Binary classification of rainfall time-series using machine learning algorithms

Summer monsoon rainfall contributes more than 75% of the annual rainfall in India. For the state of Maharashtra, India, this is more than 80% for almost all regions of the state. The high variability of rainfall during this period necessitates the classification of rainy and non-rainy days. While there are various approaches to rainfall classification, this paper proposes rainfall classification based on weather variables. This paper explores the use of support vector machine (SVM) and artificial neural network (ANN) algorithms for the binary classification of summer monsoon rainfall using common weather variables such as relative humidity, temperature, pressure. The daily data, for the summer monsoon months, for nineteen years, was collected for the Shivajinagar station of Pune in the state of Maharashtra, India. Classification accuracy of 82.1 and 82.8%, respectively, was achieved with SVM and ANN algorithms, for an imbalanced dataset. While performance parameters such as misclassification rate, F1 score indicate that better results were achieved with ANN, model parameter selection for SVM was less involved than ANN. Domain adaptation technique was used for rainfall classification at the other two stations of Maharashtra with the network trained for the Shivajinagar station. Satisfactory results for these two stations were obtained only after changing the training method for SVM and ANN.

Soil Properties Classification Using Support Vector Machine for Raver Tehsil

Soil properties are dynamic in nature and different factors are affecting to the soil quality. It is directly consequence on soil productivity and soil fertility. The heavy use of fertilizers, heavy rain fall, various agricultural practices are responsible for soil quality degradation. The soil assessment is require to maintain the soil quality. The spectroscopic techniques using Remote sensing and GIS gives the fast and accurate results as compare to traditional soil testing methods. The present study is conducted for classification of soil physicochemical properties in pre monsoon and post monsoon season. Soil samples are collected where Organic, Chemical and Mixed fertilizers treatments were applied to banana and cotton crops sites from Raver tehsil of Jalgaon district. Total 220 soil specimens are collected in pre monsoon and post monsoon season for two year respectively. ASD FieldSpec4 spectroradiometer device were used for data acquisition in the controlled laboratory environment. Acquired spectral data were processed for conversion in numeric format then various statistical methods were used for quantitative analysis of the physiochemical soil properties. The support vector machine is used for classification of the collected soil samples in pre-monsoon and post-monsoon season and classification were performed on the basis of training and testing datasets. The soil samples are divide in pre-monsoon training, pre-monsoon testing and post –monsoon training and post-monsoon testing class with support vector. The hyper plane is used for separation of pre-monsoon and post-monsoon soil samples. Misclassification rate and Mean Squared Error were calculated in the SVM classification.

Cognitive screening instruments for dementia: comparing metrics of test limitation

ABSTRACT Cognitive screening instruments (CSIs) for dementia and mild cognitive impairment are usually characterized in terms of measures of discrimination such as sensitivity, specificity, and likelihood ratios, but these CSIs also have limitations. Objective: The aim of this study was to calculate various measures of test limitation for commonly used CSIs, namely, misclassification rate (MR), net harm/net benefit ratio (H/B), and the likelihood to be diagnosed or misdiagnosed (LDM). Methods: Data from several previously reported pragmatic test accuracy studies of CSIs (Mini-Mental State Examination, the Montreal Cognitive Assessment, Mini-Addenbrooke’s Cognitive Examination, Six-item Cognitive Impairment Test, informant Ascertain Dementia 8, Test Your Memory test, and Free-Cog) undertaken in a single clinic were reanalyzed to calculate and compare MR, H/B, and the LDM for each test. Results: Some CSIs with very high sensitivity but low specificity for dementia fared poorly on measures of limitation, with high MRs, low H/B, and low LDM; some had likelihoods favoring misdiagnosis over diagnosis. Tests with a better balance of sensitivity and specificity fared better on measures of limitation. Conclusions: When deciding which CSI to administer, measures of test limitation as well as measures of test discrimination should be considered. Identification of CSIs with high MR, low H/B, and low LDM, may have implications for their use in clinical practice.

Error rate control for classification rules in multiclass mixture models

In the context of finite mixture models one considers the problem of classifying as many observations as possible in the classes of interest while controlling the classification error rate in these same classes. Similar to what is done in the framework of statistical test theory, different type I and type II-like classification error rates can be defined, along with their associated optimal rules, where optimality is defined as minimizing type II error rate while controlling type I error rate at some nominal level. It is first shown that finding an optimal classification rule boils down to searching an optimal region in the observation space where to apply the classical Maximum A Posteriori (MAP) rule. Depending on the misclassification rate to be controlled, the shape of the optimal region is provided, along with a heuristic to compute the optimal classification rule in practice. In particular, a multiclass FDR-like optimal rule is defined and compared to the thresholded MAP rules that is used in most applications. It is shown on both simulated and real datasets that the FDR-like optimal rule may be significantly less conservative than the thresholded MAP rule.

Clustering functional data using forward search based on functional spatial ranks with medical applications

Cluster analysis of functional data is finding increasing application in the field of medical research and statistics. Here we introduce a functional version of the forward search methodology for the purpose of functional data clustering. The proposed forward search algorithm is based on the functional spatial ranks and is a data-driven non-parametric method. It does not require any preprocessing functional data steps, nor does it require any dimension reduction before clustering. The Forward Search Based on Functional Spatial Rank (FSFSR) algorithm identifies the number of clusters in the curves and provides the basis for the accurate assignment of each curve to its cluster. We apply it to three simulated datasets and two real medical datasets, and compare it with six other standard methods. Based on both simulated and real data, the FSFSR algorithm identifies the correct number of clusters. Furthermore, when compared with six standard methods used for clustering and classification, it records the lowest misclassification rate. We conclude that the FSFSR algorithm has the potential to cluster and classify functional data.

Tri-Ponderal Mass Index Reference Values for Screening Metabolic Syndrome in Children and Adolescents: Results From Two National-Representative Cross-Sectional Studies in China and America

IntroductionTo ascertain the possible cut point of tri-ponderal mass index (TMI) in discriminating metabolic syndrome (MetS) and related cardio-metabolic risk factors in Chinese and American children and adolescents.MethodsA total of 57,201 Chinese children aged 7-18 recruited in 2012 and and 10,441 American children aged 12-18 from National Health and Nutrition Examination Survey (NHANES 2001-2014) were included to fit TMI percentiles. Participants were randomly assigned to a derivation set (75%) and validation set (25%). The cut points of TMI with the lowest misclassification rate under the premise of the highest area under curves (AUC) were selected for each sex, which were additionally examined in the validation set. All of data analysis was conducted between September and December in 2019.ResultsTMI showed good capacity on discriminating MetS, with AUC of 0.7658 (95% CI: 0.7544-0.7770) to 0.8445 (95% CI: 0.8349-0.8537) in Chinese and 0.8871 (95% CI: 0.8663-0.9056) to 0.9329 (95% CI: 0.9166-0.9469) in American children. The optimal cut points were 14.46 kg/m3 and 13.91 kg/m3 for Chinese boys and girls, and 17.08 kg/m3 and 18.89 kg/m3 for American boys and girls, respectively. The corresponding misclassification rates were 17.1% (95% CI: 16.4-17.8) and 11.2% (95% CI: 9.9-12.6), respectively. Performance of these cut points were also examined in the validation set (sensitivity 67.7%, specificity 82.4% in Chinese; sensitivity 84.4%, specificity 88.7% in American children).ConclusionsA sex- and ethnicity- specific single cut point of TMI could be used to distinguish MetS and elevated risk of cardio-metabolic factors in children and adolescents.

Sky and Ground Segmentation in the Navigation Visions of the Planetary Rovers

Sky and ground are two essential semantic components in computer vision, robotics, and remote sensing. The sky and ground segmentation has become increasingly popular. This research proposes a sky and ground segmentation framework for the rover navigation visions by adopting weak supervision and transfer learning technologies. A new sky and ground segmentation neural network (network in U-shaped network (NI-U-Net)) and a conservative annotation method have been proposed. The pre-trained process achieves the best results on a popular open benchmark (the Skyfinder dataset) by evaluating seven metrics compared to the state-of-the-art. These seven metrics achieve 99.232%, 99.211%, 99.221%, 99.104%, 0.0077, 0.0427, and 98.223% on accuracy, precision, recall, dice score (F1), misclassification rate (MCR), root mean squared error (RMSE), and intersection over union (IoU), respectively. The conservative annotation method achieves superior performance with limited manual intervention. The NI-U-Net can operate with 40 frames per second (FPS) to maintain the real-time property. The proposed framework successfully fills the gap between the laboratory results (with rich idea data) and the practical application (in the wild). The achievement can provide essential semantic information (sky and ground) for the rover navigation vision.

Accuracy of equations for predicting 24-h urinary potassium excretion from spot urine samples in Chinese children

Accurate assessments of potassium intake in children are important for the early prevention of CVD. Currently, there is no simple approach for accurate estimation of potassium intake in children. We aim to evaluate the accuracy of 24-h urinary potassium excretion (24UKV) estimation in children using three common equations: the Kawasaki, Tanaka and Mage formulas, in a hospital-based setting. A total of 151 participants aged 5–18 years were initially enrolled, and spot urine samples were collected in the whole 24-h duration to measure the concentrations of potassium and creatinine. We calculated the mean difference, absolute and relative difference and misclassification rate between measured 24UKV and the predicted ones using Kawasaki, Tanaka and Mage formulas in 129 participants. The mean measured 24UKV was 1193·3 mg/d in our study. Mean differences between estimated and measured 24UKV were 1215·6, −14·9 and 230·3 mg/d by the Kawasaki, Tanaka and Mage formulas, respectively. All estimated 24UKV were significantly different from the measured values in all the time point (all P < 0·05), except for the predicted values from Tanaka formula using morning, afternoon and evening spot urine. The proportions with relative differences over 40 % were 87·2%, 32·5% and 47·3 % for Kawasaki, Tanaka and Mage formulas, respectively. Misclassification rates were 91·5 % for Kawasaki, 44·4 % for Tanaka and 58·9 % for Mage formula at the individual level. Our findings showed that misclassification could occur on the individual level when using Kawasaki, Tanaka and Mage formulas to estimate 24UKV from spot urine in the child population.