International Journal of Information Technology & Decision Making

In the chronic disease diagnosis with high-dimensional clinical features, feature selection (FS) algorithms are widely applied to avoid sparse data. In current FS algorithms, only population features, which are in strong relevance with states of all patients, are extracted, while subspace features, which are in weak relevance with states of all patients but in strong relevance with states of patients under a certain state, are ignored. Eliminated relevant information in subspace features worsens the performance of current classification models. To alleviate the conflict of feature extraction in sparse data, we propose a two-phase classification model with relevant information in both population and subspace features considered. For a patient, his probability under each state is estimated in a space whose dimensions are population features in Phase 1, and in a space whose dimensions are subspace features under that state in Phase 2. The final result of the classification model is based on results in both phases. With both population and subspace features considered and probabilities under each state estimated in a low-dimensional space, the two-phase classification model outperforms other benchmark models both in accuracy and mean absolute error in the hepatic fibrosis diagnosis for patients with chronic hepatitis B.

In this research, an effort has been put to develop an integrated predictive modeling framework to automatically estimate the rental price of Airbnb units based on listed descriptions and several accommodation-related utilities. This paper considers approximately 0.2 million listings of Airbnb units across seven European cities, Amsterdam, Barcelona, Brussels, Geneva, Istanbul, London, and Milan, after the COVID-19 pandemic for predictive analysis. RoBERTa, a transfer learning framework in conjunction with $K$-means-based unsupervised text clustering, was used to form a homogeneous grouping of Airbnb units across the cities. Subsequently, particle swarm optimization (PSO) driven advanced ensemble machine learning frameworks have been utilized for predicting rental prices across the formed clusters of respective cities using 32 offer-related features. Additionally, explainable artificial intelligence (AI), an emerging field of AI, has been utilized to interpret the high-end predictive modeling to infer deeper insights into the nature and direction of influence of explanatory features on rental prices at respective locations. The rental prices of Airbnb units in Geneva and Brussels have appeared to be highly predictable, while the units in London and Milan have been found to be less predictable. Different types of amenity offerings largely explain the variation in rental prices across the cities.

Two-sided matching decision-making (TSMDM) problems widely exist in human being’s life. In practical TSMDM problems, subjects who are being matched with usually tend to provide linguistic evaluations for convenience. Owing to the fuzziness of decision-making environment, subjects may provide several linguistic terms associated with their probabilities as the evaluations, which can be described as the probabilistic linguistic term sets (PLTSs). To model such scenarios, this paper proposes a TSMDM approach with probabilistic linguistic evaluations. The probabilistic linguistic evaluations are firstly normalized with the preservation of ignored probabilities. The normalized evaluations are then aggregated into the comprehensive evaluations by using the defined Choquet integral-based probabilistic linguistic aggregation (CIPLA) operator. Thereafter, the satisfaction degrees of two sides of subjects are calculated based on the closeness coefficient in TOPSIS method. On the basis of this, a multi-objective TSMDM model aiming to maximize the comprehensive satisfaction degree is built and transformed into a single-objective TSMDM model considering the weights of two sides. To solve the single-objective TSMDM model is to determine the optimal matching result. An illustrative example of matching enterprises and knowledge-sender universities is introduced to validate the proposed approach and compare it with other existing TSMDM approaches. The results illustrate that the proposed approach can not only avoid information loss, but also effectively integrate PLTSs with correlative criteria.

This study aims to eliminate the subjectivity in the weight assignment process of Modified Kemeny Median Indicator Ranks Accordance (KEMIRA-M) and to remove the need for experts to reach a consensus on determining the criteria weights. Additionally, this study aims to apply KEMIRA-M for four different criteria groups and to prevent some criteria from taking a weight value of “0”, as in other studies using KEMIRA-M. In this context, the weighting procedure of KEMIRA-M is advanced using three different ranking-based weighting methods such as Rank Sum (RS), Rank Exponent (RE) and Rank Reciprocal (RR) to operate Median Priority Components (MPCs) more effectively. Accordingly, to determine which weighting method for which criterion group is more suitable, the selection procedure of KEMIRA-M was applied and alternative rankings were obtained for 81 different weight set combinations. Additionally, MATLAB codes have been used to provide flexibility for the application of the proposed approach in a supplier selection problem selected for a case study.

The concepts of relations and information measures have importance whenever we deal with medical diagnosis problems. The aim of this paper is to investigate the global pandemic COVID-19 scenario using relations and information measures in an interval-valued T-spherical fuzzy (IVTSF) environment. An IVTSF set (IVTSFS) allows describing four aspects of human opinions i.e., membership, abstinence, non-membership, and refusal grade that process information in a significant way and reduce information loss. We propose similarity measures and relations in the IVTSF environment and investigate their properties. Both information measures and relations are applied in a medical diagnosis problem keeping in view the global pandemic COVID-19. How to determine the diagnosis based on symptoms of a patient using similarity measures and relations is discussed. Finally, the advantages of dealing with such problems using the IVTSF framework are demonstrated with examples.

Context: Successful project management requires accurate estimation of maintenance effort and cost. Software Maintainability Prediction (SMP) plays a very important role in controlling software maintenance costs by detecting software modules with low maintainability. In previous research, numerous regression techniques were applied to predict software maintainability. The results with respect to various accuracy or performance measures are conflicting. Thus, there is a dire need to develop a method that can recommend regression techniques for predicting software maintainability in the presence of conflicting performance or accuracy measures. Objective: This paper aims to recommend suitable regression techniques for SMP based on the Multi-Criteria Decision-Making (MCDM) approach. Methodology: In our proposed approach, selecting a regression technique for SMP is modeled as the MCDM problem. To validate the proposed approach, an empirical study is done using three MCDM methods, 22 regression techniques, and eight performance measures over five software maintainability datasets. Before applying MCDM methods, a statistical test, namely the Friedman test, was conducted to ensure the significant difference between regression techniques. Results: The results of our study show that SVR, IBK, REPTree, and MLP-SVM achieve the highest-ranking score value one and are recommended as top-ranked approaches for SMP based on MCDM rankings. Conclusion: The main outcome of this study is that the proposed MCDM-based approach can be used as an efficient tool for selecting regression techniques among different available regression techniques for SMP modeling in the presence of more than one conflicting accuracy or performance measure.

Despite extensive studies on consumer behavior and decision making, the social influence of consumers on each other has not been widely investigated. To incorporate such interactions, in this study, we propose and apply an agent-based simulation model where consumers are defined as agents. The purchase behavior of each agent is characterized as a function based on the concept of the black-box model for consumer behavior. In particular, we investigate the effect of consumers’ social network and its interaction with the marketing mix parameters (4Ps). A case study of household appliances in a local market is used to demonstrate how the dynamics of preferences between domestic and foreign brands occurs. The simulation model is used to examine the effect of eight scenarios related to these interactions. The obtained results are compared and the most important factors are determined as product features and price.

The digital divide first emerged as an important social and human rights issue in the 1990s. With the rapid development of wireless network technology (e.g., Wi-Fi and cellular networks) and mobile terminal devices (e.g., mobile phones, laptops, and tablets), this issue has shifted to concern mobile Internet access. The three main Chinese telecom operators (China Mobile Communications Corporation, China Unicom Communications Corporation, and China Telecom Communications Corporation) have recently promoted unlimited data plans (UDPs) to reduce information usage costs and increase mobile data use. However, assessments of the impact of these policy changes on the mobile digital divide are limited. This study therefore offers insights into how a reduction in information costs can eliminate this divide. Using a difference-in-differences (DID) approach based on a monthly panel of system-generated mobile app data for subscribers, we find that the UDPs have various effects on promoting the use of mobile Internet and on the digital divide, depending on whether subscribers are of high or low socioeconomic status (i.e., urban versus rural and rich versus poor). We show that the relative differences are alleviated, but the absolute difference further increases after the implementation of a UDP subscription. In addition, a UDP subscription has heterogeneous effects on lifestyles. For the poor or those in rural areas, only promoting UDP does not effectively narrow the mobile digital divide because of insufficient cognition about and persistent weaknesses in the use of information technology.