Neelu Khare

An Efficient System for Heart Disease Prediction Using Hybrid OFBAT with Rule-Based Fuzzy Logic Model

The objective of the work is to predict heart disease using computing techniques like an oppositional firefly with BAT and rule-based fuzzy logic (RBFL). The system would help the doctors to automate heart disease diagnosis and to enhance the medical care. In this paper, a hybrid OFBAT-RBFL heart disease diagnosis system is designed. Here, at first, the relevant features are selected from the dataset using locality preserving projection (LPP) algorithm which helps the diagnosis system to develop a classification model using the fuzzy logic system. After that, the rules for the fuzzy system are created from the sample data. Among the entire rules, the important and relevant group of rules are selected using OFBAT algorithm. Here, the opposition based learning (OBL) is hybrid to the firefly with BAT algorithm to improve the performance of the FAT algorithm while optimizing the rules of the fuzzy logic system. Next, the fuzzy system is designed with the help of designed fuzzy rules and membership functions so that classification can be carried out within the fuzzy system designed. At last, the experimentation is performed by means of publicly available UCI datasets, i.e., Cleveland, Hungarian and Switzerland datasets. The experimentation result proves that the RBFL prediction algorithm outperformed the existing approach by attaining the accuracy of 78%.

FFBAT-Optimized Rule Based Fuzzy Logic Classifier for Diabetes

In the last two decades, developing countries are facing heavy increase in diabetes among their population that is leading to other severe diseases. Hence, there is a great need to develop some effective prediction methods to prevent diabetes. In this paper an attempt has been made to develop Firefly-BAT (FFBAT) optimized Rule Based Fuzzy Logic (RBFL) prediction algorithm for diabetes. The algorithm has two main steps. First, Locality Preserving Projections (LPP) algorithm is used for feature reduction and then classification of diabetes is done by means of RBFL classifier. LPP algorithm has been used to identify the related attributes and then the fuzzy rules are produced from RBFL. The rules are optimized using FFBAT algorithm. Next, the fuzzy system is designed with the help of optimized fuzzy rules and membership functions that will classify the diabetes data. FFBAT is the optimization algorithm which combines the features of BAT and Firefly (FF) optimization techniques. The experiment analysis shows that the RBFL-FFBAT algorithm outperforms the existing approaches.

A Review on Gender Classification Using Association Rule Mining and Classification Based on Fingerprints

Fingerprint recognition for Gender classification method done through various techniques like Support Vector Machines (SVM), Neural Network (NN), Fuzzy- C Means (FCM). This study highlights the various ridge related methods like fingerprint ridge count, ridge density, ridge thickness to valley thickness ration, ridge width and fingerprint patterns used for gender identification.[4] This paper presents Gender classification using association rule mining and classification approach. Our aim to Gender Classification uses Data Mining Techniques Association and classification to get encourage the results.

BGFS: Design and Development of Brain Genetic Fuzzy System for Data Classification

Abstract Recently, classification systems have received significant attention among researchers due to the important characteristics and behaviors of analysis required in real-time databases. Among the various classification-based methods suitable for real-time databases, fuzzy rule-based classification is effectively used by different researchers in various fields. An important issue in the design of fuzzy rule-based classification is the automatic generation of fuzzy if-then rules and the membership functions. The literature presents different techniques for automatic fuzzy design. Among the different techniques available in the literature, choosing the type, the number of membership functions, and defining parameters of membership function are still challenging tasks. In order to handle these challenges in the fuzzy rule-based classification system, this paper proposes a brain genetic fuzzy system (BGFS) for data classification by newly devising the exponential genetic brain storm optimization. Here, membership functions are optimally devised using exponential genetic brain storm optimization algorithm and rules are derived using the exponential brain storm optimization algorithm. The designed membership function and fuzzy rules are then effectively utilized for data classification. The proposed BGFS is analyzed with four datasets, using sensitivity, specificity, and accuracy. The outcome ensures that the proposed BGFS obtained the maximum accuracy of 88.8%, which is high as compared with the existing adaptive genetic fuzzy system.

