A literature review: machine learning-based stem cell investigation.

Background and Objective: Stem cell (SC) is a crucial factor of the human organ that is significantly important for clinical solutions. However, consideration of SC in the therapeutic or disease classification process is complex in terms of accurate classification and prediction. To overcome this issue, Machine learning (ML) is the most effective technique that is frequently used in cell-based clinical applications for diagnosis, treatment, and disease identification. Recently it has been implemented for SC observation which is a crucial factor for clinical solutions. Thus, the objective of this review work is to represent the effectiveness of ML techniques for SC observation from clinical perspectives with current challenges and future direction for further improvement. Methods: In this study, we conducted a short review of ML-based applications in SCs investigation and classification for the improvement of clinical solutions. We explored studies from five scientific databases (Web of Science, Google Scholar, Scopus, ScienceDirect, and PubMed) with several keywords related to the objective of our research study. After primary and secondary screening, 15 articles were utilized for this research study and summarized the observation results in terms of ten aspects (year of publication, focused area, objective, experimented datasets, selected ML classifiers, experimental procedure, classification parameter, overall performance in terms of accuracy, advancements, and limitations) with their current limitations and future improvement directions. Key Content and Findings: The majority of the existing literature review works are limited to focusing on specific SC-based investigation, limited evaluation attributes, and lack of challenges and future improvement suggestions. Also, most of the review work didn't consider the investigation of the effectiveness of the ML technique in SC biology. Therefore, in this paper, we investigate existing literature related to the development of clinical solutions considering ML techniques, in the area of SC and cell culture processes and highlight current challenges and future directions. Conclusions: The majority of studies focused on the disease identification process and implemented the convolutional neural network and support vector machine techniques. The prime limitations of the investigated studies are related to the focused area, investigated SCs, the small number of experimental datasets, and validation techniques. None of the studies provided complete evidence to determine an optimal ML technique for SC to build classification or predictive models. Therefore, further concern is required to develop and improve the developed solutions including other ML techniques, large datasets, and advanced evaluation processes.

Accessibility engineering in web evaluation process: a systematic literature review.

Several works of literature contributed to the web evaluation process in recent years to promote digital inclusion by addressing several accessibility guidelines, methods, processes, and techniques. Researchers have investigated how the web evaluation process could be facilitated by including accessibility issues to obtain an inclusive and accessible solution to improve the user experience and increase user satisfaction. Three systematic literature reviews (SLRs) have been conducted in the context of past research, considering such research focuses. This paper presents a new SLR approach concerning accessibility in the web evaluation process, considering the period from 2010 to 2021. The review of 92 primary studies showed the contribution of publications on different phases of the web evaluation process mainly by highlighting the significant studies in the framework design and testing process. To the best of our knowledge, this is the first study focused on the web accessibility literature reporting the engineering assets for evaluation of new accessible and inclusive web-based solutions (e.g., websites). Besides, in this study, we aim to provide a new direction to the web designers and developers with an updated view of process, methods, techniques, tools, and other crucial aspects to contribute to the accessible process enrichment, as well as depict the gaps and challenges that may be worthy to be investigated in the future. The findings of this SLR introduce a new dimension in web accessibility research on determining and mitigating the research gap of web accessibility issues for web designers, developers, and other practitioners.

Crash severity analysis and risk factors identification based on an alternate data source: a case study of developing country.

Road traffic injuries are one of the primary reasons for death, especially in developing countries like Bangladesh. Safety in land transport is one of the major concerns for road safety authorities and other policymakers. For this reason, contributory factors identification associated with crashes is necessary for reducing road crashes and ensuring transportation safety. This paper presents an analytical approach to identifying significant contributing factors of Bangladesh road crashes by evaluating the road crash data, considering three different severity levels (non-fetal, severe, and extremely severe). Generally, official crash databases are compiled from police-reported crash records. Though the official datasets are focusing on compiling a wide array of attributes, an assorted number of unreported issues can be observed that demands an alternative source of crash data. Therefore, this proposed approach considers compiling crash data from newspapers in Bangladesh which could be complimentary to the official crash database. To conduct the analysis, first, we filtered the useful features from compiled crash data using three popular feature selection techniques: chi-square, Two-way ANOVA, and Regression analysis. Then, we employed three machine learning classifiers: Decision Tree, Random Forest, and Naïve Bayes over the extracted features. A confusion matrix was considered to evaluate the proposed model, including classification accuracy, sensitivity, and specificity. The predictive machine learning model, namely, Random Forest using Label Encoder with chi-square and Two-way ANOVA feature selection process, seems the best option for crash severity prediction that provides high prediction accuracy. The resulting model highlights nine out of fourteen independent features as responsible factors. Significant features associated with crash severities include driver characteristics (gender, license type, seat belts), vehicle characteristics (vehicle type), road characteristics (road surface type, road classification), environmental conditions (day of crash occurred, time of crash), and injury localization. This outcome may contribute to improving traffic safety of Bangladesh.

Investigation of COVID-19 Vaccine Information Websites across Europe and Asia Using Automated Accessibility Protocols.

Websites content accessibility guidelines (WCAG) ensure that websites should be perceivable, understandable, navigable, and interactive. During the SARS-CoV-2 pandemic, the importance of accessible websites and online content grew throughout the world. Therefore, in this study, we examined COVID-19-related official government websites. This research covered 21 government websites, with 13 websites from European countries and 8 websites from Asian countries, to evaluate their accessibility following WCAG 2.0 and WCAG 2.1 guidelines. The overall goal of this study was to identify the frequent accessibility problems that might help the website owners to identify the shortcomings of their websites. The target websites were evaluated in two steps: in step-1, evaluation was performed through four automatic web accessibility testing tools such as Mauve++, Nibbler, WAVE, and WEB accessibility tools; in step-2, evaluation went through human observation, such as system usability testing and expert testing. The automatic evaluation results showed that few of the websites were accessible; a significant number of websites were not accessible for people with disabilities. In addition, system usability testing found some complexity in website organization, short explanations, and outdated information. The expert testing suggested improving the color of the websites, organization of links, buttons, and font size. This study might be helpful for associated authorities to improve the quality of the websites in the future.