درخت حوزه‌های تخصصی

Purpose: Start-up businesses have attracted considerable attention regarding the new approach in the modern economy The present study was conducted to investigate the factors affecting the improvement of customers’ online shopping experience in e-commerce start-ups in the healthcare sector.Method: This is an applied research conducted as a descriptive-survey. The target population includes all customers who used the electronic start-up services in the healthcare sector; out of which a sample of 384 individuals was selected using the Morgan table. A self-administered questionnaire was developed to collect the data which were analyzed using partial least squares and Smart-PLS software.Findings: All research hypotheses were confirmed, and it was proved that factors such as customer respect, customer enjoyment, importance of time, information security, convenience, perceived experience, valuable experience, and perceived image experience have a positive effect on improving customers’ online shopping experience in e-commerce start-ups.Conclusion: Applying the proposed model helps e-start-ups increase their performance   by eliminating their shortcomings and boosting their strengths.

This paper presents the design, simulation and realization of a patch antenna for IOT applications. The patch antenna consists of a radiating element printed on one face of a dielectric substrate, when the ground plane is placed on the other face. In this work, two techniques are used to design a miniaturized patch antenna: the set-up of slots on the radiating element and the use of defective ground plane. Also, the slot’s radius and Length of inset point effects on the performances of the antenna is illustrated. All the simulated results are performed with FEKO, a solver based on a Moments Method and measurement is made using Vector Network Analyzer Anritsu MS2026C. The propose antenna resonates in three frequency bands 3.91, 4.86 and 5.16GHz for different characteristics such as radiation pattern, gain, return loss, which makes it suitable for many wireless communication applications such as IoT applications.

This study aims to assess the efficiency of the eHealth program by conducting a comparative study between public and private healthcare settings in Bangladesh and Malaysia. A detailed literature review gathered from leading databases, articles, and case studies was conducted to achieve the study’s objective. After evaluating the literature and utilizing reducing methods the factors were organized and categorized into the four SWOT groups. The study exhibits that the government of Bangladesh has taken multiple approaches to efficient usage of their resources and providing primary healthcare services at grassroots levels. However, compared to a developing country like Malaysia it is far lag and struggling with multiple challenges like inadequate ICT infrastructure interoperability issues, low adoption of services, privacy and trust issues inadequate data security lack of policies and regulations, and insufficient funding along with multiple threats like resistance to change, cultural influence, and native environmental issues. Nonetheless, these challenges can be addressed through the development of adequate infrastructure, technology, financial support, and human resource capabilities. This will help to achieve the goal of universal health coverage and digital healthcare. This paper identified and explored numerous eHealth concerns and institutionalized favorable policy recommendations to establish an effective and successful eHealth system and boost the healthcare sectors of Bangladesh and similar emerging economies.  Being the first to incorporate SWOT analysis and eHealthcare programs, the suggested approaches can have a significant positive impact on the implementation of an effective and sustainable eHealth system.

This paper aims to conceptualize the success factors of a digital transformation (DT) strategy and analyze its impact on a company's economic performance. We explore the concepts that affect the field of DT definition and the key drivers that lead to successful DT. Through these key drivers considered as success factors, we propose a research framework linking these drivers to the DT strategy and then corporate economic performance in emerging markets. To test the research model empirically and provide a contextualized interpretation of the results, we adopted a sequential explanatory design. Initially, we performed a quantitative study through a survey among companies listed on the Casablanca Stock Exchange in Morocco. We then analyzed the collected data using the structural equation method. Next, to explain the results, we conducted a qualitative analysis through interviews with semi-structured questions. The findings show that in an emerging economy context such as Morocco, placing the customer at the core of the DT strategy, aligning the organization with the DT strategy, adopting a value system imbued with DT values, and establishing an operational roadmap to drive the change can enhance the company’s digital transformation. These drivers contribute to 59.5% of the implementation of the DT strategy. Driving a DT strategy has a significant impact on companies' economic performance, contributing to 21.5% of their commercial and financial outcomes. This study highlights that the maintenance of a "phygital" business model, which mixes digital and physical business models, and the lack of human resources involvement in the DT process are specific to the emerging market context studied.

