A hybridized red deer and rough set clinical information retrieval system for hepatitis B diagnosis

Healthcare is a big concern in the current booming population. Many approaches for improving health are imposed, such as early disease identification, treatment, and prevention. Therefore, knowledge acquisition is highly essential at different stages of decision-making. Inferring knowledge from the information system, which necessitates multiple steps for extracting useful information, is one technique to address this problem. Handling uncertainty throughout data analysis is also another challenging task. Computer intelligence is a step forward to this end while selecting characteristics, classification, clustering, and developing clinical information retrieval systems. According to recent studies, swarm optimization is a useful technique for discovering key features while resolving real-world issues. However, it is ineffective in managing uncertainty. Conversely, a rough set helps a decision system generate decision rules. This produces decision rules without any additional information. In order to assess real-world information systems while managing uncertainties, a hybrid strategy that combines a rough set and red deer algorithm is presented in this research. In the red deer optimization algorithm, the suggested method selects the optimal characteristics in terms of the degree of dependence on the rough set. In order to determine the decision rules, further a rough set is used. The efficiency of the suggested model is also contrasted with that of the decision tree algorithm and the conventional rough set. An empirical study on hepatitis disease illustrates the viability of the proposed research as compared to the decision tree and crisp rough set. The proposed hybridization of rough set and red deer algorithm achieves an accuracy of 91.7% accuracy. The acquired accuracy for the decision tree, and rough set methods is 82.9%, and 88.9%, respectively. It suggests that the proposed research is viable.

A hybrid rough set shuffled frog leaping knowledge inference system for diagnosis of lung cancer disease

Abundant medical data are generated in the digital world every second. However, gathering helpful information from these data is difficult. Gathering useful information from the dataset is very advantageous and demanding. Besides, such data also contain many extraneous features that do not influence the foreboding accuracy while diagnosing a disease. The data must eliminate these extraneous features to get a better diagnosis. Ultimately, the minimized information system will lead to a better diagnosis. In this paper, we have introduced an incremental rough set shuffled frog leaping algorithm for knowledge inference. The proposed algorithm helps find minimum features from an information system while handling complex databases with uncertainty and incompleteness. The proposed rough set shuffled frog leaping knowledge inference model works in two phases. In the initial phase, the incremental rough set shuffled frog leaping algorithm is used to get the most relevant features. Identifying the relevant features is carried out using a fitness function, which uses the rough degree of dependency. The use of the fitness function identifies the much information with the minimum number of features. The purpose of feature selection is to identify a feature subset from an original set of features without reducing the predictive accuracy and to scale back the computation overhead in the data processing. In the second phase, a rough set is utilized for knowledge discovery in perception with rule generation. The selection of decision rules is carried out based on the accuracy of the decision rule and a predefined threshold value. An empirical analysis of the lung disease information system and a comparative study is conducted. Experimental outcomes exhibit that hybrid techniques express the feasibility of the proposed model while achieving better classification accuracy.

A Hybrid Scheme for Heart Disease Diagnosis Using Rough Set and Cuckoo Search Technique

Large volumes of raw data are created from the digital world every day. Acquiring useful information from these data is challenging, and it turned into a prime zone of momentum explore. More research is done in this direction. Further, in disease diagnosis, many uncertainties are involved in the information system. To handle such uncertainties, intelligent techniques are employed. In this paper, we present an integrated scheme for heart disease diagnosis. The proposed model integrates cuckoo search and rough set for inferencing decision rules. At the underlying phase, we employ a cuckoo search to discover the main features. Further, these main features are analyzed using rough set generating rules. An empirical analysis is carried out on heart disease. Besides, a comparative study is also presented. The comparative study demonstrates the feasibility of the proposed model.

