Enhancing HE Stain Images Through an Advanced Soft Computing-Based Adaptive Ameliorated CLAHE

HE stain images play a crucial role in the medical imaging process. Often these images are regarded as of golden standards by physicians for the quality and accuracy. These images are fuzzy by nature, and hence, traditional hard-based techniques are not able to deal with this. Thereby, a decrease in the accuracy of the analysis process may be experienced. Preprocessing of these images is utmost needed so that the fuzziness may be removed to a satisfactory level. A new approach for tackling this problem is introduced in this chapter. The proposed technique is soft computing-based advanced adaptive ameliorated CLAHE. The experimental results demonstrate the superiority of the proposed approach than the other traditional techniques.

Some Aspects of Reliability Estimation of Loosely Coupled Web Services in Clustered Load Balancing Web Server

Reliability of loosely coupled services through the paradigm of service-oriented computing and observing their fault tolerance against massive load in clustered load balancing web server plays an important role while evaluating the quality aspects of software-as-a-service (SaaS), grid, and distributed systems. This chapter shows some aspects of service execution while observing their failure records against massive execution of server-side instruction. A novel reliability estimation framework is proposed that can be deployed for evaluating the reliability of service execution over clustered load balancing web server. A load generating tool is used to generate massive load over the service execution. In this study we will discuss an experimental system and its architecture by using clustered load balancing web server, the reliability estimation framework along with the goodness of fit study through statistical analysis. The overall assessment of the work will validate the applicability of the proposed framework for the loosely coupled service in clustered load balancing web server.

Automatic Ontology Construction

World wide web has information resources even on unthinkable subjects. This information may be available instantly to anyone having Internet connection. This web is growing exponentially, and it is becoming difficult to locate useful information in such a sheer volume of information. Semantic web extends the current web by emphasizing on interoperable ontologies which are capable of processing high quality information so that the agents placed on top of semantic web can automate the work or curate the content for the user. In this chapter, an extensive research in the area of ontology construction is presented, and after having a critical look over the work done in this field and considering the limitation of each, it has been observed that constructing ontology automatically is a challenging task as this task faces difficulties due to unstructured text and ambiguities in English text. In this work an ontology generation technique is devised covering all important aspects missing in the existing works giving better performance as compared to another system.

Uncertain Confidence Network-Based Collaborative Information Retrieval Relevance Feedback Algorithm

Collaborative retrieval allows increasing the amount of relevant information found and sharing history with others. The collaborative retrieval can reduce the retrieval time performed by the users of the same profile. This chapter proposes a new relevance feedback algorithm to collaborative information retrieval based on a confidence network, which performs propagation relevance between annotations terms. The main contribution in this work is the extraction of relevant terms to reformulate the initial user query considering the annotations as an information source. The proposed model introduces the concept of necessity that allows determining the terms that have strong association relationships estimated to the measure of a confidence. Since the user is overwhelmed by a variety of contradictory annotations, another contribution consists of determining the relevant annotations for a given evidence source. The experimental study gives very encouraging results.

An Approach of Documents Indexing Using Summarization

Document indexing is an active domain, which is interesting a lot of researchers. Generally, it is used in the information retrieval systems. Document indexing encompasses a set of approaches that can be applied to index a document using a corpus. This treatment has several advantages, like accelerating the research process, finding the pertinent contains related to a query, reducing storage space, etc. The use of the entire document in the indexing process affects several parameters, such as indexing time, research time, storage space of treatment, etc. The focus of this chapter is to improve all parameters (cited above) related to the indexing process by proposing a new indexing approach. The goal of proposed approach is to use a summarization to minimize the size of documents without affecting the meaning.

Search for Information in Text Files

As information becomes increasingly abundant and accessible on the web, researchers do not have a need to go to excavate books in the libraries. These require a knowledge extraction system from the text (KEST). The goal of authors in this chapter is to identify the needs of a person to do a search in a text, which can be unstructured, and retrieve the terms of information related to the subject of research then structure them into classes of useful information. These may subsequently identify the general architecture of an information retrieval system from text documents in order to develop it and finally identify the parameters to evaluate its performance and the results retrieved.

Load Balancing in Cloud Computing

Cloud computing is an effective alternative information technology paradigm with its on-demand resource provisioning and high reliability. This technology has the potential to offer virtualized, distributed, and elastic resources as utilities to users. Cloud computing offers numerous types of computing and storage means by connecting to a vast pool of systems. However, because of its large data handling property, the major issue the technology facing is the load balancing problem. Load balancing is the maximum resource utilization with effective management of load imbalance. This chapter shares information about logical and physical resources, load balancing metrics, challenges and techniques, and also gives some suggestions that could be helpful for future studies.

Mammogram Classification Using Nonsubsampled Contourlet Transform and Gray-Level Co-Occurrence Matrix

This chapter explores diagnosis of the breast tissues as normal, benign, or malignant in digital mammography, using computer-aided diagnosis (CAD). System for the early diagnosis of breast cancer can be used to assist radiologists in mammographic mass detection and classification. This chapter presents an evaluation about performance of extracted features, using gray-level co-occurrence matrix applied to all detailed coefficients. The nonsubsampled contourlet transform (NSCT) of the region of interest (ROI) of a mammogram were used to be decomposed in several levels. Detecting masses is more difficult than detecting microcalcifications due to the similarity between masses and background tissue such as F) fatty, G) fatty-glandular, and D) dense-glandular. To evaluate the system of classification in which k-nearest neighbors (KNN) and support vector machine (SVM) used the accuracy for classifying the mammograms of MIAS database between normal and abnormal. The accuracy measures through the classifier were 94.12% and 88.89% sequentially by SVM and KNN with NSCT.

Building Text Summary Generation System Using Universal Networking Language, Rhetorical Structure Theory, Sangatis and Sutra

This chapter discusses how text summaries could be generated by using a high-level semantic representation. The semantic representation is built using the discourse structure which is comprised of three text representation techniques, namely, universal networking language (UNL), rhetorical structure theory (RST), and Saṅgatis. Sangati is an ancient concept that is used in Sanskrit language literature to capture coherence. This discourse structure is indexed using a concept called sūtra which has been used in both Tamil language and Sanskrit literatures. The chapter mainly focusses on how summary could be generated by using this unique discourse structure and the indexing technique concept, sūtra. Forum for information retreival (FIRE) corpus has been used to test the system and a performance comparison has been done with the one of the state-of-art summary generation systems that is built on discourse structure.

Enhancing Information Retrieval System Using Change-Prone Classes

In today's competitive world, each company is required to change software to meet changing customer requirements. At the same time, an efficient information retrieval system is required as changes made to software in different versions can lead to complicated retrieval systems. This research aims to find the association between changes and object-oriented metrics using different versions of open source software. Earlier researchers have used various techniques such as statistical methods for the prediction of change-prone classes. This research uses execution time, frequency, run time information, popularity, and class dependency in prediction of change-prone classes. For evaluating the performance of the prediction model, sensitivity, specificity, and ROC curve are used. Higher values of AUC indicate the prediction model gives accurate results. Results are validated in two phases: Experimental Analysis I validates results using OpenClinic software and OpenHospital software and Experimental Analysis II validates result using Neuroph 2.9.2 and Neuroph 2.6.