of the most prominent and relevant technologies today. It is in everyday use in the form of AI applications and has a strong impact on society. This article presents selected results of the 2020 Dagstuhl workshop on applied machine intelligence. Selected AI applications in various domains, namely culture, education, and industrial manufacturing are presented. Current trends, best practices, and recommendations regarding AI methodology and technology are explained. The focus is on ontologies (knowledge-based AI) and machine learning.

scholarly journals Learning with Previously Unseen Features

2017 ◽  
Author(s):  
Yuan Shi ◽  
Craig A. Knoblock
Keyword(s):  
Machine Learning ◽  
Joint Distribution ◽  
Learning Systems ◽  
Test Time ◽  
Model Parameters ◽  
Training Set ◽  
Open Environment ◽  
Novel Approach ◽  
Machine Learning Model ◽  
Classification Tasks

We study the problem of improving a machine learning model by identifying and using features that are not in the training set. This is applicable to machine learning systems deployed in an open environment. For example, a prediction model built on a set of sensors may be improved when it has access to new and relevant sensors at test time. To effectively use new features, we propose a novel approach that learns a model over both the original and new features, with the goal of making the joint distribution of features and predicted labels similar to that in the training set. Our approach can naturally leverage labels associated with these new features when they are accessible. We present an efficient optimization algorithm for learning the model parameters and empirically evaluate the approach on several regression and classification tasks. Experimental results show that our approach can achieve on average 11.2% improvement over baselines.

scholarly journals Uplift modeling VS conventional predictive model: A reliable machine learning model to solve employee turnover

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Davin Wijaya ◽  
Jumri Habbeyb DS ◽  
Samuelta Barus ◽  
Beriman Pasaribu ◽  
Loredana Ioana Sirbu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Success Rate ◽  
Predictive Model ◽  
Employee Turnover ◽  
Performance Comparison ◽  
Uplift Modeling ◽  
Machine Learning Model ◽  
Average Accuracy ◽  
Synthetic Datasets ◽  
The Right

Employee turnover is the loss of talent in the workforce that can be costly for a company. Uplift modeling is one of the prescriptive methods in machine learning models that not only predict an outcome but also prescribe a solution. Recent studies are focusing on the conventional predictive models to predict employee turnover rather than uplift modeling. In this research, we analyze whether the uplifting model has better performance than the conventional predictive model in solving employee turnover. Performance comparison between the two methods was carried out by experimentation using two synthetic datasets and one real dataset. The results show that despite the conventional predictive model yields an average prediction accuracy of 84%; it only yields a success rate of 50% to target the right employee with a retention program on the three datasets. By contrast, the uplift model only yields an average accuracy of 67% but yields a consistent success rate of 100% in targeting the right employee with a retention program.

scholarly journals A machine learning based forecast model for the COVID-19 pandemic and investigation of the impact of government intervention on COVID-19 transmission in China

2020 ◽  
Author(s):  
Xingcheng Lu ◽  
Dehao Yuan ◽  
Wanying Chen ◽  
Jimmy Fung
Keyword(s):  
Machine Learning ◽  
Back Propagation ◽  
Forecast Model ◽  
Back Propagation Neural Network ◽  
Flow Index ◽  
Economic Activities ◽  
Government Interventions ◽  
Migration Index ◽  
Machine Learning Model ◽  
The Impact

Abstract The coronavirus disease 2019 (COVID-19) pandemic has killed over 0.3 million people, disrupted people’s normal lives, and severely restricted economic activities globally. In this work, a model for the next-day COVID-19 prediction in China was built based on the ensemble back-propagation neural network machine learning technique, Baidu migration index, internal travel flow index, and confirmed cases from the previous days. The 10-fold cross-validation results showed that the model performs well in estimating the next-day confirmed cases with a correlation coefficient of 0.97. To investigate the impacts of government interventions on the spread of this new coronavirus infection, the Baidu migration index and internal travel flow index multiplied by a factor of two were input into the trained machine learning model, and the results showed that the confirmed cases in the analyzed cities would increase dramatically. The correlation between the daily new confirmed cases and some meteorological factors were also analyzed, and the results revealed that these factors are not dominant in influencing the spread of this disease. Overall, the results of this work suggest that besides early diagnosis and medical treatment, a city lockdown policy is one of the most effective methods in suppressing the rapid spread of COVID-19.

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