Bidder Selection in Public Procurement using a Fuzzy Decision Support System

Bidder selection in public procurement is a decision making problem whose primary purpose is to achieve the cost effectiveness and efficiency in the expenditure of public money. This principle is also known as the principle of “value for money”. This selection is based on many alternatives and many quantitative and qualitative criteria where qualitative criteria are often expressed as linguistic uncertain variables. The theory of fuzzy sets is a tool suitable to model uncertainty when applied to a variety of problems in real life. However, many fuzzy methods require complex calculation and they are not appropriate for using in public procurement because they slow down this process. In this paper, in order to make a quick decision in public procurement, a Decision Support System based on the fuzzy extent analysis method is developed. In order to demonstrate the usefulness of this system, a real-life case scenario of public procurement is presented.

A Study on Hybridization of Intelligent Techniques in Bioinformatics

This chapter aims to study the use of Hybridization of intelligent techniques in the areas of bioinformatics and computational molecular biology. These areas have risen from the needs of biologists to utilize and help interpret the vast amounts of data that are constantly being gathered in genomic research. Also describes the kind of methods which were developed by the research community in order to search, classify and mine different available biological databases and simulate biological experiments. This chapter also presents the hybridization of intelligent systems involving neural networks, fuzzy systems, neuro-fuzzy system, rough set theory, swam intelligence and genetic algorithm. The key idea was to demonstrate the evolution of intelligence in bioinformatics. The developed hybridization of intelligent techniques was applied to the real world applications. The hybridization of intelligent systems performs better than the individual approaches. Hence these approaches might be extremely useful for hardware implementations.

Vague Correlation Coefficient of Interval Vague Sets and its Applications to Topsis in MADM Problems

Various attempts are made by researchers on the study of vagueness of data through Intuitionistic Fuzzy sets and Vague sets, and also it is shown that Vague sets are Intuitionistic Fuzzy sets. However, there are algebraic and graphical differences between Vague sets and Intuitionistic Fuzzy sets. In this chapter, an attempt is made to define the correlation coefficient of Interval Vague sets lying in the interval [0,1], and a new method for computing the correlation coefficient of interval Vague sets lying in the interval [-1,1] using a-cuts over the vague degrees through statistical confidence intervals is also presented by an example. The new method proposed in this work produces a correlation coefficient in the form of an interval. The proposed method produces a correlation coefficient in the form of an interval from a trapezoidal shaped fuzzy number derived from the vague degrees. This chapter also aims to develop a new method based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve MADM problems for Interval Vague Sets (IVSs). A TOPSIS algorithm is constructed on the basis of the concepts of the relative-closeness coefficient computed from the correlation coefficient of IVSs. This novel method also identifies the positive and negative ideal solutions using the correlation coefficient of IVSs. A numerical illustration explains the proposed algorithms and comparisons are made with some existing methods.

Fuzzy Logic Based Approach for Power System Fault Section Analysis

In this chapter, a fuzzy expert system is developed to assist the operators in fault detection. It requires much less memory to store the database (power system topology and the post fault status of circuit breakers and protective relays). The fuzzy expert system identifies two basic network section sets, Shealthy for the healthy sub network and Sisland for the fault islands, using the post fault status of circuit breakers and relays. It then calculates membership function for each possible fault section. The objective of this calculation is to determine the likelihood of each candidate fault section as the actual fault section. Moreover membership functions provide a convenient means of ranking among possible (or candidate) fault sections, and are the most important factors in decision making. During decision making, the most possible fault section is determined by maximum selection method. In this method most possible fault section is the one which is having highest membership grade. MATLAB code for the proposed scheme is developed and the results obtained in four cases for a power- system network.

Implementation of Fuzzy Technology in Complicated Medical Diagnostics and Further Decision

This chapter presents the importance of fuzzy expert systems in the medical field. Efficient and suitable medical work becomes difficult many times without the knowledge of the rules of logic. The chapter highlights the ways of implementing both classical logic and non-classical approach (e.g. temporal and fuzzy logic) in some adverse areas of medical diagnostics. The implementation of fuzzy expert systems is supported by some examples illustrating how indispensable the cognition of logic and showing how applying logic can effectively improve work in medicine. Fuzzy Expert Systems for diagnosis of urinary incontinence, Parkinson's disease, including neurological signs in domestic animals, as well as its implementation for diagnosis of prostate cancer are elaborately discussed.

