With the advancement of the internet, individuals are becoming more reliant on online applications to meet most of their needs. In the meantime, they have very little spare time to devote to the selection and decision-making process. As a result, the need for recommender systems to help tackle this problem is expanding. Recommender systems successfully provide consumers with individualized recommendations on a variety of goods, simplifying their duties. The goal of this research is to create a recommender system for farmers based on tree data structures. Recommender system has become interesting research by simplifying and saving time in the decision-making process of users. We conducted although a lot of research in various fields, there are insufficient in the agriculture sector. This issue is more necessary for farmers in Quangnam-Danang or all Vietnam countries by severe climate features. Storm from that, this research designs a system based on tree data structures. The proposed model combines the you only look once (YOLO) algorithm in a convolutional neural network (CNN) model with a similarity tree in computing similarity. By experiments on 400 samples and evaluating precision, accuracy, and the value of the predictive test as determined by its positive predictive value (PPV), the research proves that the proposed model is feasible and gain better results compared with other state-of-the-art models.

Virtual machines migration is an essential feature of virtualisation technology which brings many advantages in cloud computing management process such as proactive fault tolerance, load balancing, and power management. There have been many virtual migration algorithms that have been developed but it is hard to find one that best suits all applications as well as ensures the desire of all stakeholders. The optimal virtual machines migration of cloud computing is usually NP‐hard or NP‐complete. In addition, determining when virtual machines migrate is a challenge because of the difficulty of fault detection in cloud computing. In this study, the authors propose a virtual machines migration game approach for multi‐tier application in infrastructure‐as‐a‐service cloud computing where they first proposed fault detection model based on Takagi–Sugeno fuzzy system with metric's physical machines and then optimal or near optimal migration solution is approximated based on Nash equilibrium. The results of experiment demonstrate the efficiency of their proposal. The proposed virtual machines migration algorithm has been benchmarked in order to highlight their strength and feasibility.

Plastic pollution is an emerging entity threatening freshwater and marine ecosystems. Rivers play an important role in the transport and retention of plastic between land and sea. Tropical rivers are among the most polluted globally, and are assumed to emit large amounts of plastic into the ocean. Concurrently, tropical rivers are excellent habitats for invasive water hyacinths (Eichornia crassipes), a free-floating vegetation species. Water hyacinths grow rapidly, forming dense mats of plants and other material including plastic pollution. With only limited anecdotal and scientific evidence of plastic-water hyacinth trapping, its spatial extent along river systems remain unknown. Here, we demonstrate the consistent role of water hyacinths as carriers of plastic pollution along the lower Saigon river, Vietnam. Over 69k plastic items and 57k water hyacinths were identified at five locations along the 42 km of the lower Saigon river. We quantified plastic and water hyacinths using drones and fixed cameras, in combination with a custom-trained YOLOv8 deep learning model. Water hyacinths carried 73% of all floating plastics, ranging between 58%–82% per specific location. The highest trapping ratio was found at the most upstream locations. Although water hyacinths only covered 1.3% of the total river surface, the surface plastic concentration in water hyacinths was 197 times higher than in water not covered by water hyacinths (open water). Most downstream, the lowest water hyacinth coverage (0.2%) corresponded to the largest difference between surface plastic concentration in water hyacinths and at the open water (factor 781). Previous work demonstrated the effective trapping of plastic pollution by water hyacinths at individual sites. With our paper, we support the hypothesis that water hyacinths effectively trap and carry plastic pollution along rivers. Further work on plastic-water hyacinth co-occurrence is needed to better understand the transport, fate and impact of plastic in the world’s most polluted rivers.

Proactive Fault tolerant (FT) approaches for infrastructure's cloud computing which have been increasingly developed in research scope with the support of machine learning need consistent monitoring and highly accurate predicting systems. However, it is difficult to implement the proactive fault tolerant approach of infrastructure's cloud computing because of complex architecture, dynamic services, and many interdependent factors existing in cloud environment. To overcome this problem, a proactive FT approach for cloud‐hosted application is proposed to achieve a high degree of automation, including physical machine (PM) fault analysis and self‐learning virtual machine (VM) migration. It is based on MAPE‐K (Monitor‐Analyze‐Plan‐Execute over a shared Knowledge) loop to take into account the knowledge inside controllers in run time. To evaluate PM fault analysis model, the web e‐Commerce is deployed by OpenStack Stein while the prototype of VM migration component is built by CloudSim for VM migration solution evaluation. The effectiveness of the approach is demonstrated in experiment results.