درخت حوزه‌های تخصصی

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.

Polling system is not trusted everywhere around the world it is very important in this modern world to replace the traditional polling system with the new technology. Some countries like United States, Japan, and India suffer from corrupted polling system. Major issues are faced by current polling systems like system hacking, vote rigging, vote manipulation, distributed denial of service attack, and online polling booth capturing. This paper will lead to the problems faced by the traditional polling system and how the new technology will provide the solution to that problem. Also, our purpose is to check the feasibility of the system by recording the transaction fees and evaluate the right way to spend the amount of gas in the transaction. This will highlight blockchain frameworks including blockchain as a service and polling system which is on blockchain that addresses all constraint introducing ethereum which is a blockchain-based distributed computing platform. Ethereum is open source, and publicly available with a system featuring smart contracts. It provides the cryptocurrency wallets that let you make cheap, instant payments with gas in the form of ethers. The ethereum community is the most active and largest blockchain community in the world. There is no centralized organization that controls ethereum.

Blockchain is one of the trending emerging technologies that enables immutable data storage over distributed ledgers. The fundamental features of blockchain technology, such as record and identity traceability and immutability makes it an attractive alternative for the state-of-the-art information systems. Thus, blockchain has found its way into various fields. In Iran, blockchain technology has been employed in different areas including: commercial mining platforms, secure exchange platforms, and innovative trustless projects. One of the main applications of blockchain is secure management of sensitive information, which makes it a suitable infrastructure for providing advanced control over data in police task forces. The motivation behind this research is to provide a conceptual framework for employing blockchain in law enforcement organizations by analysis and comparison of best practices in other countries and academic case-studies. We provide a systematic overview of case-studies by qualitative content analysis. Moreover, the reliability of our statistics is evaluated by Cohen's kappa coefficient roughly 0.64. Finally, the prioritized applications were evaluated by a 14 member experts panel through fuzzy Delphi method with a Kendall’s Coefficient of Concordance of W=0.711, indicating high rates of agreement among members. To this end, “trustless authentication systems”, “distributed data/resource/information management”, and “inter-department integrated information systems” were selected as most valuable applications of blockchain for police task forces and law enforcement agencies in Iran.

Healthcare service institutions (HSIs) have sought ways to motivate patient loyalty in response to surging rates of medical tourism. Previous research indicates that Hospital Information System (HIS) is essential for HSIs to gather, measure, and analyze the massive amounts of data required to generate patient loyalty. There is currently no consensus on the factors that comprise HIS specifically geared towards motivating patient loyalty (HISPL). Furthermore, HIS requires full adoption by HSI staff to be effective. Thus, to reduce wastage of HSI resources, it is necessary to predict whether a given HIS specifically geared towards motivating patient loyalty is likely to be adopted. The purpose of this study is to reveal the factors that comprise HISPL and to modify the Unified Theory of Acceptance and Use of Technology (UTAUT) model to help predict the likelihood of an HISPL to be fully adopted by HSI staff. The results revealed that pertinent HISPL factors are capability, configurability, ease of use/help desk availability and competence (EU), and accessibility/shareability (AS). Using these factors, the UTAUT model was modified to fit the specific needs of HISPL. The modifications are theoretical and will have to be validated in future empirical studies.

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.

Security is one of the most important aspects of data transmission. To provide controlled access to the network, user authorization and authentication are often used with the help of an AAA server. RADIUS servers provide users with access to data, user authentication, and configuration information. When designing networks with such access control method implementation, it is necessary to understand how the characteristics of the communication channel affect the switching time of IP-TV channels, and therefore the overall quality of IP-TV services. The principles of the main protocols for IP-TV using a RADIUS server are described. The main parameters of the communication channel were identified. The mathematical model and the graphs demonstrate how IP-TV service access time depends on telecommunication channel parameters. The results of a practical experiment are presented to prove the formed mathematical model. The results of a practical experiment and theoretical calculation are compared.

To ensure the continuity of the training activity within the framework of the specific precau-tions for the Covid 19 health crisis , the regional centers for training and education profes-sions, such as universities and schools, have radically reversed their training strategy, from face-to-face training to alternating training mode known as `` hybrid training mode '', the training of which is provided remotely one week and face-to-face during the following week and vice versa. In this sense, digital platforms of social networks and videoconferencing soft-ware have been mobilized to succeed in the mission of trainers.  The objective of this article is to verify the contribution of videoconferencing software '' on training practices on the one hand and on the development of trainees' skills on the other hand, in order to trace a training methodology based on technological choices aimed at guar-anteeing maximum socio-professional performance. Finally, we confirm that despite the satisfaction of the majority of trainees 87% with respect to the advantages of videoconferencing tools in the process of assessing skills, technical, spa-tiotemporal and organizational disparities were noted among trainers compared to the use of digital tools in the evaluation (only 53% agree), or also in relation to the transition from one technological choice to another. 

The aim of this study is to evaluate the performance of the pre-trained models and compare them with the probability percentage of prediction in terms of execution time. This study uses the COCO dataset to evaluate both pre-trained image recognition and object detection, models. The results revealed that Tiny-YoloV3 is considered the best method for real-time applications as it takes less time. Whereas ResNet 50 is required for those applications which require a high probability percentage of prediction, such as medical image classification. In general, the rate of probability varies from 75% to 90% for the large objects in ResNet 50. Whereas in Tiny-YoloV3, the rate varies from 35% to 80% for large objects, besides it extracts more objects, so the rise of execution time is sensible. Whereas small size and high percentage probability makes SqueezeNet suitable for portable applications, while reusing features makes DenseNet suitable for applications for object identification.

The purpose of the present study is to present a model for the use of the Internet of Things in Iranian knowledge-based companies. From a purpose point of view, this research can be classified as applied research and in terms of data collection, it can be categorized as a qualitative and quantitative study. The main approach for achieving the model of using the Internet of Things is the grounded theory methodology which is based on a paradigmatic model. The research population for the qualitative phase is comprised of 25 executives and practitioners of knowledge-based companies who are somehow specialized in Internet of Things topics. The sample of this study was selected using the snowball sampling method and Cochran's formula for finite communities among all the managers and technology experts working in knowledge-based companies. After careful analysis of the interviews, 59 extracted concepts were identified in 5 main categories and 13 subcategories. Management requirements, customer requirements, and resistance economics give rise to the Internet of Things in knowledge-based companies.  The characteristics of the company, the characteristics of the human resources and the characteristics of the managers can provide a suitable ground for using the Internet of Things. And the conditions in the country, international pressures and international cooperation can be among the intermediary factors influencing the use of the Internet of Things. Raw materials and infrastructure can also be effective strategies for using the Internet of Things. Using the Internet of Things, customer-related factors, international collaborations, and expected results can be expected in knowledge-based companies.

Android malware is one of the most dangerous threats on the Internet.  It has been on the rise for several years.  As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc.  One of the most growing attacks is on Android systems due to its use in many devices worldwide.  De-spite significant efforts in detecting and classifying Android malware, there is still a long way to improve the detection process and the classification performance.  There is a necessity to provide a basic understanding of the behavior displayed by the most common Android malware categories and families.  Hence, understand the distinct ob-jective of malware after identifying their family and category.  This paper proposes an effective systematic and functional parallel machine-learning model for the dynamic detection of Android malware categories and families.  Standard machine learning classifiers are implemented to analyze a massive malware dataset with 14 major mal-ware categories and 180 prominent malware families of the CCCS-CIC-AndMal2020 on dynamic layers to detect Android malware categories and families.  The paper ex-periments with many machine learning algorithms and compares the proposed model with the most recent related work.  The results indicate more than 96 % accuracy for Android Malware Category detection and more than 99% for Android Malware family detection overperforming the current related methods.  The proposed model offers a highly accurate method for dynamic analysis of Android malware that cuts down the time required to analyze smartphone malware.

This study compared the performance of the partial least squares-structural equation modelling (PLS-SEM) and the robust partial least squares -structural equation modelling (RPLS-SEM) methods through Winsorisation approach The inputs and the outputs used in this model were based on the electricity generation data, derived from the Al-Zawiya Steam Power Plant, Libya. Furthermore, the researchers compared the novel RPLS-SEM approach with the traditional PLS-SEM approach and noted that the novel RPLS-SEM method was more efficient compared to PLS-SEM.

The Moroccan education system has undergone several reforms in recent years, thus concluded by the strategic vision, especially project 9 which has valued the continuing training of educational actors and framework law 51-17 in articles 34 and 35 which valued the continuous development of skills of teachers in Morocco, In this perspective the Ministry of National Education has reserved significant budgets to operationalize the continuing training of teachers on the one hand and it is committed to a Moroccan-American partnership within the framework of the Millennium project Challenge Account-Morocco. By committing to the establishment-challenge project, the ministry has set up a set of priorities, in particular the operationalization of the digital strategy for schools declared by decree 2.20.474, thus benefiting from the contribution of digital and while trying to overcome some constraints related to face-to-face training, a new regime that is continuous online training is implemented, it is the e-takwine platform. In addition, given the interest that evaluation represents for maintaining and improving the quality of a digital training device, we opted in this research, by evaluating the technopedagogical modality of e-takwine reserved at the start of the continuing training of novice teachers in Morocco and then developed for the training of all second cycle teachers from the start of the 2021-2022 school year. The sampling of our research is represented by 362 professors from the region of Fés –Meknes belonging to two different teaching cycles, and who followed their training for three months in six training modules in a more or less assiduous manner, Similarly, to conduct our survey, we administered two online questionnaires to participating teachers, in order to judge the functionality and ergonomics of the platform being researched. Finally, confirming to us that despite the satisfaction of the majority of participants with respect to the functional quality and the contribution of the device, some difficulties were noted at the levels: technical (compatibility, accessibility), functional or even with regard to technological knowledge.