ZeroML: A Next Generation AutoML Language

ZeroML is a new generation programming language for AutoML to drive the ML pipeline in a compiled and multi-paradigm way, with a pure functional core. Meeting the shortcomings introduced by Python, R, or Julia such as slow-running time, brittle pipelines or high dependency cost ZeroML brings the Microservices-based architecture adding the modular, reusable pieces such as DataCleaner, FeatureEngineer or ModelSelector. As a native multithread and memory-aware search optimized toolkit, and with one command deployability ability, ZeroML ensures non-coders and ML professionals to create high-accuracy models super fast and in a more reproducible way. The verbosity of the language ensures that when it comes to dropping into the backend, the code we will be creating is extremely clear but the level of repetition and boilerplate required when developing on the front end is now removed.

Towards Trustworthy Keylogger detection: A Comprehensive Analysis of Ensemble Techniques and Feature Selections through Explainable AI

Keylogger detection involves monitoring for unusual system behaviors such as delays between typing and character display, analyzing network traffic patterns for data exfiltration. In this study, we provide a comprehensive analysis for keylogger detection with traditional machine learning models - SVC, Random Forest, Decision Tree, XGBoost, AdaBoost, Logistic Regression and Naive Bayes and advanced ensemble methods including Stacking, Blending and Voting. Moreover, feature selection approaches such as Information gain, Lasso L1 and Fisher Score are thoroughly assessed to improve predictive performance and lower computational complexity. The Keylogger Detection dataset from publicly available Kaggle website is used in this project. In addition to accuracy-based classification, this study implements the approach for model interpretation using Explainable AI (XAI) techniques namely SHAP (Global) and LIME (Local) to deliver finer explanations for how much each feature contributes in assisting or hindering the detection process. To evaluate the models result, we have used AUC score, sensitivity, Specificity, Accuracy and F1 score. The best performance was achieved by AdaBoost with 99.76% accuracy, F1 score of 0.99, 100% precision, 98.6% recall, 1.0 specificity and 0.99 of AUC that is near-perfect classification with Fisher Score.

Linear Discriminant Analysis in Credit Scoring: A Transparent Hybrid Model Approach

The development of computing has made credit scoring approaches possible, with various machine learning (ML) and deep learning (DL) techniques becoming more and more valuable. While complex models yield more accurate predictions, their interpretability is often weakened, which is a concern for credit scoring that places importance on decision fairness. As features of the dataset are a crucial factor for the credit scoring system, we implement Linear Discriminant Analysis (LDA) as a feature reduction technique, which reduces the burden of the models complexity. We compared 6 different machine learning models, 1 deep learning model, and a hybrid model with and without using LDA. From the result, we have found our hybrid model, XG-DNN, outperformed other models with the highest accuracy of 99.45% and a 99% F1 score with LDA. Lastly, to interpret model decisions, we have applied 2 different explainable AI techniques named LIME (local) and Morris Sensitivity Analysis (global). Through this research, we showed how feature reduction techniques can be used without affecting the performance and explainability of the model, which can be very useful in resource-constrained settings to optimize the computational workload.