Informatica

Consensus creation is a complex challenge in decision making for conflicting or quasi-conflicting evaluator groups. The problem is even more difficult to solve, if one or more respondents are non-expert and provide uncertain or hesitant responses in a survey. This paper presents a methodological approach, the Interval-valued Spherical Fuzzy Analytic Hierarchy Process, with the objective to handle both types of problems simultaneously; considering hesitant scoring and synthesizing different stakeholder group opinions by a mathematical procedure. Interval-valued spherical fuzzy sets are superior to the other extensions with a more flexible characterization of membership function. Interval-valued spherical fuzzy sets are employed for incorporating decision makers’ judgements about the membership functions of a fuzzy set into the model with an interval instead of a single point. In the paper, Interval-valued spherical fuzzy AHP method has been applied to public transportation problem. Public transport development is an appropriate case study to introduce the new model and analyse the results due to the involvement of three classically conflicting stakeholder groups: passengers, non-passenger citizens and the representatives of the local municipality. Data from a real-world survey conducted recently in the Turkish big city, Mersin, help in understanding the new concept. As comparison, all likenesses and differences of the outputs have been pointed out in the reflection with the picture fuzzy AHP computation of the same data. The results are demonstrated and analysed in detail and the step-by-step description of the procedure might foment other applications of the model.

Convolutional neural networks (CNNs) were popular in ImageNet large scale visual recognition competition (ILSVRC 2012) because of their identification ability and computational efficiency. This paper proposes a palm vein recognition method based on CNN. The four main steps of palm vein recognition are image acquisition, image preprocessing, feature extraction, and matching. To reduce the processing steps in the recognition of palm vein images, a palm vein recognition method using a CNN is proposed. CNN is a deep learning network. Palm vein images are acquired using near-infrared light, under which the veins in the palm of the hand are relatively prominent. To obtain a good vein image, many previous methods used preprocessing to further enhance the image before using feature extraction to find feature matches for further comparison. In recent years, CNNs have been shown to have great advantages and have performed well in image classification. To reduce early-stage image processing, a CNN is used to classify and recognize palm vein images. The networks AlexNet and VGG depth CNN were trained to extract image features. The palm vein recognition rates by VGG-19, VGG-16, and AlexNet were 98.5%, 97.5%, and 96%, respectively.

A p-rung orthopair fuzzy set (p-ROFS) describes a generalization of intuitionistic fuzzy set and Pythagorean fuzzy set in the case where we face a larger representation space of acceptable membership grades, and moreover, it gives a decision maker more flexibility in expressing his/her real preferences. Under the p-rung orthopair fuzzy environment, we are going to propose a novel and parametrized score function of p-ROFSs by incorporating the idea of weighted average of the degree of membership and non-membership functions. In view of this fact, this study is further undertaken to investigate and present different properties of the proposed score function for p-ROFSs. Moreover, we indicate that this ranking technique reduces some of the drawbacks of the existing ones. Eventually, we develop an approach based on the above-mentioned ranking technique to deal with multiple criteria decision making problems with p-rung orthopair fuzzy information.

Computed tomography coronary angiography (CTCA) is a non-invasive, powerful image processing technique for assessing coronary artery disease. The aim of the paper is to evaluate the diagnostic role of CTCA using optimal scanning parameters and to investigate the effect of low kilovoltage CTCA on the qualitative and quantitative image parameters and radiation dose in overweight and obese patients. Consolidation of knowledge in medicine and image processing was used to achieve the aim, and performance was evaluated in a clinical setting. Elevated body mass index is one of the factors causing increased radiation dose to patients. This study examined the feasibility of 80-kV and 100-kV CTCA in overweight and obese adult patients, comparing radiation doses and image quality versus standardized 100-kV protocols in the group of overweight patients and 120-kV CTCA in the group of obese patients. Qualitative and quantitative image parameters were determined in proximal and distal segments of the coronary arteries. Quantitative assessment was determined by the contrast-to-noise ratio and signal-to-noise ratio. The results of the study showed that in overweight and obese patients, the low dose protocol affords radiation dose reduction of 35% and 41%, respectively. Image quality was found to be diagnostically acceptable in all cases.

