A recent study of property valuation literature indicated that the vast majority of researchers and academics in the field of real estate are focusing on Mass Appraisals rather than on the further development of the existing valuation methods. Researchers are using a variety of mathematical models used within the field of Machine Learning, which are applied to real estate valuations with high accuracy. On the other hand, it appears that professional valuers do not use these sophisticated models during daily practice, rather they operate using the traditional five methods. The Department of Lands and Surveys in Cyprus recently published the property values (General Valuation) for taxation purposes which were calculated by applying a hybrid model based on the Cost approach with the use of regression analysis in order to quantify the specific parameters of each property. In this paper, the authors propose a number of algorithms based on Artificial Intelligence and Machine Learning approaches that improve the accuracy of these results significantly. The aim of this work is to investigate the capabilities of such models and how they can be used for the mass appraisal of properties, to highlight the importance of sensitivity analysis in such models and also to increase the transparency so that automated valuation models (AVM) can be used for the day-to-day work of the valuer.

Systematic reviews may provide less biased evidence than narrative reviews because they observe a strict methodology, similarly to primary studies. Hence, for clinical research questions, systematic reviews should be the study design of choice. It would be important to evaluate the prevalence and characteristics of narrative and systematic reviews published in prominent medical journals. Researchers and clinicians give great value to articles published in such scientific journals. This study sought to evaluate the prevalence and characteristics of narrative and systematic reviews in the five highest-ranked general medical journals and investigate the associations among type of review, number of citations, and impact factor (IF). We surveyed the five highest-ranked medical journals (The New England Journal of Medicine, The Lancet, The Journal of the American Medical Association, The BMJ, and Annals of Internal Medicine) for narrative and systematic reviews published between June 2015 and June 2016. We independently selected and extracted the data from the reviews by strictly following the pre-determined eligibility criteria (Systematic and narrative reviews that focused on the management of diseases). We conducted regression analyses to investigate the associations among review type, number of citations, and IF. We also descriptively reported narrative reviews containing some methodology that might be reproducible. Two hundred seventy-five reviews were included: 75 (27%) systematic; 126 (46%) narrative with some methodology reported, and 74 (27%) narrative reviews. In comparison to systematic reviews, narrative reviews were more frequently published in journals with higher IF (risk ratio [RR] = 1.114 (95% CI 1.080 to 1.149). Systematic reviews received more citations than narrative reviews (group formed by narrative and narrative with some methodology reported (RR = 0.985 95% CI 0.978 to 0.991). Non-systematic evidence is the most prevalent type of evidence in reviews published in the five highest-ranked general medical journals. Narrative reviews were more frequently published in journals with higher IF. We recommend that journals limit their space for narrative information, and to address clinical research questions, these journals consider publishing systematic evidence exclusively.

To comprehensively report on the characteristics of retracted publications in the field of dentistry. We searched MEDLINE (via PubMed), PubMed Central, Web of Science and Google Scholar databases for dental retracted articles from database inception to 02 July 2018. In addition, we scanned the search engine Google Scholar, and the website, Retraction Watch (www.retractionwatch.com), for retracted dental articles. Two researchers independently screened titles, abstracts and full text of search results. Descriptive data was collected on each retracted article including reason for retraction, study type, journal impact factor, and time between publication and retraction. Regression models were used to evaluate the association between journal impact factor and retraction characteristics. A total of 138 retractions of dental articles were included. Reasons for retraction were misconduct (N = 100, 72.5%), with the most frequently reported misconduct being overlap/plagiarism and innacurate/falsified conducting and reporting (N = 53, 38.4%). In vitro (N = 39, 28.3%), case reports (N = 29, 21%) and narrative reviews (N = 19, 13.8%) were study design most frequently identified in retracted articles. The median time between article publication and date of retraction notice was 1 year (interquartile-range [IQR] = 0-2 years). More than half of the retracted articles (n = 82, 59.4%) were cited post-retraction. A retracted article reporting a randomized controlled trial was more likely to appear in journal with higher impact factor than a retracted case report (mean difference [MD] = 2.2; 95% confidence interval [CI] = 1.2,3.1. Articles retracted after 2012 were likely to appear in journals with a lower impact factor (MD=-1.3; 95%CI=-1.8, 0.8). Research misconduct is the main reason for retraction of dental articles. A substantial proportion of these articles were still being cited after their retraction. This report of dental retraction articles informs that more transparency is needed with data reporting in dentistry to improve writing practices in dentistry. A more complete report of retractions and their causes would provide more accurate information to inform researchers and editors to avoid or reduce future cases of retractions. More complete and accurate reporting would increase the overall trust in dental research.

The origins of forecasting can be traced back to the beginning of human civilization with attempts to predict the weather, although forecasting as a field first appeared in the 1940s and attracted more followers from the early 1950s, when the need fo

In this review paper, we computationally analyze a vast volume of published articles in the field of Adaptive Learning, as obtained by the Scopus Database. Particularly, we use a query with search terms targeting the area of Adaptive Learning Systems by utilizing a combination of specific keywords. Accordingly, we apply a multidimensional scaling algorithm to construct bibliometric maps for keywords, authors, and references. Subsequently, we present the computational results for the studied dataset, reveal significant patterns appearing in the field of adaptive learning and the inter-item associations, and interpret the findings based on the current state-of-the-art literature in the area. Furthermore, we demonstrate the time-series of the evolution of the research terms, their trends over time, as well as their prevalent statistical associations.

A new, fast, elegant, and simple stochastic optimization search method is proposed, which exhibits surprisingly good performance and robustness considering its simplicity. We name the algorithm pure random orthogonal search (PROS). The method does not use any assumptions, does not have any parameters to adjust, and uses basic calculations to evolve a single candidate solution. The idea is that a single decision variable is randomly changed at every iteration and the candidate solution is updated only when an improvement is observed; therefore, moving orthogonally towards the optimal solution. Due to its simplicity, PROS can be easily implemented with basic programming skills and any non-expert in optimization can use it to solve problems and start exploring the fascinating optimization world. In the present work, PROS is explained in detail and is used to optimize 12 multi-dimensional test functions with various levels of complexity. The performance is compared with the pure random search strategy and other three well-established algorithms: genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE). The results indicate that, despite its simplicity, the proposed PROS method exhibits very good performance with fast convergence rates and quick execution time. The method can serve as a simple alternative to established and more complex optimizers. Additionally, it could also be used as a benchmark for other metaheuristic optimization algorithms as one of the simplest, yet powerful, optimizers. The algorithm is provided with its full source code in MATLAB for anybody interested to use, test or explore.