Development of Decision Support Systems Based on Fuzzy and Binary Logic for the FOREX Foreign Exchange Market

The purpose of this paper is to present a performance comparison of expert systems with production rules, based on classical binary logic and fuzzy logic, for feedback control of dynamic systems. The expert system based on binary logic, called ES-PR-BL, was developed in Prolog language, and the system based on fuzzy logic, called ES-PR-FL, was implemented using a Mamdani type inference process. The work presents simulation results for three types of dynamic system: level control in a tank, control of the angular velocity of a DC motor, and control of the linear velocity of a vehicle. The results demonstrate the specificities of each technique and could be used to guide the development of new hybrid control methods, with the aim of improving efficiency in the control of dynamic processes employing expert systems based on production rules. The findings were highly satisfactory and demonstrated the specificities and applicabilities of the two expert systems studied.

This study conducted an integrative review investigating how fuzzy logic has been used in research with the participation of nurses. The article search was carried out in the CINAHL, EMBASE, SCOPUS, PubMed and Medline databases, with no limitation on time of publication. Articles written in Portuguese, English and Spanish with themes related to nursing and fuzzy logic with the authorship or participation of nurses were included. The final sample included 21 articles from eight countries. For the purpose of analysis, the articles were distributed into categories: theory, method and model. In nursing, fuzzy logic has significantly contributed to the understanding of subjects related to: imprecision or the need of an expert; as a research method; and in the development of models or decision support systems and hard technologies. The use of fuzzy logic in nursing has shown great potential and represents a vast field for research.

In this paper, it is attempted to substantiate that borderline disciplines are interdisciplinary in nature, and logic of interdisciplinarity is fuzzy logic. Interdisciplinarity, in turn, requires the broad use of fuzzy logic. The fact that binary logic does not allow revealing the nature and character of borderline disciplines, leads to disciplinary monism and thereby excludes other perspectives, also necessitates a switch from classical logic to fuzzy logic. Further, on the basis of fuzzy logic and the approaches based on it, a logical substantiation of the nature of borderline disciplines and character of research conducted within its framework is given, their relationship with parent disciplines is clarified, and using the linguistic variables “low”, “average”, “high” and “highest” the degrees of interdisciplinarity of studies are determined. The author, as a legal scholar, in his reasoning proceeded from the state of borderline disciplines connected to law.KeywordsBorderline disciplinesInterdisciplinarityIntersectionFuzzy logic dialogical “both-and” approachPhilosophy of lawSociology of law

Finding appropriate decision support systems (DSS) development processes and methodologies is a topic that has kept researchers in the decision support community busy for the past three decades at least. Inspired by Gibson and Nolan’s curve (Gibson & Nolan 1974; Nolan, 1979), it is fair to contend that the field of DSS development is reaching the end of its expansion (or contagion) stage, which is characterized by the proliferation of processes and methodologies in all areas of decision support. Studies on DSS development conducted during the last 15 years (e.g., Arinze, 1991; Saxena, 1992) have identified more than 30 different approaches to the design and construction of decision support methods and systems (Marakas, 2003). Interestingly enough, none of these approaches predominate and the various DSS development processes usually remain very distinct and project-specific. This situation can be interpreted as a sign that the field of DSS development should soon enter in its formalization (or control) stage. Therefore, we propose a unifying perspective of DSS development based on the notion of context. In this article, we argue that the context of the target DSS (whether organizational, technological, or developmental) is not properly considered in the literature on DSS development. Researchers propose processes (e.g., Courbon, Drageof, & Tomasi, 1979; Stabell 1983), methodologies (e.g., Blanning, 1979; Martin, 1982; Saxena, 1991; Sprague & Carlson, 1982), cycles (e.g., Keen & Scott Morton, 1978; Sage, 1991), guidelines (e.g., for end-user computer), and frameworks, but often fail to explicitly describe the context in which the solution can be applied.

