Journal of Intelligent & Fuzzy Systems - Volume 39, issue 6

Authors: Vasudev, R. Aravind | Anitha, B. | Manikandan, G. | Karthikeyan, B. | Ravi, Logesh | Subramaniyaswamy, V.

Article Type: Research Article

Abstract: Heart diseases are one of the crucial diseases that may cause fatality in both men and women. About 12 million deaths occur across the world due to heart diseases. With the advancement in information technology, it is possible for the Healthcare industry to store enormous volume of data containing millions of patient’s medical information along with their treatment details. If utilized in an efficient manner, this information helps the doctors to diagnose the diseases in a precise manner. Data mining algorithms are employed to analyse huge data sets and to discover unseen patterns. Data mining plays an essential role in …medical diagnosis. Doctors bank on different computer models which uses data mining algorithms to prefigure different kinds of diseases in patients. So, the need is to design a methodical data mining algorithm that helps for better forecast of diseases. The main goal of this work is to create an ensemble of algorithms which results in better accuracy. The ensemble is constructed by making use of stacking ensemble technique, which comprises of two categorization algorithms namely Naïve Bayes and Artificial Neural Network. The Cleveland heart disease data set acquired from UCI machine learning repository containing 14 attributes and 303 instances is given as input to these algorithms. From our experimental analysis it is evident that the proposed ensemble scheme results in a better accuracy. Show more

Keywords: Cardio vascular disease, naïve bayes, neural network, stacking, resilient back propagation

DOI: 10.3233/JIFS-189145

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8249-8257, 2020

Authors: Sengan, Sudhakar | Arokia Jesu Prabhu, L. | Ramachandran, V. | Priya, V. | Ravi, Logesh | Subramaniyaswamy, V.

Article Type: Research Article

Abstract: In the last decade, numerous researches have been focused on Image Super-Resolution (SR); this recreation or improvement model is vital in different research areas. Recently, deep learning algorithm finds useful to advance in the resolution of the medical output. Here, we devise a novel Deep Convolutional Network model along with the optimal learning rate of the Rectified Linear Unit (ReLU) intended for Medical Image Super-Resolution (MISR). For getting the optimal values of Deep Learning AlexNet structure, Modified Crow Search (MCS) is utilized, which is mainly depends on the behavior of crow sets. The chosen Alexnet lacks in a sort of …suitable supervision for upgrading execution of the proposed model that effectively aims to overfit. The proposed design, i.e., MISR, named Deep Optimal Convolutional AlexNet (DOCALN), derives the optimal values of learning rates of the ReLU activation function. Based on this optimal deep learning structure, the Low Resolution (LR) medical images can be applied. Experimentation results of our proposed model are compared with variants of Convolution Neural Networks (CNN) concerning different measures such as image quality assessment, SR efficiency analysis, and execution time. Show more

Keywords: Deep learning, super resolution, optimization, convolutional neural network, alexnet, deep learning

DOI: 10.3233/JIFS-189146

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8259-8272, 2020

Authors: Kirn Kumar, N. | Indra Gandhi, V.

Article Type: Research Article

Abstract: As the world is moving towards green energy generation to reduce the pollution by renewable sources such as wind, solar, geothermal and more. These sources are intermittent in nature, to coordinate and control with traditional power generating units a control technique is necessary. This paper mainly focuses on the design of fuzzy based classical controller using a PSO algorithm for optimal controller gains to control the frequency variations in island hybrid power system. The considered mathematical model comprises of a diesel generating model, wind turbine generator and a battery storage system. Fuzzy is an intelligent controller which is designed with …trial and error rules or on the basis of past experience provided by experts or by optimization methods for optimized gains using computational algorithms. To give best solution for these kinds of problems with FLCs traditional controllers are integrated with fuzzy logic. The PSO algorithm is applied to tune the classical controller gains to decrease the frequency deviation of the island power system, during the different load and wind disturbances. The Fuzzy PID classical controller shows the best performance compared with the only fuzzy and Fuzzy-PI controller configurations by illustrating the under shoot, overshoot and settling time and the proposed method is robust for various loading conditions and different wind changes. Show more

Keywords: Battery energy storage, fuzzy logic controller, load frequency control, particle swarm optimization, renewable energy sources

DOI: 10.3233/JIFS-189147

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8273-8283, 2020

Authors: Meena, V. | Gireesha, Obulaporam | Krithivasan, Kannan | Shankar Sriram, V.S.

