Since the COVID-19 pandemic, telemedicine or non-face-to-face medicine has increased significantly. In practice, various types of medical images are essential to achieve effective telemedicine. Medical image encryption algorithms play an irreplaceable role in the fast and secure transmission and storage of these medical images. However, most of the existing medical image encryption algorithms are full encryption algorithms, which are inefficient and time-consuming, so they are not suitable for emergency medical scenarios. To improve the efficiency of encryption, a small number of works have focused on partial or selective encryption algorithms for medical images, in which different levels of encryption strategies were adopted for different information content regions of medical images. However, these encryption algorithms have inadequate security more or less. In this paper, based on the Logistic map, we designed an improved variable dimension map. Then, an encryption algorithm for med.....

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Modern medical examinations have produced a large number of medical images. It is a great challenge to transmit and store them quickly and securely. Existing solutions mainly use medical image encryption algorithms, but these encryption algorithms, which were developed for ordinary images, are time-consuming and must cope with insufficient security considerations when encrypting medical images. Compared with ordinary images, medical images can be divided into the region of interest and the region of background. In this paper, based on this characteristic, a plain-image correlative semi-selective medical image encryption algorithm using the enhanced two dimensional Logistic map was proposed. First, the region of interest of a plain medical image is permuted at the pixel level, then for the whole medical image, substitution is performed pixel by pixel. An ideal compromise between encryption speed and security can be achieved by full-encrypting the region of interest and semi-encrypting t.....

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Choosing a quality image is the goal of image processing of two-dimensional (2D) images through computer vision. Image processing consists of stages, namely acquisition, pre-processing (enhancement), segmentation, representation and description, as well as introduction and interpretation. Edge detection is a stage in image processing that aims to find the pattern of an image. This study analyzes the quality of 2D images through edge detection techniques with a comparison of various techniques and error analysis. The comparison of edge detection in this study was performed on images produced using some techniques, such as Canny, Sobel, Prewitt, and Roberts. To analyze the error, Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) were used. This study was conducted using Matlab by comparing six different images of lung, car, leaf, apple, cat, and motorcycle. The results show that using edge detection with the Canny technique may result in the best MSE and PSNR values. Consiste.....

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With the modernization of biomedical image equipment and image processing technology, the amount of medical image data increases rapidly. These medical images need to be transmitted over a public network between doctors or hospitals to provide telemedicine services and sometimes need to be uploaded to a telemedicine data center or health cloud due to limited local computing and storage resources. Medical images involve confidential and sensitive information of patients, and the confidentiality technology to protect patient privacy is becoming more and more important. These problems have been studied by many scholars, but these studies are largely limited and scattered. To the authors knowledge, there is no systematic literature review related to medical image confidentiality technology. To provide valuable insights into the technical environment and to support researchers, the available options and gaps in this research area must be understood. Therefore, in this study, the recent 5yea.....

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Paper-capillary micropumps are advantageous as they are small in size, cost-effective, and easily disposable. However, capillary-based micropumps fabricated using the photolithography technique prevent exchanging of the pumping element. The liquid movement in paper-based micropumps is also difficult to control and manipulate as compared to non-paper-based micropumps. Therefore, in this study, the performance of a multi-angle paper-based capillary micropump that utilizes both hydrostatic and paper-capillary effects with a detachable paper matrix is presented. The design offers enhanced and controllable flow rates based on paper matrix types and platform inclination angles. In addition, the paper matrix pumping element does not involve in the analytical processes but rather only for fluid driving. Standalone operation shows a working range of flow rate comparable to some purely capillary- and hydrostatic-based micropumps reported in the literature. The proposed micropump shows that the f.....

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Online  distance  learning,  on  the  one  side,  makes  the  learning  process  more  efficient  and  flexible.  However,  on  the  other  hand,  it  produces  learning  loss  within  a  particular  educational  period  and  makes  students  less  enthusiastic  about  building  a  collaborative  attitude.  In  general,  online  learning  platform providers have provided support for collaboration activity features, but in reality, they have not been able to support effective interaction. Experts believe that using the Personal Learning Environment (PLE) as an IT tool in the learning process can help create an effective online collaboration environment. This study found that PLE was proven to increase collaborative activities in online learning significantly.  The  reflection  feature  is  the  type  of  PLE  that  has  the  most  significant influence on creating a good collaboration environment. The research also   found   th.....

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The monitoring of children's nutritional status serves as a crucial tool for assessing the health of both children and society as a whole. In this regard, machine learning (ML) has been employed to predict nutritional status for monitoring purposes. This topic has been extensively discussed. However, the question remains as to which algorithm or ML framework can yield the highest accuracy in predicting the nutritional status of children within a specific region. Furthermore, determining the appropriate dataset for predictions is also crucial. Therefore, this review aims to identify and analyze the research trends, dataset characteristics, algorithms, and frameworks utilized in studies pertaining to the nutritional status of children under the age of five from 2017 to early 2022. The selected papers focus on the application of ML techniques in predicting nutritional status. The findings of this research reveal that the Bangladesh demographic and health survey 2014 dataset is among the p.....

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Postpartum depression (PPD) is a significant mental health concern affecting mothers worldwide, irrespective of demographic factors. Detecting and managing PPD at an early stage is crucial for effective intervention. In the context of mental health, intelligent predictive models based on machine learning (ML) have emerged as valuable tools. However, there remains a relative scarcity of research specifically targeting postpartum mental health due to several prominent factors that collectively impede the widespread adoption and practical implementation of ML in the field of PPD. This paper provides an updated overview of ML approaches for PPD prediction. A systematic search across IEEE Xplore, PubMed, Science Direct, and Scopus yielded 1,074 relevant articles. The performance of ML algorithms varies depending on the dataset and the problem being addressed. Notably, the findings reveal that the random forest (RF) algorithm consistently demonstrates the highest predictive accuracy, followe.....

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Social distancing policies during the COVID-19 period have opened a space for interventions in the use of digital technology that was previously rejected by the public. Currently, most of the community activities have been carried out online, including those related to education, such as hosting virtual exhibitions with an educational purpose. Virtual exhibitions (VE) are online exhibitions that display information on specific topics. However, to the best of our knowledge, research on VE in the educational context is still lacking. This study aims to identify the variables that affect users' intentions to attend the virtual exhibition frequently. The channel expansion theory, flow theory, technology acceptance model, and expectation confirmation theory serve as the foundation for the research model that is constructed. The model was tested using data from 321 respondents who had visited VE, then analyzed using Structural Equation Modeling (SEM) with the aid of the SmartPLS application .....

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Postpartum hemorrhage (PPH) represents a significant threat to maternal health, particularly in developing countries, where it remains a leading cause of maternal mortality. Unfortunately, only 60% of pregnant women at high risk for PPH are identified, leaving 40% undetected until they experience PPH. To address this critical issue and ensure timely intervention, leveraging rapidly advancing technology with machine learning (ML) methodologies for maternal health prediction is imperative. This review synthesizes findings from 43 selected research articles, highlighting the predominant ML techniques employed in PPH prediction. Among these, logistic regression (LR), extreme gradient boosting (XGB), random forest (RF), and decision tree (DT) emerge as the most frequently utilized methods. By harnessing the power of ML, we aim to foster technological advancements in the healthcare sector, with a particular focus on maternal health and ultimately contribute to the reduction of maternal morta.....

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