This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models. Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the types of tomato diseases and Mask R-CNN is used to detect and segment the locations and shapes of the infected areas. To select the model that best fits the tomato disease detection task, four different deep convolutional neural networks are combined with the two object detection models. Data are collected from the Internet and the dataset is divided into a training set, a validation set, and a test set used in the experiments. The experimental results show that the proposed models can accurately and quickly identify the eleven tomato disease types and segment the locations and shapes of the infected areas.

In the article titled “Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques” [1], there was an error in the corresponding author affiliation, where the author Feng Qi’s affiliation “University of Texas at San Antonio, San Antonio, Texas, USA” should be corrected to “Business School, Shandong Normal University, Jinan, Shandong, China.”

This paper proposed a newly quantum-inspired Qubit neural tree network with improved Qubit neuron, cross-layer connections and distinct phase operation functions for each neurons. A hybrid evolutionary algorithm that combines the modified gene expression programming with particle swarm optimization is also introduced to obtain the optimal structure with related parameters of the Qubit neural tree network. Three nonlinear system modeling problems are selected to evaluate the effectiveness and performance of the proposed model. The simulation results indicate that the Qubit neural tree network has better nonlinear mapping and generalization ability than related methods do.

Business process simulation is the procedure of planning, modeling and simulating enterprise process to analyze the features varying with time which guides the users to make decisions or important means of process improvement. In the existing simulation methods, they considered little about dynamic resource, which may lead to a large deviation. To solve the problem above, a business process simulation method supporting resource evolution that mainly focuses on human resource is proposed. Individual differences and personnel composition are analyzed to represent the dynamic feature of resources. Evolution of human capacity and personnel changes are described to meet the simulation requirements of human resource model. And then, process simulation mechanism and algorithm are realized. An experiment is conducted by simulating the software development process of a real-world software project using the system prototype we developed, and it shows that our method is closer to the practical project situation, thus the method effectiveness can be demonstrated to a certain extent. The main contribution of our work is a novel process simulation approach based on a dynamic resource model with configurable evolution rules.