Article Type: Research Article

Abstract: Mobile Cloud Computing (MCC)’s rapid technological advancements facilitate various computational-intensive applications on smart mobile devices. However, such applications are constrained by limited processing power, energy consumption, and storage capacity of smart mobile devices. To mitigate these issues, computational offloading is found to be the one of the promising techniques as it offloads the execution of computation-intensive applications to cloud resources. In addition, various kinds of cloud services and resourceful servers are available to offload computationally intensive tasks. However, their processing speeds, access delays, computation capability, residual memory and service charges are different which retards their usage, as it becomes time-consuming …and ambiguous for making decisions. To address the aforementioned issues, this paper presents a Fuzzy Simplified Swarm Optimization based cloud Computational Offloading (FSSOCO) algorithm to achieve optimum multisite offloading. Fuzzy logic and simplified swarm optimization are employed for the identification of high powerful nodes and task decomposition respectively. The overall performance of FSSOCO is validated using the Specjvm benchmark suite and compared with the state-of-the-art offloading techniques in terms of the weighted total cost, energy consumption, and processing time. Show more

Keywords: Computational offloading, fuzzy logic, mobile cloud computing, multisite offloading, swarm optimization

DOI: 10.3233/JIFS-189148

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8285-8297, 2020

Authors: Ni, Zhiwei | Xia, Pingfan | Zhu, Xuhui | Ding, Yufei | Ni, Liping

Article Type: Research Article

Abstract: Ensemble pruning has been widely used for enhancing classification ability employing a smaller number of classifiers. Ensemble pruning extracts a part of classifiers with good overall performance to form the final ensemble. Diversity and accuracy of classifiers are of vital importance for a successful ensemble. It is hard for the members in one ensemble to achieve both good diversity and high accuracy, simultaneously, because there is a tradeoff between them. Existing works usually search for the tradeoff in terms of diversity measures, or find it utilizing heuristic algorithms, which cannot gain the exact solution without exhaustive search. To address the …above issue, a novel ensemble pruning method based on information exchange glowworm swarm optimization and complementarity measure, abbreviated EPIECM, is proposed using the combination of information exchange glowworm swarm optimization (IEGSO) and complementarity measure (COM). Firstly, multiple generated classifiers are utilized to construct a pool of learners who perform diversely. Secondly, COM is employed to pre-prune the classifiers with poor comprehensive performance, and the pre-pruned ensemble is formed utilizing the retaining classifiers. Finally, the optimal subset of classifiers is combined from the remaining constituents after pre-pruning with IEGSO. Empirical results on 27 UCI datasets indicate that EPIECM significantly outperforms other techniques. Show more

Keywords: Ensemble pruning, glowworm swarm optimization, information exchange, complementarity measure

DOI: 10.3233/JIFS-189149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8299-8313, 2020

Authors: Nath, Keshab | Dhanalakshmi, R | Vijayakumar, V. | Aremu, Bashiru | Hemant Kumar Reddy, K. | Xiao-Zhi, Gao

Article Type: Research Article

Abstract: Detection of densely interconnected nodes also called modules or communities in static or dynamic networks has become a key approach to comprehend the topology, functions and organizations of the networks. Over the years, numerous methods have been proposed to detect the accurate community structure in the networks. State-of-the-art approaches only focus on finding non-overlapping and overlapping communities in a network. However, many networks are known to possess a hidden or embedded structure, where communities are recursively grouped into a hierarchical structure. Here, we reinvent such sub-communities within a community, which can be redefined based on nodes similarity. We term those …derived communities as hidden or hierarchical communities. In this work, we present a method called H idden C ommunity based on N eighborhood Similarity C omputation (HCNC ) to uncover undetected groups of communities that embedded within a community. HCNC can detect hidden communities irrespective of density variation within the community. We define a new similarity measure based on the degree of a node and it’s adjacent nodes degree. We evaluate the efficiency of HCNC by comparing it with several well-known community detectors through various real-world and synthetic networks. Results show that HCNC has better performance in comparison to the candidate community detectors concerning various statistical measures. The most intriguing findings of HCNC is that it became the first research work to report the presence of hidden communities in Les Miserables, Karate and Polbooks networks. Show more

Keywords: Embedded Community, Intrinsic structure, Hidden community, Neighborhood similarity, Community Strength, Social graphs

DOI: 10.3233/JIFS-189150

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8315-8324, 2020

Authors: Viloria, Amelec | Lezama, Omar Bonerge Píneda | Varela, Noel

Article Type: Research Article

Abstract: As of late, traffic blockage, street mishaps, and ecological contamination brought about by traffic, alongside the need to associate and utilize constant applications, have become issues of worldwide intrigue. Different on-screen characters, for example, vehicle producers, the scholarly community, and government offices have begun to invest a ton of energy together towards the acknowledgment of the idea of huge scope vehicular interchanges. One of the primary methodologies in this kind of system is the advancement of remote advances and their assorted organizations, concentrating on the association with the Internet through WiFi systems, cell systems, or specially appointed vehicular systems. VANETs …are essentially intended to give data trade through Vehicle to Vehicle (V2V) and Vehicle to foundation (V2I) interchanges, permitting ceaseless network and being exceptionally utilized for short-range correspondence, with high transmission speed through which it is proposed that clients keep up an association and distinguish occasions about clog or street conditions. This exploration presents a vehicular situation that tries to acquire a sufficient presentation while executing a heterogeneous network in a few segments of the city of Bogotá, Colombia. Show more

