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There are more than 962 million people aged 60 and up globally. Physical activity declines as people get older, as does their capacity to undertake everyday tasks, effecting both physical and mental health. Many researchers use machine learning and deep learning methods to recognize human activities, but very few studies have been focused on human activity recognition of elderly people. This paper focuses on providing assistance to elderly people by monitoring their activities in different indoor and outdoor environments using gyroscope and accelerometer data collected from a smart phone. Smart phones have been routinely used to monitor the activities of persons with impairments; routine activities such as sitting, walking, going upstairs, going downstairs, standing, and lying are included in the dataset. Conventional Machine Learning and Deep Learning algorithms such as k-Nearest Neighbors, Random Forest, Support Vector Machine, Artificial Neural Network, and Long Short-Term Memory Network are used for human activity recognition. Long Short-Term Memory is a recurrent neural network variation that is best suited to handling temporal sequences. Two-fold and ten-fold cross-validation methods were performed to show the effect of changing the data in the training and testing dataset. Among all the classification techniques, the proposed Long Short-Term Memory Network gave the best accuracy of 95.04%. However, Support Vector Machine gave 89.07% accuracy with a very low computational time of 0.42 min using 10-fold cross-validation. [ABSTRACT FROM AUTHOR]

The proliferation of new technologies and advancements in existing ones are altering our perspective of the world. So, continuous improvements are needed. A connected world filled with a vast amount of data was created as a result of the integration of these advanced technologies in the financial sector. The advantages of this connection came at the cost of more sophisticated and advanced attacks, such as fraudulent transactions. To address these illegal transactions, researchers and engineers have created and implemented various systems and models to detect fraudulent transactions; many of them produce better results than others. On the other hand, criminals change their strategies and technologies to imitate legitimate transactions. In this article, the objective is to propose an intelligent system for detecting fraudulent transactions using various deep learning architectures, including artificial neural networks (ANNs), recurrent neural networks (RNNs), and long short-term memory (LSTM). Furthermore, the Bayesian optimization algorithm is used for hyperparameter optimization. For the evaluation, a credit card fraudulent transaction dataset was used. Based on the many experiments conducted, the RNN architecture demonstrated better efficiency and yielded better results in a shorter computational time than the ANN LSTM architectures. [ABSTRACT FROM AUTHOR]