Journal of Intelligent & Fuzzy Systems - Volume 38, issue 5

Authors: Hsiao, Shou-Ching | Liu, Zi-Yuan | Tso, Raylin | Kao, Da-Yu | Chen, Chien-Ming

Article Type: Research Article

Abstract: Gated Recurrent Unit (GRU) has wide application fields, such as sentiment analysis, speech recognition, and other sequential data processing. For efficient prediction, a growing number of model owners choose to deploy the trained GRU models through the machine-learning-as-a-service method (MLaaS). However, deploying a GRU model in cloud generates privacy issues for both model owners and prediction clients. This paper presents the architecture of PrivGRU and designs the privacy-preserving protocols to complete the secure inference. The protocols include base protocols and principal protocols. Base protocols define basic linear and non-linear computations, while principal protocols construct the gating mechanisms of GRUs. The …main benefit of PrivGRU is to address privacy problems while enjoying the efficiency and convenience of MLaaS. The overall secure inference is performed on shares, which retain two properties of security: correctness and privacy. To prove the security, this work adopts Universal Composability (UC) framework with the honest-but-curious corruption model. As each protocol is proved to UC-realize the ideal functionality, it can be arbitrarily composed in any manner. This strong security feature makes PrivGRU more flexible and practical in future implementation. Show more

Keywords: Privacy-preserving, MLaaS, gated recurrent unit, additive secret sharing, UC framework

DOI: 10.3233/JIFS-179652

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5627-5638, 2020

Authors: Luo, Tai-Li | Wu, Mu-En | Chen, Chien-Ming

Article Type: Research Article

Abstract: Quantitative trading is a crucial aspect of money management; however, conventional trading strategies are based on indicators and signals, despite the fact that position sizing is arguably the most important issue. In this study, we present a stock evaluation function that outputs the size of the stock in each fixed period as well as the consequences of increasing or decreasing the size of one’s position. The difficulties involved in using machine learning to adjust stock weighting can be attributed to difficulties in obtaining definite answers via supervised learning. We therefore train our evaluation function using reinforcement learning via CNN within …the EIIE network architecture and have the agent adjust the size of the position with the purpose of maximizing profits. Back testing was performed using the top 50 stocks in Taiwan, based on market capitalization. In experiments, most of the stock returns outperformed conventional strategies in terms of cumulative stock value. Show more

Keywords: Deep reinforcement learning, convolution neural networks, evaluation function, position sizing, money management

DOI: 10.3233/JIFS-179653

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5639-5649, 2020

Authors: Wu, Mu-En | Lin, Sheng-Hao | Wang, Jia-Ching

Article Type: Research Article

Abstract: The objective in using the Kelly criterion for money management is to maximize returns; however, in many cases, the risk level exceeds that which the investor can bear. In this study, we present an algorithm to calculate the bidding fraction, while taking into account the level of risk (i.e., the maximum drawdown). The proposed algorithm is based on ensemble learning with a combination of bagging and subset resampling. Our assessment results obtained using the FF48 (i.e., Fama-French-48) dataset revealed that when the maximum drawdown was 5% and 10%, ensemble learning outperformed the conventional approach by 2% and 4%, respectively.

Keywords: Kelly criterion, ensemble learning, Monte Carlo simulation, money managemen

DOI: 10.3233/JIFS-179654

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5651-5659, 2020

Authors: Meng, Zhenyu | Yang, Cheng | Meng, Fanjia | Chen, Yuxin | Lin, Fang

Article Type: Research Article

Abstract:  Differential Evolution (DE) was an easy-coding and efficient stochastic algorithm for global optimization, and the whole optimization process simulates biological evolution. Superior individuals of the population that were suitable for the environment were retained during the evolution, and consequently the tolerable solutions could be obtained in the end. Despite the excellent performance of DE algorithm, there were still some shortcomings. For example, the general performance of DE depended largely on mutation strategy and control parameters, how to design the appropriate control parameters and mutation strategy were difficult tasks. Here a novel DE variant was proposed to overcome these shortcomings. By …incorporating the depth information of previous generations of populations, a better diversity of trial vector candidates could be secured during the evolution process. Moreover, the thought that successful parameters should be retained to guide the update of themselves during the evolution was also incorporated into the novel algorithm. The optimization performance of the new proposed DE variant was verified under CEC 2013 test suit containing 28 benchmarks, and the results showed its competitiveness with several state-of-the-art DE variants. Show more

Keywords: Differential evolution, depth information, global optimization, real-parameter optimization

DOI: 10.3233/JIFS-179655

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5661-5671, 2020

Authors: Chu, Shu-Chuan | Chen, Yuxin | Meng, Fanjia | Yang, Chen | Pan, Jeng-Shyang | Meng, Zhenyu

Article Type: Research Article

Abstract:  Optimization demands exist everywhere in the real world especially in science studies and engineering practices, and it is important that the method to deal with intricacy optimization problems should itself be relative simple. Particle Swarm Optimization (PSO) and Differential Evolution (DE) both are simple evolutionary algorithms (EAs) which are proposed for single-objective optimization and both of them have been proved to be efficient methods for optimizing applications, however, there are still some weakness existing within them. A innovative evolutionary method named QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm which is derived from, also tackles some weaknesses of PSO and DE algorithm, and …obtains better performance on commonly used test suites. The key characteristic of QUATRE is that an automatically generated matrix named evolution matrix M is implemented in evolutionary process, which is taken as an alternative of employing the crossover rate CR . Here in this paper, we present a novel QUATRE variant, named IS-QUATRE, which can explore the search area in a better way comparison with the previous method, and relatively good optimization ability can be obtained by our proposed IS-QUATRE algorithm under CEC2013 test suit. And the conducted experimental results validate that our proposed IS-QUATRE is competitive with some other famous PSO and DE variants. Show more

Keywords: Internal search, QUATRE, single-objective optimization, stochastic optimization, real-parameter optimization

DOI: 10.3233/JIFS-179656

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5673-5684, 2020

Authors: Zhang, Fuquan | Wang, Yiou | Wu, Chensheng

Article Type: Research Article

Abstract:  Digital creativity is creative expression derived from cultural creativity and information technology. In order to overcome the problem in the creative generation in the condition of fuzzy and uncertain ideas, an automatic generation method of cross-modal fuzzy creativity (AGMCFC) is proposed. In this subject, fuzzy creative data sets and learning retrieval network are constructed for the sake of extracting original creative data effectively. And the logical correlations between creative objects are acquired dynamically based on the graph neural network. Creative objects and creative styles are generated by using generative adversarial nets technology and style transfer technology, respectively. Then, the projectiles, …boundary markers and location words of the creative scene objects are generated by analyzing related attributes of each entity. After adjusting the layout, creative works are automatically generated. A fuzzy creative generating environment is implemented. Experimental results show that the screened number of AGMCFC method is about twice as much as that of manual method, and the accuracy rate of AGMCFC method is improved compared with the manual method. AGMCFC method performs well at creative generation of fuzzy ideas automatically. Show more

Keywords: Generation of fuzzy creativity, cross-modal, graph neural network, creative works

DOI: 10.3233/JIFS-179657

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5685-5696, 2020

Authors: Zhang, Jinxin | Li, Hui | Zhang, Xiufang | Yu, Hua | Liang, Fengna | Qin, Zijun | Jiang, Miaohua

Article Type: Research Article

Abstract: Soil erosion is one of the main environmental problems in the world, and also an important branch of natural environment protection. Taking Minjiang River Basin as the research area, based on the supervised classification of land use status map, the comprehensive soil erosion modulus map was generated by referring to the soil erosion evaluation index and the revised soil loss equation (RUSLE). The results showed that the Minjiang River Basin is dominated by mild eroded area and middle eroded area, accounting for 41.50% and 54.81% of the total area respectively. High eroded area accounted for only 3.69%, mainly in the …north and west, and there was no extremely sensitive area. From the perspective of land use types, the construction land and water body are mainly slightly sensitive areas, and forest land, garden land and cultivated land are mainly mild or middle sensitive areas, while the unused lands are shown as middle sensitive areas or highly sensitive areas. Through the discussion of the sensitivity evaluation of soil erosion in Minjiang River Basin, it can provide some references for the relevant departments to the soil and water conservation work in Minjiang River Basin. Show more

Keywords: Soil erosion, sensitivity assessment, Minjiang River Basin, land use

DOI: 10.3233/JIFS-179658

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5697-5705, 2020

Authors: Liu, Wei | Mao, Yu | Ci, Linlin | Zhang, Fuquan

Article Type: Research Article

Abstract:  It is not foolproof for intrusion detection to focus only on the network level and the program level. Internal security and external security of information systems should be given equal attention. User-level intrusion detection can deter and curtail attackers from damaging information systems. Even if the mimic attacker has gained and enhanced the host user privileges that he illegally obtained. In this paper, a novel method based on recurrent neural networks (RNNs) is used to predict user command sequences and prophesy user behaviors. The experimental results show that our command sequence-to-sequence model is robust and effective for solving long sequential …problem on three different data sets including Purdue University data set, SEA data set and self-collected data set. Show more

Keywords: User behavior, recurrent neural networks, anomaly intrusion detection, attacks and defenses

DOI: 10.3233/JIFS-179659

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5707-5716, 2020

Authors: Xie, Chao-Fan | Xu, Lu-Xiong | Zhang, Fuquan

Article Type: Research Article

Abstract: Because of high efficiency and cleanness, the simulated moving bed (SMB) is the most important technology in chromatographic separation. SMB system, which contains several sectors of flow rate, the switching time of valves and many other possible influencing variables, is complex, highly sensitive and difficult to control. This paper proposes adjusted fuzzy control; the adjusted fuzzy controller is applied to the SMB system to control the separation concentration by establishing a variable direction correction formula. Compared with the traditional fuzzy controller, the adjusted fuzzy controller does not need to analyze the dynamic direction of the control force in advance, moreover, …the control accuracy is high and the fluctuation is small for SMB system. Show more

Keywords: Chromatographic, SMB, fuzzy, control

DOI: 10.3233/JIFS-179660

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5717-5729, 2020

Authors: Li, Zhanhui | Fan, Jiancong | Ren, Yande | Tang, Leiyu

Article Type: Research Article

Abstract: As a common disease, migraine has a high incidence but the pathogenesis is still not clear. Resting-state Functional Magnetic Resonance Imaging (rs-fMRI) is an important research topic in the field of brain medicine, which can classify rs-fMRI data to automatically diagnose brain diseases. However, the original features of the rs-fMRI data are difficult to be extracted and the high-dimensional characteristics, which make the data analysis an extremely complicated task. Those have also plagued many researchers and bring great challenges to the existing pattern classification methods. Aiming at the high dimensionality of rs-fMRI data, in this paper, we propose a feature …extraction approach based on the combination of neighborhood rough set and PCA, thereby improving the accuracy of migraine identification. Firstly, Resting-State fMRI Data Analysis Toolkit plus was applied for preprocessing, calculating three characteristic indices: Amplitude of Low Frequency Fluctuation (ALFF), Regional Homogeneity (ReHo) and Functional Connectivity (FC. The inter-group difference analysis was performed by two-sample T test and GRF correction. Then, correlation coefficient matrix original features extraction was performed by means of automatic anatomical label template (AAL). Finally, the original features were trained by the traditional classification algorithm in machine learning. The experimental results show that the propose approach can obtain good performance in predicting migraine. Show more

Keywords: Resting state functional magnetic resonance imaging, migraine, correlation coefficient matrix, neighborhood rough set, principal component analysis, classification

DOI: 10.3233/JIFS-179661

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5731-5741, 2020

Authors: Chen, Tao | Lv, Hongxia | Sun, Yichen | Wang, Xiaoyi

Article Type: Research Article

Abstract: As one kind of highest hierarchy node on the network, the transfer scheme of high-speed railway hub timetable should be studied at priority. After defining the problem that optimizing transfer scheme of timetable at high-speed railway hub, this paper proposes time adjusting strategy and platform adjusting strategy to optimize the problem, of which the first strategy introduces a FUZZY set of reasonable time range to reduce the possible train conflicts at adjacent stations on the network, and the second strategy helps to use different transfer time to match the arrival and departure of trains. Then, an optimization model of timetable …based on passenger transfer is established with the minimized invalid transfer waiting time for passenger and train conflicts at adjacent stations as the objective function. The model is solved by the above two strategies in MATLAB software. Finally, the rationality and effectiveness of this model are verified, taking Shanghai Hongqiao Station as an example. Show more

Keywords: High-speed railway, hub, transfer, timetable optimization

DOI: 10.3233/JIFS-179662

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5743-5752, 2020

Authors: Xu, Changan | Li, Sihan | Xu, Heying | Ni, Shaoquan

Article Type: Research Article

Abstract: Passenger train occupation rate directly affects Railway Company’s revenue and capacity utilization of railway infrastructure, and measures to improve passenger train occupation rate is always the focus of railway companies’ attention. However, researches on what, and how factors influence passenger train occupation rate remain sparse. Based on the collective data of 363 passenger trains on the Beijing-Shanghai High-speed Railway in November 2015, this paper establishes a data-driven analysis framework to explore the influencing factors of passenger train occupation rate. Specifically, we first analyze the possible factors that influence train occupation rate from the perspective of train service planning. Then, the …approach of association rules is applied to analyze the potential relationship between train occupation rate and its influencing factors. And a total of 6711 and 8133 association rules were generated for high and low train occupation rate of passenger trains, respectively. Further analysis found that train departure and arrival times, train departure and arrival station class and the type of trains are the main factors influencing the level of train occupation rate. These findings can provide reference for train service planning. Show more

Keywords: Passenger train occupation rate, association rules, train service planning, Beijing-Shanghai high-speed railway

DOI: 10.3233/JIFS-179663

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5753-5761, 2020

Authors: Yang, Tingyu | Peng, Xiaoqian | Chen, Dingjun | Yang, Feiyu | Muneeb Abid, Malik

Article Type: Research Article

Abstract: With the increasingly close links between provinces and municipalities, the complexity and social connectivity of emergencies have gradually increased. How to realize the rapid and scientific dispatch of emergency resources based on the comprehensive transportation network has become a realistic need. However, due to barriers in management and communication between different administrative regions and different modes of transportation, emergency resource scheduling can only be carried out in the region, and the utilization of integrated traffic channels is insufficient to form a good multimodal transport system. The multi-agent system has strong self-organization ability, learning ability and reasoning ability, which solves problems …in the fields of dynamic decision-making and micro-simulation and provides ideas for solving the problem of comprehensive traffic emergency dispatching across regions. In this paper, the multi-agent system is introduced into the research of the task assignment problem of trans-regional comprehensive traffic emergency materials dispatching, based on the traditional bidding rules, the emergency dispatch task assignment rules are proposed and multi-agent trans-regional comprehensive traffic emergency dispatching model based on improved bidding rules is established. The simulation results show that the proposed method can break the regional barrier and form a multimodal transport scheme for emergency materials from the reserve point to the demand point under the minimum generalized time cost, providing decision support for emergency dispatch. Show more

