Spam reviews are a pervasive problem on online platforms due to its significant impact on reputation. However, research into spam detection in data streams is scarce. Another concern lies in their need for transparency. Consequently, this paper addresses those problems by proposing an online solution for identifying and explaining spam reviews, incorporating data drift adaptation. It integrates (i) incremental profiling, (ii) data drift detection & adaptation, and (iii) identification of spam reviews employing Machine Learning. The explainable mechanism displays a visual and textual prediction explanation in a dashboard. The best results obtained reached up to 87% spam F-measure.
Artificial Intelligence (AI) in the price management process is being applied in business practice and research to a variety of pricing use cases that can be augmented or automated, providing opportunities as a forecasting tool or for price optimization. However, the complexity of evaluating the technology to prioritize implementation is challenging, especially for small and medium enterprises (SMEs), and guidance is sparse. Which are the relevant stakeholder criteria for a sustainable implementation of AI for pricing purpose? Which type of AI supported price functions meet these criteria best? Theoretically motivated by the hedonic price theory and advances in AI research, we identify nine criteria and eight AI supported price functions (AISPF). A multiple attribute decision model (MADM) using the fuzzy Best Worst Method (BWM) and fuzzy combined compromise solution (CoCoSo) is set up and evaluated by pricing experts from Germany and Spain. To validate our results and model stability, we carried out several random sensitivity analyses based on the weight of criteria exchange. The results suggest accuracy and reliability as the most prominent attribute to evaluate AISPF, while ethical and sustainable criteria are sorted as least important. The AISPF which best meet the criteria are financial prices followed by procurement prices.
Ontologies are used to semantically enrich different types of information systems (IS), ensure a reasoning on their content and integrate heterogeneous IS at the semantical level. On the other hand, fuzzy theory is employed in IS for handling the uncertainty and fuzziness of their attributes, resulting in a fully fuzzy IS. As such, ontology- and fuzzy-based IS (i.e. ontology and fuzzy IS) are being developed. So, in this paper, we present a bibliometric analysis of the ontology and fuzzy IS concept to grasp its main ideas, and to increase its body of knowledge by providing a concept map for ontology and fuzzy IS. The main results obtained show that by adding ontologies and fuzzy theory to traditional ISs, they evolve into intelligent ISs capable of managing fuzzy and semantically rich (ontological) information and ensuring knowledge recognition in various fields of application. This bibliometric analysis would enable practitioners and researchers gain a comprehensive understanding of the ontology and fuzzy IS concept that they can eventually adopt for development of intelligent IS in their work.
This work introduces ALMERIA, a decision-support tool for drug discovery. It estimates compound similarities and predicts activity, considering conformation variability. The methodology spans from data preparation to model selection and optimization. Implemented using scalable software, it handles large data volumes swiftly. Experiments were conducted on a distributed computer cluster using the DUD-E database. Models were evaluated on different data partitions to assess generalization ability with new compounds. The tool demonstrates excellent performance in molecular activity prediction (ROC AUC: 0.99, 0.96, 0.87), indicating good generalization properties of the chosen data representation and modelling. Molecular conformation sensitivity is also evaluated.
The development of various digital social network platforms has caused public opinion to play an increasingly important role in the policy making process. However, due to the fact that public opinion hotspots usually change rapidly (such as the phenomenon of public opinion inversion), both the behaviour feature and demand feature of netizens included in the public opinion often vary over time. Therefore, this paper focuses on the feature identification problem of public opinion simultaneously considering the multiple observation time intervals and key time points, in order to support the management of policy-focused online public opinion. According to the variable-scale data analysis theory, the temporal scale space model is established to describe candidate temporal observation scales, which are organized following the time points of relevant policy promulgation (policy time points). After proposing the multi-scale temporal data model, a temporal variable-scale clustering method (T-VSC) is put forward. Compared to the traditional numerical variable-scale clustering method, the proposed T-VSC enables to combine the subjective attention of decision-makers and objective timeliness of public opinion data together during the scale transformation process. The case study collects 48552 raw public opinion data on the double-reduction education policy from Sina Weibo platform during Jan 2023 to Nov 2023. Experimental results indicate that the proposed T-VSC method could divide netizens that participate in the dissemination of policy-focused public opinion into clusters with low behavioural granularity deviation on the satisfied observation time scales, and identify the differentiated demand feature of each netizen cluster at policy time points, which could be applied to build the timely and efficient digital public dialogue mechanism.
Derivative-free DIRECT-type global optimization algorithms are increasingly favoured for their simplicity and effectiveness in addressing real-world optimization challenges. This review examines their practical applications through a systematic analysis of scientific journals and computational studies. In particular, significant challenges in reproducibility have been identified with practical problems. To address this, we conducted an experimental study using practical problems from reputable CEC libraries, comparing DIRECT-type techniques against their state-of-the-art counterparts. Therefore, this study sheds light on current gaps, opportunities, and future prospects for advanced research in this domain, laying the foundation for replicating and expanding the research findings presented herein.
In this study, effect of the environmental factors on the organizational behaviour in higher education sector is analysed and these factors are prioritized. For this aim, first, the environmental criteria affecting the organizational behaviour of higher education sector are selected from the literature. Then, as a solution methodology, (i) some experts are asked to determine pairwise comparison of the criteria, (ii) the linguistic terms are converted to interval-valued Pythagorean fuzzy values, and (iii) an interval-valued Pythagorean fuzzy DEMATEL approach is developed and applied. According to the results, most of the economic, political, and professional domain criteria are of the cause category.