Flood susceptibility modeling using Radial Basis Function Classifier and Fisher’s linear discriminant function

Floods are among the most frequent highly disastrous hazards affecting life, property, and the environment worldwide. While various models are available to predict flood susceptibility, no model is accurate enough to be used for all flood-prone areas. Model development using different algorithms is a continuous process to improve the prediction accuracy of flood susceptibility. In the study, we used the Radial Basis Function and Fisher’s linear discriminant function to develop a flood susceptibility map for a case study of Quang Binh Province. The model development used ten variables (elevation, slope, curvature, river density, distance from river, geomorphology, land use, flow accumulation, flow direction, and rainfall). For model training and validation, input data was split into a 70:30 ratio according to flood locations. Statistical indexes were used to evaluate model performance such as Receiver Operating Characteristic, the Area Under the ROC Curve, Root Mean Square Error, Accuracy, Sensitivity, Specificity, and Kappa index. Results indicated that the radial basis function classifier model had better performance in predicting flood susceptible areas based on the statistical measures (PPV = 92.00%, NPV = 87.00%, SST = 87.62%, SPF = 91.58%, ACC = 89.50%, Kappa = 0.790, MAE = 0.204, RMSE = 0.292 and AUC = 0.957. Therefore, the radial basis function classifier algorithm model is appropriate for predicting flood susceptibility in Quang Binh Province.

The analysis of sacrum and coccyx length measured with computerized tomography images depending on sex

Background Sex estimation is vital in establishing an accurate biological profile from the human skeleton, as sex influences the analysis of other elements in both Physical and Forensic Anthropology and Legal Medicine. The present study was conducted to analyze the sex differences between the sacrum and coccyx length based on the measurements calculated with computed tomography (CT) images. One hundred case images (50 females, 50 males) who were between the ages of 25 and 50 and admitted by the emergency department between September 2018 and June 2019 and underwent CT were included in the study. Eighteen lengths, 4 curvature lengths, and 2 regions were measured in sagittal, coronal and transverse planes with orthogonal adjustment for three times. Results It was stated that the mean anterior and posterior sacral length, anterior and posterior sacrococcygeal length, anterior and posterior sacral curvature length, anterior coccygeal curvature length, sacral area, lengths of transverse lines 1, 2, 3 and 4, sacral first vertebra transverse and sagittal length measurements were longer in males when compared to females (p < 0.05). It was noted that the parameter with the highest discrimination value in the receiver operating characteristic (ROC) analysis was the sacral area (AUC = 0.88/Acc = 0.82). Based on Fisher’s linear discriminant analysis findings, the discrimination rate was 96% for males, 92% for females and the overall discrimination rate was 94%. Conclusions It was concluded that the fourteen parameters that were indicated as significant in the present study could be used in anthropology, Forensic Medicine and Anatomy to predict sex.

Correction to: Near-infrared auto-fluorescence spectroscopy combining with Fisher’s linear discriminant analysis improves intraoperative real-time identification of normal parathyroid in thyroidectomy

An amendment to this paper has been published and can be accessed via the original article.

Predicting COVID-19 Disease Progression with Chest CT Images

Background: Some mild patients can deteriorate to moderate or severe within a week with the natural progression of COVID-19.it has been crucial to early identify those mild cases and give timely treatment . The chest computed tomography (CT) has shown to be useful to assist clinical diagnosis of COVID-19.In this study, machine learning was used to develop an early-warning CT feature model for predicting mild patients with potential malignant progression.Methods：The total of 140 COVID-19 mild patients were collected. All patients at admission were divided into groups (alleviation group and exacerbation group) with or without malignant progression.The clinical and laboratory data at admission, the first CT, and the follow-up CT at critical stage of the two groups were compared with Chi-square test,.The CT features data (distribution, morphology,etc) were used to establish the prediction model by Fisher's linear discriminant method and Unconditional logistic regression algorithm. And the model was validated with 40 exception data.and the Area Under ROC curve (AUC) was used to evaluate the models.Results：The model filtered out three variables of CT features including distal air bronchogram, fibrosis,and reversed halo sign. Notably, the distal air bronchograms was less common in alleviation group, while the fibrosis and reversed halo sign were more common.The sensitivity, specificity and Youden index of unconditional logistic regression were 86.1%, 92.6% and 78.7%, For the analysis of Fisher's linear discriminant, the sensitivity, specificity and Youden index were 83.3%, 94.1% and 77.4%. The generalization ability of both models were consistent with sensitivity of 95.89%, specificity of 100%, and Youden index of 83.33%.Conclusions: The CT imaging features-based machine learning model has a high sensitivity for finding out the mild patients who are easy to deteriorate into severe/critical cases efficiently so that timely treatments came true for those patients,while largely help to relieve the medical pressure.

Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal

Emotion recognition using physiological signals has been a special topic frequently discussed by researchers and practitioners in the past decade. However, the use of SpO2 and Pulse rate signals for emotion recognitionisvery limited and the results still showed low accuracy. It is due to the low complexity of SpO2 and Pulse rate signals characteristics. Therefore, this study proposes a Multiscale Entropy and Multiclass Fisher’s Linear Discriminant Analysis for feature extraction and dimensional reduction of these physiological signals for improving emotion recognition accuracy in elders. In this study, the dimensional reduction process was grouped into three experimental schemes, namely a dimensional reduction using only SpO2 signals, pulse rate signals, and multimodal signals (a combination feature vectors of SpO2 and Pulse rate signals). The three schemes were then classified into three emotion classes (happy, sad, and angry emotions) using Support Vector Machine and Linear Discriminant Analysis Methods. The results showed that Support Vector Machine with the third scheme achieved optimal performance with an accuracy score of 95.24%. This result showed a significant increase of more than 22%from the previous works.

Near-infrared auto-fluorescence spectroscopy combining with Fisher’s linear discriminant analysis improves intraoperative real-time identification of normal parathyroid in thyroidectomy

Background To evaluate the efficacy of a sensitive, real-time tool for identification and protection for parathyroid glands during thyroidectomy. Methods Near-infrared (NIR) auto-fluorescence was measured intraoperatively from 20 patients undergoing thyroidectomy. Spectra were measured from suspicious parathyroid glands and surrounding neck tissues during the operation with a NIR fluorescence system. Fast frozen sections were performed on the suspicious parathyroid glands. Accuracy was evaluated by comparison with histology and NIR identification. Data were attracted for Fisher’s linear discriminant analysis. Results The auto-fluorescence intensity of parathyroid was significantly higher than that of thyroid, fat and lymph node. The peak intensity of auto-fluorescence from parathyroid was 5.55 times of that from thyroid at the corresponding wave number. Of the 20 patients, the parathyroid was accurately detected and identified in 19 patients by NIR system, compared with their histologic results. One suspicious parathyroid did not exhibit typical spectra, and was proved to be fat tissue by histology. The NIR auto-fluorescence method had a 100% sensitivity of parathyroid glands identification and a high accuracy of 95%. The positive predictive value was 95%. The parathyroid gland have specific auto-fluorescence spectrum and can be separated from the other three samples through the Fisher’s linear discriminant analysis. Conclusions NIR auto-fluorescence spectroscopy can accurately identify normal parathyroid gland during thyroidectomy. The Fisher’s linear discriminant analysis demonstrated the specificity of the NIR auto-fluorescence of parathyroid tissue and its efficacy in parathyroid discrimination.

Predicting Product Purchase using Linear Classification Algorithms

The customer buys the product based on many factors. There is no adequate and properly defined logic for such matter. The customer must satisfy when they see their product itself. They have to trust its quality, price, lifetime of the product, no side effect behavior, name of the product, packing of the product and finally cost. These factors may vary time to time, day to day and even sec to sec. The competition among sellers is also increasing day by day. The choice of choosing the product for customer is more, confused and risky also. Establishing a good relationship among seller and buyer will increase the customer. The retaining of customer is a challenging task. To solve this problem, a model is developed using machine learning algorithms svm, Naïve Bayes, Logistic Regression and fisher’s linear discriminant analysis. This model predicts the buying habit of a user/customer. The classification is performed on product purchase dataset and its performance is compared to find which algorithm performs well for this particular dataset. This work is implemented in R software.