Keywords: Multi-agent, trans-region, emergency dispatch, bidding

DOI: 10.3233/JIFS-179664

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5763-5774, 2020

Authors: Pan, Jeng-Shyang | Yang, Cheng | Meng, Fanjia | Chen, Yuxin | Meng, Zhenyu

Article Type: Research Article

Abstract:  Differential Evolution (DE) algorithm generates a population of individuals by encoding with a floating point vector, and it is a simple and effective population-based stochastic optimization algorithm for global optimization of continuous space. Because of its excellent performance, DE variants can be applied in a wide range of applications in science and engineering. However, the performance of DE is sensitive to the choice of trial vector generation strategy and the associated control parameters. Therefore, it is necessary to choose appropriate mutation strategy and control parameters when tackling optimization applications. In this paper, an adaptive update mechanism is proposed to update …control parameters F and Cr . The experimental results are verified on the CEC 2013 test suite which contains 28 benchmark functions for the evaluation of single objective real parameter optimization. The proposed algorithm is compared with jDE, iwPSO and ccPSO, and experiment results show its good performance. Show more

Keywords: Adaptive update mechanism, differential evolution, real parameter optimization, stochastic optimization

DOI: 10.3233/JIFS-179665

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5775-5786, 2020

Authors: Wu, Tsu-Yang | Lin, Jerry Chun-Wei | Yun, Unil | Chen, Chun-Hao | Srivastava, Gautam | Lv, Xianbiao

Article Type: Research Article

Abstract:  Association-rule mining (ARM) has concerned as an important and critical research issue in the field of data analytics and mining that aims at finding the correlations among the items in binary databases. However, the conventional algorithms considered the frequency of the item(set) in binary databases for ARM, which is not sufficient in real-life situations. Mining of useful information is not an easy task especially if the item(set) consists of the added values. Moreover, the discovered knowledge is not easy to understand if you are not the domain experts. For the past decades, several intelligent systems involved the fuzzy-set theory for …many domains and applications due to it is interpretable for human reasoning. Before, the Apriori-based method for discovering fuzzy frequent itemsets (FFIs) based on the type-2 fuzzy-set theory was proposed, which requires the amount of computations with enormous candidates. In this study, we then first present a fast list-based multiple fuzzy frequent itemset mining (named as LFFT2)algorithm under type-2 fuzzy-set theory. It is developed by the type-2 membership functions to retrieve the multiple fuzzy frequent itemsets for presenting more useful and meaningful knowledge for making the efficient strategies or decisions. From the results shown in the experiments, it is clear to see that the developed LFFT2 outperforms the conventional Apriori-based approach regarding the execution time and the number of examined nodes in the search space. Show more

Keywords: Data mining, fuzzy frequent itemset mining, type-2 fuzzy-set theory, list-based structure

DOI: 10.3233/JIFS-179666

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5787-5797, 2020

Authors: Lisang, Liu | Dongwei, He | Jishi, Zheng | Ying, Ma | Jing, Huang | Junfeng, Fan | Xiaoyu, Wei | Jinxin, Yao

Article Type: Research Article

Abstract: The intelligent tracking car can realize self-perception, behavioral decision-making, automatic driving, etc. for environmental information, and has a wide application in our lives. In this paper, a new intelligent car is designed based on STM32F407ZGT6 MCU. The track information is collected by the OV7725 camera. The fast OTSU adaptive threshold algorithm is used to obtain the path guiding center line, which can realize image collection, image analysis and sensor data fusion, blocked path identification and intelligent judgment, automatic tracking function. The test results show that the algorithm is effective and feasible. When there are obstacles or partial loss on the …track, it can intelligently identify the effective and obstacles and realize the independent decision making. It has good dynamic and robustness. Show more

Keywords: Machine vision, intelligent car, tracking, obstacle avoidance

DOI: 10.3233/JIFS-179667

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5799-5810, 2020

Authors: Nguyen, Trong-The | Qiao, Yu | Pan, Jeng-Shyang | Chu, Shu-Chuan | Chang, Kuo-Chi | Xue, Xingsi | Dao, Thi-Kien

Article Type: Research Article

Abstract: Metaheuristic algorithms have been applied widely for real-world problems in many fields, e.g., engineering, financial, healthcare. Bats algorithm (BA) is a recent metaheuristic algorithm with considering as a robust optimization method that can outperform existing algorithms. However, when dealing with complicated combinatorial problems such as traveling salesman problems (TSP), the BA can be fallen in a local optimum. This paper proposes a new hybridizing Parallel BA (HPBA) with a mutation in local-search to escape such its drawback scenario for TSP. A graph theory mutation method is used to embed for hybridizing BA with exploiting similarities among individuals. The proposed method …is extensively evaluated in TSP with series instances of the benchmark from TSPLIB to test its performance. The compared experimental result with the previous method and the best-known solutions (B.K.S) in the literature shows that the proposed approach offers competitive results. Show more

Keywords: Transportation applications, metaheuristic algorithms, hybridized parallel bats algorithm

DOI: 10.3233/JIFS-179668

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5811-5820, 2020

Authors: Zhang, Xiaoqing | Zheng, Yongguo | Liu, Weike | Peng, Yanjun | Wang, Zhiyong

Article Type: Research Article

Abstract: Accurate extraction of urban buildings is a key problem in urban remote sensing image processing. It can be applied to many kinds of urban problems, such as data statistics of urban management and smart cities. In recent years, the deep learning model based on convolutional neural network is widely used in the field of target recognition and semantic segmentation. In this paper, based on U-Net for urban building extraction from remote sensing image, we propose a neural network architecture for urban building extraction from remote sensing image. We use depth separable convolution to improve it and adjust the process of …network super parametric optimization according to the characteristics of building. We call this new architecture XU-Net. We evaluate the performance of XU-Net through experiments with INRIA aerial image data set. The result shows that XU-Net is not only feasible but also efficient. Moreover, XU-Net reduces number of parameters 89%, from 18.8M to 2.13M, compared to classical architecture U-Net, at the same time, it guarantees the accuracy can reach 97.5%. Show more

Keywords: U-net, depthwise separable convolution, semantic segmentation, urban building extraction

DOI: 10.3233/JIFS-179669

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5821-5829, 2020

Authors: Wu, Jimmy Ming-Tai | Teng, Qian | Lin, Jerry Chun-Wei | Yun, Unil | Chen, Hsing-Chung

Article Type: Research Article

Abstract:  HAUIM (High Average-Utility Itemset Mining) is a variation of HUIM (High-Utility Itemset Mining) that provides a reliable measure to reveal utility patterns in light of the length of the mined pattern. Several works have been studied to improve mining efficiency by designing multiple pruning strategies and efficient frameworks, but fewer studies have centered on the sophisticated database maintenance algorithm. Existing works still have to rescan the databases multiple times when it is necessary. We first use the pre-large principle in this paper to efficiently update the newly discovered HAUIs. For further updates and maintenance on the basis of the two …thresholds, the Pre-large Average Utility Itemset (PAUI) can be maintained to increase the mining performance. Experiments will then be performed to compare the batch model, the Fast-Updated (FUP)-based model, and the Apriori-like HAUIM (APHAUIM) model designed in respect of the number of maintenance patterns, scalability, runtime, and memory usage. Show more

Keywords: pre-large, high average-utility itemset mining, dynamic database, incremental, transaction insertion

DOI: 10.3233/JIFS-179670

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5831-5840, 2020

Authors: Kiss, Gabor

Article Type: Research Article

Abstract: The expectation of spreading autonomous vehicles lies in the hope of significantly decreasing the 1,3 million death toll accidents worldwide, which are caused by human factor 90% of the time. In policies of insurance companies the reaction time of a human realizing any dangerous situation, reacting to it and putting the breaks into action is two seconds. The reaction time would be reduced by the power of AI that can process the huge amount of data coming from sensors and with the information regarding the situation could make decision much faster than men. The aim of the research is to …denote several situations and possibilities that are capable of deceiving, diverting, capturing a self-driving car or even turning it against the other vehicles by influencing the decision-making of the artificial intelligence. In this paper I will discuss several situations that might be able to confuse the artificial intelligence of the autonomous vehicles or to make them come to an inadequate decision. You can see that safe decision-making depends on the teaching method of the artificial intelligence as well as the correctness of the data uploaded. The other aim of the research is to demonstrate how could work a Manchurian Artificial Intelligence in autonomous vehicles. I will introduce the idea of Manchurian artificial intelligence which can be activated by a certain event and can pose a threat to the passengers of the vehicles. If it is present in the software of several vehicles, a chain of worldwide accidents can be induced at a certain time. Show more

Keywords: Manchurian AI, self-driving car, autonomous vehicle, external manipulation

DOI: 10.3233/JIFS-179671

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5841-5845, 2020

Authors: Shakir, Hina | Rasheed, Haroon | Rasool Khan, Tariq Mairaj

Article Type: Research Article

Abstract:  Machine learning methods with quantitative imaging features integration have recently gained a lot of attention for lung nodule classification. However, there is a dearth of studies in the literature on effective features ranking methods for classification purpose. Moreover, optimal number of features required for the classification task also needs to be evaluated. In this study, we investigate the impact of supervised and unsupervised feature selection techniques on machine learning methods for nodule classification in Computed Tomography (CT) images. The research work explores the classification performance of Naive Bayes and Support Vector Machine(SVM) when trained with 2, 4, 8, 12, 16 …and 20 highly ranked features from supervised and unsupervised ranking approaches. The best classification results were achieved using SVM trained with 8 radiomic features selected from supervised feature ranking methods and the accuracy was 100%. The study further revealed that very good nodule classification can be achieved by training any of the SVM or Naive Bayes with a fewer radiomic features. A periodic increment in the number of radiomic features from 2 to 20 did not improve the classification results whether the selection was made using supervised or unsupervised ranking approaches. Show more

Keywords: Quantitative imaging features, radiomic features, nodule classification, machine learning, feature selection algorithms

DOI: 10.3233/JIFS-179672

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5847-5855, 2020

Authors: Ashraf, Muhammad Waseem | Manzoor, Saher | Shahzad Sarfraz, Muhammad | Wasim, Muhammad Faisal | Ali, Basit | Akhlaq, Maham | Rujita, Ciurea | Popa, Alexandru

Article Type: Research Article

Abstract: Nano porous anodized aluminum oxide is fabricated in acidic electrolyte using two step anodization process with varied potential and etching time. Pore diameter of the fabricated membrane increases with increasing the voltage and time of etching. The rate of pore opening of the membrane is established and optimized. Morphology of the membrane is studied by SEM micrographs and quantitative analysis is done by EDX. The pore size was in the range of 85–140 nm. Also the simulation and analysis for varied parameters is done using Fuzzy Logic Controller and it was observed that the simulated and value of pore diameter calculated …using Mamdani’s model are approximately equal with minute percentage error. The AAO membrane have potential applications in biotechnology. Show more

Keywords: AAO, fuzzy analysis, pore diameter, etching time, voltage

DOI: 10.3233/JIFS-179673

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5857-5864, 2020

Authors: Sarwar, Ghulam | Ashraf, Muhammad Waseem

Article Type: Research Article

Abstract: Fuzzy logic has been considered as a viable method for every field of study because of its large number of applications and advantages. In nano-materials, structural knowledge of material and parametric estimation can be studied using fuzzy logic. The main objective of this work is to perform fuzzy logic analyis for parametric estimation of Zinc oxide (ZnO) nano-rods based structures. The zinc oxide nano-rods when prepared with doped metal that results in change in properties of Zinc oxide nano-rods. These properties include structural, optical, mechanical and electrical properties of ZnO nano-rods. The enhancement in properties make the doped ZnO based …material suitable for energy harvesting, bio-medical, energy and electronics application. Literature depicts the effect of 2nd group elements on ZnO nano-particles which directly shows the effect of change of parameters due to change in doping concentration. In this work, the analysis of effect on bandgap and rod diameter due to change in doping concentration and synthesis time is performed on ZnO nano-rods with doping 2nd group elements. The authors concluded that the synthesis time increase the rod diameter which directly decreases the bandgap. However, the doping concentration of 2nd group elements results in increase in band gap and decrease in rod diameter. However this effect is negligible for Mg and Be due to there small atomic size. The comparison between fuzzy logic simulation and mamdani model were also analysed which shows an error of less than 1% between the value. The 2nd group doped ZnO nano-rods can be used for various application due to adjustable band-gap and rod diameter with change in doping concentration. Show more

Keywords: Zinc oxide, fuzzy analysis, magnesium, calcium, beryllium, strontium

DOI: 10.3233/JIFS-179674

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5865-5875, 2020

Authors: Sodhar, Irum Naz | Jalbani, Akhtar Hussain | Buller, Abdul Hafeez | Channa, Muhammad Ibrahim | Hakro, Dil Nawaz

Article Type: Research Article

Abstract: Sentiment Analysis have also an important role in natural language processing to evaluate and analyzing the public opinion, sentiments and views about social activities such as product, services, Academic institutes, organizations etc. Lot of work has been done on English language in natural language processing. However, it is found out from the literature that still huge research gap is available for the Romanized Sindhi and there sentiment analysis in the field of natural language processing and also no any trained data is available for the testing. Classification of sentiment of Romanized Sindhi text is very difficult task. For the evaluation …of sentiment of Romanized Sindhi text easily available online Python tool were used. In this research work thousand words of Romanized Sindhi text/data were used for the sentiment classification. Also discussed issues in sentiment classification in Python tool on Romanized Sindhi text. Show more

Keywords: Sentiment analysis, natural language processing (NLP), dataset, Romanized Sindhi, Python

DOI: 10.3233/JIFS-179675

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5877-5883, 2020

Authors: Tayyaba, Shahzadi | Ashraf, Muhammad Waseem | Tariq, Muhamamd Imran | Nazir, Mohsin | Afzulpurkar, Nitin | Balas, Marius M. | Mihalache, Sanda Florentina

Article Type: Research Article

Abstract: Computers have been used in different areas of medical technology and applications. Innovation in the field of tehnology has been considered as a fundamental consitutent of medical discipline. Advancement in the field of medical and health care results in an ease for disease diagnostic, reduction in risk of diseases and lessen pain which is eventually beneficial to human life. In this work, the designing and effect of skin puncturing of micro-needle was studied. The simulations for skin puncturing was performed in ANSYS and MATLAB fuzzy logic tool. The skin puncturing using needle based on human skin coatings including dermis, stratum …corneum and viable epidermis was studied. Fuzzy logic analysis was use to study the effect of effect of applied stress and tip diameter of the needle on the three layers of skin. A 3D model of human skin layer and needle was created in ANSYS and studied for an applied force of 0.4 to 0.9 N. Thinner the tip diameter of the needle, more penetration and puncturing of skin will occur. Similarly, for applied skin, more stress is required for proper puncturing of stratum corneum layer of human skin. The microfluidic analysis performed in the CFX environment of ANSYS shows that at the driving pressure of 140 kPa, 415μ L/min flow rate has been achieved. Show more

Keywords: Fuzzy logic, ANSYS, microneedle, skin insertion

DOI: 10.3233/JIFS-179676

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5885-5895, 2020

Authors: Duddu, Vasisht | Rajesh Pillai, N. | Rao, D. Vijay | Balas, Valentina E.