Cuckoo Search Optimized Reduction and Fuzzy Logic Classifier for Heart Disease and Diabetes Prediction

Disease forecasting using soft computing techniques is major area of research in data mining in recent years. To classify heart and diabetes diseases, this paper proposes a diagnosis system using cuckoo search optimized rough sets based attribute reduction and fuzzy logic system. The disease prediction is done as per the following steps 1 feature reduction using cuckoo search with rough set theory 2 Disease prediction using fuzzy logic system. The first step reduces the computational burden and enhances performance of fuzzy logic system. Second step is based on the fuzzy rules and membership functions which classifies the disease datasets. The authors have tested this approach on Cleveland, Hungarian, Switzerland heart disease data sets and a real-time diabetes dataset. The experimentation result demonstrates that the proposed algorithm outperforms the existing approaches.

An Adaptive Iterative PCA-SVM Based Technique for Dimensionality Reduction to Support Fast Mining of Leukemia Data

Primary Goal of a Data mining technique is to detect and classify the data from a large data set without compromising the speed of the process. Data mining is the process of extracting patterns from a large dataset. Therefore the pattern discovery and mining are often time consuming. In any data pattern, a data is represented by several columns called the linear low dimensions. But the data identity does not equally depend upon each of these dimensions. Therefore scanning and processing the entire dataset for every query not only reduces the efficiency of the algorithm but at the same time minimizes the speed of processing. This can be solved significantly by identifying the intrinsic dimensionality of the data and applying the classification on the dataset corresponding to the intrinsic dataset only. Several algorithms have been proposed for identifying the intrinsic data dimensions and reducing the same. Once the dimension of the data is reduced, it affects the classification rate and classification rate may drop due to reduction in number of data points for decision. In this work we propose a unique technique for classifying the leukemia data by identifying and reducing the dimension of the training or knowledge dataset using Iterative process of Intrinsic dimensionality discovery and reduction using Principal Components Analysis (PCA) technique. Further the optimized data set is used to classify the given data using Support Vector Machines (SVM) classification. Results show that the proposed technique performs much better in terms of obtaining optimized data set and classification accuracy.

An Efficient & Secure Content Contribution and Retrieval content in Online Social Networks using Level-level Security Optimization & Content Visualization Algorithm

Zarina Mohd Noh*, Abdul Rahman Ramli, Marsyita Hanafi, M Iqbal Saripan, Ridza Azri Ramlee 1,2,3,4 Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia 1,5 Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 75450 Durian Tunggal, Melaka, Malaysia

Review of Fuzzy Rule Based Classification systems

Fuzzy Rule Based Classification systems (FRBCs) have received significant attention among the researchers due to the good behaviour in the real time databases. An important issue in the design of fuzzy rule-based classification system is the optimized generation of fuzzy if-then rules and the membership functions. The inductive learning of fuzzy rule classifier suffers in rule generation and rule optimization when the search space or variables becomes high. This creates the new idea of making the fuzzy system with precise rules leading to less scalability and improved accuracy. Accordingly, different approaches have been presented in the literature for optimal finding of fuzzy rules using optimization algorithms. Among the different techniques available in the literature, choosing the type, number of membership functions and defining parameters of membership function are still challenging tasks. In this paper, the optimization algorithms for optimal design of membership function and optimal rule generation are reviewed.

Privacy Preserving in Data Mining Technical: A Review

Data mining produces a large amount of data that needs to be analyzed and prioritized in order to extract useful information from it and gain more knowledge from the data. The aim of data mining tools is to find useful patterns, techniques and models from the available of large data. Hence knowledge about various data mining techniques may contain private information about people or business. The data in data mining is vulnerable to data hackers and employees to take advantage of the situation and misuse data. Preservation of privacy is a significant aspect of data mining and as secrecy of sensitive information must be maintained while sharing the data among different un-trusted parties. To protect the privacy of sensitive data without losing the usability of data, various techniques have been used in privacy preserving data mining (PPDM) to achieve the goal. The aim of this paper is to present privacy preserving data mining techniques. Current application systems are suffering several data privacy during online. There is required some work for content privacy on web. This work plans to work on privacy of web content during data extraction and clustering.