Keylogging has become a crucial attacking strategy adopted by cyber criminals to fetch keystrokes of legitimate users during their interaction with computer systems. The data gathered serves as a powerful weapon for attackers to exploit. Existing techniques for detecting or preventing keylogging operations rely on mundane systems or traditional machine learning algorithms that lack deep intelligence to identify and mitigate activities of keyloggers accurately due to the volume of data that is generated from keystrokes with simple interactions from the users. This study developed a deep learning-based predictive model capable of preventing keylogging attacks in human-computer interaction using hyperband hyperparameter tuning and a hybrid genetic algorithm on stochastic gradient descent with Adam-based LSTM optimization on a UNSW-NB keylogging dataset with nine families of attacks to build the deep learning model called HH-GASA-OLSTM. From the experiments, the proposed predictive model on an 80:20 validation ratio gives the best result on six performance metrics, which are loss function of 0.0187, accuracy of 99.45%, precision of 0.9930, recall of 0.9945, F1-Score of 0.9938, MCC of 0.9889, RMSE of 0.0054, MAE of 0.0054 and MSE of 0.00003. These results are suitable for the accurate detection and prevention of keylogging attacks in HCI. The study therefore recommends further studies; the implementation of other hyperparameter tuning approaches with other parameter optimization techniques using other deep learning architectures, such as radial basis function network and deep belief network, for improved model optimization.

Olympic Games are international field and track events hosted within four years periods. Like other events, sprinting is a track event that requires rigorous and focused training. When training is done with little or no understanding of the possibilities of the games, the competition would leave more to be desired. This paper formulates, evaluates and validates a model for predicting the fastest sprinting time of Olympic athletes of 100m race for a-5 season appearances. Dataset was obtained from the Olympic official records of world best performances, typically Gold medalists in sprint for the male category from the inception in 1896 to the 2016 edition. The model was simulated on MATLAB. Cross-validation was done using residuals for whiteness and independence tests and model outputs. The results were evaluated based on Sum of Square Error (SSE), R-Square, adjusted R-Square, and Root Mean Square Error (RMSE) and benchmarked with existing models. The model outperformed the existing models with higher accuracy and goodness of fit. This prediction is a reasonable guide for predictive training, forecasting and future study on predictive algorithms.