Article Type: Research Article

Abstract:  Applications using Artificial Intelligence techniques demand a thorough assessment of different aspects of trust, namely, data and model privacy, reliability, robustness against adversarial attacks, fairness, and interpretability. While each of these aspects has been extensively studied in isolation, an understanding of the trade-offs between different aspects of trust is lacking. In this work, the trade-off between fault tolerance, privacy, and adversarial robustness is evaluated for Deep Neural Networks, by considering two adversarial settings under security and a privacy threat model. Specifically, this work studies the impact of training the model with input noise (Adversarial Robustness) and gradient noise (Differential Privacy) …on Neural Network’s fault tolerance. While adding noise to inputs, gradients or weights enhances fault tolerance, it is observed that adversarial robustness lowers fault tolerance due to increased overfitting. On the other hand, (ε dp , δ dp )-Differentially Private models enhance the fault tolerance, measured using generalisation error, which theoretically has an upper bound of e ε dp  - 1 + δ dp . This novel study of the trade-offs between different aspects of trust is pivotal for training trustworthy Machine Learning models. Show more

Keywords: Trustworthy machine learning, differential privacy, fault tolerance, adversarial robustness, deep learning

DOI: 10.3233/JIFS-179677

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5897-5907, 2020

Authors: Roy, Sanjiban Sekhar | Paraschiv, Nicolae | Popa, Mihaela | Lile, Ramona | Naktode, Ishan

Article Type: Research Article

Abstract: Air pollution is one of the major environmental concerns in recent time. The majority of the population in the developed world live in urban area, hence air pollution concern is even more in cities. The worst gaseous pollutants are Caron Monoxide (CO), Nitrogen dioxide (NO2) and OZONE (O3). In this paper, we propose two predictive models for estimation of concentration of gases in the air, namely Carbon Monoxide (CO), Nitrogen dioxide (NO2) and OZONE (O3). The first proposed model is a combination of linear regression and Genetic Algorithm (GA). The second proposed model estimates concentration of gasses using Multivariable Polynomial …Regression. First model uses a linear regression for prediction of concentration of gases, whereby errors like MAPE, R2 obtained by linear regression are optimized using a genetic algorithm (GA). Multivariable Polynomial Regression is adopted as a second proposed method for the prediction of concentration of same gases. A detailed comparative study has been carried out on the performances of GA and Multivariable Polynomial Regression. In addition, predictive equations are formed for CO, O3 and NO2 based on temperature, relative humidity, benzene and Nox (oxides of nitrogen). Show more

Keywords: Concentration of gases, genetic algorithm, polynomial regression, air quality

DOI: 10.3233/JIFS-179678

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5909-5919, 2020

Authors: Mundra, Ankit | Mundra, Shikha | Srivastava, Jai Shanker | Gupta, Punit

Article Type: Research Article

Abstract: Cryptography is the study of techniques which used to transforms the original text (plain text) to cipher text (non understandable text). Due to recent progress on digitized data exchange in electronic way, information security has become crucial in data storage and transmission. Some of the cryptographic algorithm has provided a promising solution which not only protects the data but also authenticates the systems and its participants, so the threat of various attacks is minimized. Nonetheless in the advancement of computing resources the cryptanalysis techniques also emerged and performing competitively in the field of information security with good results. In this …paper, we have proposed the optimized deep neural network approach for cryptanalysis of symmetric encryption algorithm 64-bit DES (Data encryption standard). Our approach has used back propagation technique with multiple hidden layers and advanced activation function also we have addressed the problem of vanishing gradient. Further, the implementation results show that we have achieved 90% accuracy which is significantly higher as compared to previous approaches. We have also compared the proposed technique with the existing ones against three parameters i.e. time, loss, accuracy. Show more

Keywords: Cryptography, encryption, decryption, plaintext, cipher text, DES

DOI: 10.3233/JIFS-179679

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5921-5931, 2020

Authors: Tariq, Muhammad Imran | Tayyaba, Shahzadi | Ali Mian, Natash | Sarfraz, Muhammad Shahzad | Hussain, Akhtar | Imran, Muhammad | Pricop, Emil | Cangea, Otilia | Paraschiv, Nicolae

Article Type: Research Article

Abstract: Fuzzy logic has wide adoption in every field of study due to its immense advantages and techniques. In cloud computing, there are many challenges that can be resolved with the help of fuzzy logic. The core objective of this paper is to analyze the application of fuzzy logic in most demanding research areas of cloud computing. We also analyzed the fuzzy methods that were used in the solving of problems relates to cloud computing. A systematic literature review was conducted to enlist the all the challenging areas of research relates to cloud computing, categorized the most critical and challenging areas …of cloud research, studied existing problem-solving techniques of each challenging cloud area, and finally studied the application of fuzzy logic in each aforementioned areas to redress different problems. The authors concluded that fuzzy logic can be used in every area of research including cloud computing to solve the problems and optimized the performance, as well as fuzzy logic techniques, were opted by many cloud computing researchers to conduct their study to optimize the performance of the system. Show more

Keywords: Cloud computing, fuzzy logic, scheduling algorithms, mobile cloud computing, fuzzy logic applications

DOI: 10.3233/JIFS-179680

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5933-5947, 2020

Authors: Mundra, Ankit | Mundra, Shikha | Verma, Vivek Kumar | Srivastava, Jai Shankar

Article Type: Research Article

Abstract: Stock market analysis or stock price prediction is aimed at predicting firm’s profitability based on current as well as historical data. From recent studies it is observed that machine learning approaches have outperformed traditional statistical methods in predictive analysis task. In our work we have analyzed time series data as prediction of stock price depends on historical variation in prices of stocks. To enhance the prediction accuracy, we have proposed a hybrid approach which is based on the concept of support vector machines (SVM) and Long Short-Term Memory (LSTM) as these algorithms are performing better in time series problem. On …applying proposed approach onto the TATA Global Beverages stock dataset, we have observed prediction accuracy of ninety seven percent which is outperforming, along with this to enhance the performance author have presented some observation like relative importance of the input financial variables and differences of determining factors in market comparative predictive analysis onto the experimentation dataset. Show more

Keywords: SVM, LSTM, back propagation, RNN, machine learning

DOI: 10.3233/JIFS-179681

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5949-5956, 2020

Authors: Touqeer, Muhammad | Jabeen, Salma | Irfan, Rida

Article Type: Research Article

Abstract:  Multi-criteria decision making (MCDM) problems have been solved involving various types of fuzzy sets. We know that interval type-2 fuzzy sets (IT2FSs) are the most representative known fuzzy sets since they have the ability to capture both type of linguistic uncertainties associated with a word namely, the intra-personal and inter-personal uncertainties respectively. Here for MCDM problems, we will use the three trapezoidal fuzzy numbers (TT2FNs) which are more effective in capturing the uncertainty than IT2FSs, just like triangular fuzzy numbers has a better representational power than simple interval numbers. Moreover, Entropy method is employed for evaluating the values of unknown …attribute weights. The ranking method employed here is the grey correlation projection method (GRPM), obtained by joining grey relational method (GRM) and projection method (PM) respectively. Lastly an example will be given to check the productivity of the suggested method. Show more

Keywords: Multi-criteria decision making (MCDM), three trapezoidal fuzzy number (TT2FN), Entropy method (EM), grey correlation projection method (GRPM)

DOI: 10.3233/JIFS-179682

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5957-5967, 2020

Authors: Touqeer, Muhammad | Shaheen, Kiran | Irfan, Rida

Article Type: Research Article

Abstract: Here we have employed three trapezoidal fuzzy numbers (TT2FNs) to deal with a multi-criteria decision making (MCDM) problems. The introduced technique takes into consideration the left and right areas of the three types of membership memberships involved in TT2FNs and also considers the risk attitude of decision maker. The presented method is more generalized since we have used TT2FNs, which are more effective in capturing uncertainty than IT2FSs, just like triangular fuzzy numbers has a better representational power than simple interval numbers. We have considered the unknown attribute environment where maximizing deviation method has been employed to evaluate the attribute …weights. Moreover, evaluation model for manufacturing plants with linguistic information has been provided as an illustrative example for the justification of the proposed technique. Show more

Keywords: MCDM, IT2 fuzzy set (IT2FS), maximizing deviation, generalized fuzzy number, three trapezoidal fuzzy numbers (TTFNs)

DOI: 10.3233/JIFS-179683

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5969-5978, 2020

Authors: Touqeer, Muhammad | Hafeez, Abid | Arshad, Misbah

Article Type: Research Article

Abstract: Here we present a method to deal with multi-attribute decision making(MADM)problems when the attribute values are modeled in the form of interval type-2 trapezoidal fuzzy numbers (IT2FNs), and the attribute weights are completely unknown. Grey Relation Projection Method (GRPM), which is a combination of “grey relational analysis method" and the “projection method" is employed for ranking the alternatives. The linguistic information is modeled in the form of “interval type-2 trapezoidal fuzzy numbers" (IT2TFN) which are able to capture both the intra personal and inter personal uncertainties associated with a linguistic term. Information Entropy Method (IEM) is used for calculating unknown …attribute weights. Lastly, an illustrative example is provided as a verification of the developed approach. Show more

Keywords: Multi-attribute decision making (MADM), interval type-2 trapezoidal fuzzy number (IT2TFN), Information Entropy method (IEM), grey relational projection method (GRPM)

DOI: 10.3233/JIFS-179684

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5979-5986, 2020

Authors: Gupta, Punit | Goyal, Mayank Kumar | Mundra, Ankit | Tripathi, Rajan Prasad

Article Type: Research Article

Abstract: Technology has enabled us to carry the world on our tips. Cloud computing has majorly contributed to this by providing infrastructure services on the go using pay per use model and with high quality of services. Cloud services provide resources through various distributed datacenters and client requests been fulfilled over these datacenters which act as resources. Therefore, resource allocation plays an important role in providing a high quality of service like utilization, network delay and finish time. Biogeography-based optimization (BBO) is an optimization algorithm that is an evolutionary algorithm used to find optimized solution. In this work BBO algorithm is …been used for resource optimization problem in cloud environment at infrastructure as a service level. In past several task scheduling algorithms are being proposed to find a global best schedule to achieve least execution time and high performance like genetic algorithm, ACO and many more but as compared to GA, BBO has high probability to find global best solution. Existing solutions aim toward improving performance in term of power execution time, but they have not considered network performance and utilization of the systems performance parameters. Therefore, to improve the performance of cloud in network-aware environment we have proposed an efficient nature inspired BBO algorithm. Further, the proposed approach takes network overhead and utilization of the system into consideration to provide improved performance as compared to ACO, Genetic algorithm as well as with PSO. Show more

Keywords: BBO, cloud infrastructure, meta-heuristic, resource allocation

DOI: 10.3233/JIFS-179685

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5987-5997, 2020

Authors: Gal, Viviane | Banerjee, Soumya | Rad, Dana V.

Article Type: Research Article

Abstract: Pattern of emotion identification is one of the improvised research application regarding facial expression as major concern, in those cases, conventional facial expressions for patterns identification. The present model is based on signal collected from physiological sensors followed by consecutive deployment of unsupervised machine learning model. The proposed model is unsupervised in following aspects: firstly, it introduces Expectation Maximization problem with respect to unknown emotion labels to be derived from the measures. Correlation of physiological signal and individual emotion labels can be identified. This follows a considerable emotion classification method. However, the output of EM model doesn’t ensure the …correct identification of emotion class, if any. We introduce Support Vector Regression (SVR) as output module of this model. Hence, we try to forecast the probable classes of emotion after investigating the ranges of values and appropriate standard threshold values of physiological signal with respect to respective emotion class e.g. angry, frustration and joy. This should be noted that, the proposed model doesn’t envisage facial expression analysis. However, after successful implementation of Gaussian behaviors of mixed physiological signal, we can enhance the accuracy of identification. Significant emotional context exists in output with more precise results of emotion identification phases. Show more

Keywords: Deep learning, emotion pattern, hybrid model, physiological sensors, unsupervised learning

DOI: 10.3233/JIFS-179686

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5999-6017, 2020

Authors: Nath, Malaya Kumar | Dandapat, Samarendra | Barna, Cornel

Article Type: Research Article

Abstract:  Here, an unique approach is presented for automatic detection of blood vessels and estimation of retinal disorder from color fundus image. This technique can be used to determine the progression of retinal disorders due to diabetic retinopathy, which can help in better evaluation and treatment for clinical purposes. The proposed method combines the Gaussian based matched filter with Kirsch for extraction of blood vessels, and inpainting technique to determine the pathologically affected region. This is tested on various databases (such as: DRIVE, Aria and Glaucoma etc.,). Various performance measures (such as: accuracy, sensitivity, specificity and F-score etc.,) are used to …estimate the quality of blood vessels detection. Here, we have applied the segmentation technique to the subband-2 image in 5-level wavelet decomposition by db4 mother wavelet. This reduces the computational time for inpainting. Comparing the blood vessels and the pathologies, index for blood vessel damage is calculated. This index is proportional to retinal damage in case of diabetic retinopathy. Higher index corresponds to significant amount of blood vessel damage. From the index, progression of the disease and condition of the retina can be assessed. The index for blood vessel damage for Im-24 is 2.98%, whereas for Im-18 is 68.78%. This indicates that in Im-18 more blood vessels are affected by pathologies. It also indicates that maximum portion of the retina is affected by pathology. Show more

Keywords: Retinal vascular disorder, matched filter, inpainting, F-score, wavelet decomposition

DOI: 10.3233/JIFS-179687

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6019-6030, 2020

Authors: Naga Srinivasu, P. | Srinivasa Rao, T. | Dicu, Anca M. | Mnerie, Corina Anca | Olariu, Iustin

Article Type: Research Article

Abstract: MRI image segmentation, a challenging task in medical diagnostics aids in extracting the summarized information of the anatomy of the human brain, thereby offering the potentiality for accurate treatment of the disease. The purpose of this work is to elevate the performances of different optimization techniques that are used in automated segmentation procedures. The performance of four algorithms was evaluated quantitatively over the genetic algorithm-based segmentation, which is the prevailing approach in automated segmentation. The upshot exhibits the accuracy and performance of various optimization techniques with a genetic algorithm.