Finding appropriate decision support systems (DSS) development processes and methodologies is a topic that has kept researchers in the decision support community busy for the past three decades at least. Inspired by Gibson and Nolan’s curve (Gibson & Nolan 1974; Nolan, 1979), it is fair to contend that the field of DSS development is reaching the end of its expansion (or contagion) stage, which is characterized by the proliferation of processes and methodologies in all areas of decision support. Studies on DSS development conducted during the last 15 years (e.g., Arinze, 1991; Saxena, 1992) have identified more than 30 different approaches to the design and construction of decision support methods and systems (Marakas, 2003). Interestingly enough, none of these approaches predominate and the various DSS development processes usually remain very distinct and project-specific. This situation can be interpreted as a sign that the field of DSS development should soon enter in its formalization (or control) stage. Therefore, we propose a unifying perspective of DSS development based on the notion of context. In this article, we argue that the context of the target DSS (whether organizational, technological, or developmental) is not properly considered in the literature on DSS development. Researchers propose processes (e.g., Courbon, Drageof, & Tomasi, 1979; Stabell 1983), methodologies (e.g., Blanning, 1979; Martin, 1982; Saxena, 1991; Sprague & Carlson, 1982), cycles (e.g., Keen & Scott Morton, 1978; Sage, 1991), guidelines (e.g., for end-user computer), and frameworks, but often fail to explicitly describe the context in which the solution can be applied.

This chapter presents the principles of uncertainty, imprecision, vagueness, incomplete and random data types, treatments and diagnostics principles in medicine. For example, a disease can manifest quite differently from a patient, doctor and social perspective. It is stated that knowledge-based medical treatment decision support systems will develop further with fuzzy logic evaluation due to uncertainty involvement. This chapter offers a series of recommendations on aspects of uncertainty in the medical sciences. The comparison of fuzzy and bivalent (crisp) logic and the fuzzy logic preference in medicine are explained with evidence. Fuzzy logic inference modeling with medical vague words is presented on a sample with four inputs (temperature, blood pressure, urine quality and heart rate), for diagnosing disease as an output estimate. Finally, the use of fuzzy logic in medicine is explained by a set of diseases classifications.

Leading researchers examine the usefulness and limitations of fuzzy logic for the psychology of concepts. The classical view of concepts in psychology was challenged in the 1970s when experimental evidence showed that concept categories are graded and thus cannot be represented adequately by classical sets. The possibility of using fuzzy set theory and fuzzy logic for representing and dealing with concepts was recognized initially but then virtually abandoned in the early 1980s. In this volume, leading researchers—both psychologists working on concepts and mathematicians working on fuzzy logic—reassess the usefulness of fuzzy logic for the psychology of concepts. The book begins with two tutorials—one on concepts and the other on fuzzy logic—aimed at making relevant experimental and theoretical issues accessible to researchers in both fields. The contributors then discuss the experiments that led to the rejection of the classical view of concepts; analyze the various arguments against the use of fuzzy logic in the psychology of concepts and show that they are fallacious; review methods based on sound measurement principles for constructing fuzzy sets; introduce formal concept analysis and its capabilities when generalized by using fuzzy logic; consider conceptual combinations; examine lexical concepts; and propose a research program based on cooperation between researchers in the psychology of concepts and fuzzy logic.

BackgroundThe concept of risk has pervaded medical literature in the last decades and has become a familiar topic, and the concept of probability, linked to binary logic approach, is commonly applied in epidemiology and clinical medicine. The application of probability theory to groups of individuals is quite straightforward but can pose communication challenges at individual level. Few articles by the way have tried to focus the concept of "risk" at the individual subject level rather than at population level.DiscussionThe author has reviewed the conceptual framework which has led to the use of probability theory in the medical field in a time when the principal causes of death were represented by acute disease often of infective origin.In the present scenario, in which chronic degenerative disease dominate and there are smooth transitions between health and disease the use of fuzzy logic rather than binary logic would be more appropriate. The use of fuzzy logic in which more than two possible truth-value assignments are allowed overcomes the trap of probability theory when dealing with uncertain outcomes, thereby making the meaning of a certain prognostic statement easier to understand by the patient.SummaryAt individual subject level the recourse to the term plausibility, related to fuzzy logic, would help the physician to communicate to the patient more efficiently in comparison with the term probability, related to binary logic. This would represent an evident advantage for the transfer of medical evidences to individual subjects.