From the perspective of multiple attribute decision analysis, the evaluation of decision alternatives should be based on the performance scores determined with respect to more than one attribute. Fuzzy logic concepts can equip the evaluation process with different scales of linguistic terms to let the decision-makers point out their ideas and preferences. A more recent one of fuzzy sets is the picture fuzzy set which covers three separately allocable elements: positive, neutral, and negative membership degrees. The novel and distinctive element included by a picture fuzzy set is the refusal degree which is equal to the difference between 1 and the sum of the other three. In this study, we aim to contribute to the literature of the picture fuzzy sets by (i) proposing two novel entropy measures that can be used in objective attribute weighting and (ii) developing a novel picture fuzzy version of CODAS (COmbinative Distance-based ASsessment) method which is empowered with entropy-based attribute weighting. The applicability of the method is shown in a green supplier selection problem. To clarify the differences of the proposed method, a comparative analysis is provided by considering traditional CODAS, spherical fuzzy CODAS, and spherical fuzzy TOPSIS with different entropy-based scenarios.

Nowadays, there is a lack of smart marine monitoring systems, which have possibilities to integrate multi-dimensional components for monitoring and predicting marine water quality and making decisions for their optimal operations with minimal human intervention. This research aims to extend the smart coastal marine monitoring by proposing a solar energy planning and control component. The proposed approach involves the adaptive neuro-fuzzy inference system (ANFIS) for the wireless buoys, working online during the whole year in the Baltic Sea near the Lithuanian coast. The usage of our proposed fuzzy solar energy planning and control components allows us to prolong the lifespan of batteries in buoys, so it has a positive impact on sustainable development. The novelty and advantage of the proposed approach lie in establishing the ANFIS-based model to predict and control solar energy in a buoy for different lighting and temperature conditions depending on the four year seasons and to make a decision to transfer the collected data. The energy planning and consumption system for the wireless sensor network of buoys is carefully evaluated, and its prototype is developed. The proposed approach can be practically used for environmental monitoring, providing stakeholders with relevant and timely information for sound decision-making about hydro-meteorological situations in coastal marine water.

A method for counterfactual explanation of machine learning survival models is proposed. One of the difficulties of solving the counterfactual explanation problem is that the classes of examples are implicitly defined through outcomes of a machine learning survival model in the form of survival functions. A condition that establishes the difference between survival functions of the original example and the counterfactual is introduced. This condition is based on using a distance between mean times to event. It is shown that the counterfactual explanation problem can be reduced to a standard convex optimization problem with linear constraints when the explained black-box model is the Cox model. For other black-box models, it is proposed to apply the well-known Particle Swarm Optimization algorithm. Numerical experiments with real and synthetic data demonstrate the proposed method.

There exist various types of similarity measures for intuitionistic fuzzy sets in the literature. However, in many studies the interactions among the elements are ignored in the construction of the similarity measure. This paper presents a cosine similarity measure for intuitionistic fuzzy sets by using a Choquet integral model in which the interactions between elements are considered. The proposed similarity measure is applied to some pattern recognition problems and the results are compared with some existing results to demonstrate the effectiveness of this new similarity measure.

Picture fuzzy sets (PFSs) utilize the positive, neutral, negative and refusal membership degrees to describe the behaviours of decision-makers in more detail. In this article, we expound the application of extended TODIM based on cumulative prospect theory under picture fuzzy multiple attribute group decision making (MAGDM). In addition, we adopt Information Entropy, which is used to ascertain the weighting vector of attributes to improve the availability of the TODIM method. At last, we exercise the improved TODIM into a numerical case for super market location and testify the effectiveness of this new method by comparing its results with other methods’ results.