Keywords: Genetic algorithm, harmonic mean, magnetic resonance imaging, SVM, PSO, TLBO

DOI: 10.3233/JIFS-179688

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6031-6043, 2020

Authors: Farooq, Umar | Gu, Jason | Asad, Muhammad Usman | Abbas, Ghulam | Hanif, Athar | Balas, Marius

Article Type: Research Article

Abstract: This paper proposes an online algorithm for identifying the nonlinear dynamical systems and is termed as neo-fuzzy based brain emotional learning plant identifier (NFBELPI). As the name suggests, the proposed identifier is a combination of brain emotional learning network and neo-fuzzy neurons. The integration of these two networks is realized in a way that retains the characteristics of both the networks while an enhanced performance is achieved at the same time. Precisely, the orbitofrontal cortex section of the brain emotional learning network is fused with neo-fuzzy neurons with a view to equip it with more knowledge than does the amygdala …section possesses. The proposed identifier accepts n -input and m -output samples to generate an estimate of the plant output and employs a brain emotional learning algorithm to lower the estimation error by adjusting a total of ((n  + m  + 1) × p ) + (n  + m  + 2) weights, with p being the number of neo-fuzzy neurons. The proposal is validated in a MATLAB programming environment using a simulated Narendra dynamical plant as well as against the data recorded from real forced duffing oscillator. Comparison with a brain emotional learning plant identifier (BELPI) and some other state-of-the art identifiers in terms of root mean squared error (RMSE) criterion reveals the improved performance of the proposed identifier. Show more

Keywords: System identification, brain emotional learning, neo-fuzzy neurons, MATLAB

DOI: 10.3233/JIFS-179689

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6045-6051, 2020

Authors: Thang, Tran Ngoc | Solanki, Vijender Kumar | Dao, Tuan Anh | Thi Ngoc Anh, Nguyen | Van Hai, Pham

Article Type: Research Article

Abstract: In this article, we use a monotonic optimization approach to propose an outcome-space outer approximation by copolyblocks for solving strictly quasiconvex multiobjective programming problems which include many classes of captivating problems, for example when the criterion functions are nonlinear fractional. After the algorithm is terminated, with any given tolerance, an approximation of the weakly efficient solution set is obtained containing the whole weakly efficient solution set of the problem. The algorithm is proved to be convergent and it is suitable to be implemented in parallel using convex programming tools. Some computational experiments are reported to show the accuracy and efficiency …of the algorithm. Show more

Keywords: Multiobjective programming, monotonic optimization, strictly quasiconvex, outcome space, outer approximation

DOI: 10.3233/JIFS-179690

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6053-6063, 2020

Authors: Deb, Dipankar

Article Type: Research Article

Abstract: For decentralized biomass plants using farm residue as input, the economic viability study are available in literature. The study calculates the different aspects of the cost of biomass power plants such as the cost of capital investment, running and maintenance of plants, residue processing costs, and the economic value equivalent of embedded nutrients (NPK) that are lost from residues. A comparison is drawn between the costs and revenue from the plant’s power generation. The word modest is used for giving analogy to the power plant which has capacity higher than decentralized power plants and lower than centralized power plants. In …Gujarat there exists total four biomass power plants which should be considered in the modest category. The aim is to establish the cost efficiency of adding soil nutritious chemical fertilizers and using agricultural residues for the purposes of energy-building biomass (rather than incorporating soil). To study economic viability, the discounted rate method is used. In our paper, a modest / centralized biomass power plant has been studied for economic feasibility. For this, we have selected the state of Gujarat and followed district-wise analysis. We have used the penalty method given in optimization research in order to calculate transportation and other costs. Also, we studied a soil nutrition index for measuring soil health and the proportions of N, P and K in the soil. For annual crop production, the data used is district-wise for Gujarat state for the year 2011-12. Show more

Keywords: Agricultural residue, Biomass energy, Soil incorporation, Centralized basis, NPK

DOI: 10.3233/JIFS-179691

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6065-6074, 2020

Authors: Tariq, Muhammad Imran | Tayyaba, Shahzadi | Ali Mian, Natash | Sarfraz, Muhammad Shahzad | De-la-Hoz-Franco, Emiro | Butt, Shariq Aziz | Santarcangelo, Vito | Rad, Dana V.

Article Type: Research Article

Abstract: The organizations utilizing the cloud computing services are required to select suitable Information Security Controls (ISCs) to maintain data security and privacy. Many organizations bought popular products or traditional tools to select ISCs. However, selecting the wrong information security control without keeping in view severity of the risk, budgetary constraints, measures cost, and implementation and mitigation time may lead to leakage of data and resultantly, organizations may lose their user’s information, face financial implications, even reputation of the organization may be damaged. Therefore, the organizations should evaluate each control based on certain criteria like implementation time, mitigation time, exploitation time, …risk, budgetary constraints, and previous effectiveness of the control under review. In this article, the authors utilized the methodologies of the Multi Criteria Decision Making (MCDM), Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to help the cloud organizations in the prioritization and selection of the best information security control. Furthermore, a numerical example is also given, depicting the step by step utilization of the method in cloud organizations for the prioritization of the information security controls. Show more

Keywords: Information security, Analytical Hierarchy Process, TOPSIS, fuzzy logic, MCDM, MADM

DOI: 10.3233/JIFS-179692

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6075-6088, 2020

Authors: Iqbal, Shafqat | Zhang, Chongqi | Arif, Muhammad | Hassan, Munawar | Ahmad, Shakeel

Article Type: Research Article

Abstract: Time series is a classification of data series of variables, orderly arranged with respect to time. In time series analysis, forecasting is the vital area of study besides other meaningful characteristics of the data. It has vast application in decision-making and prediction in the domain of economics, agriculture, medicine, industries, energy sector and other sciences. Fuzzy time series emerged as a robust tool to cater for historical data in linguistic values. This paper proposes the new method of fuzzy time series forecasting based on the approach of fuzzy clustering and information granules integrated with the weighted average approach to deal …with the uncertainty in data series. To distinguish the power of modeling and prediction, the strategies of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are utilized as a criterion. Findings illustrate that proposed fuzzy based time series approach is vigorous to compute the accurate estimates. Show more

Keywords: Fuzzy set, fuzzy c-means clustering, information granules, fuzzy time series, forecasting

DOI: 10.3233/JIFS-179693

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6089-6098, 2020

Authors: Datta, Rupak | Dey, Rajeeb | Saravanakumar, Ramasamy | Bhattacharya, Baby | Lin, Tsung-Chih

Article Type: Research Article

Abstract:  This paper deals with global delay-range-dependent (DRD) stability analysis for Takagi–Sugeno (T–S) fuzzy Hopfield neural network with time-varying delays. First, we proposes a new Lyapunov-Krasovskii functional (LKF) by incorporating the information of activation function, the lower and upper bound of time delay. Further, to achieve the larger delay bound results and approximating the derivatives of LKFs, Wirtinger based inequality (WBI) together with reciprocal convex lemma (RCL) is being utilized. As a result some DRD global stability conditions for the system under consideration with less conservatism are derived in an linear matrix inequality (LMI) framework. Three numerical examples are presented in …this work to exhibit the efficacy of the proposed stability criterion over the recent existing results. Show more

Keywords: Delay-range-dependent stability, takagi–sugeno fuzzy model, Hopfield neural networks, LKF, LMI

DOI: 10.3233/JIFS-179694

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6099-6109, 2020

Authors: Tamasila, Matei | Prostean, Gabriela | Ivascu, Larisa | Cioca, Lucian-Ionel | Draghici, Anca | Diaconescu, Andra

Article Type: Research Article

Abstract: The present study aims at identifying the main drivers underlying the development of municipal solid waste management (MSWM) so as to ensure an effective enhancement of the current waste management system and significantly improved recycling rates. Based on the factors identified in the qualitative evaluation of the deployed statistics and using fuzzy multi-criteria decision-making (MCDM), these factors are hierarchized and the competitive strategic alternative is selected/customized. The judgment of eight experts from the eight major regions of Romania has been used in the applied fuzzy AHP and TOPSIS methodologies. The robustness of the results is analyzed against the sensitivity analysis. …Following the sensitivity analysis, the alternatives retained their rank so that the eight experts’ assessments have been validated. By developing a sustainable MSWM, it is claimed that the recycling rate in Romania will increase. Show more

Keywords: Municipal solid waste management (MSWM), selective collection, recycle, fuzzy analytic hierarchy process

DOI: 10.3233/JIFS-179695

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6111-6127, 2020

Authors: Lin, Yu-Chen | Nguyen, Ha Ly Thi | Balas, Valentina Emilia | Lin, Tsung-Chih | Kuo, I-Chun

Article Type: Research Article

Abstract: This paper presents an adaptive prediction-based control scheme for developing an ecological cruise control (ECC) system when simultaneous running on the roads with the continuous curves and up-down slopes, which aims to guarantee the driving safety, riding comfort of a vehicle and enhance its fuel efficiency as well. This study is mainly divided into two parts: vehicle longitudinal speed control and vehicle lateral stability control. Firstly, based on GPS data, topographical information, and a fuel consumption model, the prediction based on approximate dynamic programming (ADP) using adaptive fuzzy neural networks (FNNs) is employed to calculate appropriate control signal required for …ecological driving. Secondly, to maintain the vehicle stable on the curved road, the vehicle lateral stability control is presented utilizing sliding mode control approach. The effectiveness of the proposed ECC system, in view of stability, improved riding comfort and fuel efficiency, has been validated on the CarSim environment. Show more

Keywords: Ecological cruise control (ECC) system, continuous curve, up-down slope, driving safety, fuel efficiency, approximate dynamic programming (ADP)

DOI: 10.3233/JIFS-179696

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6129-6144, 2020

Authors: Arif, Muhammad | Wang, Guojun | Peng, Tao | Balas, Valentina Emilia | Geman, Oana | Chen, Jianer

Article Type: Research Article

Abstract: Vehicular Ad-hoc Networks (VANETs) have unique capabilities and are associated with Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications to reduce traffic dynamics and provide support to the providers and the drivers to rely on security and communication alerts. Our proposed method is only supported by V2V and V2I communications and neighbourhood localization records, which are summarized in an adaptive adjustment of the forwarded information related to the messages(data communication). We optimize these message information, and only the relevant information is transmitted to the relevant vehicles, and all other unnecessary information is terminated. Because, irrelevant information causes many problems, like unnecessary resource …utilization and energy consumption. In this case, the driver will be promptly reminded to take appropriate measures. A swarm intelligence algorithm such as the Artificial Bee Colony (ABC) is utilized to optimize the communication mechanism. In this paper, we present a high-level framework for ABC based on the fuzzy logic for the VANETs. We prove the validity and efficiency of the proposed method. Our proposed method achieves higher optimization accuracy in information broadcasting with minimum delay and with the lowest energy consumption. Show more

Keywords: Communication, vehicles, optimization, fuzzy logic, ad-hoc network, artificial bee colony

DOI: 10.3233/JIFS-179697

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6145-6157, 2020

Authors: Yoseph, Fahed | Ahamed Hassain Malim, Nurul Hashimah | Heikkilä, Markku | Brezulianu, Adrian | Geman, Oana | Paskhal Rostam, Nur Aqilah

Article Type: Research Article

Abstract: Targeted marketing strategy is a prominent topic that has received substantial attention from both industries and academia. Market segmentation is a widely used approach in investigating the heterogeneity of customer buying behavior and profitability. It is important to note that conventional market segmentation models in the retail industry are predominantly descriptive methods, lack sufficient market insights, and often fail to identify sufficiently small segments. This study also takes advantage of the dynamics involved in the Hadoop distributed file system for its ability to process vast dataset. Three different market segmentation experiments using modified best fit regression, i.e., Expectation-Maximization (EM) and …K-Means++ clustering algorithms were conducted and subsequently assessed using cluster quality assessment. The results of this research are twofold: i) The insight on customer purchase behavior revealed for each Customer Lifetime Value (CLTV) segment; ii) performance of the clustering algorithm for producing accurate market segments. The analysis indicated that the average lifetime of the customer was only two years, and the churn rate was 52%. Consequently, a marketing strategy was devised based on these results and implemented on the departmental store sales. It was revealed in the marketing record that the sales growth rate up increased from 5% to 9%. Show more

Keywords: Market segmentation, data mining, customer lifetime value (CLTV), RFM model (recency frequency monetary)

DOI: 10.3233/JIFS-179698

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6159-6173, 2020

Authors: Lin, Tsung-Chih | Li, Cheng-You | Chen, Pin-Fan | Chen, Wei-Kai | Dey, Rajeeb | Balas, Marius M. | Olariu, Teodora | Wong, Wai-Shing

Article Type: Research Article

Abstract: This paper presents an identifier based intelligent adaptive fuzzy control scheme with regulating blood glucose concentration in normoglycemic level of 70 mg/dl for type 1 diabetic patients. The identifier is built with fuzzy neural network (FNN) to predict the blood glucose concentration of the diabetic patient. The fuzzy based controller with generic operating regimes which cluster all the adaptive control rules is designed to robustly reject the multiple meal disturbances resulting from food intake and deal with the parametric uncertainties in model and measurement noise. All the parameters of the FNN and of the fuzzy logic system are tuned by backpropagation …(BP), to achieve the control objectives. The numerical simulations are performed to show that the set point tracking, meal disturbances and measurement noise rejection can be realized within this method. Show more

Keywords: Adaptive control, glucose-insulin, diabetes, FNN, Backpropagation

DOI: 10.3233/JIFS-179699

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6175-6184, 2020

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6185-6185, 2020

Authors: Thampi, Sabu M. | El-Alfy, El-Sayed M. | Trajkovic, Ljiljana

Article Type: Editorial

DOI: 10.3233/JIFS-179700

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6187-6191, 2020

Authors: Ajeena Beegom, A.S. | Chinmayan, P.

