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Experts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).

In this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.

This study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.

This paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.

In response to water scarcity in Sri Lanka, the government is implementing strategies such as rainwater harvesting, efficient irrigation, wastewater treatment and desalination. Initial efforts include the establishment of a desalination plant in Jaffna, with additional plans for the dry zones (DZ). The study aims to comprehensively identify the barriers to establishing desalination plants in the DZ and provide recommendations to mitigate these barriers. Additionally, this research provides valuable insights aimed at minimizing barriers to the construction of future desalination plants within Sri Lanka.

The study used qualitative methods, using an expert survey to identify current and future barriers, along with strategies for overcoming them. The collected data were analysed using the template analysis technique.

Regarding desalination plant establishment, various barriers such as high capital costs, high energy expenses, brine discharge, pollution, emissions, technical challenges, health concerns and waste disposal have been identified. However, specific strategies exist to address and mitigate each of these obstacles.

The study offers recommendations to environmental experts and government on expediting the approval procedures for desalination plants in Sri Lanka’s DZ. Adapted to Sri Lanka’s specific challenges, it highlights strategies and barriers essential for upcoming desalination projects. Furthermore, it emphasizes the financial advantages such as increased production and job creation resulting from establishing desalination facilities.

Through this study, promoting sustainable practices and fostering community involvement, it aims to enhance livelihoods, accelerate economic development and improve overall well-being through reliable access to water. Additionally, the study aims to enhance understanding of the importance of desalination in alleviating water scarcity, promoting community engagement and ultimately facilitating improved living conditions, health outcomes and economic opportunities in Sri Lanka’s DZs.

This study provides crucial direction for decision-makers by highlighting the main barriers to the establishment of desalination plants in Sri Lanka and outlining practical solutions. Implementing these strategies helps meet the region’s increasing water demands, advance sustainable water management, improve the standard of living for nearby communities and promote the socioeconomic development of desalination plants in Sri Lanka’s DZ.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

Examines the value of expert systems in marketing organizations through a national mail survey of 117 marketing executives. All the examined respondents reported the successful use of expert systems in their organizations. The results indicate that while expert systems provide operational benefits (e.g. they assist in making decisions more quickly), they also present new problems (e.g. increased security needs) that the adopting organization must consider. Based on these results, discusses implications for managers regarding the encouragement of the adoption and use of expert systems. Also presents questions concerning expert systems which require additional investigation.

The LIS profession is beginning to place expert systems in perspective. Expert systems are no longer heralded as being the only necessary tool but rather one tool among an array of several. LIS educators are realistic, both about expert systems technology and about what can be achieved within the limitations of an LIS course. New technologies for refining and controlling information are constantly emerging; LIS schools have to keep up‐to‐date with them as they emerge, but they must also ensure that they do not overprioritize one particular development at the expense of others. They can, at best, only hope to give a taste of the possibilities and potential in different areas. Expert systems are still new enough to warrant special treatment but no doubt they will be ousted by newer technologies in the course of time. Meanwhile, LIS professionals should make the most of what is currently available. Hopefully it should pay dividends in the future.

Expert systems are frequently mentioned in business circles these days. They have the potential to assist greatly in the dissemination of scarce or complex expertise. But although they can be immensely valuable if properly understood, developed and used, they can also be a waste of resources. Aimed at managers who feel the need to know more about expert systems, but who are not themselves computing specialists, what an expert system is and is not is explained. The types of application for which it is suitable, and who is most likely to find the time, trouble and expense of creating one that is most worthwhile is discussed. Different types of expert system are explained, and the means and merits of prototyping are outlined. In order to have a successful expert system, certain essentials are required: a subject area which can be suitably defined; an expert who can provide the knowledge; users who know what they want and how they want to use it; a knowledge engineer who can translate the expertise into facts and rules for the system. A short but useful glossary of technical terms which may be encountered in the world of expert systems is included.

Expert systems are computer software packages that experts in specific fields enrich with their knowledge by distilling their expertise into a set of laws for the system. The development of expert systems and the contribution they can provide in banks, whereby financial experts can produce application programmes to help lesser experts solve problems in specialised fields by responding to program queries, eg. with regard to loan approval, cross‐selling, risk analysis, treasury operations and so on, are discussed.

Argues that expert systems are a useful tool in implementing quality customer service. Examines seven steps of customer service and illustrates how expert systems can support each step. Draws on the literature in the field to cite commercial installations of expert systems to support quality customer service.

The purpose of this paper is to compare various linear opinion pooling models for aggregating probability judgments and to determine whether Cooke's performance weighting model can sift out better calibrated experts and produce better aggregated distribution.

The leave‐one‐out cross‐validation technique is adopted to perform an out‐of‐sample comparison of Cooke's classical model, the equal weight linear pooling method, and the best expert approach.

Both aggregation models significantly outperform the best expert approach, indicating the need for inputs from multiple experts. The performance score for Cooke's classical model drops considerably in out‐of‐sample analysis, indicating that Cooke's performance weight approach might have been slightly overrated before, and the performance weight aggregation method no longer dominantly outperforms the equal weight linear opinion pool.

The results show that using seed questions to sift out better calibrated experts may still be a feasible approach. However, because the superiority of Cooke's model as discussed in previous studies can no longer be claimed, whether the cost of extra efforts used in generating and evaluating seed questions is justifiable remains a question.

Understanding the performance of various models for aggregating experts' probability judgments is critical for decision and risk analysis. Furthermore, the leave‐one‐out cross‐validation technique used in this study achieves more objective evaluations than previous studies.

Neural networks and expert systems are two major branches of artificial intelligence (AI). Their emergence has created the potential for a new generation of computer‐based applications in the area of financial decision making. Both systems are used by financial institutions and corporations for a variety of new applications from credit scoring to bond rating to detection of credit card fraud. While both systems belong to the applied field of artificial intelligence, there are many differences between them which differentiate their potential capabilities in the field of business. Presents an analysis of both neural networks and expert systems applications in terms of their capabilities and weaknesses. Uses examples of financial applications of expert systems and neural networks to provide a unified context for the comparison.