Improving the Security of Digital Images in Hadamard Transform Domain Using Digital Watermarking

In recent days, due to the advancement in technology there are increasing numbers of threats to multimedia data which are floating around in the Internet especially in the form of image data. Many methods exist to provide security for digital images but transform domain based digital watermarking could be considered as a promising method. Many transformation techniques are used to insert watermark in cover data, but this chapter deals with watermarking approaches in Hadamard transform domain. In traditional watermarking approaches the scaling parameter is empirically considered for inserting watermark but to maintain the quality of underlying cover images it needs to be calculated based on the content of the cover images. In order to make the watermarking algorithm completely automated the embedding and scaling parameters are calculated using the content of cover images. Many methods are existing for calculating scaling parameter adaptively but this chapter discusses various approaches using computational intelligence to arrive at optimum value of scaling and embedding parameters.

Intelligence-Based Adaptive Digital Watermarking for Images in Wavelet Transform Domain

Advances in technologies facilitate the end users to carry out unauthorized manipulation and duplication of multimedia data with less effort. Because of these advancements, the two most commonly encountered problems are (1) copyright protection and (2) unauthorized manipulation of multimedia data. Thus a scheme is required to protect multimedia data from those two above said problems. Digital Watermarking is considered as one of the security mechanisms to protect copyrights of multimedia data. The literature review reveals that the calculation of scaling and embedding parameters are not completely automated. In order to automate the procedure of calculating scaling and embedding parameters the computational intelligence need to be incorporated in the watermarking algorithm. Moreover the quality of the watermarked images could also be preserved by combining computational intelligence concepts. Thus watermarking schemes utilizing computational intelligence concepts could be called as intelligence based watermarking schemes and it is presented in this chapter in detail.

A Hybrid Scheme for Breast Cancer Detection Using Intuitionistic Fuzzy Rough Set Technique

Diagnosis of cancer is of prime concern in recent years. Medical imaging is used to analyze these diseases. But, these images contain uncertainties due to various factors and thus intelligent techniques are essential to process these uncertainties. This paper hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction techniques. The hybrid scheme starts with image segmentation using intuitionistic fuzzy set to extract the zone of interest and then to enhance the edges surrounding it. Further feature extraction using gray-level co-occurrence matrix is presented. Additionally, rough set is used to engender all minimal reducts and rules. These rules then fed into a classifier to identify different zones of interest and to check whether these points contain decision class value as either cancer or not. The experimental analysis shows the overall accuracy of 98.3% and it is higher than the accuracy achieved by hybridizing fuzzy rough set model.

A Hybrid Scheme for Breast Cancer Detection using Intuitionistic Fuzzy Rough Set Technique

Diagnosis of cancer is of prime concern in recent years. Medical imaging is used to analyze these diseases. But, these images contain uncertainties due to various factors and thus intelligent techniques are essential to process these uncertainties. This paper hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction techniques. The hybrid scheme starts with image segmentation using intuitionistic fuzzy set to extract the zone of interest and then to enhance the edges surrounding it. Further feature extraction using gray-level co-occurrence matrix is presented. Additionally, rough set is used to engender all minimal reducts and rules. These rules then fed into a classifier to identify different zones of interest and to check whether these points contain decision class value as either cancer or not. The experimental analysis shows the overall accuracy of 98.3% and it is higher than the accuracy achieved by hybridizing fuzzy rough set model.

Neural network and rough set hybrid scheme for prediction of missing associations

Currently, internet is the best tool for distributed computing, which involves spreading of data geographically. But, retrieving information from huge data is critical and has no relevance unless it provides certain information. Prediction of missing associations can be viewed as fundamental problems in machine learning where the main objective is to determine decisions for the missing associations. Mathematical models such as naive Bayes structure, human composed network structure, Bayesian network modelling, etc., were developed to this end. But, it has certain limitations and failed to include uncertainties. Therefore, effort has been made to process inconsistencies in the data with the introduction of rough set theory. This paper uses two processes, pre-process and post-process, to predict the decisions for the missing associations in the attribute values. In preprocess, rough set is used to reduce the dimensionality, whereas neural network is used in postprocess to explore the decision for the missing associations. A real-life example is provided to show the viability of the proposed research.