Article Type: Research Article

Abstract: In natural language processing, the problem of finding the intended meaning or “sense” of a word which is activated by the use of that word in a particular context is generally known as word sense disambiguation (WSD) problem. The solution to this problem impacts many other fields of natural language processing including sentiment analysis and machine translation. Here, WSD problem is modelled as a combinatorial optimization problem where the goal is to find a sequence of meanings or senses that maximizes the semantic meaning among the targeted words. In this work, an algorithm is proposed that uses a combinatorial version …of particle swarm optimization algorithm for solving WSD problem. The test results show that the algorithm performs better than existing methods. Show more

Keywords: Word sense disambiguation, particle swarm optimization, knowledge-based approach, combinatorial PSO

DOI: 10.3233/JIFS-179701

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6193-6200, 2020

Authors: Akhtar, Nadeem | Sufyan Beg, M.M. | Hussain, Md. Muzakkir

Article Type: Research Article

Abstract:  Most extractive multi-document summarization (MDS) methods relies on extraction of content relevant sentences ignoring sentence relationships. In this work, we propose a unified framework for extractive MDS that also considers sentence relationships. We argue that adding a sentence to the summary increases summary score by relevance score of the new sentence plus some additional score which depends on the relationships of new sentence with other summary sentences. The quantification of additional score depends on how coherent the new sentence is with respect to the existing sentences in the summary. Simultaneously, some score is decreased from the summary score due to …the redundancy which depends on overlap between new and existing summary sentences. To find the exact solution, sentence extraction problem is modeled as integer linear problem. The sentence relevance score is found using content and surface features of the sentence using topic model and regression framework. To find the relative coherence score, transition probabilities in the entity grid model are used. Redundancy between sentences is found using support vector regression that uses sentence overlapping features. The proposed method is evaluated on DUC datasets over query based multi-document summarization task. DUC 2006 dataset is used as training and development set for tuning parameters. Experimental results produce ROUGE score comparable to the state-of-the-art methods demonstrating the effectiveness of the proposed method. Show more

Keywords: Multi-document summarization, topic model, support vector regression, entity grid, rouge

DOI: 10.3233/JIFS-179702

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6201-6210, 2020

Authors: El-Alfy, El-Sayed M. | Al-Azani, Sadam

Article Type: Research Article

Abstract:  With the proliferation of social media and mobile technology, huge amount of unstructured data is posted daily online. Consequently, sentiment analysis has gained increasing importance as a tool to understand the opinions of certain groups of people on contemporary political, cultural, social or commercial issues. Unlike western languages, the research on sentiment analysis for dialectical Arabic language is still in its early stages with several challenges to be addressed. The main goal of this study is twofold. First, it compares the performance of core machine learning algorithms for detecting the polarity in imbalanced Arabic tweet datasets using neural word embedding …as a feature extractor rather than hand-crafted or traditional features. Second, it examines the impact of using various oversampling techniques to handle the highly-imbalanced nature of the sentiment data. Intensive empirical analysis of nine machine learning methods and six oversampling methods has been conducted and the results have been discussed in terms of a wide range of performance measures. Show more

Keywords: Social network, sentiment analysis, polarity detection, word embedding, machine learning, imbalanced dataset, Arabic tweets

DOI: 10.3233/JIFS-179703

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6211-6222, 2020

Authors: Sanagar, Swati | Gupta, Deepa

Article Type: Research Article

Abstract: Sentiment analysis research has evolved over the years to extract relevant information from opinionated raw text. Sentiment lexicon is a compiled list of sentiment words and a core component of sentiment analysis tasks. These words play a key role in domain adaptation. Domain adaptation is challenging due to variation in sentiments across the domains. We propose a solution to this research problem by presenting a genre-level sentiment lexicon adaptation approach. The model uses a language domain sense to represent the genre pertaining to the distinct characteristics of the communicated text. The approach addresses the generalization of knowledge at the genre …level by learning the multi-source domain lexicon for the selected source domains. The novelty of our approach lies in the genre level relevancy of the source lexicon to the target domains. The model uses unlabeled training data for the source and target domain sentiment lexicon learning. The lexicon adaptation is demonstrated on a long list of target domains that address the three domain adaptation challenges. Experimental results have proved that the model learns the relevant scores and polarities of sentiment words, in addition, it identifies new domain-based sentiment words. The model is evaluated in comparison with standard baselines. Show more

Keywords: Lexicon adaptation, sentiment lexicon, domain adaptation, multiple source, transfer learning, sentiment analysis

DOI: 10.3233/JIFS-179704

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6223-6234, 2020

Authors: Richa, | Bedi, Punam

Article Type: Research Article

Abstract: Group recommender system provides suggestions for a group of users by exploring the choices of individual users of the group. Popularity of group recommender systems is increasing because many activities such as listening to music, watching movies, traveling, etc. are normally performed in groups rather individually. Group recommender systems like personal recommender systems also suffer from cold start and sparsity issues. The cold start and sparsity issues result into inaccurate recommendation computation which degrades the recommendation quality. To handle the cold start and sparsity issues in a Group Recommender System (GRS), this paper proposes to use cross domain approach and …introduces Cross Domain Group Recommender System (CDGRS). The recommendations provided by trustworthy and reputed users in the group enhance the acceptance towards the presented recommendations as compared to the other individuals in the group. We have combined the social factors e.g. trust and reputation to get influential user in the group recommendation. A prototype of the system is developed for tourism domain that incorporates four sub-domains i.e. restaurants, hotels, tourist places and shopping places. The performance of CDGRS is compared with GRS. Spearman’s Correlation Coefficient, MAE, RMSE, Precision, Recall and F-measure are used to find the accuracy of the generated recommendations. Show more

Keywords: Recommender system, cross domain, group recommender system, multi-agent system, user influence

DOI: 10.3233/JIFS-179705

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6235-6246, 2020

Authors: Sujitha, P. | Simon, Philomina

Article Type: Research Article

Abstract: For the last three decades human activity recognition has shown a huge technological advancement due to less expensive RGB-D cameras and the increase in the large volume of video data. As a result of the increase in number of surveillance cameras, manual annotation becomes difficult and need for automatic recognition and annotation of video arises. In this paper, we introduce a computationally and storage efficient method for recognizing human activities from depth videos and a new frame selection method based on the mean value of motion energy. We extract normal vectors from the points in the boundary curve. Then polynormals …are obtained by sequentially attaching the normals from a neighborhood of each of the points in the boundary curve. These polynormals from a spatio-temporal cuboid constructed from the input video and it is pooled to form the Super Normal vectors. These Super Normal vectors are the final feature vectors, which are given as input to the classifier. The classifier used is lib-linear SVM. The results on MSRAction3D dataset show that the algorithm we put forward is fast and the accuracy obtained is comparable with the existing methods. The method which we proposed here gives an accuracy of 88% while taking whole frames and 89.82% when frame selection method is applied. The proposed method is also tested on UTD-MHAD dataset. Show more

Keywords: Motion energy, depth videos, frame selection, boundary curves, polynormal, dictionary learning

DOI: 10.3233/JIFS-179706

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6247-6255, 2020

Authors: Alpana, | Chand, Satish

Article Type: Research Article

Abstract: Coal is a primary natural resource of fuel that is efficiently used for electricity generation, steel or iron production, and household usage. Characterization is needed for industries to understand the quality of coal before shipping. Currently, industries follow chemical, microscopical, and machine-based analysis as the gold standard for coal characterization. These conventional analyses of coal are an indispensable method over the years and have tested by high skilled petrologists. Though, these types of optical or machine-dependent recognition of coal category samples are quite slow, expensive, and restricted by subjective analyses with less accuracy. The main aim of this research is …to propose an accurate, time, and cost-effective machine learning-based automated characterization system by analyzing coal color and textural features. This paper comes up with a quantitative approach toward the characterization of dissimilar types of coal for better utilization in industries. The proposed ensemble learning coal characterization method provides an accuracy of around 97% and takes less computational time than conventional methods. Hence, the proposed automated coal characterization system provides support to industries in the development of computer-aided assessment of coal category samples. Show more

Keywords: Coal, HSV, GLCM, image processing, machine learning

DOI: 10.3233/JIFS-179707

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6257-6267, 2020

Authors: Ramachandran, Sivakumar | Kochitty, Shymol | Vinekar, Anand | John, Renu

Article Type: Research Article

Abstract: The identification of landmark features such as optic disc is of high prognostic significance in diagnosing various ophthalmic diseases. A retinal fundus photograph provides a non-invasive observation of the optic disc. The wide variability present in fundus images poses difficulties in its detection and further analysis. The reported work is a part of the fundus image screening for the diagnosis of Retinopathy of Prematurity (ROP), a sight threatening disorder seen in preterm infants. The diagnostic procedure for this disease estimates blood vessel tortuosity in a pre-defined area around the optic disc. Hence accurate optic disc localization is very important for …the disease diagnosis. In this paper, we present an optic disc localization technique using a deep neural network based framework. The proposed system relies on the underlying architecture of YOLOv3, a fully convolutional neural network pipeline for object detection and localization. The new approach is tested in 10 different data sets and has achieved an overall accuracy of 99.25%, outperforming other deep learning-based OD detection methods. The test results guarantees the robustness of the proposed technique, and hence may be deployed to assist medical experts for disease diagnosis. Show more

Keywords: Optic disc, deep learning, convolutional neural network, retinal images, ROP diagnosis

DOI: 10.3233/JIFS-179708

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6269-6278, 2020

Authors: Shiji, T. P. | Remya, S. | Lakshmanan, Rekha | Pratab, Thara | Thomas, Vinu

Article Type: Research Article

Abstract: Intelligent lesion detection system for medical ultrasound images are aimed at reducing physicians’ effort during cancer diagnosis process. Automatic separation and classification of tumours in ultrasound images is challenging owing to the low contrast and noisy behavior of the image. A Computer aided detection (CAD) system that automatically segment and classify breast tumours in ultrasound (US) images is proposed in this paper. The proposed method is invariant to scale changes and does not require an operator defined initial region of interest. Wavelet modulus maxima points of the US image are analyzed to extract the tumour seed point. The lesions segmented …using a region-based approach are classified using a support vector machine (SVM) classifier. Evaluation of various performance measures show that the performance of the proposed CAD system is promising. Show more

Keywords: Breast ultrasound, Shearlet transform, tumour detection, wavelet modulus maxima, SVM classifier

DOI: 10.3233/JIFS-179709

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6279-6290, 2020

Authors: Ravikumar, Sourav | Vinod, Dayanand | Ramesh, Gowtham | Pulari, Sini Raj | Mathi, Senthilkumar

Article Type: Research Article

Abstract:  Human-Elephant Conflict (HEC) and its mitigation have always been a serious conservation issue in India. It occurs mainly due to the encroachment of forests by humans as part of societal development. Consequently, these human settlements are highly affected by the intrusion of wild elephants as they cause extensive crop-raiding, injuries and even death in many cases. HEC is a growing problem in rural areas of India which shares a border with forests and other elephant habitats. Based on the studies, it is very explicit that HEC is an important conservation issue which affects the peaceful co-existence of both humans and …elephants near the forest areas. The desirable solution for this problem would be to facilitate co-existence among humans and elephants, but this often fails because of technical difficulties. Hence, this paper presents an end-to-end technological solution to facilitate smoother coexistence of humans and elephants. The proposed work deploys a live video surveillance system along with deep learning strategies to effectively detect the presence of elephants. From the numerical analysis, it is revealed that the post-training accuracy of the deep learning model used in the proposed approach is evaluated at 98.7% and outperforms an an out-of-the-box image detector. The layered approach used in the proposed work improves resource management which is a major bottleneck in real-time deployment scenarios. Show more

Keywords: Human elephant conflict, machine learning, convolutional neural network, support vector machine

DOI: 10.3233/JIFS-179710

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6291-6298, 2020

Authors: Jyotsna, C. | Amudha, J. | Rao, Raghavendra | Nayar, Ravi

Article Type: Research Article

Abstract: Trail making test is a cognitive impairment test used for understanding the visual attention during the visual search task. The classical paper pencil method measures the completion time of the participant and there was no mechanism for comparison across the participant with similar feature. The psychologist has to observe the reactions of the participants during the trial process and there is no mechanism to capture it. This study made an attempt to resolve the above problem and tried to infer additional parameters which can support psychologist to understand the participant performance in trail making test. The insight provided by the …approach is to extract various features which helps a psychologist by providing individual profiling and group profiling of a person and can understand the group of people who show similar cognitive impairment while performing trail Making Test. The proposed Intelligent Gaze Tracking approach could classify the participant into three different groups like low, high and medium cognitive impairment based on the extracted gaze features. The proposed approach has been compared across existing literature survey to significantly show the advantage of the system in terms of identifying the people with similar characteristics in terms of cognitive impairment. Show more

Keywords: Eye tracking, cognitive impairment, trail making test, area of interest, scanpath, fixation, adaptive neuro fuzzy inference system, k-means clustering

DOI: 10.3233/JIFS-179711

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6299-6310, 2020

Authors: Amudha, J. | Divya, K.V. | Aarthi, R.

Article Type: Research Article

Abstract: Top–down influences play a major role in the primate’s visual attention mechanism. Design of top-down influences for target search problems is the recommended approach to develop better computational models. Existing top down computational visual attention models mainly exploit three factors namely the context information, target information and task demands. Here in this paper we propose a Fuzzy based System for Target Search (FSTS) which makes use of target information as the top-down factor. The system uses Fuzzy logic to predict the salient locations in an image based on the prior information about a target object to be detected in a …scene or frame. The performance of the system was analysed using multiple evaluation parameters and is found to have a better average hit number, number of first hits and elapsed CPU time than the existing system. The saliency map comparison is performed with human eye fixation map and is found to predict the human fixations with better accuracy than existing systems. Show more

Keywords: Visual attention, saliency, regions of interest, fuzzy system, computer vision

DOI: 10.3233/JIFS-179712

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6311-6323, 2020

Authors: Navdeep, | Singh, Vijander | Rani, Asha | Goyal, Sonal

Article Type: Research Article

Abstract: This paper presents an improved hyper smoothing function based methodology for efficient edge detection. The main aim of this work is to obtain localized edges of noisy and blurred images without duplicate ones and integrating them into meaningful object boundaries. Therefore, logarithmic hyper-smoothing function is introduced in local binary pattern leading to improved hyperfunction based local binary pattern (IHLBP) algorithm. The proposed technique uses an improved counting scheme to correctly evaluate the number of image points having pixel value greater than or equal to the central pixel. The IHLBP algorithm is tested on synthetic images, radiography images, real-life pictures from …USC-SIPL and BSDS database. Improved local binary pattern (ILBP), hyper local binary pattern (HLBP), Canny and Sobel methods are also used for comparative analysis. The results reveal that the proposed algorithm performs well on all synthetic and real images in the presence of blur and salt & pepper noise. Thus IHLBP proves to be an effective approach for edge detection in comparison to conventional methods. Show more

Keywords: Edge detection, digital radiography images, real images, noise images

DOI: 10.3233/JIFS-179713

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6325-6335, 2020

Authors: Jahnavi, B. Sai | Supraja, B. Sai | Lalitha, S.

