It is a well-known fact that the quality of a seed highly impacts the germination of a rice seed. The age of the seed is one of the primary key points in assessing the seed quality. Therefore, this study aims to develop an AI-based machine-learning model to classify age-wise rice seeds. This study employs the SURF-BOF-based Cascaded-ANFIS algorithm for the implementation of the classifier. The proposed model performances were compared to the VGG16. Moreover, this research contributes a novel Japanese rice seed dataset to the scientific community. Furthermore, a 10-Fold cross-validation is performed to evaluate the robustness of the novel approach. The K-fold cross-validation’s mean accuracy confirmed the proposed algorithm’s higher robustness in the age-wise classification of rice seeds. Nevertheless, the results were evaluated using the confusion matrix and metrics such as precision, recall, and F1-Score. The Accuracy of Akitakomachi, Koshihikari, Yandao-8, and rice variety classification is 99%, 99%, 92%, and 97%. Analysis of the results determines the ability to classify rice by age and the general robustness of the algorithm.

Performing an accurate and smooth trajectory of a quad-copter is a crucial aspect in autonomous controls due to its non-linearity and under-actuated characteristic. Adaptive Neuro-Fuzzy Inference System (ANFIS) is well-known for nonlinear controls. This paper focuses on comparing the performance of ANFIS based quad-copter systems to identify the best optimization algorithm. Two famous algorithms called Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) was used as the optimization algorithms and to tune the gains of the Fuzzy Inference Systems (FIS). The analysis was performed using two different simulations namely, altitude control and trajectory navigation. The final results were compared between traditional PID, conventional ANFIS, GA-ANFIS and PSO-ANFIS. PSO-ANFIS obtained the highest performance in our experiments.

This paper describes a study of a feed forward neural network trained by particle swarm optimization with a control of velocity (NN-PSOCV). A hardware implementation of NN-PSOCV coded using SystemVerilog has been developed. Details of each module in the proposed architecture are presented. This paper also shows results of the implementation when tested on a device called a DE1-SoC board. Experimental results demonstrate that NN-PSOCV was successfully implemented. Results also show that the hardware implementation of NN-PSOCV achieved a better performance over the hardware implementation of the neural network trained by original particle swarm optimization.

This paper describes a study of the relation between facial expression and customer impression of service quality. Based on the results, a facial expression warning system will be designed to improve the service quality of the Customer Service Representative when they practice in training sessions. The system, based on existing systems, has three modules: facial recognition, feature extraction and facial expression recognition. This paper also uses Haar-like features for face detection and a Support Vector Machine for facial expression recognition in an attempt to improve the recognition rate by using a novel feature extraction method. This paper also presents results of the method when tested on standard facial expression databases.