Brain games (BGs) have long been recognized as one of the most cost-effective tools for cognitive stimulation. However, the underlying mechanisms that could enhance their effectiveness and acceptance remain underexplored. This research aims to shed light on how motivation and engagement influence performance across single vs. dual player desktop computer BGs among children. Performance as well as motivation and engagement were assessed through the mixed methods research in a sample of 117 participants during a series of BGs play across both modes. The BGs include Picture Puzzle, Letter and Number, and Find the Difference, which focuses on the player’s memory, analytical ability, and vision, respectively. The results revealed, in the context of BGs, single-player mode tended to be more engaging, while the dual player mode was generally more motivating. The dual player mode generally performed better in the memorization-and-recalling activity of Picture Puzzle and the visualization-and-searching activity of Find the Difference. In the analytical activity of Letters and Numbers, accuracy was higher in the dual player mode. Still, their efficiency was higher in single-player mode. A significant correlation was found between and within the performance subcomponents, motivation, and engagement across both modes of BGs. Particularly, they were reported as generally correlated and enhancing each other in dual player mode, while in single-player mode, motivation was reported as a non-contributing factor in enhancing performance subcomponents. Further quantitative findings concerning the employed BGs in each mode are also discussed.

Due to the increase in world population, arable land has been reduced. Consequently, the concept of urban greenhouses is on the rise. Smart greenhouses need to monitor physical parameters for the healthy growth of plants from remote locations. A digital twin is a representation of physical assets in the digital world, and this emerging technology has opened up opportunities for efficient system development for Industry 4.0. The digital twin receives real-time operational data to monitor the asset in the digital domain. It performs real-time processing, data analysis, and machine learning to predict optimized decisions. In the era of next-generation mobile networks, IoT devices can communicate and perform their remote operations in a timely manner. In smart greenhouse technology, the digital twin could be a revolutionary substitute for real-time remote monitoring and process management. However, there has been limited work on digital twin-driven smart greenhouse technology. In this paper, a process management framework is developed that can be interpreted as a machine learning and cloud-based data-driven digital twin for smart greenhouses. The proposed framework consists of three layers: the physical, fog, and cloud layers. The physical greenhouse measurements are monitored using a highly immersive cloud-based, real-time 3D environment. We present an example architecture using commercial cloud and open-source tools to verify the proof of concept. Additionally, different ML techniques are utilized to predict the operational requirements for smart greenhouses.

The liver is the largest organ of the human body with more than 500 vital functions. In recent decades, a large number of liver patients have been reported with diseases such as cirrhosis, fibrosis, or other liver disorders. There is a need for effective, early, and accurate identification of individuals suffering from such disease so that the person may recover before the disease spreads and becomes fatal. For this, applications of machine learning are playing a significant role. Despite the advancements, existing systems remain inconsistent in performance due to limited feature selection and data imbalance. In this article, we reviewed 58 articles extracted from 5 different electronic repositories published from January 2015 to 2023. After a systematic and protocol‐based review, we answered 6 research questions about machine learning algorithms. The identification of effective feature selection techniques, data imbalance management techniques, accurate machine learning algorithms, a list of available data sets with their URLs and characteristics, and feature importance based on usage has been identified for diagnosing liver disease. The reason to select this research question is, in any machine learning framework, the role of dimensionality reduction, data imbalance management, machine learning algorithm with its accuracy, and data itself is very significant. Based on the conducted review, a framework, machine learning‐based liver disease diagnosis (MaLLiDD), has been proposed and validated using three datasets. The proposed framework classified liver disorders with 99.56%, 76.56%, and 76.11% accuracy. In conclusion, this article addressed six research questions by identifying effective feature selection techniques, data imbalance management techniques, algorithms, datasets, and feature importance based on usage. It also demonstrated a high accuracy with the framework for early diagnosis, marking a significant advancement.