Article Type: Research Article

Abstract: The main motive of this work is to discriminate a vital neurodegenerative condition of Parkinson Disease (PD) affected patients from individuals with no history of such a disorder. Excitation source features, voice quality features and prosodic features are the speech constituents considered. Voice samples of PD patients are extracted from the University of California-Irvine (UCI) Machine Learning Parkinson’s database. Random Forest (RF) decision trees and Support Vector Machine (SVM) are considered for classification. Feature reduction is applied with the Correlation based Feature Selection (CFS) attribute selector classifier that utilizes Best First Selector (BFS) as a search algorithm. The work involves …recognizing a PD patient from a healthy individual using only two speech sounds of /a/ and /o/. The speech sounds are extracted without the association of a certified clinician, that adds novelty. The proposed algorithm is non-invasive and accomplished 94.77% accuracy with feature selection process and applying RF classifier. Show more

Keywords: Best first selector, correlation based feature selection, feature reduction, parkinson, random forest, support vector machine

DOI: 10.3233/JIFS-179714

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6337-6345, 2020

Authors: Singh, Utkarsh | Gupta, Akshay | Bisharad, Dipjyoti | Arif, Wasim

Article Type: Research Article

Abstract: Speech analysis for extracting attributes such as the speaker, gender, accent and like has been a field of great interest and has been widely studied. The paper presents a novel architecture for accent identification by using a cascade of two deep-learning architecture. We design and test our proposed architecture on common voice dataset. The architecture consists of a cascade of Convolutional Neural Network (CNN) and Convolutional Recurrent Neural Network (CRNN). It is trained on Mel-spectrogram of the audios. We consider five of the most popular English accents groups namely India, Australia, US, England, Canada in this study. The proposed model …has an accuracy of 78.48% using CNN and 83.21% using CRNN. Show more

Keywords: Mel-spectrogram, deep neural networks, foreign accent classification, recurrent neural network

DOI: 10.3233/JIFS-179715

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6347-6352, 2020

Authors: Vyshnav, M.T. | Sachin Kumar, S. | Mohan, Neethu | Soman, K.P.

Article Type: Research Article

Abstract: The present paper proposes Random Kitchen Sink based music/speech classification. The temporal and spectral features such as spectral centroid, Spectral roll-off, spectral flux, Mel-frequency cepstral coefficients, entropy, and Zero-crossing rate are extracted from the signals. In order to show the competence of the proposed approach, experimental evaluations and comparisons are performed. Even though both speech and music signals differ in their production mechanisms, those share many common characteristics such as a common spectrum of frequency and are comparatively non-stationary which makes the classification difficult. The proposed approach explicitly maps the data to a feature space where it is linearly separable. …The evaluation results shows that the proposed approach provides competing scores with the methods in the available literature. Show more

Keywords: Music/speech, random kitchen sink, feature vector, GTZAN database, S&S database, spectral features

DOI: 10.3233/JIFS-179716

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6353-6363, 2020

Authors: Shinde, Hemendra Vijay | Patil, Devashri Manohar | Edla, Damodar Reddy | Bablani, Annushree | Mahananda, Malkauthekar

Article Type: Research Article

Abstract: Background: Students have to manage the strain of rising education level and their future career, accompanying the hormonal changes during their pubescence. This creates a great impact on their education as well as personal life. In this paper, an analysis has been made to study the impact of yoga on engineering students. To understand the impact. Brain-Computer Interface (BCI) approaches have been utilized. An EEG based BCI is used which will give a direct view of whats going on in the students’ brains. Methodology: In this work, an experiment has been performed on engineering students and their brain …activity is recorded before and after practicing yoga. In the experimental procedure, EEG signals are acquired from 8 electrodes which are associated with the cognitive and memory-related tasks of the brain. During each trial, participants solve the set of mathematical questionnaire. EEG signals are acquired during test trials before and after the yoga session. A bandpass filter is applied to preprocess the EEG signals. A discrete wavelet transform is implemented for feature extraction of the preprocessed signals. Results: Different classification algorithms are applied to classify the EEG signals before and after the yoga session. To measure the classification performance, measures such as accuracy, sensitivity, and specificity are presented in the paper. The highest accuracy of 95 % is achieved with Probabilistic Neural Network. Classification concluded the variations in signals before and after yoga. Further, in this work analysis of frequency bands, accuracy and score of the subjects before and after the yoga session are also done. Show more

Keywords: Brain Computer Interface, EEG signals, yoga, wavelets, Probabilistic Neural Network

DOI: 10.3233/JIFS-179717

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6365-6376, 2020

Authors: Manu, D.K. | Karthik, P.

Article Type: Research Article

Abstract: Increasingly challenging problems have been addressed in the field of underwater acoustics. The critical topics of research have been increasing the sensitivity of Sensors, measurement of sound intensity, locating target from the source, measurement of radiating power, etc. In this proposed research work, the fiber optic hydrophone sensitivity is increased by varying different parameters like dimensions of the materials, Poisson’s Ratio and Young’s Modulus of the mandrel. The fiber optic hydrophone is composed of different materials- Nylon, Aluminum, Polystyrene, Fiber, and Polyurethane. The design of the hydrophone is carried out using finite element analysis tools. The parameters of the hydrophone …(mandrel) have been varied, and the analytical result shows that there is a considerable increase in sensitivity. These results demonstrate that there is an improvement in the hydrophone sensitivity by around 20 db in contrast with the existing hydrophone. From this result, we are now focusing on customizing the design and further validating the design, in the future. Show more

Keywords: Fiber optic-hydrophone, sensitivity, poissons ratio, Young’s modulus, mandrel, finite element analysis

DOI: 10.3233/JIFS-179718

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6377-6382, 2020

Authors: Chaithanya Krishna, D.C. | Tripathi, Shikha

Article Type: Research Article

Abstract: A hybrid architecture for transforms such as N-point Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Sine Transform (DST) and Discrete Wavelet Transform (DWT) has been proposed and implemented using triple matrix product method. There is limited work reported on efficient single architecture that can perform multiple transforms simultaneously or serially depending on the application requirement. The Hybrid architecture implemented, can compute various transforms efficiently. A controller is designed which can perform different transforms using the hybrid architecture based on the input provided. The implemented systolic array can be used for computing the diagonal elements of triple-matrix product. The …designed architecture produces the output of transform sequence in order, which avoids reordering at output. The implemented architecture can be used to handle large sized transforms by repeatedly using fixed size architecture for a large number of points without increasing the number of Processing Elements (PEs). The proposed architecture has been validated with a watermarking algorithm that uses DCT and DWT transforms and its performance analyzed. The proposed hybrid architecture is implemented on Spartan-7 xc7s100fgga676-1. The simulation results are given and analyzed against standalone architecture. Show more

Keywords: Hybrid architecture, transforms implementation, triple matrix product method

DOI: 10.3233/JIFS-179719

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6383-6390, 2020

Authors: Remya Revi, K. | Wilscy, M.

Article Type: Research Article

Abstract: Nowadays the manipulations of digital images are common due to easy access of many online photo editing applications and image editing softwares. Forged images are widely used in social media for creating deceitful propaganda of an individual or a particular event and for cooking up fake evidences even in court proceedings. Hence ensuring the integrity of digital images is of prime significance and it has become a hot research area. In this paper, a novel technique for image forgery detection is proposed. The method utilizes the layer activation of inception-ResNet-v2, a pretrained Convolutional Neural Network(CNN)to extract the deep textural features …from Rotation Invariant – Local Binary Pattern (RI-LBP) map of the chrominance image. Non-negative Matrix Factorization (NMF) technique is used to reduce the dimensionality of the extracted features. The dimensionality reduced features are used to train a quadratic Support Vector Machine(SVM) classifier to classify images into forged or authentic. The method is assessed on four benchmark datasets (CASIA ITDE v1.0, CASIA ITDE v2.0, CUISDE and IFS-TC). Extensive experimental analysis is done and the results show an improved detection accuracy compared to the state-of-the-art methods. Show more

Keywords: Deep learning, rotation invariant-local binary pattern, pretrained convolutional neural etworks, deep textual features, image forgery detection

DOI: 10.3233/JIFS-179720

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6391-6401, 2020

Authors: Anushiadevi, R. | Pravinkumar, Padmapriya | Rayappan, John Bosco Balaguru | Amirtharajan, Rengarajan

Article Type: Research Article

Abstract: Reversible Data Hiding (RDH) is a technique that is used to protect the secret by using digital cover media to hide it and to retrieve the cover after extracting the secret. The Reversible Data Hiding In an Encrypted Image (RDHIEI) protects the privacy of secret information and also the cover by hiding confidential information in a cipher image. Some of the algorithms in RDHIEI can extract the information if and only if the cipher image is already decrypted and some algorithms can decrypt the image if only if the data has already been extracted from the cipher image. In those …algorithms, the extraction and decryption cannot be separated. But some applications like healthcare and army image processing require that image recovery and information extraction to be separate processes; this new technique is called Separable Reversible Data Hiding In an Encrypted Image (SRDHIEI). In this paper, a novel SRDHIEI is suggested with high payload and good quality decipher image by embedding information in a cipher image on two levels. In the first level data is embedded by the Least Significant Bit(LSB) substitution method and in the second level data is embedded by using Pixel Expansion (PE) method. For image confidentiality, the cover image is encrypted by using an additive homomorphism technique. The benefits of the proposed method is to transfer the cover image in an extremely secure manner with PSNR of 8.6813 dB, – 0.0077 correlation and 7.998 entropy. The average embedding capacity of the proposed method is 217579 bits, and the decrypted image PSNR is 29.5 dB. 100% restoration of the host image and 100% lossless secret information extraction can be achieved. Show more

Keywords: Reversible data hiding, pixel expansion, embedding capacity, imperceptibility, SRDHIEI

DOI: 10.3233/JIFS-179721

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6403-6414, 2020

Authors: Sadasivan, Santhu | Sivakumar, Trivandrum T. | Joseph, Anna P. | Zacharias, Geevar C. | Nair, Madhu S.

Article Type: Research Article

Abstract:  The need of newer biometric traits is increasing, as the conventional biometric systems are found to be vulnerable to forging. Nowadays, tongue print is gaining importance as a biometric trait, especially in the area of forensics. Tongue is a well protected vital organ which exhibits rich structural patterns. Success of tongue print as a biometric tool depends on how well the discriminating features are extracted from it. Advancements in the field of deep neural network and availability of high-end computing environments facilitate remarkable progress in the area of image recognition. CNN follows a hierarchical learning to extract feature maps that …highly characterize the training data. However, obtaining a tongue print dataset large enough to train a CNN for recognition poses a huge challenge. Alternatively, two techniques can be used to successfully employ CNN for recognition: fine-tuning pre-trained CNN models, to use as a classifier, with the new input dataset and class labels to perform tongue-print image recognition. Another effective method is to use a pre-trained CNN model as a feature extractor, to extract features from the input tongue dataset and then use a state-of-the-art classifier to perform image recognition. In this paper, we addressed three important factors regarding the deployment of tongue-print as a biometric tool. Since, a tongue-print dataset is not publicly available, our first objective to create a challenging tongue-print dataset. We then explored and evaluated different state-of-the-art CNN architectures for image recognition. These models are varied in their architecture and contain 5 million to 144 million parameters. Finally, we analyzed different approaches to use the pre-trained CNN models for the tongue-print identification task. Show more

Keywords: Tongue print, biometric, identification, CNN, support vector machine, forensics

DOI: 10.3233/JIFS-179722

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6415-6422, 2020

Authors: Madarkar, Jitendra | Sharma, Poonam

Article Type: Research Article

Abstract:  Today’s world is facing threats from terrorism, for safety concerns system needs to strengthen security. Security is a challenging task and it can be strengthened by technology such as biometric and surveillance cameras. These technologies are deployed everywhere but it is the need of the days a strong automatic face recognition applications so they can be used to recognize the person in an unconstrained environment. In an unconstrained environment, images are affected by occlusion such as a scarf, goggle, random but these variations decrease the performance of face recognition. Also, the accuracy of face recognition depends on the number of …labeled samples and variation available in the training dataset. But some applications of face recognition such as passport verification, identification of these applications have fewer training samples without or with very less occlusion hence, it is not enough to solve the issue of unconstrained conditions. This problem has been targeted by many researchers using an occlusion based training dataset where common variation exists in both training and testing datasets. This paper tackles the occlusion issues by designing a NonCoherent dictionary. The proposed dictionary is designed by two steps firstly it extracts the occlusion from the face image and secondly creates NonCoherent samples. The extensive experimentation is done on benchmark face databases and compared the results on state-of-the-art SRC methods by using NonCoherent and normal dictionary also compared the sparse coefficients of each method. The results show the effectiveness of proposed model. Show more

Keywords: Face recognition, sparse representation, occlusion, dictionary

DOI: 10.3233/JIFS-179723

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6423-6435, 2020

Authors: Rajan, R. Ashoka | Kumaran, P.

Article Type: Research Article

Abstract: A recent trend of the information technology is cloud computing technology where many complex tasks are simplified with increased speed and low cost. However, cloud authentication plays a crucial role once all the data’s are uploaded in the cloud. In this paper, multi-biometric template security based on generation of unique graph is proposed to ensure a safe and secured cloud authentication mechanism. To overcome the vulnerability issues of traditional password and token based authentication methods, in this work, a multi-biometric system is proposed. The left, right fingerprint and palm print multi modal traits are given as input to the system. …After preprocessing, all the features are combined to generate a weighted graph called as branching factor graph. In the end, the node and edge values of the branching factor graph will be stored in the cloud database. Experimental study shows that the proposed method achieved a very low equal error rate than the other existing works. Show more

Keywords: Cloud security, template security, biometric authentication, cloud database security, secure cloud authentication, biometric cloud security, feature extraction, thinning

DOI: 10.3233/JIFS-179724

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6437-6444, 2020

Authors: Kumar, Malay | Mishra, Vaibhav | Shukla, Anurag | Singh, Munendra | Vardhan, Manu

Article Type: Research Article

Abstract: Computation of complex mathematical problems are always a challenge of resource constrained clients. A client can outsource the computations to resource abundant cloud server for execution. But this arrangement brings many security and privacy challenges. In this paper, we have presented a secure and efficient algorithm for general computation and scientific problem i.e. matrix multiplication . The proposed algorithm is inspired by the existing algorithm, but we believe that it is imperative to improve the algorithm to enable secure outsourcing of computation. The previous state-of-the art algorithm for matrix multiplication is vulnerable to the Cipher-Text Only Attack (COA) along with …Chosen Cipher-Text Attack (CCA) and Known Plain-Text Attack (KPA) and reveal information about the client’s data. Hence fails the security requirements of the outsourcing algorithm. The proposed work retains the efficiency benefit of state-of-the-art algorithm, additionally defended the client data against (COA) along with (CCA) and Known Plain-Text Attack (KPA). Show more

Keywords: Matrix multiplication, cloud computing, secure outsourcing, security, verifiability, Cipher-Text Only Attack (COA), Chosen Cipher-Text Attack (CCA) and Known Plain-Text Attack (KPA)

DOI: 10.3233/JIFS-179725

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6445-6455, 2020

Authors: Preethi, D. | Vimala, J. | Davvaz, B. | Rajareega, S.