Purpose: The libraries of medical sciences universities have much more diverse goals and services than can be publically conceived, so providing services related to information resources is one of the most important challenges facing these organizations.Method: The present study is an applied research based on its purpose and descriptive-analytical as regards method. The study utilized the semi-structured interview method to obtain the factors affecting the provision of health information resources services. Sampling was conducted based on a purposeful method and experts’ viewpoints. The sampling was carried out the fuzzy Delphi technique which aimed to provide a solution for the issues. A researcher-made questionnaire was used to check the factors obtained from 62 central libraries of medical sciences.Findings: In general, six types of services were determined as health information resource services in specific central libraries, including tools for exploring the online book registration network, selection tools, interlibrary loan, free educational tools, additional book publishing tests, and content evaluation. Additionally, eight factors were identified, including providing services of specialized bibliographic review by librarians, consultation services to users, dissemination and provision of selective information services on Persian and Non-Persian sources, overcoming the weaknesses of librarians without library education, increasing the diversity of medical sciences library services, utilizing new technologies to compensate for deficiencies and lack of resources, improving the social function of the library as a social institution, and offering various electronic services such as databases and digital resources. These identified factors were found to influence the provision of health information resources.Conclusion: Various factors may affect the provision of health information resources services in universities of medical sciences, most importantly improvement of the social functions of libraries.

The term "clickbait" refers to content specifically designed to capture readers' attention, often through misleading headlines, leading to frustration among social media users. In this study, titled "Mushakkal," which translates to "variety" in Arabic, we utilized a Convolutional Neural Network (CNN)—a deep learning approach—to detect clickbait within an Arabic dataset. We compared three optimizers: RMSprop, Adam, and Adadelta, evaluating various parameter settings to determine the most effective combination for detecting clickbait in Arabic content. Our findings revealed that the CNN model performed best when both pre-processing and Word2Vec techniques were applied. The Adam optimizer outperformed the others, achieving a Macro-F1 score of 77%. The RMSprop optimizer closely followed, attaining a Macro-F1 score of 76%. In contrast, Adadelta proved to be the least effective for classifying Arabic text.

In today’s world, there is a notable focus on incorporating disabled people within the community and the workplace. Advancements in artificial intelligence (AI) have the potential to significantly impact empowering individuals with disabilities to enhance inclusivity and autonomy for this demographic in the era of Society 5.0. This paper aims to offer a bibliometric analysis of the increasing number of publications addressing the potential impact of AI on disabled individuals and their future employment. To conduct this analysis, Scopus served as the bibliographic source, using Disability, Employment, and Future Workforce as the search terms, yielding 203 publications on the subject of study from 1973 to 2024. The analysis was conducted using VOS viewer. The results indicate that as Society 5.0 evolves, advancements in AI have the potential to significantly empower individuals with disabilities, enhancing inclusivity and autonomy for this demographic.

Today, users generate various data increasingly using the Internet when choosing a product or service. This leads to the generation of data about the purchases and services of various consumers. In addition, consumers often leave feedback about the purchase. At the same time, consumers discuss their attitudes about goods and services on social networks, messengers, thematic sites, etc. This leads to the emergence of large volumes of data that contain useful information about various manufacturers of goods and services. Such information can be useful to both ordinary users and large companies. However, it is practically impossible to use this information due to the fact that it is located in different places, that is, it has a raw, unstructured character. At the same time, depending on the target group of users, not the entire data set is needed, but a specific target sample. To solve this problem, it is necessary to have a tool for structuring information arrays and their further analysis depending on the set goal. This can be done with the help of various frameworks that use methods of machine learning and work with data. This work is devoted to elucidating the problem of creating means for evaluating consumer preferences based on the analysis of large volumes of data for its further use by the target audience.  The goal of the development of big data analysis systems is obtaining new, previously unknown information. The methodology of application of algorithms of work with large data sets and methods of machine learning is used, namely the pandas library for operations on a data set and logistic regression for information classification As a result, a system was built that allows the analysis of lexical information, translate it into numerical format and create on this basis the necessary statistical samples. The originality of the work lies in the use of specialized libraries of data processing and machine learning to create data analysis systems. The practical value of the work lies in the possibility of creating data analysis systems built using specialized machine learning libraries.