The aim of this paper is to development of decision support system based on fuzzy logic for green manufacturing (GM) process. The fuzzy logic-based decision support system (FLDS) consists of subtractive clustering with Takagi–Sugeno–Kang fuzzy logic (TSK-FL) model which predicts and optimizes the process parameters of GM process. Here, subtractive clustering method is used for extraction of cluster centers which influences the process parameters of GM process, while TSK-FL method is used for prediction and optimization of GM process parameters. An experiment has been performed on machining of natural filler-reinforced polymer composites using abrasive water jet machining process to show the strength and working significance of proposed model. Initially, the historical database has been created using the results of the theoretical experiment of Taguchi (L27) orthogonal array. Second, normalization process has been performed on the historical data to transform original data sequence to comparable sequence data, which then provided as input to the FLDS system for optimization of the process parameters of GM process. In addition, prediction model has been developed for optimum prediction of response parameters for GM process using proposed FLDS system. Finally, the confirmatory and performance analysis has been tested to verify the experimental results. The result shows that predictions through proposed model are comparable with experimental results with accuracies more than 95% and establishes the most optimal combinations of process parameters for GM process which directly or indirectly improves the efficiency as well as performance of GM process. The research suggests that the developed model can be used as systematic approach for prediction and parameter optimization in GM applications.

This paper develops a Mamdani fuzzy logic system (FLS) that has stochastic fuzzy input variables designed to identify cash-flow deficits in bank lending policies. These deficits do not cover the available cash-flow (CFA) resulting from the company's operating activity. Thus, due to these deficits, solutions must be identified to avoid companies' financial difficulties. The novelty of this paper lies in its using stochastic fuzzy variables, or those categories of variables that are defined by fuzzy sets, characterized by normally distributed density functions specific to random variables, and characterized by fuzzy membership functions. The variation intervals of the stochastic fuzzy variables allow identification of the probabilistic risk situations to which the company is exposed during the crediting period using the Mamdani-type fuzzy logic system. The mechanism of implementing the fuzzy logic system is based on two stages. The first is based on the determination of the cash-flow requirements resulting from loan reimbursement and interest rates. This stage has the role of determining the need for financial resources to cover the liabilities. The second stage is based on the identification of the stochastic fuzzy variables which have a role in influencing the cash flow deficits and the probability values estimation of these variables taking into account probability calculations. Based on these probabilistic values, using the Mamdani fuzzy logic system, estimations are computed for the available cash-flow (the output variable). The estimated values for CFA are then used to detect probability risk situations in which the company will not have enough resources to cover its liabilities to financial creditors. All the FLS calculations refer to future time periods. Testing and simulating the fuzzy controller confirms its functionality.

Applications of artificial intelligence (AI) are increasingly becoming an indispensable component of orthodontic practices. The number of AI studies focused on orthodontics is growing rapidly. The fundamental operations of the AI algorithms utilized in these studies typically rely on binary logic (black or white, or 0 or 1). However, binary logic does not fully reflect the complexities of real life. Reality is more nuanced, with shades of gray and irregularities. Therefore, fuzzy logic-based AI systems, which mimic human-like reasoning, may facilitate more optimal and realistic decision-making in the healthcare and orthodontics fields. This article aims to introduce the concept of fuzzy logic to orthodontists, demonstrate its application through clinical case examples, emphasize the importance of adopting a fuzzy logic approach in treatment planning, and raise awareness among orthodontists about the potential for fuzzy logic AI to play a more prominent role in orthodontic practice in the future.

