A fuzzy consensus aggregation operator

Abstract An aggregation operator of quantitative opinions about the acceptance or rejection of a certain alternative is proposed. The main idea is to include a degree of consensus between the experts in computing the final value. We aim at strengthening the acceptance or rejection rate if the experts agree in their assessments. The proposed operator is directly applicable to the two-level classification paradigm where a pool of classifiers is used to infer the decision. The operator allows us to form a complex classification boundary in the space of experts' (classifiers') decisions expressed in terms of degrees of membership. The connective can easily be extended to define a classifier with a "refuse-to-decide" option

Factors Influencing the Process of Decision Making in Telecommunication Sector

Decision Making is the most important element for every organization. It can raise a firm to the market leader or can fall a market leader to disappear.  Therefore it is important to know, what factors have strong influence over decision making. This paper finds out the factors which have a great impact over the suitability of decision making. These are individual experience, intelligence, information availability and organizational capabilities. The research is based on questionnaire survey from telecommunication organization in Pakistan. The data was analyzed through SPSS tools for getting results. Broad recommendations are made decision making. The research is equally applicable to all type of organizations irrespective of their type and products. Keywords: Intelligence (I), Individual Experience (IE), Availability of Information (AOI), Organizational Capabilities, Decision Making (DM) and Strategically Suitable Decision making (SSDM)

Developing A Machine Learning Based Approach For Fractured Zone Detection By Using Petrophysical Logs

Oil reservoirs are divided into three categories: carbonate (fractured), sandstone and unconventional reservoirs. Identification and modeling of fractures in fractured reservoirs are so important due to geomechanical issues, fluid flood simulation and enhanced oil recovery.Image and petrophysical logs are individual tools, run inside oil wells, to achieve physical characteristics of reservoirs, e.g. geological rock types, porosity, and permeability. Fractures could be distinguished using image logs because of their higher resolution. Image logs are an expensive and newly developed tool, so they have run in limited wells, whereas petrophysical logs are usually run inside the wells. Lack of image logs makes huge difficulties in fracture detection, as well as fracture studies. In the last decade, a few studies were done to distinguish fractured zones in oil wells, by applying data mining methods over petrophysical logs. The goal of this study was also discrimination of fractured/non-fractured zones by using machine learning techniques and petrophysical logs. To do that, interpretation of image logs was utilized to label reservoir depth of studied wells as 0 (non-fractured zone) and 1 (fractured zone). We developed four classifiers (Deep Learning, Support Vector Machine, Decision Tree, and Random Forest) and applied them to petrophysics logs to discriminate fractured/non-fractured zones. Ordered Weighted Averaging was the data fusion method that we utilized to integrate outputs of classifiers in order to achieve unique and more reliable results. Overall, the frequency of non-fractured zones is about two times of fractured zones. This leads to an imbalanced condition between two classes. Therefore, the aforementioned procedure relied on the balance/imbalance data to investigate the influence of creating a balanced situation between classes. Results showed that Random Forest and Support Vector Machines are better classifiers with above 95 percent accuracy in discrimination of fractured/non-fractured zones. Meanwhile, making a balanced situation in the wells by a higher imbalance index helps to distinguish either non-fractured or fractured zones. Through imbalance data, non-fractured zones (dominant class) could be perfectly distinguished, while a significant percentage of fractured zones were also labeled as non-fractured ones

Integración de modelos de representación de preferencias en problemas de toma de decisiones con múltiples expertos

Universidad de Granada. Departamento de Ciencias de la Computación e Inteligencia Artificia

Fuzzy Sets, Fuzzy Logic and Their Applications

The present book contains 20 articles collected from amongst the 53 total submitted manuscripts for the Special Issue “Fuzzy Sets, Fuzzy Loigic and Their Applications” of the MDPI journal Mathematics. The articles, which appear in the book in the series in which they were accepted, published in Volumes 7 (2019) and 8 (2020) of the journal, cover a wide range of topics connected to the theory and applications of fuzzy systems and their extensions and generalizations. This range includes, among others, management of the uncertainty in a fuzzy environment; fuzzy assessment methods of human-machine performance; fuzzy graphs; fuzzy topological and convergence spaces; bipolar fuzzy relations; type-2 fuzzy; and intuitionistic, interval-valued, complex, picture, and Pythagorean fuzzy sets, soft sets and algebras, etc. The applications presented are oriented to finance, fuzzy analytic hierarchy, green supply chain industries, smart health practice, and hotel selection. This wide range of topics makes the book interesting for all those working in the wider area of Fuzzy sets and systems and of fuzzy logic and for those who have the proper mathematical background who wish to become familiar with recent advances in fuzzy mathematics, which has entered to almost all sectors of human life and activity

Seismic Risk Management

Seismic risk management is a problem of many dimensions, involving multiple inputs, interactions within risk factors, criteria, alternatives and stakeholders. The deployment of this process is inherently fraught with the issues of complexity, ambiguity and uncertainty, posing extra challenges in the assessment, modelling and management stages. The complexity of earthquake impacts and the uncertain nature of information necessitate the establishment of a systematic approach to address the risk of many effects of seismic events in a reliable and realistic way. To fulfill this need, the study applies a systematic approach to the assessment and management of seismic risk and uses an integrated risk structure. The fuzzy set theory was used as a formal mathematical basis to handle uncertainties involved within risk parameters. Throughout the process, the potential impacts of an earthquake as the basic criteria for risk assessment were identified and relations between them were accommodated through a hierarchical structure. The various impacts of an earthquake are then aggregated through a composite fuzzy seismic risk index (FSRi) to screen and prioritize the retrofitting of a group of school buildings in Iran. Given the imprecise data which is the prime challenge for development of any risk model, the proposed model demonstrates a more reliable and robust methodology to handle vague and imprecise information. The significant feature of the model is its transparency and flexibility in aggregating, tracing and monitoring the risk impacts. The novelty of this study is that it serves as the first attempt of the process of a knowledge base risk-informed system for ranking and screening the retrofitting group of school buildings. The model is capable of integrating various forms of knowledge (quantitative and qualitative information) extracted from different sources (facts, algorithms, standards and experience). The outcomes of the research collectively demonstrate that the proposed system supports seismic risk management processes effectively and efficiently