Consumer financial fraud has become a serious problem because it often causes victims to suffer economic, physical, mental, social, and legal harm. Identifying which individuals are more likely to be scammed may mitigate the threat posed by consumer financial fraud. Based on a two-stage conceptual framework, this study integrated various individual factors in a nationwide survey (36,202 participants) to construct fraud exposure recognition (FER) and fraud victimhood recognition (FVR) models by utilizing a machine learning method. The FER model performed well (f1 = 0.727), and model interpretation indicated that migration status, financial status, urbanicity, and age have good predictive effects on fraud exposure in the Chinese context, whereas the FVR model shows a low predictive effect (f1 = 0.565), reminding us to consider more psychological factors in future work. This research provides an important reference for the analysis of individual differences among people vulnerable to consumer fraud.

The rapid growth of computational power demand from scientific, business, and Web applications has led to the emergence of cloud-oriented data centers. These centers use pay-as-you-go execution environments that scale transparently to the user. Load prediction is a significant cost-optimal resource allocation and energy saving approach for a cloud computing environment. Traditional linear or nonlinear prediction models that forecast future load directly from historical information appear less effective. Load classification before prediction is necessary to improve prediction accuracy. In this paper, a novel approach is proposed to forecast the future load for cloud-oriented data centers. First, a hidden Markov model (HMM) based data clustering method is adopted to classify the cloud load. The Bayesian information criterion and Akaike information criterion are employed to automatically determine the optimal HMM model size and cluster numbers. Trained HMMs are then used to identify the most appropriate cluster that possesses the maximum likelihood for current load. With the data from this cluster, a genetic algorithm optimized Elman network is used to forecast future load. Experimental results show that our algorithm outperforms other approaches reported in previous works.

Sap flow is widely used to estimate the transpiration and water consumption of canopies and to manage water resources. In this paper, an improved time series prediction model was proposed by integrating three basic networks—CNN, GRU and BiLSTM—to assess sap flow with historical environment variables. A dataset with 17,569 records of each, including 9 environment variables and 1 sap flow, was applied from a public database of SAPFLUXNET. After normalization, the environment variables were analyzed and composed with the factor analysis method. After the CNN-GRU-BiLSTM structure was designed, N records of three main factors were computed from environment variables, which were measured at N previous moments, and the sap flow was measured at the current moment, and they were applied for each training, validation, and testing cycle. To improve and compare the CNN-GRU-BiLSTM-based model, nine other models, using the methods of multiple linear regression, support vector regression, random forest, LSTM, GRU, BiLSTM, CNN-GRU, CNN-BiLSTM, and CNN-GRU-LSTM, were constructed in this study, respectively. Results show that the performance of the CNN-GRU-BiLSTM-based model has more accuracy than the other nine models we built in this paper, with the mean absolute error, mean squared error, mean absolute percentage error, and coefficient of determination (R²) being 0.0410, 0.0029, 0.2708 and 0.9329, respectively. Furthermore, for a comparison of the descending dimension method of factor analysis, principal component analysis (PCA) and singular value decomposition (SVD) methods were applied and compared, respectively. Results show that the performance of the factor analysis-based model is better than the PCA- or SVD-based model, with the R² results of the factor analysis-based model being higher than the PCA- and SVD-based models by 5.06% and 10.63%, respectively. This study indicates that the CNN-GRU-BiLSTM-based sap flow prediction model established with a factor analysis of historical environmental variables has optimistic applications for analyzing the transpiration of trees and evaluating water consumption.

The ‘Kyoho’ (Vitis labruscana) grape is one of the mainly fresh fruits; it is important to accurately segment the grape bunch and to detect its maturity level for the construction of an intelligent grape orchard. Grapes in the natural environment have different shapes, occlusion, complex backgrounds, and varying illumination; this leads to poor accuracy in grape maturity detection. In this paper, an improved Mask RCNN-based algorithm was proposed by adding attention mechanism modules to establish a grape bunch segmentation and maturity level detection model. The dataset had 656 grape bunches of different backgrounds, acquired from a grape growing environment of natural conditions. This dataset was divided into four groups according to maturity level. In this study, we first compared different grape bunch segmentation and maturity level detection models established with YoloV3, Solov2, Yolact, and Mask RCNN to select the backbone network. By comparing the performances of the different models established with these methods, Mask RCNN was selected as the backbone network. Then, three different attention mechanism modules, including squeeze-and-excitation attention (SE), the convolutional block attention module (CBAM), and coordinate attention (CA), were introduced to the backbone network of the ResNet50/101 in Mask RCNN, respectively. The results showed that the mean average precision (mAP) and mAP_0.75 and the average accuracy of the model established with ResNet101 + CA reached 0.934, 0.891, and 0.944, which were 6.1%, 4.4%, and 9.4% higher than the ResNet101-based model, respectively. The error rate of this model was 5.6%, which was less than the ResNet101-based model. In addition, we compared the performances of the models established with MASK RCNN, adding different attention mechanism modules. The results showed that the mAP and mAP_0.75 and the accuracy for the Mask RCNN50/101 + CA-based model were higher than those of the Mask RCNN50/101 + SE- and Mask RCNN50/101 + CBAM-based models. Furthermore, the performances of the models constructed with different network layers of ResNet50- and ResNet101-based attention mechanism modules in a combination method were compared. The results showed that the performance of the ResNet101-based combination with CA model was better than the ResNet50-based combination with CA model. The results showed that the proposed model of Mask RCNN ResNet101 + CA was good for capturing the features of a grape bunch. The proposed model has practical significance for the segmentation of grape bunches and the evaluation of the grape maturity level, which contributes to the construction of intelligent vineyards.