Over the past few years, total financial investment in the agricultural sector has increased substantially. Palm tree is important for many countries' economies, particularly in northern Africa and the Middle East. Monitoring in terms of detection and counting palm trees provides useful information for various stakeholders; it helps in yield estimation and examination to ensure better crop quality and prevent pests, diseases, better irrigation, and other potential threats. Despite their importance, this information is still challenging to obtain. This study systematically reviews research articles between 2011 and 2021 on artificial intelligence (AI) technology for smart palm tree detection. A systematic review (SR) was performed using the PRISMA approach based on a four-stage selection process. Twenty-two articles were included for the synthesis activity reached from the search strategy alongside the inclusion criteria in order to answer to two main research questions. The study's findings reveal patterns, relationships, networks, and trends in applying artificial intelligence in palm tree detection over the last decade. Despite the good results in most of the studies, the effective and efficient management of large-scale palm plantations is still a challenge. In addition, countries whose economies strongly related to intelligent palm services, especially in North Africa, should give more attention to this kind of study. The results of this research could benefit both the research community and stakeholders.

Long-standing data sparsity and cold-start constitute thorny and perplexing problems for the recommendation systems. Cross-domain recommendation as a domain adaptation framework has been utilized to efficiently address these challenging issues, by exploiting information from multiple domains. In this study, an item-level relevance cross-domain recommendation task is explored, where two related domains, that is, the source and the target domain contain common items without sharing sensitive information regarding the users' behavior, and thus avoiding the leak of user privacy. In light of this scenario, two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation. The first method aims to simultaneously learn a pair of autoencoders in order to reveal the intrinsic representations of the items in the source and target domains, along with a coupled mapping function to model the non-linear relationships between these representations, thus transferring beneficial information from the source to the target domain. The second method is derived based on a new joint regularized optimization problem, which employs two autoencoders to generate in a deep and non-linear manner the user and item-latent factors, while at the same time a data-driven function is learnt to map the item-latent factors across domains. Extensive numerical experiments on two publicly available benchmark datasets are conducted illustrating the superior performance of our proposed methods compared to several state-of-the-art cross-domain recommendation frameworks.

Trundling around the hallways of Hong Kong's Cyberport innovation hub, the little Rice Robot is on a mission. The stocky white cuboid resembles Star Wars' R2D2 robot in its build, but has the wide-eyed expression of Pixar's WALL-E. It's delivering drinks to patrons of the HFT Life cafe in a compartment in its "head" which is unlocked by the customer using a PIN code sent to their phone. While Rice's operations at the cafe are limited to distributing drinks, the compact robot is already providing a range of services at venues in Hong Kong and Japan. Rice is deployed as a bellhop at Hong Kong's Dorsett Wanchai hotel, providing room service to guests.

In this article, we are going to see what is a recommendation system, the use cases of recommendation systems, why we use recommendation systems, and what are the types of recommendation systems. So without wasting any time let's start this article with a short intro about recommendation systems in Machine Learning. As we know that Netflix uses a recommendation system to recommend movies and web series on the behalf of user interest and Youtube also uses a recommendation system to recommend videos so that users can spend more time on their platforms. The use cases of recommendation systems have been increasing consistently and there could be no better time than now to dive deeper into this excellent machine learning technology so that we can also utilize this technique in the right direction. Recommendation systems are like filtering systems that attempt to predict the rating or preference a user might give an item.

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Social media platforms have become a place where users collaborate, share their ideas and also have conflicts (Hansson et al., 2019; Hansson and Ludwig, 2019). With 126 million active daily users (Shaban, 2019), Twitter is the dominant microblogging platform on which users discuss a breadth of subjects and even play a role in influencing current trends. Users on Twitter post short and often informal messages (tweets) in which they share information and project opinions and sentiments about what is going on in the world. Twitter has been a major platform for sharing scholarly articles, and many researchers have used it to develop various metrics for scholarly articles (Haustein, 2019). Other social media platforms like Facebook and Weibo have also been sources to study online users' responses (Kou et al., 2017). Social media platforms have become a hub where users express their opinions and emotions related to multiple fields of interest (Chatterjee et al., 2019). Researchers have studied the sentiments and emotions associated with research articles on these platforms (Freeman et al., 2019, 2020).

Netflix has so far only managed to convince 1.7 million people among its 221 million subscribers to play games on its platform daily,