Essential Feature - Cooperative Gameplay

Although single player and multiplayer is very important in today game, cooperative mode is an essential part of a great game. There are a lot of benefits of playing co-op mode in a game such as education and joy. Communicating, solving problems, handling stress, managing time, making decision, following instructions, acting fast as well as working in a team are skills that students can learn and practice while they are playing cooperative games. These skills are valuable for students to use in education and even in careers

Wavelet-Based Kernel Construction for Heart Disease Classification

© 2019 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERINGHeart disease classification plays an important role in clinical diagnoses. The performance improvement of an Electrocardiogram classifier is therefore of great relevance, but it is a challenging task too. This paper proposes a novel classification algorithm using the kernel method. A kernel is constructed based on wavelet coefficients of heartbeat signals for a classifier with high performance. In particular, a wavelet packet decomposition algorithm is applied to heartbeat signals to obtain the Approximation and Detail coefficients, which are used to calculate the parameters of the kernel. A principal component analysis algorithm with the wavelet-based kernel is employed to choose the main features of the heartbeat signals for the input of the classifier. In addition, a neural network with three hidden layers in the classifier is utilized for classifying five types of heart disease. The electrocardiogram signals in nine patients obtained from the MIT-BIH database are used to test the proposed classifier. In order to evaluate the performance of the classifier, a multi-class confusion matrix is applied to produce the performance indexes, including the Accuracy, Recall, Precision, and F1 score. The experimental results show that the proposed method gives good results for the classification of the five mentioned types of heart disease.Peer reviewedFinal Published versio

A TWO-YEAR FIELD STUDY OF PHYTO REMEDIATION USING SOLANUM NIGRUM L. IN DONGNAI, VIETNAM

A two- year in-situ phytoremediation trial was conducted in Dongnai province. The phytoremediation efficiency of Solanum nigurm L. was detected, by monitoring the change of soil Cadmium level in the 0 - 20 cm soil depth. The results indicate that soil Cd decreased significantly by planting S. nigrum. The Cd concentrations decreased averagely from 2.75 mg kg-1 to 2.45 mg kg-1 in the first year and 2.33 mg kg-1 to 1.53 mg kg-1 in the second year, separately. Decrease by a factor of 10.6 % in first year and 12 % second year. After two years phytoremediation by S. nigrum, Cd concentrations of the seven experimental plots with S. nigrum growth decreased from 2.75 mg kg-1 to 1.53 mg kg-1, and decrease by a factor of 24.9%. Therefore, using S. nigrum for phytoremediation of Cd contaminated farmland soils seems very promising, and we can conclude that S. nigrum will get a better performance in the warmer area, as the temperature of the experimental area is relatively lower

A Novel Explainable Artificial Intelligence Model in Image Classification problem

In recent years, artificial intelligence is increasingly being applied widely in many different fields and has a profound and direct impact on human life. Following this is the need to understand the principles of the model making predictions. Since most of the current high-precision models are black boxes, neither the AI scientist nor the end-user deeply understands what's going on inside these models. Therefore, many algorithms are studied for the purpose of explaining AI models, especially those in the problem of image classification in the field of computer vision such as LIME, CAM, GradCAM. However, these algorithms still have limitations such as LIME's long execution time and CAM's confusing interpretation of concreteness and clarity. Therefore, in this paper, we propose a new method called Segmentation - Class Activation Mapping (SeCAM) that combines the advantages of these algorithms above, while at the same time overcoming their disadvantages. We tested this algorithm with various models, including ResNet50, Inception-v3, VGG16 from ImageNet Large Scale Visual Recognition Challenge (ILSVRC) data set. Outstanding results when the algorithm has met all the requirements for a specific explanation in a remarkably concise time.Comment: Published in the Proceedings of FAIC 202

Bounded Distributed Flocking Control of Nonholonomic Mobile Robots

There have been numerous studies on the problem of flocking control for multiagent systems whose simplified models are presented in terms of point-mass elements. Meanwhile, full dynamic models pose some challenging problems in addressing the flocking control problem of mobile robots due to their nonholonomic dynamic properties. Taking practical constraints into consideration, we propose a novel approach to distributed flocking control of nonholonomic mobile robots by bounded feedback. The flocking control objectives consist of velocity consensus, collision avoidance, and cohesion maintenance among mobile robots. A flocking control protocol which is based on the information of neighbor mobile robots is constructed. The theoretical analysis is conducted with the help of a Lyapunov-like function and graph theory. Simulation results are shown to demonstrate the efficacy of the proposed distributed flocking control scheme