Cybersecurity is one of the major issues to be considered while designing a communication network for Smart Grid (SGCN). However, due to the openness and unpredictable nature of the wireless network, it is often prone to different adversarial attacks that exploit vulnerabilities to launch cross-layer attacks during data transmission. To address this issue, we proposed a novel trust-based routing framework with Bayesian Inference to calculate direct trust and Dempster-Shafer (D-S) theory is used to compute the indirect trust by combining the evidence collected from reliable neighbours. The analytical hierarchy process (AHP) calculates the node's creditability trust by taking cross-layer metrics like transmission rate, buffering capacity and received signal strength. Further, the fuzzy theory is combined with BDS-AHP to incorporate fairness in utilizing cross-layer metrics for calculating the link trust to enable reliable routing. Extensive experiments are carried to evaluate the performance of the proposed fuzzy-based trusted routing algorithm (FBDS-AHP) with the presence of malicious nodes launching packet-dropping, bad-mouthing, on-off and Sybil attacks. Simulation result proves that the proposed trust evaluation algorithm achieves promising results in defending against these attacks thereby providing improved reliability and ensures secured routing in SGCN.

Finding an optimal route for reliable data delivery in the smart grid communication network (SGCN) is a challenging task due to its dynamic nature. Even though the rule set (RS) and membership function (MF) framed intuitively in our previous fuzzy logic (FL) approach performs the trusted routing satisfactorily, it consumes computational memory and reduces the energy efficiency of the node. To address this issue, in this article, we proposed a novel trust evaluation framework that employs water cycle algorithm (WCA) for automatic tuning of the rule set and membership function for the decision variable to route the packet in an adaptable manner. Variables like distance, link stability, and node honesty are considered for evaluation using WCA in three iterative processes, namely, exploitation, evaporation, and raining to find the near optimal if-then rules and points for the membership functions. An experimental setup is created using Network Simulator-2 (NS2) to evaluate the performance of the proposed trusted routing algorithm in SGCN. Extensive experiments are conducted for three cases, namely, 1) evaluating RS with fixed MF; 2) evaluating MF with fixed RS; and 3) combined evaluation of MF and RS to evaluate the performance of the proposed model. From the simulation results, it is clear that the RS and MF generated by the proposed model is small and compact enough to provide reliable routing in SGCN.

Data produced out of microarray experiments are of great use for the physician when it is presented in a meaningful manner. This paper proposes hybrid intelligent methods for addressing the challenges in analyzing the microarray data. The concept of fuzzy and rough set is hybridized with FInformation (FRFI) for gene selection. An optimal fuzzy logic based classifier (FLC) is developed for sample classification using a hybrid Genetic Swarm Algorithm (GSA). Detailed experiments are conducted using microarray data related to Cancer and Rheumatoid Arthritis. From the simulation study, it is found that the proposed FRFI-FLC-GSA produces compact classification system with reasonably good informative genes that can be used for disease diagnosis.

Accuracy maximization and complexity minimization are the two main goals of a fuzzy expert system based microarray data classification. Our previous Genetic Swarm Algorithm (GSA) approach has improved the classification accuracy of the fuzzy expert system at the cost of their interpretability. The if-then rules produced by the GSA are lengthy and complex which is difficult for the physician to understand. To address this interpretability-accuracy tradeoff, the rule set is represented using integer numbers and the task of rule generation is treated as a combinatorial optimization task. Ant colony optimization (ACO) with local and global pheromone updations are applied to find out the fuzzy partition based on the gene expression values for generating simpler rule set. In order to address the formless and continuous expression values of a gene, this paper employs artificial bee colony (ABC) algorithm to evolve the points of membership function. Mutual Information is used for idenfication of informative genes. The performance of the proposed hybrid Ant Bee Algorithm (ABA) is evaluated using six gene expression data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with highly interpretable and compact rules for all the data sets when compared with other approaches.

Treatment to diabetes using microarray data has gained much attention among the physician as it provides important information about pathological states as well as information that can lead to earlier diagnosis. But its high dimensional low sample nature poses a lot of difficulties when it is analyzed by hand and needs an automatic system. As against statistical and machine learning approaches, fuzzy expert system provides an understandable diagnostic system. An important issue in the design of fuzzy expert system is knowledge acquisition. This paper presents a hybrid colony algorithm to extract if-then rules and to form membership functions from diabetes microarray data. During the run, Ant Colony Optimization (ACO) is used to generate optimal rule set and Artificial Bee Colony (ABC) is used to evolve the points of membership function. Mutual Information is used for identification of informative genes. The performance of the proposed approach is evaluated using two diabetes microarray data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with interpretable rules when compared with other approaches.

This paper proposes an Enhanced Particle Swarm Optimization (EPSO) for extracting optimal rule set and tuning membership function for fuzzy logic based classifier model. The standard PSO is more sensitive to premature convergence due to lack of diversity in the swarm and can easily get trapped into local minima when it is used for data classification. To overcome this issue, BLX-α crossover and Non-uniform mutation from Genetic Algorithm (GA) are incorporated in addition to standard velocity and position updating of PSO. The performance of the proposed approach is evaluated using ten publicly available bench mark data sets. From the simulation study, it is found that the proposed approach enhances the convergence and generates a comprehensible fuzzy classifier system with high classification accuracy for all the data sets. Statistical analysis of the test result shows the suitability of the proposed method over other approaches reported in the literature.

An important issue in the design of FRBS is the formation of fuzzy if-then rules and the membership functions. This paper presents a Mixed Genetic Algorithm (MGA) approach to obtain the optimal rule set and the membership function of the fuzzy classifier. While applying genetic algorithm for fuzzy classifier design, the membership functions are represented as real numbers and the fuzzy rules are represented as binary string. Modified forms of crossover and mutation operators are proposed to deal with the mixed string. The proposed genetic operators help to improve the convergence of GA and accuracy of the classifier. The performance of the proposed approach is evaluated through development of fuzzy classifier for seven standard data sets. From the simulation study it is found that the proposed algorithm produces a fuzzy classifier with minimum number of rules and high classification accuracy. Statistical analysis of the test results shows the superiority of the proposed algorithm over the existing methods.