Vague Variables Research Articles

From computing science to intelligent computing: A review of artificial and computational intelligence in data and information analysis

Artificial intelligence put forward using human intelligence index by use of advanced algorithms and models that transform computational principles of AI. Deep learning is a category of Artificial intelligence that shows the best problem-solving techniques and ways across different domains. Also, Computational Intelligence (CI) enhances conventional computing strategies through processes such as fuzzy logic, evolutionary algorithms, and neural networks that enables smarter decision-making among vague variables. Intelligent computing's impact is expanded over technological domains, that shows transformative changes in healthcare systems, finance industry, and other industrial processes. Having access to these technologies through collective platforms and open-source initiatives look after innovation. This review shatter perspectives from research scholars, industrial persons, and research communities, highlighting the current landscape and future prospects of A.I. and computational intelligence. Interdisciplinary applications and cross-cutting research initiatives represent the transformative potential of integrating intelligent computing techniques into expanded strategies. This wide scattering of A.I. and computational intelligence gives rise to the ethical concerns that majorly includes algorithmic biases and privacy issues to the end user. Meaningful dialogues and ethical practices are imperative to harnessing A.I. for social good in our interconnected world. This article represents the interdependent relationship between A.I., computational intelligence, and data science, offering deep knowledges into their synergistic applications and societal implications. It highlights the need for responsible A.I. deployment and ethical considerations to ensure the beneficial use of intelligent computing technologies.

THE FUZZY OPTIMISTIC-REASONABLE-PESSIMISTIC INVENTORY MODEL

In inventory and production decision problems, decision makers are interested to identify the optimal inventory and production level. In a certain decision environment, the optimal inventory level could be determined through traditional inventory methods and the optimal ptoduction level could be determined through linear programming algorithms. In an uncertain decision environment, the traditional methods and algorithms can not provide efficient and relevant solutions for these levels, due to the vague and changing parameters. In this case it is neccesary to develop new methods and models that can deal with vague variables and provide optimal levels. In this paper, the optimal inventory and production levels are determined through a single model that uses fuzzy linear programming. This new model is Fuzzy Optimistic-Reasonable-Pessimistic Inventory Model. It has three scenario: optimistic, reasonable and pessimistic, that are defined through triangular fuzzy numbers. In this way, decision makers can deal with vague parameters. These scenarios help managers to divide the Fuzzy ORP Model into three sub-models, that can be easily solved through traditional Simplex Algorithms. Each sub-model provides a crisp solution for each scenario. The solutions forms the final fuzzy optimal solution. The Fuzzy PRO Inventory Model helps managers to identify three optimal levels and to rank them according to their evaluations. This is useful, also, in predictions, where the decision makers should predict different scenarios for the production process. The limit of this model is the definition of the variables and scenarios. This model consider that all values for all variables and coefficients have the same definition: the inferior limit is related to the optimistic sceanrio, the peak is represents the reasonable limit and the superior limit is related to the pessimistic scenario. In real problem, the decision variables could have different definition than coefficients. The inferior limit of the cost is related to the optimistic scenario, but the superior limit of the production level can be related to the optimistic scenario. There are different representations for the scenarios.

Measurement of Security Quality of E-Commerce Using Fuzzy Analytical Hierarchy Process

The importance of e-commerce website security is to protect e-business and user information. A number of e-commerce security problems arise along with increasing transactions through e-commerce. This research uses Fuzzy Analytic Hierarchy Process (FAHP) method because Fuzzy is used for uncertainty or vague variables, while AHP is very suitable in considering both qualitative and quantitative characteristics that are closer to the real situation. From the research that has been done, the Tokopedia.com site is the best e-commerce in terms of website security. Tokopedia is on first on secure web tests, and on second rank on web asafa tools and observatory tools.

