A multi-attribute decision making procedure using fuzzy numbers and hybrid aggregators

The classical Analytical Hierarchy Process (AHP) has two limitations. Firstly, it disregards the aspect of uncertainty that usually embedded in the data or information expressed by human. Secondly, it ignores the aspect of interdependencies among attributes during aggregation. The application of fuzzy numbers aids in confronting the former issue whereas, the usage of Choquet Integral operator helps in dealing with the later issue. However, the application of fuzzy numbers into multi-attribute decision making (MADM) demands some additional steps and inputs from decision maker(s). Similarly, identification of monotone measure weights prior to employing Choquet Integral requires huge number of computational steps and amount of inputs from decision makers, especially with the increasing number of attributes. Therefore, this research proposed a MADM procedure which able to reduce the number of computational steps and amount of information required from the decision makers when dealing with these two aspects simultaneously. To attain primary goal of this research, five phases were executed. First, the concept of fuzzy set theory and its application in AHP were investigated. Second, an analysis on the aggregation operators was conducted. Third, the investigation was narrowed on Choquet Integral and its associate monotone measure. Subsequently, the proposed procedure was developed with the convergence of five major components namely Factor Analysis, Fuzzy-Linguistic Estimator, Choquet Integral, Mikhailov‘s Fuzzy AHP, and Simple Weighted Average. Finally, the feasibility of the proposed procedure was verified by solving a real MADM problem where the image of three stores located in Sabak Bernam, Selangor, Malaysia was analysed from the homemakers‘ perspective. This research has a potential in motivating more decision makers to simultaneously include uncertainties in human‘s data and interdependencies among attributes when solving any MADM problems

The improvement of strategic crops production via a goal programming model with novel multi-interval weights

Nowadays, the need to increase agricultural production has becomes a challenging task for most of the countries. Generally, there are many resource factors which affect the deterioration of production level, such as low water level, desertification, soil salinity, low on capital, lack of equipment, impact of export and import of crops, lack of fertilizers, pesticide, and the ineffective role of agricultural extension services which are significant in this sector. The main objective of this research is to develop fuzzy goal programming (FGP) model to improve agricultural crop production, leading to increased agricultural benefits (more tons of produce per acre) based on the minimization of the main resources (water, fertilizer and pesticide) to determine the weight in the objectives function subject to different constraints (land area, irrigation, labour, fertilizer, pesticide, equipment and seed). FGP and GP were utilized to solve multi-objective decision making problems (MODM). From the results, this research has successfully presented a new alternative method which introduced multi-interval weights in solving a multi-objective FGP and GP model problem in a fuzzy manner, in the current uncertain decision making environment for the agricultural sector. The significance of this research lies in the fact that some of the farming zones have resource limitations while others adversely impact their environment due to misuse of resources. Finally, the model was used to determine the efficiency of each farming zone over the others in terms of resource utilization

Median-Based Aggregation Operators for Prototype Construction in Ordinal Scales

This article studies aggregation operators in ordinal scales for their application to clustering (more specifically, to microaggregation for statistical disclosure risk). In particular, we consider these operators in the process of prototype construction. This study analyzes main aggregation operators for ordinal scales [plurality rule, medians, Sugeno integrals (SI), and ordinal weighted means (OWM), among others] and shows the difficulties for their application in this particular setting. Then, we propose two approaches to solve the drawbacks and we study their properties. Special emphasis is given to the study of monotonicity because the operator is proven nonsatisfactory for this property. Exhaustive empirical work shows that in most practical situations, this cannot be considered a problem. 2003 Wiley Periodicals, In

Методи опрацювання вимірювальної та експертної інформації з застосуванням шкал класифікації

В роботі набув подальшого розвитку метод непараметричної ідентифікації форми розподілу, на основі чого був розроблений метод непараметричної класифікації форми розподілу вибірок малого об’єму, який може бути застосований для удосконалення методів опрацювання результатів багаторазових вимірювань, а також при побудові контрольних карт технологічних процесів. Набули подальшого розвитку технології побудови лінгвістичних шкал в інтелектуальних вимірювальних системах при застосуванні метричної класифікації для переходу від числових даних до вербальних. Запропонована послідовність етапів встановлення або відтворення лінгвістичної шкали класифікації, причому основна увага приділена аналізу чинників, що характеризують нечіткість правил і невизначеність вимірювання, а також способам їх урахування при побудові терм-множини шкали класифікації, визначена характеристика якості шкали класифікації у вигляді матриці відповідності. Розроблено метод визначення узгодженості вибірок класифікованих даних, заснований на непараметричних оцінках центру вибірки і шкалі метричної класифікації, що дозволяє провести оцінку узгодженості при нерівномірному розташуванні класів еквівалентності. Розроблено метод класифікації стану об’єкту або технологічного процесу за вербальними даними, що ґрунтується на технології використання декількох критеріїв і оцінок і дозволяє вирішити задачу класифікації стану з використанням або встановлених класів еквівалентності або з додатковими (проміжними). Основні наукові положення дисертаційної роботи підтверджені експериментальними дослідженнями і впровадженням в конкретні інформаційно-вимірювальні системи

Computational methods for physiological data

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2009.Author is also affiliated with the MIT Dept. of Electrical Engineering and Computer Science. Cataloged from PDF version of thesis.Includes bibliographical references (p. 177-188).Large volumes of continuous waveform data are now collected in hospitals. These datasets provide an opportunity to advance medical care, by capturing rare or subtle phenomena associated with specific medical conditions, and by providing fresh insights into disease dynamics over long time scales. We describe how progress in medicine can be accelerated through the use of sophisticated computational methods for the structured analysis of large multi-patient, multi-signal datasets. We propose two new approaches, morphologic variability (MV) and physiological symbolic analysis, for the analysis of continuous long-term signals. MV studies subtle micro-level variations in the shape of physiological signals over long periods. These variations, which are often widely considered to be noise, can contain important information about the state of the underlying system. Symbolic analysis studies the macro-level information in signals by abstracting them into symbolic sequences. Converting continuous waveforms into symbolic sequences facilitates the development of efficient algorithms to discover high risk patterns and patients who are outliers in a population. We apply our methods to the clinical challenge of identifying patients at high risk of cardiovascular mortality (almost 30% of all deaths worldwide each year). When evaluated on ECG data from over 4,500 patients, high MV was strongly associated with both cardiovascular death and sudden cardiac death. MV was a better predictor of these events than other ECG-based metrics. Furthermore, these results were independent of information in echocardiography, clinical characteristics, and biomarkers.(cont.) Our symbolic analysis techniques also identified groups of patients exhibiting a varying risk of adverse outcomes. One group, with a particular set of symbolic characteristics, showed a 23 fold increased risk of death in the months following a mild heart attack, while another exhibited a 5 fold increased risk of future heart attacks.by Zeeshan Hassan Syed.Ph.D