Data mining is defined as a search through large amounts of data for valuable information. The association rules, grouping, clustering, prediction, sequence modeling is some essential and most general strategies for data extraction. The processing of data plays a major role in the healthcare industry's disease detection. A variety of disease evaluations should be required to diagnose the patient. However, using data mining strategies, the number of examinations should be decreased. This decreased examination plays a crucial role in terms of time and results. Heart disease is a death-provoking disorder. In this recent instance, health issues are immense because of the availability of health issues and the grouping of various situations. Today, secret information is important in the healthcare industry to make decisions. For the prediction of cardiovascular problems, (Weka 3.8.3) tools for this analysis are used for the prediction of data extraction algorithms like sequential minimal optimization (SMO), multilayer perceptron (MLP), random forest and Bayes net. The data collected combine the prediction accuracy results, the receiver operating characteristic (ROC) curve, and the PRC value. The performance of Bayes net (94.5%) and random forest (94%) technologies indicates optimum performance rather than the sequential minimal optimization (SMO) and multilayer perceptron (MLP) methods.

This paper is devoted to the design of a trajectory-following control for a differentiation nonholonomic wheeled mobile robot. It suggests a kinematic nonlinear controller steer a National Instrument mobile robot. The suggested trajectory-following control structure includes two parts; the first part is a nonlinear feedback acceleration control equation based on backstepping control that controls the mobile robot to follow the predetermined suitable path; the second part is an optimization algorithm, that is performed depending on the Crossoved Firefly algorithm (CFA) to tune the parameters of the controller to obtain the optimum trajectory. The simulation is achieved based on MATLAB R2017b and the results present that the kinematic nonlinear controller with CFA is more effective and robust than the original firefly learning algorithm; this is shown by the minimized trackingfollowing error to equal or less than (0.8 cm) and getting smoothness of the linear velocity less than (0.1 m/sec), and all trajectory-following results with predetermined suitable are taken into account. Stability analysis of the suggested controller is proven using the Lyapunov method.

A novel median filter based on crow optimization algorithms (OMF) is suggested to reduce the random salt and pepper noise and improve the quality of the RGB-colored and gray images. The fundamental idea of the approach is that first, the crow optimization algorithm detects noise pixels, and that replacing them with an optimum median value depending on a criterion of maximization fitness function. Finally, the standard measure peak signal-to-noise ratio (PSNR), Structural Similarity, absolute square error and mean square error have been used to test the performance of suggested filters (original and improved median filter) used to removed noise from images. It achieves the simulation based on MATLAB R2019b and the results present that the improved median filter with crow optimization algorithm is more effective than the original median filter algorithm and some recently methods; they show that the suggested process is robust to reduce the error problem and remove noise because of a candidate of the median filter; the results will show by the minimized mean square error to equal or less than (1.38), absolute error to equal or less than (0.22) ,Structural Similarity (SSIM) to equal (0.9856) and getting PSNR more than (46 dB). Thus, the percentage of improvement in work is (25%).

RATIONALE Dynamic Chest radiography (DCR) involves taking sequential x-ray images throughout the patients breathing cycle. This allows for quantifiable measurements of diaphragmatic movement, cardiac motion, pulmonary ventilation and circulation. Recent studies have begun to correlate DCR obtained values with conventional measures of pulmonary physiology, such as spirometry. This technique therefore has the potential to become a rapid means of assessing both pulmonary anatomy and physiology. Two key DCR measurements, 'maximal distance from lung apex to the diaphragm' and 'total lung area' can used to calculate the change in area occurring between inspiration and expiration, or 'DCR equivalent Forced Vital Capacity' (DCR FVC). The aim of this study is to investigate the use of DCR FVC and assess the level of agreement between the two key measurements. METHODS The DCR images for 50 individual patients were obtained. The values for 'maximal distance from lung apex to the diaphragm' and 'total lung area' were calculated for both lungs during maximal inspiration and expiration. The difference between inspiration and expiration was used to generate DCR FVC and data was collated in a spreadsheet. Summary statistics were calculated, and Pearson's correlation coefficients were used to compare 'maximal distance from lung apex to the diaphragm' and 'total lung area' values. RESULTS Values were obtained for all 50 patients. The

RATIONALE Dynamic chest radiography (DCR) is a new imaging technique that involves taking sequential chest radiographs throughout the respiratory cycle. This allows the real time observation of the changes in lung and diaphragmatic movement occurring during respiration. Recent studies have correlated DCR acquired measures with those of pulmonary function testing, indicating that it can be an alternative measure of pulmonary physiology. Currently DCR values are manually recorded introducing an element of subjectivity. If this technique is to be widely adopted the nature of the inter-operator variability will need to be characterised. This study evaluates the inter-operator variability between two observers recording two key DCR obtained values. METHODS Two independent reviewers separately recorded measurements from the DCR image sequences of 50 patients. Their values for 'maximal distance from lung apex to diaphragm' and 'total lung area' at the point of maximal inspiration and expiration for both the right and left lungs were collated in a database. The inter-operator variability (IOV) for their inspiratory and expiratory values was assessed using Bland-Altman plots and Deming regression analysis was used to investigate statistical error in the observations. RESULTS The Bland-Altman pIot demonstrates the majority of the reviewer's observations for 'maximum distance' in the inspiratory images were within the 95% limits of agreement, with only a few outliers (right lung maximum distance; slope= 1.01 (SE 0.02, 95% CI 0.97-1.05) left lung max distance; slope= 0.98 (SE 0.04 95% CI 0.90-1.05). This was a similar picture for lung area (right lung area; slope= 1.0 (SE 0.01 95% CI 0.98-1.03), left lung area; slope= 1.02 (SE 0.01 95% CI 0.97-1.05)). Deming regression analysis showed good agreement. The two reviewers also had good agreement for expiratory images (right lung maximum distance; slope= 1.01 (SE 0.06, 95% CI 0.98-1.04) left lung max distance; slope= 0.99 (SE 0.05 95% CI 0.89-1.09). Again 'lung area' measurements were similar (right lung area; slope= 0.16 (SE 0.86 95% CI -1.58-1.88), left lung area; slope= 1.03 (SE 0.01 95% CI 1.0-1.06)). Deming regression showed poor fit however this was influenced by an outlying measurement. CONCLUSIONS In general there was good agreement between the two reviewers when interpreting the various images. However, as this imaging technique becomes more widespread the image analysis would benefit from being uniformly interpreted.