Diabetes, also known as chronic illness, is a group of metabolic diseases due to a high level of sugar in the blood over a long period. The risk factor and severity of diabetes can be reduced significantly if the precise early prediction is possible. The robust and accurate prediction of diabetes is highly challenging due to the limited number of labeled data and also the presence of outliers (or missing values) in the diabetes datasets. In this literature, we are proposing a robust framework for diabetes prediction where the outlier rejection, filling the missing values, data standardization, feature selection, K-fold cross-validation, and different Machine Learning (ML) classifiers (k-nearest Neighbour, Decision Trees, Random Forest, AdaBoost, Naive Bayes, and XGBoost) and Multilayer Perceptron (MLP) were employed. The weighted ensembling of different ML models is also proposed, in this literature, to improve the prediction of diabetes where the weights are estimated from the corresponding Area Under ROC Curve (AUC) of the ML model. AUC is chosen as the performance metric, which is then maximized during hyperparameter tuning using the grid search technique. All the experiments, in this literature, were conducted under the same experimental conditions using the Pima Indian Diabetes Dataset. From all the extensive experiments, our proposed ensembling classifier is the best performing classifier with the sensitivity, specificity, false omission rate, diagnostic odds ratio, and AUC as 0.789, 0.934, 0.092, 66.234, and 0.950 respectively which outperforms the state-of-the-art results by 2.00 % in AUC. Our proposed framework for the diabetes prediction outperforms the other methods discussed in the article. It can also provide better results on the same dataset which can lead to better performance in diabetes prediction. Our source code for diabetes prediction is made publicly available. INDEX TERMS Diabetes prediction, ensembling classifier, machine learning, multilayer perceptron, missing values and outliers, Pima Indian Diabetic dataset.

Trajectory-based writing system refers to writing a linguistic character or word in free space by moving a finger, marker, or handheld device. It is widely applicable where traditional pen-up and pen-down writing systems are troublesome. Due to the simple writing style, it has a great advantage over the gesture-based system. However, it is a challenging task because of the non-uniform characters and different writing styles. In this research, we developed an air-writing recognition system using three-dimensional (3D) trajectories collected by a depth camera that tracks the fingertip. For better feature selection, the nearest neighbor and root point translation was used to normalize the trajectory. We employed the long short-term memory (LSTM) and a convolutional neural network (CNN) as a recognizer. The model was tested and verified by the self-collected dataset. To evaluate the robustness of our model, we also employed the 6D motion gesture (6DMG) alphanumeric character dataset and achieved 99.32% accuracy which is the highest to date. Hence, it verifies that the proposed model is invariant for digits and characters. Moreover, we publish a dataset containing 21,000 digits; which solves the lack of dataset in the current research.

No abstract

No abstract

Severe acute malnutrition (SAM) is a major global public health problem. We aimed to assess the effects of probiotic and synbiotic supplementation on rate of weight gain and change in length in young SAM infants. This study was substudy of a single-blind randomized clinical trial (NCT0366657). During nutritional rehabilitation, 67 <6 months old SAM infants were enrolled and randomized to receive either probiotic (Bifidobacterium. infantis EVC001) or synbiotic (B. infantis EVC001 + Lacto-N-neotetraose [LNnT]) or placebo (Lactose) for four weeks and were followed for four more weeks after supplementation. In multivariable linear regression model, the mean rate of weight gain in the probiotic arm compared to placebo was higher by 2.03 unit (P < 0.001), and 1.13 unit (P = 0.030) in the synbiotic arm. In linear mixed-effects model, mean WAZ was higher by 0.57 unit (P = 0.018) in probiotic arm compared to placebo. Although not statistically significant, delta length for age z score (LAZ) trended to be higher among children in probiotc (β = 0.25) and synbiotic (β = 0.26) arms compared to placebo in multivariable linear regression model. Our study describes that young SAM infants had a higher rate of weight gain when supplemented with probiotic alone, compared to their counterparts with either synbiotic or placebo.