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ObjectivesThis study aimed to profile the cognitive aging research landscape from 1956 to 2021.MethodsA total of 3,779 documents were retrieved from the Scopus database for the bibliometric analysis and network visualization. By comparing each keyword’s overall connection strength (centrality), frequency (density), and average year of publication (novelty) to the calculated median values acquired from the overlay view of the VOSviewer map, the enhanced strategic diagrams (ESDs) were constructed.ResultsThe findings showed an increasing trend in the number of publications. The United States leads the contributing countries in cognitive aging research. The scientific productivity pattern obeyed Lotka’s law. The most productive researcher was Deary, I. J., with the highest number of publications. The collaborative index showed an increasing trend from 1980 onwards. Frontiers in Aging Neuroscience is the most prestigious journal in the field of cognitive aging research. In Bradford core journals zone 1, the top 10 core journals of cognitive aging research provided more than half of the total articles (697, or 55.36 percent).ConclusionsFor the next decades, the trending topics in cognitive aging research include neuropsychological assessment, functional connectivity, human immunodeficiency virus (HIV), decision-making, gender, compensation, default mode network, learning and memory, brain-derived neurotrophic factor (BDNF), obesity, D-galactose, epigenetics, frailty, mortality, mini-mental state examination (MMSE), anxiety, and gait speed.

Determining the cluster number in k-means is problematic since it affects the quality of cluster for numerous applications in the data mining. The automatic clustering differential evolution (ACDE) is one of the most used clustering methods that are able to determine the cluster number automatically. However, ACDE still makes use of the manual strategy to determine a value k activation threshold thereby affecting its performance. In this study, the u-control chart (UCC) method use to tackle the ACDE method problem. UCC method used for the initial step to get a value of the variables sought before initialization of the variable vector on ACDE. The UCC is a method from statistical process control (SPC) field which has proved to be effective in solving the problem of management control attributes. The performance of the proposed method was tested using seven public datasets from the UCI repository and Clustering basic benchmark repository and evaluated with Davies Bouldin Index (DBI) and CS measure. The results show that, the proposed method yields excellent performance compared to prior researches for most datasets with optimal cluster number yet lowest DBI and CS measure. It can be concluded that the UCC method is able to determine k activation threshold in ACDE that caused effective determination of the cluster number for k-means clustering.

The cost of finding and correcting the software defects are high and increases exponentially in the software development. The software defect prediction (SDP) can be used in the early phases to reduce the testing and maintenance time, cost and effort; thus, improves the quality of the software. SDP performance is poor caused by imbalanced class in datasets where defective modules as minority compared to defect-free ones. In this study, we propose the combination of random undersampling based on two-step cluster and stacking technique for improving the accuracy of SDP. In stacking technique, Decision Tree, Logistic Regression and k-Nearest Neighbor are used as base learner while Naive Bayes as stacking model learner. The proposed method is evaluated using nine datasets from NASA metrics data program repository and area under curve (AUC) as main evaluation. Results have indicated that the proposed method yield excellent performance for 5 of 9 datasets (AUC > 0.9). Compared to the prior researches, the proposed method has first position for 3 datasets, second position for 5 datasets and only 1 dataset in third position for AUC value comparison. Therefore, it can be concluded that the proposed method has an impressive and promising result in prediction performance for most datasets compared with prior research performance.

Recent interest in multiple sclerosis research warrants literature analysis to evaluate the current state of the discipline and new research domains. This bibliometric review summarised the research trends and analysed research areas in multiple sclerosis over the last decade. The documents containing the term ‘multiple sclerosis’ in the article title were retrieved from the Scopus database. We used Harzing’s Publish or Perish and VOSviewer for citation analysis and data visualisation, respectively. We found a total of 18,003 articles published in journals in the English language between 2012 and 2021. The emerging keywords identified utilising the enhanced strategic diagram were ‘covid-19’, ‘teriflunomide’, ‘clinical trial’, ‘microglia’, ‘b cells’, ‘myelin’, ‘brain’, ‘white matter’, ‘functional connectivity’, ‘pain’, ‘employment’, ‘health-related quality of life’, ‘meta-analysis’ and ‘comorbidity’. This study demonstrates the tremendous growth of multiple sclerosis literature worldwide, which is expected to grow more than double during the next decade especially in the identified emerging topics.

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