MODELLING THE DYNAMICS OF THE SUBJECT OF MASS COMMUNICATION MENTALITY

The paper outlines the modelling concept of the dynamics of susceptibility of the subjects of mass communication to the effects of inductors. The level of susceptibility is one of the key characteristics of the subject's mentality, as a system of feelings (likes and dislikes), stereotypes, values, ideas about the world, which are formed in the mind during key periods of his life (childhood, becoming, social and political disasters, etc.). The changes in the structure of this system, its key characteristics – levels of perception, as well as the impact (induction) of certain views on social phenomena are determined as the dynamics of mentality. The probability of transition of the recipient from one socio-psychological state to another depends on the levels of perception and influence in a pair of recipient-inductor. Such dependence is adopted as multiplicative. Transitions from state to state under the influence of various factors may be accompanied by changes in the levels of perception and impact, which are determined by the dynamics of mentality. Mathematical models of this dynamics are offered in the form of iterative schemes, finite probabilistic automata. The states themselves can be viewed as structures (vectors, in particular), their components (impact and perception levels, types of behavior, in particular) - as linguistic variables that take on the values of vague variables, which, in turn, are determined by the characteristic functions of vague sets. It was determined that iterative schemes should be applied with variability (possible changes) of the state structure, dynamism of the set of states (some states are not known in advance, possible emergence of new states). For impact and susceptibility levels (the number of which, in case of using a vague option, is limited and known in advance, for example, zero, low, medium, high, maximum), it is recommended to use finite probabilistic automata. Fixing the transition from state to state can be carried out by sociological tools (by interviewing, questioning, etc.).

Clinicopathological changes of perinatal mortality during the last 20 years in a tertiary hospital of Greece

Introduction: Perinatal period is the period that includes fetuses weighing > 500 grams (22nd week of gestation) and newborns aged up to seven days. Perinatal mortality is one of the earliest quantitative measurements of quality in obstetric care and affects approximately 0.5% to 1% of all pregnancies. Aim: The purpose of this study was the identification, classification, and frequency of causes of perinatal mortality in premature infants during 20 years (1992-2012) in a tertiary Maternity Hospital in Athens. Materials and Methods: This was a retrospective study based on Pathology Department record and contains autopsy findings of fetuses, newborns, and membranes of the period 1992-2012 in conjunction with clinical information. The authors excluded pharmaceutical miscarriage and those containing vague variables. The total population birth to the mentioned years in this Hospital was 23,703 and there were 278 deaths. The authors used the classification system of ReCoDe (2005) which best suited the present data. Changes in perinatal death cause were estimated and compared every five years during this period (1993-1997, 1998-2002, 2003-2007, and 2008-2012) and also divided according to the following gestational ages: 22-27, 28-31, 32-36, and 37-43 weeks using the SPSS 19.0. Results: Perinatal mortality was reduced up to 72.3% during these years. The vast majority of stillbirths were in their 22-27 week of gestation. Almost half of the fetal deaths were caused by fetal abnormalities, while in 78% the placenta had a main or secondary role. A detailed description of embryo-membranes and clinical status of the mother was performed. Finally the authors identified 15 newborns who had reached the 28th day of their life, of which 12 (80%) were premature. The majority were females and the mean age of the mothers was 28 years. Seven out of 12 newborns died of fetal problems, while three out of 12 due to intrapartum pathology. Conclusion: Pathogenesis of perinatal mortality is often unclear and associated to multiple causes. Impressive reduction of neonatal mortality has been realized during recent years due to the developments in obstetric and neonatal intensive care, but still many improvements are needed to be done.

Collective action in organizational structures

Public goods games played by a group of individuals collectively performing actions towards a common interest characterize social dilemmas where both competition and cooperation are present. By using agent-based simulation, this paper investigates how collective action in the form of repeated n-person linear public goods games is affected when the interaction of individuals is driven by the underlying hierarchical structure of an organization. The proposed agent-based simulation model is based on generally known empirical findings about public goods games and takes into account that individuals may change their profiles from conditional cooperators to rational egoists or vice versa. To do so, a fuzzy logic system was designed to allow the cumulative modification of agents’ profiles in the presence of vague variables such as individuals’ attitude towards group pressure and/or their perception about the cooperation of others. From the simulation results, it can be concluded that collective action is affected by the structural characteristics of hierarchical organizations. The major findings are as follows: (1) Cooperation in organizational structures is fostered when there is a collegial model defining the structure of the punishment mechanisms employed by organizations. (2) Having multiple, and small organizational units fosters group pressure and highlights the positive perception about the cooperation of others, resulting in organizations achieving relatively high aggregate contribution levels. (3) The greater the number of levels, the higher the aggregate contribution level of an organization when effective sanctioning systems are introduced.

A theory for non-linear prediction approach in the presence of vague variables: with application to BMI monitoring

Abstract In the statistical literature, truncated distributions can be used for modeling real data. Due to error of measurement in truncated continuous data, choosing a crisp trimmed point caucuses a fault inference, so using fuzzy sets to define a threshold pointmay leads us more efficient results with respect to crisp thresholds. Arellano-Valle et al. [2] defined a selection distribution for analysis of truncated data with crisp threshold. In this paper, we define fuzzy multivariate selection distribution that is an extension of the selection distributions using fuzzy threshold. A practical data set with a fuzzy threshold point is considered to investigate the relationship between high blood pressure and BMI.

