Journal of Intelligent & Fuzzy Systems - Volume 37, issue 3

Authors: Catak, Ferhat Ozgur | Mustacoglu, Ahmet Fatih

Article Type: Research Article

Abstract: Today, many companies are faced with the huge network traffics mainly consisting of the various type of network attacks due to the increased usage of the botnet, fuzzier, shellcode or network related vulnerabilities. These types of attacks are having a negative impact on the organization because they block the day-to-day operations. By using the classification models, the attacks could be identified and separated earlier. The Distributed Denial of Service Attacks (DDoS) primarily focus on preventing or reducing the availability of a service to innocent users. In this research, we focused primarily on the classification of network traffics based on the …deep learning methods and technologies for network flow models. In order to increase the classification performance of a model that is based on the deep neural networks has been used. The model used in this research for the classification of network traffics evaluated and the related metrics showing the classification performance have been depicted in the figures and tables. As the results indicate, the proposed model can perform well enough for detecting DDoS attacks through deep learning technologies. Show more

Keywords: cyber security, ddos, deep learning, autoencoder

DOI: 10.3233/JIFS-190159

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 3969-3979, 2019

Authors: Leninfred, A. | Dhanya, D. | Kavitha, S. | Ashwini, M.

Article Type: Research Article

Abstract: Cloud computing is used for processing resources that are conveyed as an administration over a network and a prototype to enable beneficial on-interest network access to a general loch of configurable reckoning resources which are rapidly provisioned and discharged. While adopting cloud computing, major challenges like resource provisioning, resource allocation and security are arising. Only prevailing resource provisioning algorithm are depending upon single tier application utilizing meta-heuristic methodology. Here, we presented a multi-tier application for provisioning dynamic resources utilizing meta-heuristic methodology like Ant Colony Optimization algorithm (ACO), Simulated Annealing (SA) algorithm and hybrid algorithm which fuses ACO and SA and …also an improved cost based scheduling is used to schedule jobs within the cloud with reduced cost. Implementation outcomes displays the efficiency of provisioning resources using ACO-SA algorithm in multitier application of hybrid cloud is greater than other resource provisioning algorithms in cloud computing. Show more

Keywords: Hybrid cloud, cloud computing, resource provisioning, meta-heuristic technique, hybrid ACO-SA, improved cost based scheduling

DOI: 10.3233/JIFS-190160

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 3981-3990, 2019

Authors: Liu, Xiaoyong | Yun, Zhonghua | Yang, Hang | Zhang, Qiang

Article Type: Research Article

Abstract: In this paper, a novel method for fault detection based on an adaptive interval regression model characterized by the upper regression model (URM) and lower regression model (LRM) has been proposed. Applying the proposed method, a confidence band for the measured data, derived in the normal operating conditions of a system, is constructed.The method combines the superiorities of model sparse representation and computational efficiency of linear programming support vector regression (LP-SVR) with some ideas from L 1 -norm on approximation errors. First, the upper and lower L 1 -norms with respect to upper bound approximation error are considered, and the …both norms subject to respective constraints are integrated into LP-SVR to form new upper and lower optimization problems, respectively. Following that, optimization problem corresponding to URM and LRM are solved by linear programming and interval regression model is thus constructed to judge whether the fault occurs or not. The proposed method returns an interval output as opposed to a point output. Finally, the efficacy of this method is demonstrated by applying it on the benchmark Tennessee Eastman problem, and has been compared with conventional techniques such as principal component analysis (PCA), dynamic-PCA (DPCA) and One-Class Support Vector Machine(1-class SVM). It is shown that the proposed method is superior to those approaches in terms of performance measure of detection latency. Show more

Keywords: Fault detection, LP-SVR, L1-Norm minimization, linear programming, interval regression model

DOI: 10.3233/JIFS-190176

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 3991-4001, 2019

Authors: Too, Edna C. | Li, Yujian | Kwao, Pius | Njuki, Sam | Mosomi, Mugendi E. | Kibet, Julius

