Classification algorithms that help to identify software defects or faults play a crucial role in software risk management. Experimental results have shown that ensemble of classifiers are often more accurate and robust to the effects of noisy data, and achieve lower average error rate than any of the constituent classifiers. However, inconsistencies exist in different studies and the performances of learning algorithms may vary using different performance measures and under different circumstances. Therefore, more research is needed to evaluate the performance of ensemble algorithms in software defect prediction. The goal of this paper is to assess the quality of ensemble methods in software defect prediction with the analytic hierarchy process (AHP), which is a multicriteria decision-making approach that prioritizes decision alternatives based on pairwise comparisons. Through the application of the AHP, this study compares experimentally the performance of several popular ensemble methods using 13 different performance metrics over 10 public-domain software defect datasets from the NASA Metrics Data Program (MDP) repository. The results indicate that ensemble methods can improve the classification results of software defect prediction in general and AdaBoost gives the best results. In addition, tree and rule based classifiers perform better in software defect prediction than other types of classifiers included in the experiment. In terms of single classifier, K-nearest-neighbor, C4.5, and Naïve Bayes tree ranked higher than other classifiers.

The intersection between digital technologies and tourism emerges as a prominent area of research within the tourism industry. This is primarily driven by the immense complexity of digital tourism itself as a multifaceted social, economic, and cultural phenomenon. This paper reveals the future implications of tourism in the context of digital intelligence. To gain insights into the latest applications of digital tourism, this work reviews 278 studies spanning from 2012 to 2024. A systematic literature analysis method is proposed to identify the research hotspots and trends. Through value co-creation theory and Schumpeter’s innovation theory, five key trajectories are suggested, as follows: in-depth application of digital technologies in tourism, spawning new scenarios, new forms and new modes of digital tourism, and deep integration of digital economy and tourism real economy. This state-of-the-art review will help to reflect the efforts towards promoting the smart development of digital technologies in tourism.

Investment strategy selection relies heavily on personal experience and behavior. This paper proposes an improved Analytical Hierarchy Process-group decision making (IAHP-GDM) model to reduce investment risk. This model applies the method of least squares to adjust group decision matrix in order to satisfy the property of positive reciprocal matrix in AHP. In addition, five experts from related fields are invited to evaluate investment risk that takes group wisdom to eliminate personal bias. An empirical study is conducted to compare the proposed model to AHP for group decision making model. The results show that the IAHP-GDM model is not only accurate and effective, but also consistent with realistic investment environment.

Individual decision-making largely influences the effectiveness of decisions and benefits of investments. Methods: In this article, a consensus model for group decision-making (GDM), based on the analytic hierarchy process (AHP), is developed to gather group ideas and analyze the real estate investment environment under multi-criteria problems. Twelve evaluation procedures of the developed model, which increase the convergence of the opinions of multiple experts, are proposed. An empirical case about the real estate investment environment is applied to certify the feasibility of this developed model. the evaluation procedures have been fully observed with several rounds of discussions, and have manifested the experiences of experts. Besides, the evaluation results are in accordance with real-world situations, which demonstrates that our developed model is a feasible analysis tool for real estate investors to obtain better profits and lower risk.