Hybrid Intelligent System for the Diagnosis of Typhoid Fever

Hybrid Intelligent System for the Diagnosis of Typhoid Fever The diagnosis of Typhoid Fever (TF) is often complicated due to the significant number of vague variables involved. As a result of this complexity, several lives have been lost while others are living with deteriorated health status. This research proposes a Hybrid Intelligent System which provides an efficient means of handling the complexity associated with the diagnosis of TF.

A simulation of vendor managed inventory dynamics using fuzzy arithmetic operations with genetic algorithms

This paper develops a new simulation method for vendor managed inventory (VMI) model based on fuzzy arithmetic in the supply chain (SC). The traditional VMI model has been successfully used to reduce the “Bullwhip Effect” in the SC. Thus, in real industry the VMI model can be observed that some variables/parameters may belong to the uncertain factors. Therefore, the traditional VMI model may need to be extended to treat the vague variables or parameters. This study develops a fuzzy system dynamic to simulate vendor managed inventory, automatic pipeline, inventory and order based production control system (VMI-APIOBPCS) model based on fuzzy difference equations, and these operators of difference equations adopt the weakest t-norm ( T W ) operators. Based on the weakest t-norm operators we can get the approximate result using sup-min convolution for simulating fuzzy VMI model, and the fuzzy VMI model can be easier simulated under uncertain environment Moreover, the results of fuzzy VMI-APIOBPCS model can provide the whole extended information regarding the system behavior uncertainties for the decision-makers with fuzzy interval. Furthermore, the study uses genetic algorithms (GA) to search optimal parameters of fuzzy VMI-APIOBPCS model. The performance of Bullwhip measures shows that fuzzy VMI-APIOBPCS model also can reduce the “Bullwhip Effect” as crisp VMI model which can be evidenced by analysis of variance (ANOVA), and the performance of integral of time × absolute error (ITAE) shows that the fuzzy VMI model outperforms previous method with fixing customer service level.

Fuzzy expert marketing-mix model

A marketing-mix setting model is presented. The all four P’s are integrated and identified. The model makes strong utilization of relevant experts’ knowledge and expertise through a fuzzy-decision-making system tailored to dynamically set the marketing-mix periodically in response to changing business environment. Quantitative, qualitative, uncertain, and vague variables are handled necessary to provide a realistic solution. The proposed model is particularly applicable to changing business environments that are full of such qualitative, stochastic, uncertain, and vague variables as in the case of agricultural business (e.g., agro-food companies, fertilizers and agro-chemical producers), and generally applicable to any other business sector. The method can be considered as the fuzzy-expert system for determining values of the marketing decision variables. 

Fuzzy logic identifier of the short-term objectives

This article presents a new science-based approach which identifies the most effective current-period short-term objective of an enterprise, which is usually set based on the pure judgment of top management members or enterprise’s experts. The method is based on fuzzy-decision-making system and makes robust utilization of enterprise’s experts’ knowledge, intuition and expertise. A simple illustrative example is provided to demonstrate how the method can be efficiently and effectively used in practice. The proposed approach can be used to identify the short-term objective of any profit-making-enterprise, and is specially applicable to business industries that are full of qualitative, stochastic, uncertain, and vague variables such as the case in agricultural business (e.g., agro-food companies, fertilizers, and agro-chemical producers), and industrial firms (e.g., electronics, home appliances, machine tools companies ... etc.), and is generally applicable to any other business sector.

A semi-arid grazing ecosystem simulation model with probabilistic and fuzzy parameters

