جستجوی مقالات مرتبط با کلیدواژه « systematic review » در نشریات گروه « ریاضی »

Many countries and governments that seek to achieve economic prosperity and benefit from the power and other benefits derived from it; they consider the tourism industry as a golden opportunity. Considering the vast amount of mines in Iran and stating that mining has many positive and negative effects on the environment due to its vastness. Scientometrics is one of the reliable tools for evaluating materials and information sources and identifying research areas and drawing connections between these areas in the form of a thematic map. Using this technique to gain a comprehensive insight related to the potential of mining tourism for the sustainable development of mines is a new action and provides a comprehensive understanding of published articles and research for researchers. This study, which follows the interpretative paradigm, is a type of descriptive study that was conducted using a systematic review method and using the search terms defined in the web of science database in the title of the articles and in the time period of 1955 to 2022. Published research in the field of mining tourism has been deeply investigated. After searching, screening and qualitative evaluation of studies, the final analysis of 52 articles and in-depth analysis of selected articles showed that the most research in the field of tourism in the field of mining is related to 2020 and China, Australia and Slovakia respectively. They have the highest number of research and communication between authors.

In the latest decade, the concept of service delivery and software-as-a-service have evolved into two important evolution paradigms which have affected the information systems area. A paradigm shift in software development has been triggered by this change, in which software is developed through the use of ready-made services in the cloud. Since service composition in the cloud environment must be done on-the-fly, realizing this requires a trade-off between the optimality of the composite service and the time it takes. As QoS-aware service composition has several potential solutions, some of which are usually considered optimal, which should be considered an NP-hard problem. A growing number of services leads to a larger problem search space, which is why in recent years many researchers have looked into methods that use meta-heuristic algorithms to solve the problem of service composition in a cloud environment. Thus, it is crucial that researchers have access to up-to-date and specialized review articles. Based on a systematic review of the research literature, the paper aims to extract important questions that are relevant to meta-heuristic QoS-aware service composition methods. Then, after classifying the studies and studying the proposed methods, goals, and priorities of researchers in articles, useful results and statistics for future research in this field are presented.

Machine learning plays a vital role in construction estimation which could make improve the project's safety, and reliability. Many studies have been proposed to explore the potential opportunities to review this technology in the construction cost in structure and transport fields. However, no comprehensive study to review the global research trends on this area's advancement in construction cost. The goal is to taxonomy, review, and summarize the state-of-the-art knowledge body on this topic in a systematic manner based on machine learning (ML) and deep learning (DL) approaches. To achieve this, this paper considered many studies in construction management related to bibliographic records retrieved from the Scopus database by adopting a quantitative analysis approach. This paper found that from 2017 to 2021, there has been a considerable increase in the number of publications in this domain. We categorized and explained civil projects into structures and transport cost, ML/DL as supervised and unsupervised approaches, and the evaluation metrics proposed to evaluate the performance of ML-Cost estimations in the civil area. The findings will help both professionals and researchers to understand and evolve the recent trend research ML/DL methodologies and their role played in the construction management domain.