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Big2 is one of the most popular card games in Chinese residential regions; however, there is lack of advanced computer players with challenging artificial intelligence. In this study, we propose the Big2 artificial intelligence (Big2AI) framework consisting of card superiority analysis, dynamic weight adjustment, game feature learning, and multi-opponent movement prediction based on Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo Tree Search (ISMCTS). According to our review of relevant research, this is the first artificial intelligence framework that can perform self-playing with various computer players, improve win rates through historical game features, and predict multiple movements of multiple opponents in the card game Big2. An Android-based prototype of four-player Big2 game is implemented to verify the feasibility and superiority of Big2AI. Experimental results show that Big2AI outperforms existing artificial intelligence and can achieve the highest win rate and the least losing points against computer and human players in Big2 games.

Following the rapid development of information and communication technology, and the huge amounts of data that have undergone explosive growth, artificial intelligence and machine learning have been used for predictive analysis in many fields. However, the prediction accuracy of these machine learning recognition models depends on the quality of the features selected for training. It is therefore very important to analyse characteristics that are meaningful and in line with the target variables as the training conditions for machine learning recognition models. In this paper, we analyse the correlation between features and target variables using the Pearson product-moment correlation coefficient, and integrate transfer learning technology for sequential feature extraction to enhance the prediction accuracy of a machine learning recognition model for the prediction of multiple crop pests and diseases as the performance verification target of the proposed method. The performance of our machine learning recognition model is compared with schemes in related work, and our approach is shown to increase the prediction accuracy by between 3% and 15%.

In this paper, we propose a smart campus care and guiding framework with deep learning-based face recognition, called DeepGuiding, for students through Internet of Things technologies. The DeepGuiding framework can construct the dedicated video trajectory of a campus student, where the recorded video for each student can be automatically classified to achieve efficient footprint review as necessary. In addition, DeepGuiding can provide time-efficient indoor and outdoor guiding in a campus to quickly reach places, meet friends, and find students. To the best of our knowledge, DeepGuiding is the first campus care and guiding system which provides the following features: 1) it achieves the seamless outdoor and indoor navigation between buildings in a campus; 2) it keeps additional construction cost low by utilizing existing surveillance cameras in a campus; and 3) it reduces the total searching time for finding a specific event/target in a campus by alleviating time-consuming labor overhead to review a huge amount of video data. An Android-based prototype using iBeacon indoor localization and global positioning system outdoor positioning with surveillance cameras is implemented to verify the feasibility and superiority of our DeepGuiding framework. The Experimental results show that DeepGuiding outperforms existing face recognition methods and can achieve high recognition accuracy for students not close to surveillance cameras.

A monitoring system for smart machinery has been considered to be one of the most important goals in recent enterprises. This monitoring system will encounter huge difficulties, such as more data uploaded by smart machines, and the available internet bandwidth will influence the transmission speed of data and the reliability of the equipment monitoring platform. This paper proposes reducing the periodical information that has been uploaded to the monitoring platform by setting an upload event through the traits of production data from machines. The proposed methods reduce bandwidth and power consumption. The monitoring information is reconstructed by the proposed methods, so history data will not reduce storage in the cloud server database. In order to reduce the halt time caused by machine error, the proposed system uses machine-learning technology to model the operating status of machinery for fault prediction. In the experimental results, the smart machinery monitoring system using the Industrial Internet of Things reduces the volume of information uploaded by 54.57% and obtains a 98% prediction accuracy.

In this paper, we propose a cyber-physical signage interacting framework for the interaction between digital signage and mobile users using smart handheld devices, such as smartphones and tablets. The proposed framework can provide diverse multimedia/feedback services to mobile users interacting with digital signage through face detection, classification, and recognition techniques based on mobile cloud computing. Mobile users only need to click and drag the interested service over the face of advertising celebrities/endorsers displayed on digital signage and can obtain supplementary multimedia information or feedback related comments. Using the intuitive way of gesture-based operations, mobile users can directly interact with digital signage through their handheld devices. Our framework reveals an innovative human-machine interface for signage interacting between digital signage and mobile users. In addition, we integrate our framework with a face cache mechanism that can make the interaction delay as small as possible for popular signage. Furthermore, an Android-based signage interacting system is implemented to verify the feasibility and superiority of our framework. Experimental results show that our approach outperforms the existing methods and can significantly reduce the average consumption time of obtaining interested multimedia contents from digital signage.

Mobile ad hoc networks (MANETs) have gained a lot of interests in research communities for the infrastructure-less self-organizing nature. A MANET with fleet cyclists using smartphones forms a two-tier mobile long-thin network (MLTN) along a common cycling route, where the high-tier network is composed of 3G/LTE interfaces and the low-tier network is composed of IEEE 802.11 interfaces. The low-tier network may consist of several path-like networks. This work investigates cooperative sensing data collection and distribution with packet collision avoidance in a two-tier MLTN. As numbers of cyclists upload their sensing data and download global fleet information frequently, serious bandwidth and latency problems may result if all members rely on their high-tier interfaces. We designed and analyzed a cooperative framework consisting of a distributed grouping mechanism, a group merging and splitting method, and a sensing data aggregation scheme. Through cooperation between the two tiers, the proposed framework outperforms existing works by significantly reducing the 3G/LTE data transmission and the number of 3G/LTE connections.