Employee turnover prediction and retention policies design: a case study

This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design \& test employee retention policies. This type of retention discussion is, to our knowledge, innovative and constitutes the main value of this paper

Basic research planning in mathematical pattern recognition and image analysis

Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis

IMPROVING CREDIT CARD FRAUD DETECTION USING TRANSFER LEARNING AND DATA RESAMPLING TECHNIQUES

This Culminating Experience Project explores the use of machine learning algorithms to detect credit card fraud. The research questions are: Q1. What cross-domain techniques developed in other domains can be effectively adapted and applied to mitigate or eliminate credit card fraud, and how do these techniques compare in terms of fraud detection accuracy and efficiency? Q2. To what extent do synthetic data generation methods effectively mitigate the challenges posed by imbalanced datasets in credit card fraud detection, and how do these methods impact classification performance? Q3. To what extent can the combination of transfer learning and innovative data resampling techniques improve the accuracy and efficiency of credit card fraud detection systems when dealing with imbalanced datasets, and what novel strategies can be developed to address this common challenge? The main findings are: Q1. Unconventional cross-domain methods improved fraud detection, holding promise for enhanced security. Q2. The problems caused by unbalanced datasets in credit card fraud detection were effectively addressed by the synthetic data generation techniques SMOTE and ADASYN, resulting in a more balanced dataset suitable for fraud classification. Q3. The combination of neural networks and data resampling techniques, such as SMOTE and ADASYN, significantly improved credit card fraud detection accuracy. The main conclusions are: Q1. Cross-domain methods are useful for credit card fraud detection, especially when it comes to online transactions. Q2. When used with various classifiers, neural networks show remarkable accuracy rates: 97% for unbalanced data, 99.47% for SMOTE, and 99.11% for ADASYN Q3. A fraud recall of 0.99 is obtained by the model evaluation on imbalanced data, with 12,155 right predictions out of 12,336 and 181 incorrect ones. The identified areas for further study encompass the testing of our model on larger datasets and the optimization of hyperparameters for further enhancement

SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach

This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank's control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naive Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environmentsComment: E-Preprin

Handling minority class problem in threats detection based on heterogeneous ensemble learning approach.

Multiclass problem, such as detecting multi-steps behaviour of Advanced Persistent Threats (APTs) have been a major global challenge, due to their capability to navigates around defenses and to evade detection for a prolonged period of time. Targeted APT attacks present an increasing concern for both cyber security and business continuity. Detecting the rare attack is a classification problem with data imbalance. This paper explores the applications of data resampling techniques, together with heterogeneous ensemble approach for dealing with data imbalance caused by unevenly distributed data elements among classes with our focus on capturing the rare attack. It has been shown that the suggested algorithms provide not only detection capability, but can also classify malicious data traffic corresponding to rare APT attacks

LANDSAT information for state planning

The transfer of remote sensing technology for the digital processing of LANDSAT data to state and local agencies in Georgia and other southeastern states is discussed. The project consists of a series of workshops, seminars, and demonstration efforts, and transfer of NASA-developed hardware concepts and computer software to state agencies. Throughout the multi-year effort, digital processing techniques have been emphasized classification algorithms. Software for LANDSAT data rectification and processing have been developed and/or transferred. A hardware system is available at EES (engineering experiment station) to allow user interactive processing of LANDSAT data. Seminars and workshops emphasize the digital approach to LANDSAT data utilization and the system improvements scheduled for LANDSATs C and D. Results of the project indicate a substantially increased awareness of the utility of digital LANDSAT processing techniques among the agencies contracted throughout the southeast. In Georgia, several agencies have jointly funded a program to map the entire state using digitally processed LANDSAT data