Diabetes is one of the most common diseases worldwide. Many Machine Learning (ML) techniques have been utilized in predicting diabetes in the last couple of years. The increasing complexity of this problem has inspired researchers to explore the robust set of Deep Learning (DL) algorithms. The highest accuracy achieved so far was 95.1% by a combined model CNN-LSTM. Even though numerous ML algorithms were used in solving this problem, there are a set of classifiers that are rarely used or even not used at all in this problem, so it is of interest to determine the performance of these classifiers in predicting diabetes. Moreover, there is no recent survey that has reviewed and compared the performance of all the proposed ML and DL techniques in addition to combined models. This article surveyed all the ML and DL techniques-based diabetes predictions published in the last six years. In addition, one study was developed that aimed to implement those rarely and not used ML classifiers on the Pima Indian Dataset to analyze their performance. The classifiers obtained an accuracy of 68%–74%. The recommendation is to use these classifiers in diabetes prediction and enhance them by developing combined models.
<span>Nowadays the entire world depends on emails as a communication tool. Spammers try to exploit various vulnerabilities to attack users with spam emails. While it is difficult to prevent spam email attacks, many research studies have been developed in the last decade in an attempt to detect spam emails. These studies were conducted using machine learning techniques and various types of neural networks. However, with all their attempts the highest accuracy acquired was 94.2% by random forest classifier. Deep learning techniques have demonstrated higher accuracy performance compared to the traditional machine learning algorithms. In this paper, deep recurrent neural network was used to determine whether an email is a spam email. After investigating different configurations for this method, the best setting that generated the highest accuracy was based on using Tanh as the activation function with the dropout rate equals to 0.1 and the number of epochs achieving 100. The proposed approach attained a high accuracy of 99.7% which surpassed the best accuracy (98.7%) obtained by the hybrid gated recurrent unit recurrent neural network approach.</span>
Assistive technology has been a significant topic in both the research and manufacturing industry for the past decades focusing on improving social interaction, supporting health care, business, education, and daily activities. There are different types of assistive technologies such as wearable devices, mobile applications, automated home appliances, and robots. There is lack of study conducted to investigate the use of robots among disabled users, such as adult users with motor disabilities.
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