درخت حوزه‌های تخصصی

In today's world, the Internet of Things (IoT), which is a fairly new technology, has become a popular topic for discussion. Meanwhile, the increasing demand for personalized healthcare with the assistance of new technologies has created new applications called e-health IoT applications; however, researchers are still attempting to find its applications, therefore they have not been able to focus on comparing these applications. We have aimed at understanding the benefits of e-health IoT applications in comparison to one another. Therefore, this study is an attempt to provide a list of e-health IoT applications for individuals and to prioritize them. The Fuzzy Analytical Hierarchy Process (FAHP) method has been used, which is a method for Multi-Criteria Decision Making (MCDM) and a useful tool for prioritizing multiple alternatives based on criteria. Eight important criteria, based on a comprehensive literature review and experts’ opinions, were determined. Then, by using the FAHP method, the weight of each criterion was calculated. As a result, seven applications identified for individuals were prioritized based on the weight of each criterion and the score of each application in each criterion. Health Effectiveness, Empowerment, Safety, Privacy, and Peace of Mind are the most important criteria in e-health IoT applications for individuals; Cost Saving, round-the-clock Access, and Time-Saving are in the next levels of importance. The results also show that Chronic disease management, Medication reminders, Health monitoring, Air quality, Fall detection, Sleep control and Fitness were respectively ranked as first, second, third, fourth, fifth, sixth and seventh among the IoT applications.

Persian language is one of the most widely used languages in the Web environment. Hence, the Persian Web includes invaluable information that is required to be retrieved effectively. Similar to other languages, ranking algorithms for the Persian Web content, deal with different challenges, such as applicability issues in real-world situations as well as the lack of user modeling. CF-Rank, as a recently proposed learning to rank data, aims to deal with such issues by the classifier fusion idea. CF-Rank generates a few click-through features, which provide a compact representation of a given primitive dataset. By constructing the primitive classifiers on each category of click-through features and aggregating their decisions by the use of information fusion techniques, CF-Rank has become a successful ranking algorithm in English datasets. In this paper, CF-Rank is customized for the Persian Web content. Evaluation results of this algorithm on the dotIR dataset indicate that the customized CF-Rank outperforms baseline rankings. Especially, the improvement is more noticeable at the top of ranked lists, which are observed most of the time by the Web users. According to the NDCG@1 and MAP evaluation criteria, comparing the CF-Rank with the preeminent baseline algorithm on the dotIR dataset indicates an improvement of 30 percent and 16.5 percent, respectively.

In attempts to examine the mapped spaces of a literary narrative, various quantitative approaches have been deployed to extract data from texts to graphs, maps, and trees. Though the existing methods offer invaluable insights, they undertake a rather different project than that of literary scholars who seek to examine privileged or unprivileged representations of certain spaces. This study aims to propose a computerized method to examine how matters of space and spatiality are addressed in literary writings. As the primary source of data, the study will focus on Viet Thanh Nguyen’s The Sympathizer (2015), which explores the lives of Vietnamese diaspora in two geographical locations, Vietnam, and America. To examine the portrayed spatial relations, that is which country is privileged over the other, and to find out the underlying opinion about the two places, this study performs topic modelling with Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) by using TextBlob. In addition, Python is used as the analytical tool for this project as it supports two LDA algorithms: Gensim and Mallet. To overcome the limitation that the performance of the model relies on the available libraries in Python, the study employs machine learning approach. Even though the results indicated that both geographical spaces are portrayed slightly positively, America achieves a higher polarity score than Vietnam and hence seems to be the favored space in the novel. This study can assist literary scholars in analyzing spatial relations more accurately in large volumes of works.

In this paper a novel digital watermarking algorithm is proposed. The proposed method comprises of a watermarking embedding and extraction algorithm using bio inspired optimization technique. Dragonfly algorithm (DA) is based on the static and dynamic swarming behaviors of dragonflies in nature. The dragonfly algorithm is used to optimize the scaling factor of the watermarking so that an optimal watermark is embedded. Watermarking algorithms take as input a cover image and the message. The cover image in the proposed method is decomposed into sub bands using discrete wavelet transform (DWT). Thereafter, it is converted to discrete cosine blocks (DCT). An optimal scaling factor is required for performing the watermarking. In this paper, DA is used for computing the scaling factor. The DA generated scaling factor is optimal and improves the performance of the watermarking. The inverse DWT and DCT are computed to extract the watermarked image from the cover image. The proposed method is applied on different images to evaluate the performance. The results obtained are compared with other state of the art methods.

The Architecture, Engineering, and Construction (AEC) industry rely heavily on Building Information Modeling (BIM). BIM is the collection of Information and Communication Technologies (ICT), interacting policies, and procedures. BIM is a tool for managing digital project data during the life cycle of a building. Despite the many benefits and features of BIM, the Malaysian construction industry's proliferation is confronted with adoption issues. Therefore, this research study intends to find the effect of BIM adoption factors in Malaysian AEC. Quantitative data collection from construction firms is gathered. The proposed model's theoretical foundations are based on Technology, Organization Environment framework. The model is tested and validated with the Smart PLS tool. The study's findings indicate that Perceived benefits, Organizational Capabilities, and Trialability are drivers of BIM adoption. Perceived cost and Insecurity are the barriers to BIM adoption. Perceived ease of use and compatibility does not affect BIM adoption. Finally, this study performs Importance Performance Map Analysis to provide recommendations to AEC stakeholders to address the BIM adoption issues for enhancing its diffusion in Malaysia.

The purpose of this study is to develop proposals and recommendations for the implementation of a process model for the development of leadership qualities of public servants and justification of the conditions for ensuring its effectiveness in terms of digital transformation. The relevance of this study is due to the need to ensure development of the process of professionalization of the senior civil service personnel on the basis of development of leadership qualities that will contribute to the effective operation of the civil service of Ukraine, change management and successful implementation of reforms in Ukraine, taking into account the best world practices. The methodology for assessing the level of managerial competencies of public servants according to the degree of implementation of strategic (key) competencies has been developed. The assessment of managerial competencies according to the degree of their significance for civil servants, the expert group identified the most important management competencies. An approach to understanding has proposed interaction of leadership competencies with managerial competencies, a diagnostic model for assessing the leadership of public servants has been developed. To implement the model, a system of indicators has been developed - single, complex and integrated indicators of civil servants' leadership, using tools: a tree of civil servants' leadership indicators, matrices for the calculated civil servants' leadership indicator, measurement scales for the corresponding level of indicators.