Integrating Growth and Environmental Parameters to Discriminate Powdery Mildew and Aphid of Winter Wheat Using Bi-Temporal Landsat-8 Imagery

Monitoring and discriminating co-epidemic diseases and pests at regional scales are of practical importance in guiding differential treatment. A combination of vegetation and environmental parameters could improve the accuracy for discriminating crop diseases and pests. Different diseases and pests could cause similar stresses and symptoms during the same crop growth period, so combining growth period information can be useful for discerning different changes in crop diseases and pests. Additionally, problems associated with imbalanced data often have detrimental effects on the performance of image classification. In this study, we developed an approach for discriminating crop diseases and pests based on bi-temporal Landsat-8 satellite imagery integrating both crop growth and environmental parameters. As a case study, the approach was applied to data during a period of typical co-epidemic outbreak of winter wheat powdery mildew and aphids in the Shijiazhuang area of Hebei Province, China. Firstly, bi-temporal remotely sensed features characterizing growth indices and environmental factors were calculated based on two Landsat-8 images. The synthetic minority oversampling technique (SMOTE) algorithm was used to resample the imbalanced training data set before model construction. Then, a back propagation neural network (BPNN) based on a new training data set balanced by the SMOTE approach (SMOTE-BPNN) was developed to generate the regional wheat disease and pest distribution maps. The original training data set-based BPNN and support vector machine (SVM) methods were used for comparison and testing of the initial results. Our findings suggest that the proposed approach incorporating both growth and environmental parameters of different crop periods could distinguish wheat powdery mildew and aphids at the regional scale. The bi-temporal growth indices and environmental factors-based SMOTE-BPNN, BPNN, and SVM models all had an overall accuracy high than 80%. Meanwhile, the SMOTE-BPNN method had the highest G-means among the three methods. These results revealed that the combination of bi-temporal crop growth and environmental parameters is essential for improving the accuracy of the crop disease and pest discriminating models. The combination of SMOTE and BPNN could effectively improve the discrimination accuracy of the minor disease or pest

Klasifikasi Sentimen Wisatawan Candi Borobudur pada Situs TripAdvisor Menggunakan Support Vector Machine dan K-Nearest Neighbor

Candi Borobudur merupakan salah satu destinasi wisata di Indonesia yang telah dikenal hingga dunia internasional dan kini menjadi satu dari sepuluh destinasi prioritas yang ditetapkan oleh Kementerian Pariwisata. Oleh sebab itu pengelola wisata Candi Borobudur perlu memperhatikan berbagai persepsi wisatawan sebagai bagian dari proses evaluasi. Klasifikasi sentimen wisatawan berdasarkan data ulasan yang tersedia di situs TripAdvisor dilakukan dengan metode Support Vector Machine (SVM) dan K-Nearest Neighbor (K-NN), dengan penerapan teknik N-gram di kedua metode tersebut. Selain itu digunakan pula metode Synthetic Minority Oversampling Technique (SMOTE) untuk menangani kasus data imbalance. Hasil yang diperoleh dari penelitian ini adalah SVM kernel Radial Basis Function (RBF) dengan penerapan unigram merupakan metode terbaik untuk kasus klasifikasi sentimen wisatawan Candi Borobudur. Kinerja klasifikasi yang dihasilkan oleh metode tersebut tergolong sangat baik

Using recency, frequency and monetary variables to predict customer lifetime value with XGBoost

CRM) will continue to gain prominence in the coming years. A commonly used CRM metric called Customer Lifetime Value (CLV) is the value a customer will contribute while they are an active customer. This study investigated the ability of supervised machine learning models constructed with XGBoost to predict future CLV, as well as the likelihood that a customer will drop to a lower CLV in the future. One approach to determining CLV, called the RFM method, is done by isolating recency (R), frequency (F) and (M) monetary values. The produced models used these RFM variables and also assessed if including temporal, product, and other customer transaction information assisted the XGBoost classifier in making better predictions. The classification models were constructed by extracting each customer's RFM values and transaction information from a Fast Mover Consumer Goods dataset. Different variations of CLV were calculated through one- and two-dimensional K-means clustering of the M (Monetary), F and M (Profitability), F and R (Loyalty), as well as the R and M (Burgeoning) variables. Two additional CLV variations were also determined by isolating the M tercile segments and a commonly used weighted-RFM approach. To test the effectiveness of XGBoost in predicting future timeframes, the dataset was divided into three consecutive periods, where the first period formed the features used to predict the target CLV variables in the second and third periods. Models that predicted if CLV dropped to a lower value from the first to the second and from the first to the third periods were also constructed. It was found that the XGBoost models were moderately to highly effective in classifying future CLV in both the second and third periods. The models also effectively predicted if CLV would drop to a lower value in both future periods. The ability to predict future CLV and CLV drop in the second period, was only slightly better than the ability to predict the future CLV in the third period. Models constructed by adding additional temporal, product, and customer transaction information to the RFM values did not improve on those created that used only the RFM values. These findings illustrate the effectiveness of XGBoost as a predictor for future CLV and CLV drop, as well as affirming the efficacy of utilising RFM values to determine future CLV