Engineering and Information Technology Collected Works - Research Publications
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ItemA parametric similarity measure for extended picture fuzzy sets and its application in pattern recognition(University of Sistan & Baluchestan, 2022-11-01)This article advances the idea of extended picture fuzzy set (E-PFS), which is especially an augmentation of generalised spherical fuzzy set (GSFS) by releasing the restricted selection of p in the description of GSFSs. Moreover, by the use of triangular conorm term in the description of E-PFS, it indeed widens the scope of E-PFS not only compared to picture fuzzy set (PFS) and spherical fuzzy set (SFS), but also to GSFS. In the sequel, a given fundamental theorem concerning E-PFS depicts its more ability in comparison with the special types to deal with the ambiguity and uncertainty. Further, we propose a parametric E-PFS similarity measure which plays a critical role in information theory. In order for revealing the advantages and authenticity of E-PFS similarity measure, we exhibit its applicability in multiple criteria decision making entitling the recognition of building material, the recognition of patterns, and the selection process of mega project(s) in developing countries. Furthermore, through the experimental studies, we demonstrate that E-PFS is able to handle uncertain information in real-life decision procedures with no extra parameter, and it has a prominent role in decision making whenever the concepts of PFS, SFS and GSFS do not make sense.
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ItemExpert-Machine Collaborative Decision Making: We Need Healthy Competition(IEEE COMPUTER SOC, 2022-09-01)Much has been written and discussed in previous years about human-AI interaction. However, the debate so far has mainly concentrated on "Aaverage" decision makers, neglecting important differences when it is experts who require support. In this article, we are going to talk about expert-machine collaboration for decision-making. We investigate the current approaches for expert decision support and exemplify the inefficiency of this approach for a real clinical decision-making problem. We propose two solutions for expert-machine collaboration to overcome the shortcomings of the current state of the art. We think that the proposed approaches open new horizons for expert-machine collaborative decision-making.
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ItemA cooperative robust human resource allocation problem for healthcare systems for disaster management(Elsevier, 2022-08-01)Similar to other human-made institutes, healthcare systems often experience post-disaster disruptions in performance, which can pose significant threats to the people's lives in the affected zone. In this study, we develop a cooperative game theory approach to alleviate the negative impacts of such catastrophic events, minimize normal hospital service levels, and reduce undesired expenses. Hence, we propose a linear robust formulation to enable the observation of collaborative behaviors among medical centers, including transferring staff, beds, and patients between hospitals. In our proposed model, information uncertainty is considered the right-hand side parameter (i.e., as coefficients for the decision variables of the constraints). Moreover, the existence of a core in the developed game structure is investigated to demonstrate the stability of the developed cooperative structure. Finally, we generated many numerical examples to evaluate the performance of the model under various circumstances and presented a number of managerial insights.
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ItemA hybrid projection method for resource-constrained project scheduling problem under uncertainty(SPRINGER LONDON LTD, 2022-09)Resource constraint project scheduling problem (RCPSP) is one of the most important problems in the scheduling environment. This paper introduces a new framework to collect the activities’ duration and resource requirement by group decision-making, solve the RCPSP with variable durations, and obtain the buffer to protect the schedule. Firstly, the duration and resources of the project’s activities are determined by a new expert weighting method. In the group decision-making, hybrid projection measure is introduced to construct the aggregated decision about some RCPSP parameters. The hybrid projection includes the projection, normalized projection, and bi-directional projection. In the second step, a RCPSP model is presented where the duration of activities can change within certain intervals. Thus, the problem is called the RCPSP with variable durations. The intervals for activities’ duration and resource requirements are obtained from the group decision-making in the first step. Genetic algorithm and vibration damping optimization are applied to solve the RCPSP with variable durations. In the third step, the project’s buffer is determined to protect the schedule. In this step, the intervals for activities’ duration are converted into interval-valued fuzzy (IVF) numbers and the buffer sizing method is extended using IVF numbers. Finally, the presented framework is solved for a practical example and the results are reported.
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ItemEngineering Blockchain Based Software Systems: Foundations, Survey, and Future Directions(Association for Computing Machinery (ACM), 2022)Many scientific and practical areas have shown increasing interest in reaping the benefits of blockchain technology to empower software systems. However, the unique characteristics and requirements associated with Blockchain Based Software (BBS) systems raise new challenges across the development lifecycle that entail an extensive improvement of conventional software engineering. This article presents a systematic literature review of the state-of-the-art in BBS engineering research from the perspective of the software engineering discipline. We characterize BBS engineering based on the key aspects of theoretical foundations, processes, models, and roles. Based on these aspects, we present a rich repertoire of development tasks, design principles, models, roles, challenges, and resolution techniques. The focus and depth of this survey not only give software engineering practitioners and researchers a consolidated body of knowledge about current BBS development but also underpin a starting point for further research in this field.