Keywords: Coverage, heterogeneous, throughput, VANET, WIFI, DSRC

DOI: 10.3233/JIFS-189151

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8325-8332, 2020

Authors: Karthikeyan, M.P. | Venkatesan, R. | Vijayakumar, V. | Ravi, Logesh | Subramaniyaswamy, V.

Article Type: Research Article

Abstract: Due to the wide acceptance of White Blood Cells (WBCs) in disease diagnosis, detection and classification of WBC are hot topic. Existing methodologies have some drawbacks such as significant degree of error, higher accuracy, time bound and higher misclassification rate. A WBCs detection and classification called, Jenks Optimized Logistic Convolutional Neural Network (JO-LCNN) method has proposed. Initally, Eulers Principal Axis is used as a convolution model to obtain a rotation invariant form of image by differentiating the background and RBCs, then eliminating them which leaves only the WBCs. By eliminating the wanton features, inherent features are detected contributing to minimum …misclassification rate. According to above, Jenks Optimization function is used as a pooling model to obtain feature map for lower resolution. Therefore JO-LCNN is used for removing tiny objects in image and complete nuclei. Finally, Multinomial Logistic classifier is used to classify five types of classes by means of loss function and updating weight according to the loss function, therefore classifying with higher accuracy rate. Using LISC database for WBCs with different parameters as classification accuracy, false positive rate and time complexity are performed. Result shows that JO-LCNN, efficiently improves accuracy with less time, misclassification rate than the state-of-art methods. Show more

Keywords: White blood cell, Eulers Principal Axis, Jenks Optimization, pooling, Multinomial Logistic Classification

DOI: 10.3233/JIFS-189152

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8333-8343, 2020

Authors: Alamelu, M. | Pradeep Kumar, T.S. | Vijayakumar, V.

Article Type: Research Article

Abstract: Service Level Agreement (SLA) is an agreement between the service provider and consumer to provide the verifiable quality of services. Using the valuable metrics in SLA, a service consumer could easily evaluate the service provider. Though there are different types of SLA models are available between the consumer and provider, the proposed approach describes the Fuzzy rule base SLA agreement generation among multiple service providers. A negotiation system is designed in this work to collect the different sets of provider services. With their desired quality metrics, a common Fuzzy based SLA report is generated and compared against the existing consumer …requirements. From the analysis of the common agreement report, consumers can easily evaluate the best service with the desired Impact service, cost and Quality. The main advantage of this approach is that it reduces the time consumption of a consumer. Moreover, the best service provider can be selected among multiple providers with the desired QoS parameters. At the same time, the bilateral negotiation is enhanced with the approach of multilateral negotiation to improve the searching time of consumers. Show more

Keywords: Multiparty negotiation, SLA, expert advice, service level agreement, fuzzy based support system

DOI: 10.3233/JIFS-189153

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8345-8356, 2020

Authors: Stephan, Thompson | Rajappa, Ananthnarayan | Sendhil Kumar, K.S. | Gupta, Shivang | Shankar, Achyut | Vijayakumar, V.

Article Type: Research Article

Abstract: Vehicular Ad Hoc Networks (VANETs) is the most growing research area in wireless communication and has been gaining significant attention over recent years due to its role in designing intelligent transportation systems. Wireless multi-hop forwarding in VANETs is challenging since the data has to be relayed as soon as possible through the intermediate vehicles from the source to destination. This paper proposes a modified fuzzy-based greedy routing protocol (MFGR) which is an enhanced version of fuzzy logic-based greedy routing protocol (FLGR). Our proposed protocol applies fuzzy logic for the selection of the next greedy forwarder to forward the data reliably …towards the destination. Five parameters, namely distance, direction, speed, position, and trust have been used to evaluate the node’s stability using fuzzy logic. The simulation results demonstrate that the proposed MFGR scheme can achieve the best performance in terms of the highest packet delivery ratio (PDR) and minimizes the average number of hops among all protocols. Show more

Keywords: Vehicular ad hoc network (VANET), fuzzy logic, routing, mobility, trust

DOI: 10.3233/JIFS-189154

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8357-8364, 2020