Article Type: Research Article

Abstract:  Deep learning is a field of Artificial Intelligence that has recently drawn a lot of attention with the desire to build up a quick, automatic and accurate system for image identification and classification. Deep learning serves as a fundamental part of modern computer vision solutions. However, as the architectures become deep and powerful new challenges in the process of training emerge. This includes the computational cost associated with training deep and large networks. In this work, the focus is on pruning and evaluation of state-of-the-art deep convolutional neural network for image-based plant disease and plants species classification. Pruning filters allow …the reduction of parameters by removing unimportant filters and its feature maps. In this paper, the performance of pruned networks is evaluated across three datasets. It is observed that pruned DenseNet with Self-Normalization Neural Network (SNN) approach learns 2x faster compared to the initial DenseNet architecture. Additionally, pruning filters allow the reduction of the number of parameters and FLOPs by approximately 14% and 25% respectively. The aim is to create a fast and efficient model for the purpose of identification of plant diseases. Fast methods are desired for early identifications of diseases before damages occur. The proposed method achieves a satisfactory accuracy performance on PlantVillage, LeafSnap and Swedish-leaf dataset using held-out dataset. Our best pruned model gives an accuracy of 99.24%, 86.64%, and 97.5% on PlantVillage, LeafSnap, and Swedish-leaf datasets respectively. Show more

Keywords: Deep learning, convolutional neural network, pruning, image-based disease classification

DOI: 10.3233/JIFS-190184

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4003-4019, 2019

Authors: Yang, Jie | Yu, Shujuan | Zhang, Yun

Article Type: Research Article

Abstract: The increase of depth is essential for the success of Deep Neural Networks while also leads to the difficulty of training. In light of this, the authors propose a novel multi-layer LSTM model called Highway-DC via introducing Highway Networks (Highway) to Densely Connected Bi-LSTM (DC-Bi-LSTM) which representation of each layer concatenates the output of itself and all preceding layers. Highway is applied to control the volume of input or output of each layer in DC-Bi-LSTM to the next. However, results reveal that Highway-DC shows no improvement over DC-Bi-LSTM, thus an extended version of Highway named Highway II is proposed via …eliminating the multiplicative connections between transform gate and the output in Highway thus preserve the learning of each layer. And the Highway II-based model is named Highway II-DC. Evaluated on 7 benchmark datasets of text classification with compare to DC-Bi-LSTM and other state-of-the-art approaches, results indicate that Highway II-DC shows promising performance for achieving state-of-the-art on 3 datasets and surpassing DC-Bi-LSTM on 6 datasets with faster speed to converge. Besides, it can still enjoy the gain of increased layers with depth up to 30, while DC-Bi-LSTM gets saturated early at a depth of 15. Show more

Keywords: Deep neural networks, Bi-LSTM, text classification, highway

DOI: 10.3233/JIFS-190191

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4021-4032, 2019

Authors: Rubio, José de Jesús | Cruz, David Ricardo | Elias, Israel | Ochoa, Genaro | Balcazar, Ricardo | Aguilar, Arturo

Article Type: Research Article

Abstract: Recently, the Adaptive-Network-Based Fuzzy Inference System (ANFIS) is applied in many areas of knowledge, and there are multiple optimization algorithms for its learning. This work shows the design of a novel optimization algorithm for an ANFIS system that learns and classifies the behavior of brain signals between normal and abnormal. For this goal, different types of optimization algorithms for the learning of an ANFIS system are evaluated, such as the backpropagation, the mini-lots, and the Adam algorithm (adaptive moment estimation). As a result, utilizing the ANFIS with Adam and mini-lots provides the most accurate, fastest, and with least computational …costs results. Show more

Keywords: Adam algorithm, ANFIS system, mini-lots, classification of brain signals

DOI: 10.3233/JIFS-190207

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4033-4041, 2019

Authors: Xia, Yaowei | Qin, Jiejie

Article Type: Research Article

Abstract: In this paper, a new optimization methodology to assess the designs of the various renewable generation systems of electrical energy is used. This methodology utilizes Whale Optimization Algorithm (WOA) to minimize the cost of the electrical energy generated. The methodology permits to examine and to combine different sources of energy as to touch base at an optimal configuration of the hybrid system. This system is capable of providing energy to the predefined site in an achievable way as indicated by certain specialized and financial criteria. The system incorporates wind generation, photovoltaic generation and batteries for energy storage. The recreation results …have been acquired with the help of MATLAB programming. Moreover, the outcomes of the proposed methodology have been compared with Particle Swarm Optimization (PSO) Algorithm for validation. The recreation results demonstrated the predominance of the proposed methodology. Show more

Keywords: Hybrid renewable energy system, whale optimization algorithm, optimization, photovoltaic, wind, off-grid

DOI: 10.3233/JIFS-190213

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4043-4053, 2019

Authors: Mantas, C.J.