Many of today's grazing ecosystem management problems require a predictive understanding of the interactions between ecological processes acting across different spatiotemporal scales with complex soil, vegetation, climate, topographic and geologic characteristics. Due to complexity of ecosystems and the incomplete nature of empirical data for specific relationships involved in ecosystem dynamics, resource managers usually select management strategies without complete information or knowledge. Such vagueness in real world situations is also an obstacle to understanding and modeling ecosystem dynamics by means of conventional mathematical and computer simulation techniques. Modeling, simulating, and analyzing actual ecosystems can be significantly improved if modeling is extended to deal with imprecise and vague variables, relationships, and events. In this paper, a simulation model combining fuzzy imprecision with probabilistic uncertainty is formulated to study climate-plant-herbivore interactions in grassland ecosystems. This approach provides a unifying simulation framework to integrate numerical data, linguistic statements, and expert experience. The model includes treatment of imprecise and vague input variables as fuzzy variables, use of fuzzy arithmetic in equations when fuzzy and probability numbers are involved, and replacement of some of the relationships in the dynamic systems either with fuzzy conditional statements, or with fuzzy algorithms. The temporal patterns of herbivore population and primary production for a laissez-faire, extensive system in both Australia and China are modeled. The consistency of results obtained from the two simulations suggests the underlying mechanisms for semi-arid grassland ecosystems are those included in the model. Models combining fuzzy sets to quantify subjective parameters and traditional mechanistic techniques can be used to form a basis of long-term managerial policies for a sustainable grassland ecosystem.

Comparative Analysis of Investment Conditions in Japan, United States, Korea and Taiwan

Econometric studies based on the so-called q theory have so far been often unsuccessful. One of the main reasons for this seems to exist in the way the average q (denoted by qA) is estimated. The q theory itself, until now, has provided no specific expressions for the qA. As a proxy for this, some financial and other vague variables such as stock prices have been adopted in prior econometric analyses. The present paper demonstrates the significance of the qA in our econometric analysis.Our findings are as follows.1. A new interpretation of the qA is obtained along with a new formulation of its microeconomics foundations, namely the qA is the ratio of Net Cash Flow to the adjustment cost of investment. Utilizing our definition of the qA, within the confines of the model, we show this value to be determined by such variables as profit rates and investment rates except for the discount rate. We can thus obtain the qA in a consistent manner with the model.2. The marginal q being equal to unity or not is of no relevance to the criterion for an investment decision making and we derive a new investment decision rule from the q theory. In particular, the optimal investment behavior at any time is to invest so as to maintain the maximal value of the qA, namely so as to maximize the ratio of Net Cash Flow to the adjustment cost of investment.3. By estimating the q values for Japan, United States, Korea and Taiwan, and by attempting a comparative analysis of investment conditions and rationality of investment behavior of these conutries, we find the following in particular:(a) Throughout the sample periods, from 1970 to 1991, while the qA values for both Japan and United States are more decreasing than increasing those for Korea and Taiwan are increasing. This means that for Japan and The United States the investment conditions are getting worse, contrary to those for Korea and Taiwan that are improving. Especially, the damage to Japan and the United States from the first oil crisis was more serious than it was to Korea and Taiwan, although the investment conditions thereafter have become similar and competitive among these nations.(b) Japan and the United States do not show a tendency to follow optimal paths implied by our investment criterion or decision rule, implying instead their investment behavior not fully rational. In contrast, there was a positive correlation between the investment level and the direction of changes in the investment conditions of Korea and Taiwan. Thus their investment behaviors were apparently rational.

Modelling the software development process using an expert simulation system having fuzzy logic

AbstractThis paper describes an intelligent computerized tool designed to aid managers of software development projects in planning, managing and controlling the development process of medium‐ to large‐scale software projects. Systems dynamics is used to model and simulate the dynamic process of software development. The software development process is affected by some imprecise and vague variables that are treated as fuzzy variables. The simulation model is integrated with two expert systems. The fuzzy input variables to the system dynamics simulation model are handled by an input expert system having fuzzy logic. This expert system is designed to check the consistency of input variables. The simulation results are analysed by an output expert system having fuzzy logic. This expert system is designed to make recommendations based on experimentation with the simulation model.

Systems dynamics with fuzzy logic

Systems dynamics has been used to model and simulate a variety of environments, e.g. economic, social and political, which require quantification or some types of human behaviour. The lack of empirical verification of the relationships in the systems dynamics models has often been criticised. Nevertheless, the methodology is effective in dealing with time-varying (dynamic) interactions among components of the analysed system. The effectiveness of systems dynamics as a methodology for modelling, simulating and analysing real-life systems can be significantly increased if it is extended to deal with imprecise and vague variables or events. Such an extension requires: (1) treatment of imprecise and vague input variables as fuzzy variables: (2) use of fuzzy arithmetic in the level, rate and auxiliary equations when fuzzy numbers are involved; and (3) replacement of some of the relationships in the systems dynamics models either with conditional statements including fuzzy variables, or with fuzzy algorithms.