METHODS: We first tested ten traditional machine learning algorithms, and then the three-best performing algorithms (three types of SVM) were used in the rest of the study. To improve the performance of these algorithms, a data preprocessing with normalization was carried out. Moreover, a genetic algorithm and particle swarm optimization, coupled with stratified 10-fold cross-validation, were used twice: for optimization of classifier parameters and for parallel selection of features.
RESULTS: The presented approach enhanced the performance of all traditional machine learning algorithms used in this study. We also introduced a new optimization technique called N2Genetic optimizer (a new genetic training). Our experiments demonstrated that N2Genetic-nuSVM provided the accuracy of 93.08% and F1-score of 91.51% when predicting CAD outcomes among the patients included in a well-known Z-Alizadeh Sani dataset. These results are competitive and comparable to the best results in the field.
CONCLUSIONS: We showed that machine-learning techniques optimized by the proposed approach, can lead to highly accurate models intended for both clinical and research use.
METHODS: This study used the MIT-BIH Normal Sinus Rhythm (nsrdb) and MIT-BIH Atrial Fibrillation (afdb) databases for healthy human (NSR) and atrial fibrillation patient (N and AF) ECG signals, respectively. The extraction of features was based on the dynamic system concept to determine the ω of the ECG signals. There were 35,031 samples used for classification.
RESULTS: There were significant differences between the N & NSR, N & AF, and NSR & AF groups as determined by the statistical t-test (p<0.0001). There was a linear separation at 0.4s(-1) for ω of both databases upon using the thresholding method. The feature ω for afdb and nsrdb falls within the high frequency (HF) and above the HF band, respectively. The feature classification between the nsrdb and afdb ECG signals was 96.53% accurate.
CONCLUSIONS: This study found that features of the ω of atrial fibrillation patients and healthy humans were associated with the frequency analysis of the ANS during parasympathetic activity. The feature ω is significant for different databases, and the classification between afdb and nsrdb was determined.
RESULTS: At present, the classifier used has achieved an accuracy of 100% based on skulls' views. Classification and identification to regions and sexes have also attained 72.5%, 87.5% and 80.0% of accuracy for dorsal, lateral, and jaw views, respectively. This results show that the shape characteristic features used are substantial because they can differentiate the specimens based on regions and sexes up to the accuracy of 80% and above. Finally, an application was developed and can be used for the scientific community.
CONCLUSIONS: This automated system demonstrates the practicability of using computer-assisted systems in providing interesting alternative approach for quick and easy identification of unknown species.
METHODOLOGY: The Clarivate Analytics' Web of Science 'All Databases', Elsevier's Scopus, Google Scholar and PubMed Central were searched to retrieve the 50 most-cited articles in the IEJ published from April 1967 to December 2018. The articles were analysed and information including number of citations, year of publication, contributing authors, institutions and countries, study design, study topic, impact factor and keywords was extracted.
RESULTS: The number of citations of the 50 selected papers varied from 575 to 130 (Web of Science), 656 to164 (Elsevier's Scopus), 1354 to 199 (Google Scholar) and 123 to 3 (PubMed). The majority of papers were published in the year 2001 (n = 7). Amongst 102 authors, the greatest contribution was made by four contributors that included Gulabivala K (n = 4), Ng YL (n = 4), Pitt Ford TR (n = 4) and Wesselink PR (n = 4). The majority of papers originated from the United Kingdom (n = 8) with most contributions from King's College London Dental Institute (UK) and Eastman Dental Hospital, London. Reviews were the most common study design (n = 19) followed by Clinical Research (n = 16) and Basic Research (n = 15). The majority of topics covered by the most-cited articles were Outcome Studies (n = 9), Intracanal medicaments (n = 8), Endodontic microbiology (n = 7) and Canal instrumentation (n = 7). Amongst 76 unique keywords, Endodontics (n = 7), Mineral Trioxide Aggregate (MTA) (n = 7) and Root Canal Treatment (n = 7) were the most frequently used.
CONCLUSION: This is the first study to identify and analyse the top 50 most-cited articles in a specific professional journal within Dentistry. The analysis has revealed information regarding the development of the IEJ over time as well as scientific progress in the field of Endodontology.
METHODS: An electronic search was conducted on the Clarivate Analytics Web of Science "All Databases" to identify and analyze the top 50 most frequently cited scientific articles. After ranking the articles in a descending order based on their citation counts, each article was then crossmatched with the citation counts in Scopus, Google Scholar, and PubMed.
RESULTS: The citation counts of the 50 selected most cited articles ranged between 218 and 731 (Clarivate Analytics Web of Science). The years in which most top 50 articles were published were 2004 and 2008 (n = 5). Among 131 authors, the greatest contribution was made by M. Torabinejad (n = 14). Most of the articles originated from the United States (n = 38) with the greatest contributions from the School of Dentistry, Loma Linda University, Loma Linda, CA (n = 15). Basic research-technology was the most frequent study design (n = 18). A negative, significant correlation occurred between citation density and publication age (correlation coefficient = -0.708, P < .01).
CONCLUSIONS: Several interesting differences were found between the main characteristics of the most cited articles and the most downloaded articles.
RESULTS: The Condorcet fusion method was examined. This approach combines the outputs of similarity searches from eleven association and distance similarity coefficients, and then the winner measure for each class of molecules, based on Condorcet fusion, was chosen to be the best method of searching. The recall of retrieved active molecules at top 5% and significant test are used to evaluate our proposed method. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints.
CONCLUSIONS: Simulated virtual screening experiments with the standard two data sets show that the use of Condorcet fusion provides a very simple way of improving the ligand-based virtual screening, especially when the active molecules being sought have a lowest degree of structural heterogeneity. However, the effectiveness of the Condorcet fusion was increased slightly when structural sets of high diversity activities were being sought.
MATERIALS AND METHODS: PubMed and Semantic Scholar databases were scoured for articles using 10 search terms. In vitro studies satisfying the inclusion criteria were probed which were meticulously screened and scrutinized for eligibility adhering to the 11 exclusion criteria. The quality assessment tool for in vitro studies (QUIN Tool) containing 12 criteria was employed to assess the risk of bias (RoB).
RESULTS: A total of 48 studies assessing shear bond strength (SBS) and 15 studies evaluating tensile bond strength (TBS) were included in the qualitative synthesis. Concerning SBS, 33.4% moderate and 66.6% high RoB was observed. Concerning TBS, 26.8% moderate and 73.2% high RoB was discerned. Seventeen and two studies assessing SBS and TBS, respectively, were included in meta-analyses.
CONCLUSIONS: Shear bond strength and TBS increased for the primed alloys. Cyclic disulfide primer is best-suited for noble alloys when compared with thiol/thione primers. Phosphoric acid- and phosphonic acid ester-based primers are opportune for base alloys.
CLINICAL SIGNIFICANCE: The alloy-resin interface (ARI) would fail if an inappropriate primer was selected. Therefore, the selection of an appropriate alloy adhesive primer for an alloy plays a crucial role in prosthetic success. This systematic review would help in the identification and selection of a congruous primer for a selected alloy.