Systematic Literature Review and Meta-Analysis Journal http://slr-m.com/index.php/home <p>The Systematic Literature Review and Meta-Analysis Journal is a multidisciplinary journal focused on the research articles, reviews and empirical research that has used Systematic Literature Review and Meta-Analysis (SLR-M) methods in their research. The journal aimed to facilitate the research in all fields of life until the SLR-M methods have been applied. </p> en-US info@theapra.org (Dr. Muhammad Imran Qureshi) systematliterature@gmail.com (Nohman Khan) Fri, 21 Oct 2022 14:46:11 -0400 OJS 3.3.0.13 http://blogs.law.harvard.edu/tech/rss 60 A comprehensive systematic literature review on traffic flow prediction (TFP) http://slr-m.com/index.php/home/article/view/44 <p class="RiAbstractText">Nowadays, traffic congestion is becoming a severe problem for almost every urban area. It badly hampers the economic growth of a country because it has negative effects on productivity and business. Increasing populations and urbanization are the main reasons for traffic congestion in most cities. However, traffic prediction, forecasting, and modeling can help provide appropriate routes and times for traveling and can significantly impact traffic jam reduction. Currently, there is a lot of research being done on traffic flow analysis in all developed countries, and they are planning their future accordingly. The objective of this review paper is to provide a comprehensive and systematic review of the traffic prediction literature, containing 98 papers published from 2010 to 2020. The papers are extracted from four well-known publishers and databases: Scopus, ScienceDirect, IEEE Xplore, and ACM. This article concentrates on the research approaches, directions, and gaps in traffic flow prediction. It also talks about current trends in predicting traffic flow and what might be taken into account in the future.</p> Md. Moshiur Rahman, Md. Mahbubul Alam Joarder, Naushin Nower Copyright (c) 2022 Systematic Literature Review and Meta-Analysis Journal https://creativecommons.org/licenses/by/4.0 http://slr-m.com/index.php/home/article/view/44 Fri, 21 Oct 2022 00:00:00 -0400 Automatic cell type annotation using supervised classification: A systematic literature review http://slr-m.com/index.php/home/article/view/45 <p>Single-cell sequencing gives us the opportunity to analyze cells on an individual level rather than at a population level. There are different types of sequencing based on the stage and portion of the cell from where the data are collected. Among those Single Cell RNA seq is most widely used and most application of cell type annotation has been on Single-cell RNA seq data. Tools have been developed for automatic cell type annotation as manual annotation of cell type is time-consuming and partially subjective. There are mainly three strategies to associate cell type with gene expression profiles of single cell by using marker genes databases, correlating expression data, transferring levels by supervised classification. In this SLR, we present a comprehensive evaluation of the available tools and the underlying approaches to perform automated cell type annotations on scRNA-seq data.</p> Nazifa Tasnim Hia, Sumon Ahmed Copyright (c) 2022 Systematic Literature Review and Meta-Analysis Journal https://creativecommons.org/licenses/by/4.0 http://slr-m.com/index.php/home/article/view/45 Fri, 21 Oct 2022 00:00:00 -0400 Trajectory inference in single cell data: A systematic literature review http://slr-m.com/index.php/home/article/view/46 <p class="RiAbstractText">Recent advances in single-cell transcriptomics have made it possible to explore the dynamic mechanisms of immunology in a high-throughput and objective manner. Unsupervised trajectory inference methods attempt to automatically reconstruct the developmental path cells are following by using a mixture of cells at various stages of development. In the past few years, there have been a multitude of new techniques for deducing the trajectory of a single cell from its data. This paper proposes that new researchers might focus on these criteria by examining the strategies and challenges of existing methodologies. Using specific databases (Scopus, Google Scholar and IEEE Xplore), these single cell data trajectory inference studies from 2016 to 2022 were reviewed. We have adhered to the PRISMA structure. Three databases and the most recent works on trajectory inference have been selected. The majority of studies compared their results to those of previously established methods. Several challenges were identified. Additionally, we attempted to identify the most recent work strategies. This may aid future researchers in locating suitable strategies.</p> Ishrat Jahan Emu, Sumon Ahmed Copyright (c) 2022 Systematic Literature Review and Meta-Analysis Journal https://creativecommons.org/licenses/by/4.0 http://slr-m.com/index.php/home/article/view/46 Fri, 21 Oct 2022 00:00:00 -0400