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ItemBased on neutrosophic fuzzy environment: a new development of FWZIC and FDOSM for benchmarking smart e-tourism applications(SPRINGER HEIDELBERG, 2022-08)Abstract The task of benchmarking smart e-tourism applications based on multiple smart key concept attributes is considered a multi-attribute decision-making (MADM) problem. Although the literature review has evaluated and benchmarked these applications, data ambiguity and vagueness continue to be unresolved issues. The robustness of the fuzzy decision by opinion score method (FDOSM) and fuzzy weighted with zero inconsistency (FWZIC) is proven compared with that of other MADM methods. Thus, this study extends FDOSM and FWZIC under a new fuzzy environment to address the mentioned issues whilst benchmarking the applications. The neutrosophic fuzzy set is used for this purpose because of its high ability to handle ambiguous and vague information comprehensively. Fundamentally, the proposed methodology comprises two phases. The first phase adopts and describes the decision matrices of the smart e-tourism applications. The second phase presents the proposed framework in two sections. In the first section, the weight of each attribute of smart e-tourism applications is calculated through the neutrosophic FWZIC (NS-FWZIC) method. The second section employs the weights determined by the NS-FWZIC method to benchmark all the applications per each category (tourism marketing and smart-based tourism recommendation system categories) through the neutrosophic FDOSM (NS-FDOSM). Findings reveal that: (1) the NS-FWZIC method effectively weights the applications’ attributes. Real time receives the highest importance weight (0.402), whereas augmented reality has the lowest weight (0.005). The remaining attributes are distributed in between. (2) In the context of group decision-making, NS-FDOSM is used to uniform the variation found in the individual benchmarking results of the applications across all categories. Systematic ranking, sensitivity analysis and comparison analysis assessments are used to evaluate the robustness of the proposed work. Finally, the limitations of this study are discussed along with several future directions.
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ItemNo Preview AvailableCall for Papers IEEE Transactions on Evolutionary Computation Special Issue on Multi-objective Evolutionary Optimization in Machine Learning(Institute of Electrical and Electronics Engineers (IEEE), 2022-02-01)
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ItemMulti-objective Semi-supervised clustering for finding predictive clusters(Elsevier, 2022-06-01)This study concentrates on clustering problems and aims to find compact clusters that are informative regarding the outcome variable. The main goal is partitioning data points so that observations in each cluster are similar and the outcome variable can be predicted using these clusters simultaneously. We model this semi-supervised clustering problem as a multi-objective optimization problem with considering deviation of data points in clusters and prediction error of the outcome variable as two objective functions to be minimized. For finding optimal clustering solutions, we employ a non-dominated sorting genetic algorithm II approach and local regression is applied as the prediction method for the output variable. For comparing the performance of the proposed model, we compute seven models using five real-world data sets. Furthermore, we investigate the impact of using local regression for predicting the outcome variable in all models and examine the performance of the multi-objective models compared to single-objective models.
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ItemShape and size optimization of truss structures by Chaos game optimization considering frequency constraints(Elsevier, 2022-01-01)Introduction: An engineering system consists of properly established activities and is put together to achieve a predefined goal. These activities include analysis, design, construction, research, and development. Designing and assembling structural systems, including buildings, bridges, highways, and other complex systems, have been developed over centuries. However, the evolution of these systems has been prolonged because the overall process is very costly and time-consuming, requiring primary human and material resources to be utilized. One of the options for overcoming these shortcomings is the use of metaheuristic algorithms as recently developed intelligent techniques. These algorithms can be utilized as upper-level search techniques for optimization procedures to achieve better results. Objectives: Shape and size optimization of truss structures are considered in this paper, utilizing the Chaos Game Optimization (CGO) as one of the recently developed metaheuristic algorithms. The principles of chaos theory and fractal configuration are considered inspirational concepts. For the numerical purpose, the 10-bar, 37-bar, 52-bar, 72-bar, and 120-bar truss structures as five of the benchmark problems in this field are considered as design examples in which the frequency constraints are considered as limits that have to be dealt with during the optimization procedure. Multiple optimization runs are also conducted for having a comprehensive statistical analysis, while a comparative investigation is also conducted with other algorithms in the literature. Results: Based on the results of the CGO and other approaches from the literature, the CGO can provide better and competitive results in dealing with the considered truss design problems. Conclusion: In summary, the CGO can provide better solutions in dealing with the considered real-size structural design problems with higher levels of complexity.
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ItemMulti-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy(PERGAMON-ELSEVIER SCIENCE LTD, 2021-12)A substantial impediment to widespread Coronavirus disease (COVID-19) vaccination is vaccine hesitancy. Many researchers across scientific disciplines have presented countless studies in favor of COVID-19 vaccination, but misinformation on social media could hinder vaccination efforts and increase vaccine hesitancy. Nevertheless, studying people's perceptions on social media to understand their sentiment presents a powerful medium for researchers to identify the causes of vaccine hesitancy and therefore develop appropriate public health messages and interventions. To the best of the authors' knowledge, previous studies have presented vaccine hesitancy in specific cases or within one scientific discipline (i.e., social, medical, and technological). No previous study has presented findings via sentiment analysis for multiple scientific disciplines as follows: (1) social, (2) medical, public health, and (3) technology sciences. Therefore, this research aimed to review and analyze articles related to different vaccine hesitancy cases in the last 11 years and understand the application of sentiment analysis on the most important literature findings. Articles were systematically searched in Web of Science, Scopus, PubMed, IEEEXplore, ScienceDirect, and Ovid from January 1, 2010, to July 2021. A total of 30 articles were selected on the basis of inclusion and exclusion criteria. These articles were formed into a taxonomy of literature, along with challenges, motivations, and recommendations for social, medical, and public health and technology sciences. Significant patterns were identified, and opportunities were promoted towards the understanding of this phenomenon.