Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

Nowadays, due to the great accessibility to the internet, people seek out and consume news via social media due to its low cost, ease of access, and quick transmission of information. The tremendous leverage of social media applications in daily life makes them significant information sources. Users can post and share different types of information in all their forms with a single click. However, the cost becomes expensive and dangerous when non-experts say anything about anything. Fake news are rapidly dominating the dissemination of disinformation by distorting people’s views or knowledge to influence their awareness and decision-making. Therefore, we have to identify and prevent the problematic effects of falsified information as soon as possible. In this paper, we conducted three experiments with machine learning classifiers, deep learning models, and transformers. In all experiments, we relied on word embedding to extract contextual features from articles. Our experimental results showed that deep learning models outperformed machine learning classifiers and the BERT transformer in terms of accuracy. Moreover, results showed almost the same accuracy between the LSTM and GRU models. We showed that by combining an augmented linguistic feature set with machine or deep learning models, we can, with high accuracy, identify fake news.

The revolutionary in cyber attacks, especially in smartphones are rising. The Android operating system is becoming one of the most leading operating systems. Therefore, Android malware is rising in terms of popularity. Malware makers are using novel techniques to develop malicious Android applications, drastically diminishing the capabilities of traditional malware detectors. In consequence, those Anti-malware detectors become unable to detect these unexplained malicious apps. Currently, machine learning techniques are extensively used to discover new unknown Android viruses by analyzing the functionality of static and dynamic app reviews. In this paper, we introduce an Android malware detection technique based on API and permissions. Our purpose is to evaluate and examine the incorporation of machine learning classifiers with featured Android features such as APIs and permissions. We investigated several classification methods in characterizing Android malware with respect to the used feature. We discovered varied performance when we analyses all Android malware detection classifiers that use machine learning, suggesting that machine learning algorithms are effectively utilized in this area of identifying Android malicious apps.

The revolutionary in cyber attacks, especially in smartphones are rising. The Android operating system is becoming one of the most leading operating systems. Therefore, Android malware is rising in terms of popularity. Malware makers are using novel techniques to develop malicious Android applications, drastically diminishing the capabilities of traditional malware detectors. In consequence, those Anti-malware detectors become unable to detect these unexplained malicious apps. Currently, machine learning techniques are extensively used to discover new unknown Android viruses by analysing the functionality of static and dynamic app reviews. In this paper, we introduce an Android malware detection technique based on API and permissions. Our purpose is to evaluate and examine the incorporation of machine learning classifiers with featured Android features such as APIs and permissions. We investigated several classification methods in characterizing Android malware with respect to the used feature. We discovered varied performance when we analyses all Android malware detection classifiers that use machine learning, suggesting that machine learning algorithms are effectively utilised in this area of identifying Android malicious apps.