Engineering and Information Technology Collected Works - Research Publications
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ItemStability Classification of Stock Market Using Temporal Rule-Based Classification( 2022-10-10)Abstract Economists have sought to predict stock market prices for decades with varying degrees of success. This study classifies stocks according to their stability in two consequent financial quarters (depending on whether the majority of stocks remain in the same stability group, which can indicate forecastability). However, classifying temporal information like stock market data using available methods produces complicated rules that cannot be easily interpreted by human experts. To reduce this complication a new approach of rule-based temporal classification is used. The method combines human-provided rules with machine optimisation to produce classes that can be easily interpreted by experts, enabling them to comprehend the complicated temporal dimension of the data. Rules provided by human experts might be via generalization of the temporal data using statistical functions like standard deviation and averages. Initially, each rule will have a range of possible values that can be reduced to a single one. For this study, we classify the stability of stock market data of the S&P 500 for two constitutive financial quarters to test if stocks have the same stability or not. The results show that the classes for stock markets are different beyond random chance, which might be an indication of the viability of forecasting stock markets depending on their old trends.
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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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ItemCluster-based Diversity Over-sampling: A Density and Diversity Oriented Synthetic Over-sampling for Imbalanced Data(SCITEPRESS - Science and Technology Publications, 2022)In many real-life classification tasks, the issue of imbalanced data is commonly observed. The workings of mainstream machine learning algorithms typically assume the classes amongst underlying datasets are relatively well-balanced. The failure of this assumption can lead to a biased representation of the models’ performance. This has encouraged the incorporation of re-sampling techniques to generate more balanced datasets. However, mainstream re-sampling methods fail to account for the distribution of minority data and the diversity within generated instances. Therefore, in this paper, we propose a data-generation algorithm, Cluster-based Diversity Over-sampling (CDO), to consider minority instance distribution during the process of data generation. Diversity optimisation is utilised to promote diversity within the generated data. We have conducted extensive experiments on synthetic and real-world datasets to evaluate the performance of CDO in comparison with SMOTE-based and diversity-based methods (DADO, DIWO, BL-SMOTE, DB-SMOTE, and MAHAKIL). The experiments show the superiority of CDO.
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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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ItemLimitations(Springer Nature Singapore, 2022-01-01)
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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.