Today, the elderly population is increasing, and there are many drawbacks for them, especially defects in their knee joints which lead to improper gait. To solve this problem, their knee joint can be replaced with knee arthroplasty. In this letter, level of improvement in the human gait before and after total knee arthroplasty (TKA) surgery is investigated using the dynamic time warping (DTW) algorithm. For this purpose, several volunteers who have problems with their knees are incorporated in a test before and after TKA surgery. Then, the data of gait analysis is collected and the data is compared with a reference using the DTW algorithm. The outcome results illustrate an improvement of 89%–97% by the proposed algorithm after TKA surgery. Therefore, patients can see improvement with high accuracy and very fast that result in more use this technique in TKR surgery.

In this letter, the level of improvement in the human gait before and after total knee arthroplasty (TKA) surgery is investigated using the Dynamic Time Wrapping (DTW) algorithm. For this purpose, several volunteers who have problems with their knees incorporate in a test before and after TKA surgery, and data of gait analysis is collected. Then, the obtained data is compared with a reference using the DTW algorithm.image

Chronic obstructive pulmonary disease (COPD) affects the lives of millions of patients worldwide. Patients with advanced COPD may require non‐invasive ventilation (NIV) to support the resultant deficiencies of the respiratory system. The purpose of this study was to evaluate the effects of varying the continuous positive airway pressure (CPAP) and oxygen supplementation components of NIV on simulated COPD patients by using an established and detailed model of the human respiratory system. The model used in the study simulates features of advanced COPD including the effects on the changes in ventilation control, increases in respiratory dead space and airway resistance, and the acid–base shifts in the blood seen in these patients over time. The results of the study have been compared with and found to be in general agreement with available clinical data. Our results demonstrate that under non‐emergency conditions, low levels of oxygen supplementation combined with low levels of CPAP therapy seem to improve hypoxemia and hypercapnia in the model, whereas prolonged high‐level CPAP and moderate‐to‐high levels of oxygen supplementation do not. The authors conclude that such modelling may be useful to help guide beneficial interventions for COPD patients using NIV.

• Optimal application of Non‐Invasive Ventilation (NIV) can help mitigate the negative impacts of Chronic Obstructive Pulmonary Disease (COPD).

• Optimal applications of continuous positive airway pressure (CPAP) component of NIV can improve oxygenation and reduce carbon dioxide retention for COPD patients.

• Optimal applications of supplemental oxygen treatment component of NIV can result in normoxia and reduced hypercapnia in COPD patients.

• Mathematical modelling and computerized decision support systems can be used to help guide beneficial interventions for COPD patients using NIV. image

Recently, medical technologies have developed, and the diagnosis of diseases through medical images has become very important. Medical images often pass through the branches of the network from one end to the other. Hence, high‐level security is required. Problems arise due to unauthorized use of data in the image. One of the methods used to secure data in the image is encryption, which is one of the most effective techniques in this field. Confusion and diffusion are the two main steps addressed here. The contribution here is the adaptation of the deep neural network by the weight that has the highest impact on the output, whether it is an intermediate output or a semi‐final output in additional to a chaotic system that is not detectable using deep neural network algorithm. The colour and grayscale images were used in the proposed method by dividing the images according to the Region of Interest by the deep neural network algorithm. The algorithm was then used to generate random numbers to randomly create a chaotic system based on the replacement of columns and rows, and randomly distribute the pixels on the designated area. The proposed algorithm evaluated in several ways, and compared with the existing methods to prove the worth of the proposed method.

This study proposes a deep learning‐based secure medical image transmission in e‐health applicationsimage