Intelligence in Web Technology

End users also start days with Internet. This has become the scenario. One of the most burgeoning needs of computer science research is research on web technologies and intelligence, as that has become one of the most emerging nowadays. A big area of other research areas like e-marketing, e-learning, e-governance, searching technologies, et cetera will be highly benefited if intelligence can be added to the Web. The objective of this chapter is to create a clear understanding of Web technology research and highlight the ways to implement Semantic Web. The chapter also discusses the tools and technologies that can be applied to develop Semantic Web. This new research area needs enough care as sometimes data are open. Thus, software engineering issues are also a focus.

Computer Intelligence in Healthcare

Low cost solutions for the development of intelligent bio-medical devices that not only assist people to live in a better way but also assist physicians for better diagnosis are presented in this chapter. Two such devices are discussed here, which are helpful for prevention and diagnosis of diseases. Statistical analysis reveals that cold and fever are the main culprits for the loss of man-hours throughout the world, and early pathological investigation can reduce the vulnerability of disease and the sick period. To reduce this cold and fever problem a household cooling system controller, which is adaptive and intelligent in nature, is designed. It is able to control the speed of a household cooling fan or an air conditioner based on the real time data, namely room temperature, humidity, and time for which system is active, which are collected from environment. To control the speed in an adaptive and intelligent manner, an associative memory neural network (Kramer) has been used. This embedded system is able to learn from training set; i.e., the user can teach the system about his/her feelings through training data sets. When the system starts up, it allows the fan to run freely at full speed, and after certain interval, it takes the environmental parameters like room temperature, humidity, and time as inputs. After that, the system takes the decision and controls the speed of the fan.

Modernization of Healthcare and Medical Diagnosis System Using Multi Agent System (MAS)

The healthcare system that prevailed some years ago was a mere pen and paper based system. A number of workers, staff, and written records were the main components of the prevailing system of healthcare. This had a number of drawbacks, and a number of mishaps occurred due to mismanagement of data and information. There was a need for development. Then, the concept of telemedicine came, which revolutionized the healthcare paradigm to a great extent. With the advancement of telemedicine, many major problems of the prevailing system were removed. But, still there were many other aspects which could be further improved to make healthcare facilities more enhanced. Keeping this in mind, the concept of Multi Agent System (MAS) was introduced in the healthcare system later. MASes are considered as the best and most appropriate technology that can be used in the development of applications in healthcare paradigm where the presence of multiple agents, heterogeneous and loosely coupled components, the data management in a dynamic and distributed environment, and multi-user collaborations are considered the most pertinent requirements for healthcare system. This chapter focuses mainly about MAS, its applications, and some systems that were developed by the authors.

An Evolving System in the Text Classification Problem

Traditional machine learning techniques have been successful in yielding good results when the data are stable along the time horizon. However, in many cases, these techniques may be inefficient for data that are constantly expanding and changing over time. To address this problem, new learning techniques have been proposed in the literature. In this chapter, the authors discuss some improvements on their technique, called Evolving Probabilistic Neural Network (ePNN), and present the aspects of this recent learning paradigm. This technique is based on the Probabilistic Neural Networks. In this chapter the authors compare their technique against two other competitive techniques that can be found in the literature: Incremental Probabilistic Neural Network (IPNN) and Evolving Fuzzy Neural Network (EFuNN). To show the better performance of their technique, the authors present and discuss a series of experiments that demonstrate the efficiency of ePNN over both the IPNN and EFuNN approaches.

Data Clustering Algorithms Using Rough Sets

Developing Data Clustering algorithms have been pursued by researchers since the introduction of k-means algorithm (Macqueen 1967; Lloyd 1982). These algorithms were subsequently modified to handle categorical data. In order to handle the situations where objects can have memberships in multiple clusters, fuzzy clustering and rough clustering methods were introduced (Lingras et al 2003, 2004a). There are many extensions of these initial algorithms (Lingras et al 2004b; Lingras 2007; Mitra 2004; Peters 2006, 2007). The MMR algorithm (Parmar et al 2007), its extensions (Tripathy et al 2009, 2011a, 2011b) and the MADE algorithm (Herawan et al 2010) use rough set techniques for clustering. In this chapter, the authors focus on rough set based clustering algorithms and provide a comparative study of all the fuzzy set based and rough set based clustering algorithms in terms of their efficiency. They also present problems for future studies in the direction of the topics covered.

