درخت حوزه‌های تخصصی

Available research suggests using Blockchain in electronic medical files to aggregate and integrate information. While reviewing authoritative articles and books, we analyzed the challenges and opportunities of using Blockchain this way. By using the specialists' opinion, completing a specialist questionnaire, using the DEMETEL technique and interpretive structural modeling (ISM) technique, this research investigated and analyzed the possibilities of using Blockchain in electronic medical files. The pair comparison technique was used to study and analyze the challenges of using Blockchain this way. The results are stated in this research. Research achievements include the design of a model using Blockchain for electronic medical files, analysis of Block chain's opportunities and challenges, measurement of the components, design and presentation of the model, and prioritizing the challenges of using Blockchain. This research suggests that the use of Blockchain in electronic medical files can reduce extra costs, create a comprehensive system for medical records to avoid additional costs, facilitate access to patient information, and reduce medical errors. The results obtained reveal that the components of immutability and the impossibility of data forgery of patient records (based on data integration in Blockchain) have the most impact on the system. Examination of the challenges reveals that blockchain technology's challenges (low speed, difficulty working with Blockchain, poor integrations between the software for various electronic health records) are of the utmost importance.

The reconstruction of 3D images is always a difficult task for the researchers. The 3D reconstruction of the image is a core technique of various fields such as Computer graphics, computer vision, CAD systems, medical science, computer application, etc. Reconstruction of the 3D image allows us to gather the quantitative features of the objects such as the shape, size, and volume of the objects. The existing computer algorithms need spatial dimension information to make the distinguished inference from the given 3D image which is not always possible. This paper simplifies the 3D reconstruction of the image. This research paper introduced a novel algorithm for the representation of the Three Dimensional images into a textual form.  The syntactic approach is used for the extraction of the features of the image and these are called knowledge vectors. The knowledge vector consists of the direction information and length information. This a new approach in the field of image processing where images can be represented as a knowledge vector and it could be a great contribution in the field where security is a major concern. Further, the knowledge vector is used for the reconstruction of the 3D image. The performance of the algorithm is evaluated on the PASCAL 3D + and example-based Synthesis of the 3D Object Arrangements dataset. According to the obtained results, the proposed methodology is having better accuracy, and the processing time of reconstruction of the original 3D image is 1.02 Seconds. Single-pass is sufficient for reconstructing the original image

This study aims to test the relationship between knowledge generation and organizational creativity. The second method was surveyed (analytical) using exploratory research through the survey as a data collection tool. The validity coefficient was measured as a reasonable degree of stability was obtained for a sample of (46) individuals. The data was analyzed using statistical package (SPSS V25). Descriptive Statistic Among the findings is that CASNOS has a medium level of the knowledge generation process, and there is a statistically significant effect of knowledge generation on the organizational innovation of CASNOS (El-Oued), according to the dimensions adopted in the study model.

Healthcare service institutions (HIS) seeking to motivate patient loyalty have identified Hospital Information Systems (HIS) as a potential solution to gather, measure, and analyze the healthcare data necessary for this goal. The purpose of this systematic review of the literature is to reveal how prevalent the use of HIS with respect to motivating patient loyalty, and to investigate the efficacy of HIS in doing so. To generate data, published empirical studies and conference papers from the past five years were compiled from the following online databases: Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, and Emerald Insight. The search results indicate that, while the use of HIS in motivating patient loyalty is rare relative to other topics within the general field of HIS, HIS use have a significant positive impact on patient satisfaction, which is understood in the literature to be directly related to patient loyalty. There remains a gap in empirical studies on the direct application of HIS with the purpose of increasing patient loyalty. Future research may be required on the development of an HIS focused on motivating patient loyalty, which can be empirically tested in a real-world HSI setting.

Falls are a serious concern among the elderly people, causing severe physical pain to them and placing a strain on medical infrastructure. The global elderly population is expected to grow significantly in the coming years, as advances in healthcare allow lifespans to increase globally. This will bring more chances for falls to occur. With this in mind, there is a need for new research to be conducted on finding ways to reduce this problem. One area which shows promise is the use of Machine Learning to perform fall detection. Machine Learning is a rapidly growing field, and it has many applications in various fields such as finance, technology and medicine. When it comes to fall detection, Machine Learning systems are often able to detect falls much better and efficiently than a human can, given the same input data. The goal of this paper is to conduct a survey study on the main and most common machine learning algorithms implemented in the field of early fall detection for elderly people and the characteristics. The paper will discuss the different types of fall detection systems, algorithms, tools, datasets, applications, and challenges.  By conducting this research, a better understanding of the context, progress and trends in the field will be possible so that future research will have a guide to build upon.

LoRaWAN is one of the new and updated standards for IoT applications. However, the expected high density of peripheral devices for each gateway, and the absence of an operative synchronization mechanism between the gateway and peripherals, all of which challenges the networks scalability. In this paper, we propose to normalize the communication of LoRaWAN networks using a Reservation-ALOHA (R-ALOHA) instead of the standard ALOHA approach used by LoRa. The implementation is a library package placed on top of the standard LoRaWAN; thus, no modification in pre-existing LoRaWAN structure and libraries is required. Our proposed approach is based on a distributed synchronization service that is suitable for low-cost IoT end-nodes. R-ALOHA LoRaWAN gives better performance in comparison with the previous models; Pure-ALOHA LoRaWAN, Slotted-ALOHA LoRaWAN, and TDMA LoRaWAN. It significantly improves the performance of network regarding the probability of collision, the maximum throughput, and the maximum duty cycle.

In recent years, brain tumors become the leading cause of death in the world. Detection and rapid classification of this tumor are very important and may indicate the likely diagnosis and treatment strategy. In this paper, we propose deep learning techniques based on the combinations of pre-trained VGG-16 CNNs to classify three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). The scope of this research is the use of gray level of co-occurrence matrix (GLCM) features images and the original images as inputs to CNNs. Two GLCM features images are used (contrast and energy image). Our experiments show that the original image with energy image as input has better distinguishing features than other input combinations; accuracy can achieve average of 96.5% which is higher than accuracy in state-of-the-art classifiers.

This research deals with integrating RE and CBC in the product design process. RE improves the product value based on evaluate the competition products, whereas CBC aims to use a process approach to define activities and prelates those activities to products or customers using the product’s Characteristics. Integrating RE and CBC together leads to the improvement of product value, an increase of customer satisfaction, and support of competitive advantage. In this study first, a conceptual model of integration of these two approaches is provided and then the implementation procedures in product design cycle are explained, and finally, the results got from implementing it in an Electronic Industries company are discussed.