Article Type: Research Article

Abstract:  In this manuscript we proposed the concept of fuzzy hyperlattice ordered group. Algebraic hyperstructures represent a natural extension of classical algebraic structures. In a classical algebraic structure, the composition of two elements is an element, while in an algebraic hyperstructure, the composition of two elements is a set. Algebraic hyperstructure theory has many applications in other disciplines. The foremost intendment of the manuscript is to contribute some properties of fuzzy hyperlattice ordered group and also an application of fuzzy hyperlattice ordered group on inheritance.

Keywords: Hyperlattice, fuzzy hyperlattice, lattice ordered group, fuzzy lattice ordered group, fuzzy hyperlattice ordered group AMS MSC: 03E72, 06D72, 20N20, 03E72, 06D72, 20N20

DOI: 10.3233/JIFS-179726

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6457-6464, 2020

Authors: Gonge, Sudhanshu Suhas

Article Type: Research Article

Abstract: The computer system represents data in various format viz (i) Audio, (ii) Video, (iii) Text, (iv) Message, and (v) Image format. There are many ways through which, data can be easily transferred. The process of transferring digital bank cheque image is done with the help of cheque truncation system. It transfers cheque from home branch to clearing bank branch for faster clearance of customer cheque. This helps the banking system to keep transparency of transaction. During the flow of digital bank cheque image, there may be possibility of various attacks like, Cropping, JPEG compression, Median filtering, Gaussian Blur noise, Rotation, …Salt & Pepper noise, etc. This arise the issues of copyright protection and security for digital bank cheque image. In this research work, The Combination of Digital Image Watermarking Using Neural Network and Advanced Encryption and Decryption Technique is Used for Providing Copyright Protection & Security Technique to Digital Bank Cheque Image. Show more

Keywords: Digital image watermarking, attacks, AES, encryption, decryption, neural network

DOI: 10.3233/JIFS-179727

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6465-6474, 2020

Authors: Suriya Praba, T. | Meena, V. | Sethukarasi, T. | Prachetha, K. | Aravind, B. | Bharathkumar, K.C.S.

Article Type: Research Article

Abstract: Wireless sensor networks (WSNs) is a network of resource constrained sensors deployed in unattended region for environmental monitoring. The resource constrained and ad-hoc nature of WSN stances lot of challenges to the research community when designing protocols for such environments. Now a days WSN is widely deployed from environmental monitoring to military applications. So secure data transmission is mandated in WSNs when it is used for mission critical applications. Data aggregation is a widely used method in WSNs for reducing communication overhead by mitigating unwanted data transmissions. But upholding accuracy of such aggregated data and providing security for the same …is a challenging task. In this paper we propose Cluster based Concealed data Aggregation for Confidentiality and Integrity(C-CASIN) in WSN. It uses Elliptic Curve Cryptography based Elgamal additive homomorphic encryption scheme for providing Confidentiality and Integrity.EC-Elgamal Signature algorithm supports for authenticity. By supporting end-to-end encryption proposed method provides security with reduced computation and communication overheads. Results show that proposed method defend against various possible attacks and malicious behavior with the extended network lifetime of 15 to 20 percentage when comparing with basic secure model. Show more

Keywords: WSNs, concealed data aggregation, clustering, end-to-end encryption, homomorphic encryption

DOI: 10.3233/JIFS-179728

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6475-6482, 2020

Authors: Arunkumar, B. | Kousalya, G.

Article Type: Research Article

Abstract: Authentication Encryption with Associated Data (AEAD) is a scheme that preserves the integrity of both the cipher text and authenticated data. In AEAD, cipher suites like GCM_SIV and AES_GCM_SIV provides the message integrity through nonce-based authentication encryption technique. The problem of nonce-based authentication encryption is the repetition of nonce in two different messages that violates message integrity property when the number of message blocks is maximized to 232 . This paper verifies the maximum limit of nonce usefulness and proves better security bounds attained in GCM_SIV and AES_GCM_SIV using nonce-reuse/misuse resistance authentication encryption (NRMR-AE) technique. The NRMR-AE resistance property achieves …better security bounds and performance even when the nonces are repeated in different messages. But nonce repetition in NRMR-AE property reduces the number of message encryption and message length (in blocks) in GCM_SIV and AES_GCM_SIV AEAD methods used in QUIC(Quick UDP Internet Communications) and TLS Cipher suites which is found to be a greater drawback. This paper increases the number of messages encrypted even with maximum number of nonce repetition ensuring that the message length in AES_GCM_SIV meets the standard NIST bound 2-32 . Show more

Keywords: AEAD, GCM_SIV, AES_GCM_SIV, TLS, QUIC

DOI: 10.3233/JIFS-179729

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6483-6493, 2020

Authors: Rauthan, J.S. | Vaisla, K.S.

Article Type: Research Article

Abstract:  This paper presents the core algorithms behind DB-Query-Encryption, a proposal that supports private information retrieval (PIR) explorations. DB-Query-Encryption permits users for selectively retrieve information from a cloud database whereas keeping sensitive data terms secretive. As an example use case, a medical research institute may, as part of a sensitive data exploration, requisite to look up facts about an individual person from a cloud database deprived of reveling the person’s identity. The basic idea behind DB-Query-Encryption is to uses homomorphic encryption, which allows the cloud server to fulfill this request, whereas making it infeasible for the database owner (or a hacker …who might compromised) to conclude the name being explored for, either which records are retrieved. The query, which retrieved the information, still secretive even if the spectator be able to search all the data over the cloud server and all the actions as they are being executed. Within that period, the query response produced by the cloud server is considerable smaller than the whole cloud database, making it more convenient when it is not feasible or appropriate for the user to transfer the entire database. Show more

Keywords: Cloud database, homomorphic encryption, privacy, sensitive information retrieval, information security

DOI: 10.3233/JIFS-179730

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6495-6505, 2020

Authors: Ramadas, Rithvik | Chowdhury, Anirban

Article Type: Research Article

Abstract: Voice User Interfaces have become popular with the advent of Alexa, Google Home, Cortana and other commercial speech recognition interfaces; however, the privacy of the end users is compromised while using these interfaces in public. In addition, users can feel a bit awkward while using these interfaces with loud voice while they are outside their homes. Contextually, ‘Hypnosis’ and ‘Hypnotherapy’ have not been frequently applied as a way of human communications although the principle of suggestion induced behavior changes in an interesting approach to interact with machines. In this paper, GEORGIE a prototype AI was used to achieve a novel …means of interaction inspired from the principles of hypnotherapy, which is a discrete interface ensuring that end-users’ privacy is not compromised. It is envisaged that people who prefer secret communication and interaction might love to use this hypnotic computer interface (hypCI). The hypCI would be the novel means of human robot interface (HRI) or human computer interface (HCI). Show more

Keywords: Artificial intelligence, cognitive science, defense, hypnotic triggers, user experience design, voice user interfaces

DOI: 10.3233/JIFS-179731

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6507-6516, 2020

Authors: Murali, Ritwik | Shunmuga Velayutham, C.

Article Type: Research Article

Abstract:  This paper attempts to employ Evolutionary Algorithm(EA) techniques to evolve variants of a computer virus(Timid ) that successfully evades popular antivirus scanners. Generating authentic variants of a specific malware results in a valid database of malware variants, which is sought by anti-malware scanners, so as to identify the variants before they are released by malware developers. This preliminary investigation applies EAs to mutate the Timid virus with a simple code evasion strategy, i.e., insertion and deletion(if available) of a specific assembly code instruction directly into the virus source code. Starting with a database of over 60 popular antivirus scanners, …this EA based approach for malware variant generation successfully evolves Timid variants that evade more than 97% of the antivirus scanners. The results from these preliminary investigations demonstrate the potential for EA based malware generation and also opens up avenues for further analysis. Show more

Keywords: Anti-malware research, cyber security, evolutionary algorithms, malware, malware creation, virus

DOI: 10.3233/JIFS-179732

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6517-6526, 2020

Authors: Patil, Nilesh Vishwasrao | Rama Krishna, C. | Kumar, Krishan

Article Type: Research Article

Abstract:  A Distributed Denial of Service (DDoS) attack is the biggest threat to Internet-based applications and consumes victim service by sending a massive amount of attack traffic. In the literature, numerous approaches are available to protect the victim from the DDoS attacks. However, the attack incidents are increasing year by year. Further, several issues exist in the traditional framework based detection system such as itself becoming a victim, slow detection, no real-time response, etc. Therefore, the traditional framework based system is not capable of processing live traffic in the big data environment. This paper proposes a novel Spark streaming-based distributed and …real-time DDoS detection system called S-DDoS. The proposed S-DDoS system employs the K-Means clustering algorithm to recognize the DDoS attack traffic in real-time. The proposed detection model designed on the Apache Hadoop framework using highly scalable H2O sparkling water. The detection model deployed on the Spark framework to classify live traffic flows. The results show that the proposed S-DDoS detection system efficiently detects the DDoS attack from network traffic flows with higher detection accuracy (98% ). Show more

Keywords: Distributed denial of service (DDoS), K-means clustering algorithm, big data, entropy, network security, apache spark

DOI: 10.3233/JIFS-179733

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6527-6535, 2020

Authors: Dickson, Anne | Thomas, Ciza

Article Type: Research Article

Abstract:  Intrusion detection system is a second layer of defence in a secured network environment. When comes to an IoT platform, the role of IDS is very critical since it is highly vulnerable to security threats. For a trustworthy intrusion detection system in a network, it is necessary to improve the true positives with minimum false positives. Research reveals that the true positive and false positive are conflicting objectives that are to be simultaneously optimized and hence their trade-off always exists as a major challenge. This paper presents a method to solve the tradeoff among these conflicting objectives using multi-objective particle …swarm optimization approach. We conducted empirical analysis of the system with multiple machine learning classifiers. Experimental results reveals that this technique with J48 classifier gives the highest gbest value 10.77 with minimum optimum value of false positive 0.02 and maximum true positive 0.995. Empirical evaluation shows an incredible improvement in Pareto set in the objective function space by attaining an optimum point. Show more

Keywords: Intrusion detection system, receiver operating characteristics, particle swarm optimization, pareto front, multi-objective optimization, non-linear programming

DOI: 10.3233/JIFS-179734

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6537-6547, 2020

Authors: Hussain, Muzakkir | Saad Alam, Mohammad | Sufyan Beg, M.M. | Akhtar, Nadeem

Article Type: Research Article

Abstract:  Vehicular Fog Computing (VFC) is a natural extension of Fog Computing (FC) in Intelligent Transportation Systems (ITS). It is an emerging computing model that leverages latency aware and energy aware application deployment in ITS. However, due to heterogeneity, scale and dynamicity of vehicular networks (VN), deployment of VFC is a challenging task. In this paper, we propose a multi-objective optimization model towards minimizing the response time and energy consumption of VFC applications. Using the concepts of probability and queuing theory, we propose an efficient offloading scheme for the fog computing nodes (FCN) used in VFC architecture. The optimization model is …then solved using a modified differential evolution (MDE) algorithm. Extensive experimentations performed on real-world vehicular trace of Shenzhen, reveals the superiority of proposed VFC framework over generic cloud platforms. Show more

Keywords: Intelligent transportation systems, modified differential evolution, vehicular fog computing

DOI: 10.3233/JIFS-179735

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6549-6560, 2020

Authors: Chandrawanshi, Veervrat Singh | Tripathi, Rajiv Kumar | Pachauri, Rahul

Article Type: Research Article

Abstract:  The design of a wireless sensor network (WSN) faces many constraints. Mostly, WSN is energy constraint because the sensor nodes are battery operated. Available power expenditure in WSN largely depends on the efficient use of limited resources and appropriate routing of the data packets. The power consumption can be minimized by balancing the energy consumption between the sensor nodes and selecting the minimum power consumption route for the data packets. Clustering is one of the most effective technique that not only uniformly distributes the energy among all the sensor nodes but also play a vital role in the designing of …routing protocols. So based on these advantages, a low power consumption routing protocol is proposed that makes use of fuzzy c-means++ algorithm. The proposed approach minimizes the power consumption of the sensor network by the excellent management of the WSN and also raises the lifespan. The simulation result illustrates the effectiveness of the proposed routing method when compared with the recently developed protocols based on k-means and fuzzy c-means algorithms. Show more

Keywords: Wireless sensor networks, clustering, cluster head, k-means, k-means++, fuzzy c-means algorithm, fuzzy c-means++ algorithm, energy efficient network

DOI: 10.3233/JIFS-179736

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6561-6570, 2020

Authors: Suriya Praba, T. | Sethukarasi, T. | Venkatesh, Veeramuthu

Article Type: Research Article

Abstract: In wireless sensor networks (WSN), the establishment of large-scale sensor networks has always needed attention. One of the many challenges is to set up an architecture that is different from the rest and find mechanisms that can efficiently scale up with the growing number of nodes that may be essential to ensure sufficient coverage of large areas under study. Concurrently, these new architectures and mechanisms are supposed to maintain low power consumption per node to comply with energy guaranty acceptable network lifetime. The researchers utilized numerous Data collection techniques for the prompt data aggregation, yet still those outcomes the node …with path failures. To solve this issue, the mobile sink is being extensively used for data aggregation in large scale wireless sensor networks (WSNs). This technique avoids imbalances in energy consumption due to multi-hop transmission but might lead to extended delay time. In this paper, our focus is on shortening the length of the mobile sink’s travelling path to reduce the delay time during data gathering in large scale WSN. To achieve this, the mobile sink visits the cluster heads in an optimized path instead of sensors one by one. Here Hierarchical clusters are efficiently formed by modified K- means with outlier elimination and node proximity and residual energy based second level clustering algorithm. Next, we determine the optimal path for the mobile sink by formulating KH based Travelling Salesman Problem solving optimization algorithm. This technique proposed reduces not only the length of the path travelled by the mobile sink but also lessens the computational effort that is required for travelling-path planning and enhances the lifetime of nodes. And to ensure aggregation accuracy in cluster heads iterative filtering is implemented. Our experimental results show the proposed algorithm shortens the tour length by 40–60 percent compared to Bacterial foraging optimization-based TSP algorithm. Also delivers better results compared to other’s in terms of the computational effort, time, energy use, and enhances the network lifetime. Show more

Keywords: Wireless sensor networks, data aggregation, clustering, travelling salesman problem, krill herd optimization

DOI: 10.3233/JIFS-179737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6571-6581, 2020

Authors: Mathi, Senthilkumar | Joseph, Eric | Advaith, M.S. | Gopikrishna, K.S. | Gopakumar, Rohit

Article Type: Research Article

Abstract: The rapid increase in internet usage for the past few decades has steered to higher demand and for adequate support for the network mobility in heterogeneous networks. However, in the existing mobile IPv6 (Internet Protocol version 6) protocols such as traditional, hierarchical, proxy and related methodologies have been stated to manage the recurrent mobility of the devices in a network with the centralized feature. However, the single point of failure, route optimization, handoff latency and security threats are highly exposed when the number of mobile device increases in the centralized approach. Also, it leads to the limitation of the size …of binding information when the mobile host needs to update its place. Hence, this paper suggests a secure and optimized architecture by distributing mobility functions as distributed access points. Also, the paper addresses the prevention measures for security attacks such as false binding message, rerun and hijack. The proposed scheme is simulated and validated using network simulator and security model verifier – AVISPA. Finally, the numerical and experimental outcomes demonstrate that the proposed scheme offers a substantial diminution in the cost of the binding update, binding refresh, and packet delivery. Show more

Keywords: IP networks, distributed access point, mobile agents, routing, integrity, authentication

DOI: 10.3233/JIFS-179738

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6583-6593, 2020

Authors: Sujatha, M. | Geetha, K. | Balakrishnan, P. | Renugadevi, T.

