Mobile applications based on machine learning are reshaping and affecting many aspects of our lives. Implementing machine learning on mobile devices faces various challenges, including computational power, energy, latency, low memory, and privacy risks. In this article, we…

Mobile health (mHealth) is considered one of the most transformative drivers for health informatics delivery of ubiquitous medical applications. Machine learning has proven to be a powerful tool in classifying medical images for detecting various diseases. However, supervised machine learning requires a large amount of data to train the model, whose storage and processing pose considerable system requirements challenges for mobile applications. Therefore, many studies focus on…

Online reviews are valuable sources of relevant information that can support users in their decision making. An estimated 92% of online shoppers read online reviews, 88% trust online reviews as much as personal recommendations and they typically read more than 10 reviews to form an opinion. The objective

is to propose a framework aimed at improving user experience when faced with an…

Even though the most online review systems offer star rating in addition to free text reviews, this only applies to the overall review. However, different users may have different preferences in relation to different aspects of a product or a service and may struggle to extract relevant information from a massive amount of consumer reviews available online. In this paper, we present a framework for extracting prevalent topics from online reviews and automatically rating them on a 5-star…