Adaptive Median Filtering Based on Unsupervised Classification of Pixels

This chapter deals with a novel approach which aims at detection and filtering of impulses in digital images through unsupervised classification of pixels. This approach coagulates directional weighted median filtering with unsupervised pixel classification based adaptive window selection toward detection and filtering of impulses in digital images. K-means based clustering algorithm has been utilized to detect the noisy pixels based adaptive window selection to restore the impulses. Adaptive median filtering approach has been proposed to obtain best possible restoration results. Results demonstrating the effectiveness of the proposed technique are provided for numeric intensity values described in terms of feature vectors. Various benchmark digital images are used to show the restoration results in terms of PSNR (dB) and visual effects which conform better restoration of images through proposed technique.

Soft Computing Based Statistical Time Series Analysis, Characterization of Chaos Theory, and Theory of Fractals

The present work is an attempt to analyze the various researches already carried out from the theoretical perspective in the field of soft computing based time series analysis, characterization of chaos, and theory of fractals. Emphasis has been given in the analysis on soft computing based study in prediction, data compression, explanatory analysis, signal processing, filter design, tracing chaotic behaviour, and estimation of fractal dimension of time series. The present work is a study as a whole revealing the effectiveness as well as the shortcomings of the various techniques adapted in this regard.

Computational Intelligence for Pathological Issues in Precision Agriculture

Plant Pathology is the scientific study of plant diseases, caused by pathogens and environmental conditions (physiological factors). Detection and grading of plant diseases by machine vision is an essential research topic as it may prove useful in monitoring large fields of crops. This can be of great benefit to those users, who have little or no information about the crop they are growing. Also, in some developing countries, farmers may have to go long distances to contact experts to dig up information which is expensive and time consuming. Therefore, looking for a fast, automatic, less expensive, and accurate method to detect plant diseases is of great realistic significance. Such an efficient system can be modeled by integrating the various tools/techniques of information and communication technology (ICT) in agriculture. The objective of the present chapter is to model an intelligent decision support system for detection and grading of plant diseases which encompasses image processing techniques and soft computing/machine learning techniques.

Particle Swarm Optimization Algorithm and its Hybrid Variants for Feature Subset Selection

Selecting an optimum subset of features from a large set of features is an important pre- processing step for pattern classification, data mining, or machine learning applications. Feature subset selection basically comprises of defining a criterion function for evaluation of the feature subset and developing a search strategy to find the best feature subset from a large number of feature subsets. Lots of mathematical and statistical techniques have been proposed so far. Recently biologically inspired computing is gaining popularity for solving real world problems for their more flexibility compared to traditional statistical or mathematical techniques. In this chapter, the role of Particle Swarm Optimization (PSO), one of the recently developed bio-inspired evolutionary computational (EC) approaches in designing algorithms for producing optimal feature subset from a large feature set, is examined. A state of the art review on Particle Swarm Optimization algorithms and its hybrids with other soft computing techniques for feature subset selection are presented followed by author’s proposals of PSO based algorithms. Simple simulation experiments with benchmark data sets and their results are shown to evaluate their respective effectiveness and comparative performance in selecting best feature subset from a set of features.

Database Anonymization Techniques with Focus on Uncertainty and Multi-Sensitive Attributes

Publication of Data owned by various organizations for scientific research has the danger of sensitive information of respondents being disclosed. The policy of removal or encryption of identifiers cannot avoid the leakage of information through quasi-identifiers. So, several anonymization techniques like k-anonymity, l-diversity, and t-closeness have been proposed. However, uncertainty in data cannot be handled by these algorithms. One solution to this is to develop anonymization algorithms by using rough set based clustering algorithms like MMR, MMeR, SDR, SSDR, and MADE at the clustering stage of existing algorithms. Some of these algorithms handle both numerical and categorical data. In this chapter, the author addresses the database anonymization problem and briefly discusses k-anonymization methods. The primary focus is on the algorithms dealing with l-diversity of databases having single or multi-sensitive attributes. The author also proposes certain algorithms to deal with anonymization of databases with involved uncertainty. Also, the aim is to draw attention of researchers towards the various open problems in this direction.