Ramalingaswamy Cheruku

SM-RuleMiner

Diabetes is a major health challenge around the world. Existing rule-based classification systems have been widely used for diabetes diagnosis, even though they must overcome the challenge of producing a comprehensive optimal ruleset while balancing accuracy, sensitivity and specificity values. To resolve this drawback, in this paper, a Spider Monkey Optimization-based rule miner (SM-RuleMiner) has been proposed for diabetes classification. A novel fitness function has also been incorporated into SM-RuleMiner to generate a comprehensive optimal ruleset while balancing accuracy, sensitivity and specificity. The proposed rule-miner is compared against three rule-based algorithms, namely ID3, C4.5 and CART, along with several meta-heuristic-based rule mining algorithms, on the Pima Indians Diabetes dataset using 10-fold cross validation. It has been observed that the proposed rule miner outperforms several well-known algorithms in terms of average classification accuracy and average sensitivity. Moreover, the proposed rule miner outperformed the other algorithms in terms of mean rule length and mean ruleset size.

Diabetes Classification using Radial Basis Function Network by Combining Cluster Validity Index and BAT Optimization with Novel Fitness Function

Diabetes is one of the foremost causes for the increase in mortality among children and adults in recent years. Classification systems are being used by doctors to analyse and diagnose the medical data. Radial basis function neural networks are more attractive for classification of diseases, especially in diabetes classification, because of it’s non iterative nature. Radial basis function neural networks are four layer feed forward neural network with input layer, pattern layer, summation layer and the decision layer respectively. The size of the pattern layer increases on par with training data set size. Though various attempts have made to solve this issue by clustering input data using different clustering algorithms like k-means, k-medoids, and SOFM etc. However main difficulty of determining the optimal number of neurons in the pattern layer remain unsolved. In this paper, we present a new model based on cluster validity index with radial basis neural network for classification of diabetic patients data. We employ cluster validity index in class by class fashion for determining the optimal number of neurons in pattern layer. A new convex fitness function has also been designed for bat inspired optimization algorithm to identify the weights between summation layer and pattern layer. The proposed model for radial basis function neural network is tested on Pima Indians Diabetes data set and synthetic data sets. Experimental results proved that our approach performs better in terms of accuracy, sensitivity, specificity, classification time, training time, network complexity and computational time compared to conventional radial basis function neural network. It is also proved that proposed model performs better compared to familiar classifiers namely probabilistic neural network, feed forward neural network, cascade forward network, time delay network, artificial immuine system and GINI classifier.

RST-BatMiner: A fuzzy rule miner integrating rough set feature selection and Bat optimization for detection of diabetes disease

Abstract Fuzzy classification rules are more interpretable and cope better with pervasive uncertainty and vagueness with respect to crisp rules. Because of this fact, fuzzy classification rules are extensively used in classification and decision support systems for disease diagnosis. But, most of the rule mining techniques failed to generate accurate and comprehensive fuzzy rules. This paper presents a hybrid decision support system based on Rough Set Theory (RST) and Bat optimization Algorithm (BA) called RST-BatMiner. It consists of two stages. In the first stage, redundant features have been removed from the data set through RST-based QUICK-REDUCT approach. In the second stage, for each class BA is invoked to generate fuzzy rules by minimizing proposed fitness function. Further, an Ada-Boosting technique is applied to the rules generated by BA to increase the accuracy rate of generated fuzzy rules. Moreover, to generate comprehensive fuzzy rules, a new ≠ (not equal) operator along with = (equal) operator is introduced into BA encoding scheme. The proposed RST-BatMiner builds consolidated fuzzy ruleset by learning the rules associated with each class separately. The proposed RST-BatMiner is experimented on six bench-mark datasets namely Pima Indians Diabetes, Wisconsin Breast Cancer, Cleveland Heart disease, iris, wine and glass, in order to validate its generalization capability. These experimental results show that except for wine dataset the proposed RST-BatMiner yields high accuracy and comprehensible ruleset when compared to other state-of-the-art bio-inspired based fuzzy rule miners and Fuzzy Rule Based Classification Systems (FRBCS) in the literature. In the case of wine dataset, the proposed RST-BatMiner yields second highest accuracy along with comprehensible ruleset.

PSO-RBFNN: A PSO-Based Clustering Approach for RBFNN Design to Classify Disease Data

The Radial Basis Function Neural Networks (RBFNNs) are non-iterative in nature so they are attractive for disease classification. These are four layer networks with input, hidden, output and decision layers. The RBFNNs require single iteration for training the network. On the other side, it suffers from growing hidden layer size on par with training dataset. Though various attempts have been made to solve this issue by clustering the input data. But, in a given dataset estimating the optimal number of clusters is unknown and also it involves more computational time. Hence, to address this problem in this paper, a Particle Swarm Optimization (PSO)-based clustering methodology has been proposed. In this context, we introduce a measure in the objective function of PSO, which allows us to measure the quality of wide range of clusters without prior information. Next, this PSO-based clustering methodology yields a set of High-Performance Cluster Centers (HPCCs). The proposed method experimented on three medical datasets. The experimental results indicate that the proposed method outperforms the competing approaches.