I: General Issues.- Modeling Knowledge: Model-based Decision Support and Soft Computations.- Benefits of Decision Support Using Soft Computing.- Evolving Connectionist-based Decision Support Systems.- An Agent-based Soft Computing Society with Application Applications in Financial Investment Planning.- A Rough Sets/Neoral Networks Approach to Knowledge Discovery for the Development of Decision Support Systems.- II: Applications and Implementations.- Decision Support Systems in Healthcare: Emerging Trends and Success Factors.- A View of Public and Private Sectors for Taiwan's BOT Project Financing Using Fuzzy Multi-Criteria Methods.- Relational Structures for the Analysis of Decision Information in an Electronic Market.- A Fuzzy Evaluation Model: A Case for Intermodal Terminals in Europe.- Application of Kemeny's Median for Group Decision Support.- An Internet-based Group Decision and Consensus Reaching Support System.- Limpio: A DSS for the Urban Waste Collection Problem.- A Decision Support System for Air Quality Control Based on Soft Computing Methods.- Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems.- Intelligent Information Systems for Crime Analysis.- Application of Fuzzy Decision Trees to Reservoir Recognition.- Introducing SACRA: A Decision Support System for the Construction of Cattle Diets.- Prediction of Parthenium Weed Dispersal Using Fuzzy Logic on GIS Spatial Image.

A study of the DSS implementation area reveals an increased emphasis on methodologies for DSS development. As the DSS field matures, a larger number of methodological options are becoming available to the DSS developer. Existing methodologies have adopted various methods for performing the important task of DSS requirements analysis and specification. In envisaging a hierarchy of requirements analysis methods, a method for improved requirements analysis is proposed to remedy observed deficiencies within it. This method is based on a view of generic types of inquiries or solicitations made by the DSS user during decision-making. This formalism constitutes the basis of a proposed DSS development methodology that offers several benefits to DSS development. The intended benefits include a sharper and more focused requirements analysis to improve the DSS development process. This paper describes the model and methodology, alongside details of a real-world example of their use in developing a Marketing Decision Support System (MKDSS) for the support of marketing decision-making.

Finding appropriate decision support systems (DSS) development processes and methodologies is a topic that has kept researchers in the decision support community busy for the past three decades at least. Inspired by Gibson and Nolan’s curve (Gibson & Nolan 1974; Nolan, 1979), it is fair to contend that the field of DSS development is reaching the end of its expansion (or contagion) stage, which is characterized by the proliferation of processes and methodologies in all areas of decision support. Studies on DSS development conducted during the last 15 years (e.g., Arinze, 1991; Saxena, 1992) have identified more than 30 different approaches to the design and construction of decision support methods and systems (Marakas, 2003). Interestingly enough, none of these approaches predominate and the various DSS development processes usually remain very distinct and project-specific. This situation can be interpreted as a sign that the field of DSS development should soon enter in its formalization (or control) stage. Therefore, we propose a unifying perspective of DSS development based on the notion of context. In this article, we argue that the context of the target DSS (whether organizational, technological, or developmental) is not properly considered in the literature on DSS development. Researchers propose processes (e.g., Courbon, Drageof, & Tomasi, 1979; Stabell 1983), methodologies (e.g., Blanning, 1979; Martin, 1982; Saxena, 1991; Sprague & Carlson, 1982), cycles (e.g., Keen & Scott Morton, 1978; Sage, 1991), guidelines (e.g., for end-user computer), and frameworks, but often fail to explicitly describe the context in which the solution can be applied.

Fuzzy logic is a logical calculus which operates with many truth values (while classical logic works with the two values of true and false). Since fuzzy logic considers the truth of scientific statements like something softened, it is fruitfully applied to the study of biological phenomena, biology is indeed considered the field of complexity, uncertainty and vagueness. In this paper fuzzy logic is successfully applied to the clinical diagnosis of a patient who suffers from different diseases bound by a complex causal chain. In this work it is presented a mathematical foundation of fuzzy logic (with connectives and inference rules) and then the application of fuzzy reasoning to the study of a clinical case. Probabilistic logic is widely considered the unique logical calculus useful in clinical diagnosis, thus the usefulness of fuzzy logic and its relation with probabilistic logic is here explored. The presentation of the case is supplied with all the features necessary to affect a clinical diagnosis: physical exam, anamnesis and tests.