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>
Android ransomware is one of the most threatening attacks that is increasing at an alarming rate. Ransomware attacks usually target Android users by either locking their devices or encrypting their data files and then requesting them to pay money to unlock the devices or recover the files back. Existing solutions for detecting ransomware mainly use static analysis. However, limited approaches apply dynamic analysis specifically for ransomware detection. Furthermore, the performance of these approaches is either poor or often fails in the presence of code obfuscation techniques or benign applications that use cryptography methods for their APIs usage. Additionally, most of them are unable to detect ransomware attacks at early stages. Therefore, this paper proposes a hybrid detection system that effectively utilizes both static and dynamic analyses to detect ransomware with high accuracy. For the static analysis, the proposed hybrid system considered more than 70 state-of-the-art antivirus engines. For the dynamic analysis, this research explored the existing dynamic tools and conducted an in-depth comparative study to find the proper tool to integrate it in detecting ransomware whenever needed. To evaluate the performance of the proposed hybrid system, we analyzed statically and dynamically over one hundred ransomware samples. These samples originated from 10 different ransomware families. The experiments’ results revealed that static analysis achieved almost half of the detection accuracy—ranging around 40–55%, compared to the dynamic analysis, which reached a 100% accuracy rate. Moreover, this research reports some of the high API classes, methods, and permissions used in these ransomware apps. Finally, some case studies are highlighted, including failed running apps and crypto-ransomware patterns.
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