An Efficient Load Balancing of Gateways Using Improved Shuffled Frog Leaping Algorithm and Novel Fitness Function for WSNs

Energy consumption is one of the important factors in wireless sensor networks (WSNs) design. As energy is a limited resource, energy consumption problem in WSNs has become a fast growing problem, and there is a need of efficient and robust algorithms for load balancing in WSNs. This energy is needed for sensor nodes operations. In order to maximize the network lifetime, energy consumption should be optimized. In cluster-based WSNs, cluster heads or gateways perform activities, such as data collection from its member nodes, data aggregation, and data exchange with the base station. Hence, load balancing of gateways in WSNs is one of the crucial and challenging tasks to maximize network lifetime. In order to address this problem, in this paper, shuffled frog leaping algorithm (SFLA) is improved by suitably modifying the frog’s population generation and off-spring generation phases in SFLA and by introducing a transfer phase. A novel fitness function is also designed to evaluate the quality of the solutions produced by the improved SFLA. We performed extensive simulations of the proposed load balancing algorithm in terms of various performance parameters. The experimental results are encouraging and demonstrated the efficiency of the proposed algorithm.

Automatic disease diagnosis using optimised weightless neural networks for low-power wearable devices

Low-power wearable devices for disease diagnosis are used at anytime and anywhere. These are non-invasive and pain-free for the better quality of life. However, these devices are resource constrained in terms of memory and processing capability. Memory constraint allows these devices to store a limited number of patterns and processing constraint provides delayed response. It is a challenging task to design a robust classification system under above constraints with high accuracy. In this Letter, to resolve this problem, a novel architecture for weightless neural networks (WNNs) has been proposed. It uses variable sized random access memories to optimise the memory usage and a modified binary TRIE data structure for reducing the test time. In addition, a bio-inspired-based genetic algorithm has been employed to improve the accuracy. The proposed architecture is experimented on various disease datasets using its software and hardware realisations. The experimental results prove that the proposed architecture achieves better performance in terms of accuracy, memory saving and test time as compared to standard WNNs. It also outperforms in terms of accuracy as compared to conventional neural network-based classifiers. The proposed architecture is a powerful part of most of the low-power wearable devices for the solution of memory, accuracy and time issues.

A PSO Based Routing with Novel Fitness Function for Improving Lifetime of WSNs

Wireless sensor networks (WSNs) consist of spatially distributed low power sensor nodes and gateways along with base station to monitor physical or environmental conditions. In cluster-based WSNs, the cluster head is treated as the gateway. The gateways perform the multiple activities, such as data gathering, aggregation, and transmission etc. The collected data is transmitted from gateways to the base station using routing information. Routing is a key challenge in WSNs design as gateways are constrained by energy, processing power, and memory. Moreover, heavily loaded gateways die in early stages and cause changes in network topology. It is necessary to conserve gateways energy for prolonging the WSNs lifetime. To address this problem, particle swarm optimization (PSO)-based routing is proposed in this paper. Also, a novel fitness function is designed by considering the number of relay nodes, the distance between the gateway to base station and relay load factor of the network. The proposed algorithm is validated under two different scenarios. The experimental results show that the proposed PSO-based routing algorithm prolonged WSNs lifetime when compared to other bio-inspired approaches.

Shuffled Complex Evolution Approach for Load Balancing of Gateways in Wireless Sensor Networks

Energy consumption is one of the important factor of Wireless Sensor Networks (WSN). It has much attention in many fields. From recent studies, it is observed that energy consumption in WSN is challenging task as the energy is limited resource. This energy is needed for sensor nodes operation. In order to maximize the network lifetime, energy consumption should be mitigated. In cluster based WSN, cluster head i.e., the leader of cluster performs various activities, such as data collection from its member nodes, data aggregation and data exchange with base station. Hence, load balancing in WSNs is one of the challenging task to maximize network lifetime. In order to address this problem, in this paper, Shuffled Complex Evolution (SCE) algorithm is used. A novel fitness function is also designed to evaluate fitness of solutions produced by SCE algorithm. In SCE, the solutions with best and worst fitness value exchange their information to produce new off-spring. We have simulated proposed load balancing algorithm along with other state-of-the-art load balancing algorithms, namely Node Local Density Load Balancing, Score Based Load Balancing, Simple Genetic Algorithm based load balancing, Novel Genetic Algorithm based Load Balancing. It is observed from experimental results that proposed load balancing algorithm outperforms state-of-the-art load balancing algorithms in terms of load balancing, energy consumption, execution time, number of sensor nodes and number of heavy loaded sensor nodes.

Spatial Co-location Pattern Mining Using Delaunay Triangulation

Spatial data mining is the process of finding interesting patterns that may implicitly exist in spatial database. The process of finding the subsets of features that are frequently found together in a same location is called co-location pattern discovery. Earlier methods to find co-location patterns focuses on converting neighbourhood relations to item sets. Once item sets are obtained then can apply any method for finding patterns. The criteria to know the strength of co-location patterns is participation ratio and participation index. In this paper, Delaunay triangulation approach is proposed for mining co-location patterns. Delaunay triangulation represents the closest neighbourhood structure of the features exactly which is a major concern in finding the co-location patterns. The results show that this approach achieves good performance when compared to earlier methodologies.

Review on RBFNN Design Approaches: A Case Study on Diabetes Data

Radial Basis Function Neural Networks (RBFNNs) are more powerful machine learning technique as it requires non-iterative training. However, the hidden layer of RBFNN grows on par with the growing dataset size. This results in increase in network complexity, training time, and testing times. It is desirable to design appropriate RBFNN which balance between simplicity and accuracy. In the literature, many approaches are proposed for reducing the neurons in the RBFNN hidden layer. In this paper, a comprehensive survey is performed on hidden layer reduction techniques with respect to Pima Indians Diabetes (PID) dataset.