A Military Human Performance Management System Design using Machine Learning Algorithms

The case highlights the key concerns and issues an organization may face if its performance management system (PMS) does not serve its intended purpose and how it can cause demotivation and dissatisfaction among employees and result in an overall decline in organizational performance. It also illustrates how employees’ organizational culture and perception can be as important as the system design and can present challenges even if an elaborate system is planned. In the face of increasing turmoil among employees towards management and the decreasing performance of the organization, TECHNO21 decided to change its current PMS, which seemed a difficult but necessary step to achieve the desired performance levels and overall strategic objectives of the organization.

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Predicting Student Failure in University Examination using Machine Learning Algorithms

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.e2643.039520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 956-959

Keyword(s):

Machine Learning ◽

Data Mining ◽

Performance Management ◽

Student Performance ◽

Learning Algorithms ◽

Educational Data Mining ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Social Characteristics ◽

Student Failure

Student Performance Management is one of the key pillars of the higher education institutions since it directly impacts the student’s career prospects and college rankings. This paper follows the path of learning analytics and educational data mining by applying machine learning techniques in student data for identifying students who are at the more likely to fail in the university examinations and thus providing needed interventions for improved student performance. The Paper uses data mining approach with 10 fold cross validation to classify students based on predictors which are demographic and social characteristics of the students. This paper compares five popular machine learning algorithms Rep Tree, Jrip, Random Forest, Random Tree, Naive Bayes algorithms based on overall classifier accuracy as well as other class specific indicators i.e. precision, recall, f-measure. Results proved that Rep tree algorithm outperformed other machine learning algorithms in classifying students who are at more likely to fail in the examinations.

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Waste Management System Fraud Detection Using Machine Learning Algorithms to Minimize Penalties Avoidance and Redemption Abuse

Recycling ◽

10.3390/recycling6040065 ◽

2021 ◽

Vol 6 (4) ◽

pp. 65

Author(s):

Ali Hewiagh ◽

Kannan Ramakrishnan ◽

Timothy Tzen Vun Yap ◽

Ching Seong Tan

Keyword(s):

Machine Learning ◽

Random Forest ◽

Waste Management ◽

Management System ◽

Learning Algorithms ◽

Fraud Detection ◽

Machine Learning Algorithms ◽

Support Vector ◽

Data Set ◽

Waste Management System

Online frauds have pernicious impacts on different system domains, including waste management systems. Fraudsters illegally obtain rewards for their recycling activities or avoid penalties for those who are required to recycle their own waste. Although some approaches have been introduced to prevent such fraudulent activities, the fraudsters continuously seek new ways to commit illegal actions. Machine learning technology has shown significant and impressive results in identifying new online fraud patterns in different system domains such as e-commerce, insurance, and banking. The purpose of this paper, therefore, is to analyze a waste management system and develop a machine learning model to detect fraud in the system. The intended system allows consumers, individuals, and organizations to track, monitor, and update their performance in their recycling activities. The data set provided by a waste management organization is used for the analysis and the model training. This data set contains transactions of users’ recycling activities and behaviors. Three machine learning algorithms, random forest, support vector machine, and multi-layer perceptron are used in the experiments and the best detection model is selected based on the model’s performance. Results show that each of these algorithms can be used for fraud detection in waste managements with high accuracy. The random forest algorithm produces the optimal model with an accuracy of 96.33%, F1-score of 95.20%, and ROC of 98.92%.

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