A Hybrid System Based on FMM and MLP to Diagnose Heart Disease

In the medical field diagnosis of a disease at an early stage is very important. Nowadays soft computing techniques such as fuzzy logic, artificial neural network and Neuro- fuzzy networks are widely used for the diagnosis of various diseases at different levels. In this chapter, a hybrid neural network is designed to classify the heart disease data set the hybrid neural network consist of two types of neural network multilayer perceptron (MLP) and fuzzy min max (FMM) neural network arranged in a hierarchical manner. The hybrid system is designed for the dataset which contain the combination of continuous and non continuous attribute values. In the system the attributes with continuous values are classified using the FMM neural networks and attributes with non-continuous value are classified by using the MLP neural network and to synthesize the result the output of both the network is fed into the second MLP neural network to generate the final result.

An Optimal Fuzzy Load Balanced Adaptive Gateway Discovery for Ubiquitous Internet Access in MANET

A mobile ad hoc network (MANET) is an autonomous collection of independent nodes cooperating together to form an infrastructure less network spontaneously. For increasing usability of MANET domain which finds application in natural disaster such as earthquake, floods etc. it is also desired to be connected with Internet through Internet gateways. Therefore, an efficient gateway discovery mechanism is required for MANET-Internet integration. Existing schemes use one or multiple parameters for optimal selection of gateway which causes a particular gateway to be selected many times which results in higher delay latency and packet drops due to prevailing congestion at a particular gateway. To avoid this situation, the authors have utilized the potential of fuzzy logic to ascertain the decision of load balancing at the Internet gateway. Besides this, their scheme also incorporates an effective adaptive gateway discovery mechanism. Consequently, enhanced performance is achieved as compared to existing state-of-the-art related schemes. The proposed approach is evaluated by simulation and analytical validation.

Movie Recommendation System Based on Fuzzy Inference System and Adaptive Neuro Fuzzy Inference System

One of most prominent features that social networks or e-commerce sites now provide is recommendation of items. However, the recommendation task is challenging as high degree of accuracy is required. This paper analyzes the improvement in recommendation of movies using Fuzzy Inference System (FIS) and Adaptive Neuro Fuzzy Inference System (ANFIS). Two similarity measures have been used: one by taking account similar users' choice and the other by matching genres of similar movies rated by the user. For similarity calculation, four different techniques, namely Euclidean Distance, Manhattan Distance, Pearson Coefficient and Cosine Similarity are used. FIS and ANFIS system are used in decision making. The experiments have been carried out on Movie Lens dataset and a comparative performance analysis has been reported. Experimental results demonstrate that ANFIS outperforms FIS in most of the cases when Pearson Correlation metric is used for similarity calculation.

A Two-Level Fuzzy Value-Based Replica Replacement Algorithm in Data Grids

One of the challenges of data grid is to access widely distributed data fast and efficiently and providing maximum data availability with minimum latency. Data replication is an efficient way used to address this challenge by replicating and storing replicas, making it possible to access similar data in different locations of the data grid and can shorten the time of getting the files. However, as the number and storage size of grid sites is limited and restricted, an optimized and effective replacement algorithm is needed to improve the efficiency of replication. In this paper, the authors propose a novel two-level replacement algorithm which uses Fuzzy Replica Preserving Value Evaluator System (FRPVES) for evaluating the value of each replica. The algorithm was tested using a grid simulator, OptorSim developed by European Data Grid projects. Results from simulation procedure show that the authors' proposed algorithm has better performance in comparison with other algorithms in terms of job execution time, total number of replications and effective network usage.

Classification of EEG Signals for Motor Imagery Based on Mutual Information and Adaptive Neuro Fuzzy Inference System

Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to BCI Competition III dataset IVa and IVb and BCI Competition II dataset III.