Labeled projective dictionary pair learning: application to handwritten numbers recognition

Dictionary learning was introduced for sparse image representation. Today, it is a cornerstone of image classification. We propose a novel dictionary learning method to recognise images of handwritten numbers. Our focus is to maximise the sparse-representation and discrimination power of the class-specific dictionaries. We, for the first time, adopt a new feature space, i.e., histogram of oriented gradients (HOG), to generate dictionary columns (atoms). The HOG features robustly describe fine details of hand-writings. We design an objective function followed by a minimisation technique to simultaneously incorporate these features. The proposed cost function benefits from a novel class-label penalty term constraining the associated minimisation approach to obtain class-specific dictionaries. The results of applying the proposed method on various handwritten image databases in three different languages show enhanced classification performance (~98%) compared to other relevant methods. Moreover, we show that combination of HOG features with dictionary learning enhances the accuracy by 11% compared to when raw data are used. Finally, we demonstrate that our proposed approach achieves comparable results to that of existing deep learning models under the same experimental conditions but with a fraction of parameters

The Effect of Sports and Physical Activity on Elderly Reaction Time and Response Time

Objectives: Physical activities ameliorate elderly motor and cognitive performance. The aim of this research is to study the effect of sport and physical activity on elderly reaction time and response time. Methods & Materials: The research method is causal-comparative and its statistical population consists of 60 active and non-active old males over 60 years residing at Mahabad city. Reaction time was measured by reaction timer apparatus, made in Takei Company (YB1000 model). Response time was measured via Nelson’s Choice- Response Movement Test. At first, reaction time and then response time was measured. For data analysis, descriptive statistic, K-S Test and One Sample T Test were used Results K-S Test show that research data was parametric. According to the results of this research, physical activity affected reaction time and response time. Results: of T test show that reaction time (P=0.000) and response time (P=0.000) of active group was statistically shorter than non- active group. Conclusion: The result of current study demonstrate that sport and physical activity, decrease reaction and response time via psychomotor and physiological positive changes

Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

ABSTRACTMonitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations