درخت حوزه‌های تخصصی

The purpose of this research was to identify the skills and abilities learners need in a digital learning environment during the Corona crisis. The research approach was applied in terms of objective and the data collected using the mixed-method approach. In the first part, using a qualitative approach and a document search method, 33 components (related to the skills and abilities of e-learners) were identified, and then19 components out of 33 with the highest frequencies were selected. The target population for this study included all MA students of the University of Tehran who had the experience of studying in an online learning environment, then 374 individuals were selected as a sample using the random sampling method. In the second part, using a quantitative approach and a descriptive-analytical research method, the questionnaire developed by the researcher relies on 19 components identified in the previous step. The experts' opinions proved the validity of the questionnaire—moreover, its reliability in 0.94 (Cronbach alpha). The data was analyzed by SPSS and LISREL, and the results of the data analysis demonstrate that the proposed model is a good fit. Among the 19 components surveyed, all are above average. Finally, the results indicated that the most critical components from e-learners’ view which more critical in digital learning were effective communication, use of the computer, active learning, learning skills, resilience, motivation toward e-learning, emotional intelligence, interaction with e-learning system, self-direct learning, time management, flexibility and adaptability, critical thinking, creativity, asking someone for help, leadership skills, stress management, self-organized, cognitive skills and self-assessment

Focusing on the problems faced by blind people, this paper has come up with the technology solution for the assistance of blind people. The solution is based on the intelligent data transmission to the earphone of a person based on task associated. The solution consists of a jacket to detect the obstacles along with a wearable box with task priority switchs. The system helps in detection of the obstacle and its height, one-touch cab booking and support of relatives, Ambulance services, Police services, etc. in the case of emergency. Either wired and wireless headphones or speakers can be interfaced with the device (box) to get audio notifications. The various tasks are triggered using multiple switches. The system will use a definitive SOC (System on Chip) platform recognized as Rasp-Pi-Pi along with ultrasonic sensor HC-SR04, Neo-6M GPS (Global Positioning System) module, and different switches. The system uses a 20,000 mAh lion battery for the power supply. The voice signals can be provided in more than fifty languages. A fall detection system is also discussed in this paper. This system will be beneficial not only for blind but also for care of old aged people

Heart and circulatory system diseases are often referred to as cardiovascular disease (CVD). The health and efficiency of the heart are crucial to human survival. CVD has become a primary cause of demise in recent years. According to data provided by the World-Health-Organization (WHO), CVD were conscientious for the deaths of 18.6M people in 2017. Biomedical care, healthcare, and disease prediction are just few of the fields making use of cutting-edge skills like machine learning (ML) and deep learning (DL). Utilizing the CVD dataset from the UCI Machine-Repository, this article aims to improve the accuracy of cardiac disease diagnosis. Improved precision and sensitivity in diagnosing heart disease by the use of an optimization algorithm is possible. Optimization is the process of evaluating a number of potential answers to a problem and selecting the best one. Support-Machine-Vector (SVM), K-Nearest-Neighbor (KNN), Naïve-Bayes (NB), Artificial-Neural-Network (ANN), Random-Forest (RF), and Gradient-Descent-Optimization (GDO) are just some of the ML strategies that have been utilized. Predicting Cardiovascular Disease with Intelligence, the best results may be obtained from the set of considered classification techniques, and this is where the GDO approach comes in. It has been evaluated and found to have an accuracy of 99.62 percent. The sensitivity and specificity were likewise measured at 99.65% and 98.54%, respectively. According to the findings, the proposed unique optimized algorithm has the potential to serve as a useful healthcare examination system for the timely prediction of CVD and for the study of such conditions.

The purpose of this paper is to measure the relationship between the functional service recovery and technical service recovery towards customer satisfaction in banking industry. Both functional and technical is equally important in service quality especially in service recovery. The competition among the banking industry especially in major cities in Malaysia is very crucial. The study was conducted in Kuala Lumpur based on the top 10 banks based on the market share. 250 questionnaire distributed but only 212 were usable for the data analysis. The results indicated that both functional and technical service recovery plays important role towards customer satisfaction. Banking services or any financial institution should have focused on both elements in order to provide excellent services that may contribute to customer satisfaction and in the long term lead to customer loyalty

The quest for improving the software quality has given rise to various studies which focus on the enhancement of the quality of software through various processes. Code smells, which are indicators of the software quality have not been put to an extensive study for as to determine their role in the prediction of defects in the software. This study aims to investigate the role of code smells in prediction of non-faulty classes. We examine the Eclipse software with four versions (3.2, 3.3, 3.6, and 3.7) for metrics and smells. Further, different code smells, derived subjectively through iPlasma, are taken into conjugation and three efficient, but subjective models are developed to detect code smells on each of Random Forest, J48 and SVM machine learning algorithms. This model is then used to detect the absence of defects in the four Eclipse versions. The effect of balanced and unbalanced datasets is also examined for these four versions. The results suggest that the code smells can be a valuable feature in discriminating absence of defects in a software.

Penetration of smartphones and increasing use of social media on always-on devices has attracted the attention of enterprises and organizations to benefit from such platforms for better understanding of customers’ needs and more effectively communicate with potential consumers. For this purpose, the present study investigates the impact of social media adoption and media needs on online purchasing behavior. This study has also examined the moderating role of media type, gender and age in the relationship between variables of social media adoption and online purchasing behavior. A total of 410 questionnaires were collected on social media such as Instagram, Telegram and WhatsApp. SEM technique using PLS used as the analytical method to empirically test the proposed hypotheses. Results show that the effects of tension dimension on online purchasing behavior is mediated by social media adoption. Moreover, the effects of social media adoption on online purchasing behavior are moderated by age and medium type based on Multi-Group Analysis. The IPMA matrix shows that social media adoption had the highest importance, but the lowest performance. This study is significant in terms of innovation due to the use of new indicators effective in the area of statistical analyses. The use of FIMIX, CTA, permutation test, MGA, and IPMA matrix analyses is part of it.

Achieved wireless networks since its beginning the prevalent wide due to the increasing wireless devices represented by smart phones and laptop, and the proliferation of networks coincides with the high speed and ease of use of the Internet and enjoy the delivery of various data such as video clips and games. Here's the show the congestion problem arises and represent   aim of the research is to avoid congestion at APs to wireless networks by adding a control before congestion occurs. A wireless connection was made using the Android system, and congestion was predicted based on the analysis of wireless communication packages around the access point using the LSTM deep learning model. The results show that if the amount of information in the input data is large, a more accurate prediction can be made.

Digital technologies are actively implemented in all areas of tourism. The article identifies the main areas of balancing interests in the field of tourism on the basis of optimization of digital marketing tools. Analytical research has shown that a set of sales channels in the Internet environment and a set of tools are formed, depending on the marketing strategy of the travel company. The main tactics and tools of digital marketing in tourism are highlighted. These are: the company's website, digital marketing channels - online channels for promoting and attracting customers: SEO, online advertising, email marketing, sales funnel, content marketing, teaser advertising, SMM, etc. The study proposes the application of portfolio investment theory to optimize investment in digital marketing tools to achieve a specific goal. The article uses a method of building a mathematical model that will optimally distribute financial investments in digital marketing. Thus balancing of interests in the field of tourism on the basis of digital marketing tools is offered on the basis of KPI indicators, according to the allocated purposes.

In the theory of traffic flows the main characteristics are: intensity, speed, and density.  They make it possible to use hydrodynamic models. In connection with the development of modern highways and road networks, traffic flows behavior is becoming more and more complex and diverse. In particular, the B.Kerner studies have shown that the laminar solution of hydrodynamic models is poorly correlated with experimental data. Our research team is developing tools for intelligent monitoring of traffic flows on fragments of the road network with different geometries.  The paper presents a project of a client-server system, which allows obtaining, in real-time, information regarding the basic characteristics of traffic flows at the junction of any configuration using mobile devices. The automation of obtaining characteristics is based on the application of image recognition algorithms (virtual detection method).