Informatica

This paper focuses on the aggregation or scoring methods to evaluate the alternatives in Multiple Attribute Decision Making problems (MADM), e.g. Weighted Sum Model (WSM) and Weighted Product Model (WPM). The paper deals with the incorporation of the two concepts into the scoring methods, which has not been studied yet. These concepts are decision maker’s Indifference Thresholds (IT) and Yearning Thresholds (YT) on the decision making criteria. Reviewing the related literature reveals that the existent scoring methods do not have a suitable structure to involve the IT, and there is no scoring method which addresses a way to take the YT into account. The paper shows that there is an important drawback to the famous Aspiration Level (AL) concept. Hence, the YT idea is given to resolve the AL limitation. Based on the IT and YT concepts, two new scoring methods are developed: Extended WPM (EWPM) and Extended WSM (EWSM). The EWPM and EWSM are compared with the other scoring methods using a set of simulation analysis. A real-world case extracted from Exploration and Production (E&P) companies in oil industry is examined.

Smart Farming (SF) has garnered interest from computer science researchers for its potential to address challenges in Smart Farming and Precision Agriculture (PA). This systematic review explores the application of Fuzzy Logic (FL) in these areas. Using a specific anonymous search method across five scientific web indexing databases, we identified relevant scholarly articles published from 2017 to 2024, assessed through the PRISMA methodology. Out of 830 selected papers, the review revealed four gaps in using FL to manage imprecise data in Smart Farming. This review provides valuable insights into FL for potential applications and areas needing further investigation in SF.

This paper develops a two-stage decision approach with probabilistic hesitant fuzzy data. Research challenges in earlier models are: (i) the calculation of occurrence probability; (ii) imputation of missing elements; (iii) consideration of attitude and hesitation of experts during weight calculation; (iv) capturing of interdependencies among experts during aggregation; and (v) ranking of alternatives with resemblance to human cognition. Driven by these challenges, a new group decision-making model is proposed with integrate methods for data curation and decision-making. The usefulness and superiority of the model is realized via an illustrative example of a logistic service provider selection.

Green communication is important for businesses to achieve customer satisfaction and gain a significant competitive advantage. Therefore, improving the indicators is very significant for increasing the green communication performance of businesses. However, these improvements cause cost increase for businesses. Hence, there is a significant need for a priority analysis on the variables that will affect the green communication performance of businesses to use the budget more effectively. The purpose of this study is to evaluate important indicators of effective green communication for the companies. For this purpose, a novel model is proposed that has mainly two different parts. In this process, the evaluations of three decision makers are taken into consideration. At the first stage, selected indicators are examined by using artificial intelligence-based sine trigonometric Pythagorean fuzzy decision-making trial and evaluation laboratory (DEMATEL). Secondly, emerging seven countries are ranked according to the performance of the green communication. In this context, artificial intelligence-based sine trigonometric Pythagorean fuzzy ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS) technique is taken into consideration. Moreover, these countries are also ranked by using additive ratio assessment (ARAS) methodology to make a comparative evaluation. The main contribution of this study is that artificial intelligence methodology is integrated with the fuzzy decision-making model. Artificial intelligence methodology is considered to generate decision matrix. With the help of this situation, more appropriate calculations can be made. Proposing RATGOS methodology to the literature by the authors is another significant contribution of this proposed model. To overcome criticisms regarding the existing ranking decision-making techniques in the literature, RATGOS model is generated by making computations with geometrical mean. Owing to this issue, it can be possible to reach more effective solutions. The findings demonstrate that informativeness is the most crucial issue for the improvement of green communication performance of the companies. Meeting customer expectation is another important situation that should be taken into consideration in this manner. Considering these findings, it would be appropriate to establish sectoral standards and guidelines to provide information in green communication. Thanks to these standards, it is possible for companies to provide detailed and comprehensive information to their customers. The ranking results of both RATGOS and ARAS are the same that gives information about the consistency and coherency of the proposed model. The ranking results indicate that China and Russia are the most successful emerging countries with respect to the green communication performance.

Intensity Modulated Radiation Therapy is an effective cancer treatment. Models based on the Generalized Equivalent Uniform Dose (gEUD) provide radiation plans with excellent planning target volume coverage and low radiation for organs at risk. However, manual adjustment of the parameters involved in gEUD is required to ensure that the plans meet patient-specific physical restrictions. This paper proposes a radiotherapy planning methodology based on bi-level optimization. We evaluated the proposed scheme in a real patient and compared the resulting irradiation plans with those prepared by clinical planners in hospital devices. The results in terms of efficiency and effectiveness are promising.

Neural networks (NNs) are well established and widely used in time series forecasting due to their frequent dominance over other linear and nonlinear models. Thus, this paper does not question their appropriateness in forecasting cryptocurrency prices; rather, it compares the most commonly used NNs, i.e. feedforward neural networks (FFNNs), long short-term memory (LSTM) and convolutional neural networks (CNNs). This paper contributes to the existing literature by defining the appropriate NN structure comparable across different NN architectures, which yields the optimal NN model for Bitcoin return forecasting. Moreover, by incorporating turbulent events such as COVID and war, this paper emerges as a stress test for NNs. Finally, inputs are carefully selected, mostly covering macroeconomic and market variables, as well as different attractiveness measures, the importance of which in cryptocurrency forecasting is tested. The main results indicate that all NNs perform the best in an environment of bullish market, where CNNs stand out as the optimal models for continuous dataset, and LSTMs emerge as optimal in direction forecasting. In the downturn periods, CNNs stand out as the best models. Additionally, Tweets, as an attractiveness measure, enabled the models to attain superior performance.

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.