Article Type: Research Article

Abstract: First-order recurrent neural networks can be trained to recognize strings of a regular language. Finite state automata can be extracted from these neural networks. Normally, a search process in the output domain of the neurons is necessary for carrying out this extraction procedure. On the other hand, studies about fuzzy rules extraction from feedforward multilayered neural networks can be considered to define new techniques that transform first-order recurrent neural networks into finite state automata. With these new techniques, a fuzzy description of the action of each neuron can be obtained. From these descriptions, the transition function of the automaton can …be directly found and, in this way, the search process is not necessary. A technique with this approach is presented in this paper. Besides, the used method to extract fuzzy rules from a neuron has the advantage that the inputs of the fuzzy system coincide with the inputs of the neuron. Thus, the fuzzy system is more intuitive. Once the transition function is obtained, the automaton structure can be found with the analysis of the transitions for every state and input from the initial state. Finally, several examples are presented to illustrate the method. Show more

Keywords: First-order recurrent neural networks, regular grammars, fuzzy rules, finite state automata

DOI: 10.3233/JIFS-190215

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4055-4070, 2019

Authors: Athira, T.M. | John, Sunil Jacob | Garg, Harish

Article Type: Research Article

Abstract:  A Pythagorean fuzzy soft set is a parameterized family of Pythagorean fuzzy sets and a generalization of intuitionistic fuzzy soft sets. In this paper, the notions of entropy and distance measures are defined for the Pythagorean fuzzy soft sets (PFSSs). Since, the already existing techniques for finding entropy and distance measures are not working for PFSSs, it is necessary to introduce these techniques in the contest of PFSSs. This work proposes a characterization of the Pythagorean fuzzy soft entropy. Also, the expressions for the standard distance measures like Hamming distance and Euclidean distance are obtained. Further, the applications of PFSSs …in decision making problem and pattern recognition problem are discussed. Finally, comparative studies with other existing equations are also carried out. Show more

Keywords: Pythagorean fuzzy soft sets, fuzzy soft sets, entropy, distance measure, decision making problem, pattern recognition problem

DOI: 10.3233/JIFS-190217

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4071-4084, 2019

Authors: Sree Priya, S. | Sivarani, T.S.

Article Type: Research Article

Abstract: This paper proposes an optimal control of Induction Motor (IM) drives using a new optimization technique. The optimization technique is the joined execution of both the Improved Moth flame Optimization (IMFO) algorithm and Radial Basis Function Neural Network (RBFNN). The main objective of the proposed strategy is to enhance the control performance of the IM while reducing the Total Harmonic Distortion (THD), eliminating the oscillation period of the stator current, torque, and speed. Here, the IMFO technique is optimized the gain parameters of the PI controller based on the IM speed variation and generates the reference quadrature axis current. By …using the RBFNN, the reference three-phase current for accurate control pulses of the voltage source inverter (VSI) is predicted. The RBFNN is trained by the input motor actual quadrature axis current and the reference quadrature axis current with the corresponding target reference three-phase current. Furthermore, the proposed method control signals are connected with random pulse width modulation (RPWM) scheme and appropriate pulses are generated and applied to the inverter. With the proposed strategy, the control pulses of VSI are optimized and the proposed system offers a reliable solution. The proposed methodology is implemented in MATLAB/Simulink working platform. The performance of the IM drive is assessed by utilizing the comparative analysis with the existing techniques. The result obtained using the proposed optimization strategy showed that; it can provide the optimal control of IM drive. Also, the proposed strategy is effective in minimize the acoustic noise, torque ripple, eliminate the oscillation period with less computation, and reduces the complexity of the algorithm. Show more

Keywords: Induction motor (IM), IMFO, RBFNN, total harmonic distortion (THD), random pulse width modulation (RPWM)

DOI: 10.3233/JIFS-190244

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4085-4102, 2019