Article Type: Research Article

Abstract: The unprecedented growth in personal, business and research data motivates users to lease storage from multiple cloud storage providers like Amazon, Azure, etc. Selection of cost-effective cloud storage service by considering different pricing policies along with their performance characteristics is a challenging task. This research proposes a model named as OUTFIT (Optimal sUgeno Type Fuzzy Inference sysTem) an optimal data storage hosting model by suggesting an appropriate storage type based on user demands. In the first phase, we have surveyed Amazon, Google Cloud, Azure and Rackspace cloud storage providers and consolidated the different cloud storage types supported by them. In …the second phase the cloud service providers are ranked by using Sugeno fuzzy inference system based on the user preference. The third phase designates the appropriate service that incurs minimal estimated storage usage cost. The proposed approach is able to categorize various cloud service providers with an optimal grading process by including multiple decision criteria for fine-grained storage type selection. The observed results prove it to be a more favourable selection tool in comparison with its counterpart tools like Cloudorado, RightCloudz in terms of cost. Show more

Keywords: CSP selection, QoS Attributes, Sugeno Inference system, Iaas storage selection, Cloud computing

DOI: 10.3233/JIFS-179739

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6595-6605, 2020

Authors: Pradeep Kumar, K.A. | Thiruvengadathan, Rajagopalan | Shanmugha Sundaram, G.A.

Article Type: Research Article

Abstract:  The perfect Y antenna array configuration is among the most prevalent antenna array arrangement used in radio interferometry for synthesis imaging. It is crucial to determine an antenna array configuration that could offer further higher quality radio-images. In this paper, a novel and an efficient L-band log-periodic spiral antenna array design is presented. The radio-imaging performance of the log-periodic spiral antenna array is compared and shown to outperform an equivalent perfect Y antenna array. Radio imaging performance is evaluated using the computational simulation for the proposed L-band log-periodic spiral antenna array and the equivalent perfect Y antenna array. The metric …used for evaluation is the Structural Similarity Index (SSIM) and Surface Brightness Sensitivity (SBS). The L-band log-periodic spiral antenna array was observed to have about five times higher bandwidth, 2.24 times greater sensitivity, angular resolution better by a factor of five, and 10% wider field of view than the perfect Y configuration antenna array of comparable extent. It has been analytically demonstrated that the log-periodic spiral antenna array is an optimum configuration based on Chow’ s optimization technique. The L-band log-periodic spiral antenna array has outperformed the perfect Y configuration in many different imaging aspects. Show more

Keywords: Radio telescope, interferometry, synthesis array, log-periodic antenna array, antenna array configuration

DOI: 10.3233/JIFS-179740

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6607-6618, 2020

Authors: Zhilenkov, Anton A. | Chernyi, Sergei G. | Sokolov, Sergei S. | Nyrkov, Anatoliy P.

Article Type: Research Article

Abstract: The safe and reliable navigation of such autonomous systems as unmanned aerial vehicles (UAV) is a complex open problem in robotics, where a robotic system must simultaneously do many tasks of perception, control and localization. This task is especially complicated when working in an uncontrolled, unpredictable environment, for example, on city streets, in wooded areas, etc. In these cases, the autonomous agent must not only be guided to avoid collisions, but also interact safely with other agents in the environment. The developed system allows navigation of unmanned aerial vehicles in difficult environmental conditions. The results of training and the operation …of the autonomous navigation system in the forest are presented. The system finds and follows the paths that are fairly difficult to distinguish. The results of field experiments are presented. Presentation of the model is presented on the youtube.com channel. Show more

Keywords: Localization, agent, robotic, control

DOI: 10.3233/JIFS-179741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6619-6625, 2020

Authors: Mor, Navdeep | Sood, Hemant | Goyal, Tripta

Article Type: Research Article

Abstract: Over the last few years, road accidents in developing countries are increasing at an alarming rate. In India, almost 3% of GDP is getting wasted in road accidents, which not only cause social problems but, also, imposes a huge burden on the Indian economy. Various researches have been done to analyze this situation using different methods and techniques on different stretches and intersections. This paper makes one of the first attempts to develop an Accident Prediction Model (APM) in the Indian State of Haryana. This study describes the procedure for collection and analysis of accident data, as well as the …detailed methodology used to develop APMs. The Models were developed using one of the most common algorithms of machine learning i.e. linear regression technique. Results obtained from APM of Haryana State were compared with the results given by some of the highly successful APMs like Smeed’s Model, Valli’s Model and their comparisons were discussed to find the most efficient model. It was observed that the proposed model shows highly accurate results in predicting road accidents in Haryana. The output of this work can be used for theoretical as well as practical applications like road safety management for improving existing conditions of the road network in Haryana and to regulate new traffic safety policies in the future. Show more

Keywords: Accident prediction model, linear regression, road safety, accidents

DOI: 10.3233/JIFS-179742

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6627-6636, 2020

Authors: Shukla, Alok Kumar | Pippal, Sanjeev Kumar | Gupta, Srishti | Ramachandra Reddy, B. | Tripathi, Diwakar

Article Type: Research Article

Abstract: Feature selection is a pre-processing method that identifies the significant features from high-dimensional data and able to diminish the computational cost of the learning algorithm because of removing the irrelevant and redundant features. It has traditionally been applied in a wide range of problems that include biological data processing, pattern recognition, and computer vision. The aim of this paper is to identify the best feature subsets from the benchmark datasets which improve the performance of the classifiers. Existing filter-based feature selection approaches fail to choose the relevant features from the original feature sets. To obtain the tiny subset of relevant …features, we have introduced a novel filter-based feature selection method, called ReCFS. The proposed method is a combination of both feature-feature correlation and nearest neighbor weighted features to find an optimal subset of features to minimize correlation among features. The effectiveness of the selected feature subset by proposed method is evaluated by using two classifiers such as Naïve Bayes and K-Nearest Neighbour on real-life datasets. For the diverse performance measurements, the experiments are conducted on eight real-life datasets of varied dimensionality and number of instances. The result demonstrates that the proposed method has found promising feature subsets which improved the classification accuracy over competing feature selection methods Show more

Keywords: Machine learning, relief-F, correlation feature selection, classification, naïve bayes

DOI: 10.3233/JIFS-179743

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6637-6648, 2020

Authors: Pradhan, Rosy | Majhi, Santosh Kumar | Jaypuria, Jemarani

Article Type: Research Article

Abstract: Moth-Flame optimization is a meta-heuristic algorithm based on the navigation behaviour of moths. Generally, moth’s poses a very effective mechanism called transverse orientation while moving a long distance in night and maintain of fixed angle with respect to the moon. MFO suffers with local optima and stagnation problem, in order to improve the performance and exploration rate of the existing algorithm and for solving the complex real world problems, a new version of MFO algorithms is proposed by adding the concept of orthogonality feature. The modified algorithm is termed as orthogonal Moth-Flame optimization (OMFO) algorithm. The main objective of this …OMFO is going to solve the convergence problem to minimization of the search space and avoid the local optima. The proposed method can also be used to maintain the balance between exploration and exploitation. In this work, a set of 28 standard IEEE CEC 2017 benchmark test functions with 10 and 30 dimensions are used to evaluate and compare between the obtained results which prove that the proposed OMFO gives very promising and competitive performance as well as achieve better performance over original MFO algorithm with high stability over searching method. The efficiency of the proposed method is verified by applying in model order reduction problem. The performed analysis such as statistical measure, convergence analysis and complexity measure reveal that the proposed method is reliable and efficient in solving practical optimization problems. Show more

Keywords: Meta-heuristic, optimization, MFO, orthogonality, model order reduction

DOI: 10.3233/JIFS-179744

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6649-6661, 2020

Authors: Jayachitra Devi, Salam | Singh, Buddha

Article Type: Research Article

Abstract: Link prediction tremendously gained interest in the field of machine learning and data mining due to its real world applicability on various fields such as in social network analysis, biomedicine, e-commerce, scientific community, etc. Several link prediction methods have been developed which mainly focuses on the topological features of the network structure, to figure out the link prediction problem. Here, the main aim of this paper is to perform feature extraction from the given real time complex network using subgraph extraction technique and labeling of the vertices in the subgraph according to the distance from the vertex associated with each …target link. This proposed model helps to learn the topological pattern from the extracted subgraph without using the topological properties of each vertex. The Geodesic distance measure is used in labeling of the vertices in the subgraph. The feature extraction is carried out with different size of the subgraph as K  = 10and K  = 15. Then the features are fit to different machine learning classification model. For the evaluation purpose, area under the ROC curve (AUC) metric is used. Further, comparative analysis of the existing link prediction methods is performed to have a clear picture of their variability in the performance of each network. Later, the experimental results obtained from different machine learning classifiers based on AUC metric have been presented. From the analysis, we can conclude that AdaBoost, Adaptive Logistic Regression, Bagging and Random forest maintain great performance comparatively on all the network. Finally, comparative analysis has been carried out between some best existing methods, and four best classification models, to make visible that link prediction based on classification models works well across several varieties of complex networks and solve the link prediction problem with superior performance and with robustness. Show more

Keywords: Link prediction, geodesic distance, classification model, complex network, data mining

DOI: 10.3233/JIFS-179745

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6663-6675, 2020

Authors: Panda, Nibedan | Majhi, Santosh Kumar | Singh, Sarishma | Khanna, Abhirup

Article Type: Research Article

Abstract: Success behind nature inspired evolutionary metaheuristic algorithms lies in its seemly combination of operator’s castoff for smooth balance between exploration and exploitation. The deficit in such combination leads to untimely convergence of an algorithm, simultaneously failed to attain global optimum by stocking in local optimum. This work represents atypical algorithm termed as OBL-MO-SHO to improve the performance of existing SHO. To deal with more intricate realistic problems and to enhance the explorative and exploitative strength of SHO, we have integrated the oppositional learning concept with mutation operator. The proposed algorithm OBL-MO-SHO (oppositional spotted hyena optimizer with mutation operator) reveals promising …performance in terms of achieving global optimum and superior convergence rate which confirms its improved exploration and exploitation capability within searching region. To establish competency of proposed OBL-MO-SHO algorithm the same is appraised by means of standard functions set belongs to IEEE CEC 2017. The efficacy of said method has been proven by means of various performance metrics and the outcomes also compared with state-of-the-art algorithms. To scrutinize its uniqueness statistically, Friedman and Holms test has been performed as one non-parametric test. Additionally as an application to unravel real world intricate difficulties the said OBL-MO-SHO algorithm has been castoff to train wavelet neural network by considering datasets selected from UCI depository. The reported results unveils that the evolved OBL-MO-SHO might be one potential algorithm for enlightening different optimization difficulties effectively. Show more

Keywords: Swarm intelligence, spotted hyena optimizer, opposition-based learning, mutation operator, optimization, classification, wavelet neural network (WNN)

DOI: 10.3233/JIFS-179746

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6677-6690, 2020

Authors: Bhardwaj, Shubham | Geraldine Bessie Amali, D | Phadke, Amrut | Umadevi, K.S. | Balakrishnan, P.

Article Type: Research Article

Abstract: Metaheuristic algorithms are a family of algorithms that help solve NP-hard problems by providing near-optimal solutions in a reasonable amount of time. Galactic Swarm Optimization (GSO) is the state-of-the-art metaheuristic algorithm that takes inspiration from the motion of stars and galaxies under the influence of gravity. In this paper, a new scalable algorithm is proposed to help overcome the inherent sequential nature of GSO and helps the modified version of the GSO algorithm to utilize the full computing capacity of the hardware efficiently. The modified algorithm includes new features to tackle the problem of training an Artificial Neural Network. The …proposed algorithm is compared with Stochastic Gradient Descent based on performance and accuracy. The algorithm’s performance was evaluated based on per-CPU utilization on multiple platforms. Experimental results have shown that PGSO outperforms GSO and other competitors like PSO in a variety of challenging settings. Show more

Keywords: nature inspired metaheuristic, parallel computation, galactic swarm optimization, artificial neural networks

DOI: 10.3233/JIFS-179747

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6691-6701, 2020

Authors: Rawat, Anuj | Jha, S.K. | Kumar, Bhavnesh | Mohan, Vijay

Article Type: Research Article

Abstract: This paper presents a fractional order nonlinear Proportional Integral Derivative (FONPID) controller to efficiently achieve the Maximum Power Point Tracking (MPPT) in Photovoltaic (PV) systems working under rapidly varying solar intensity and the temperature. In this paper, comparisons have been made among different techniques in respect of the extent of energy extracted from the photovoltaic (PV) system using MATLAB platforms. Gains of the proposed FONPID controllers are optimally tuned using a meta-heuristic based Elitist Teaching Learning Based Optimization (ETLBO) algorithm. The performance assessment of the FONPID controller is made in terms of efficiency, settling time, rise time and ripple. The …ETLBO tuned FONPID controller outperforms the other controller such as PID, Nonlinear PID (NPID), Fractional order PID (FOPID) and perturb and observe (P & O) technique. Therefore, in view of the meticulous investigation it is inferred that the proposed FONPID controller is an emerging MPPT technique with highest tracking efficiency and negligible ripple. Show more

Keywords: Fractional order nonlinear proportional-integral derivative (FONPID), maximum power point tracking (MPPT), elitist teaching learning based optimization (ETLBO)

DOI: 10.3233/JIFS-179748

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6703-6713, 2020

Authors: Chauhan, Urvashi | Singh, Vijander | Kumar, Bhavnesh | Rani, Asha

Article Type: Research Article

Abstract: This article proposes an improved multiverse optimization (IMVO) assisted maximum power point tacking (MPPT) algorithm for attaining maximum global power from photovoltaic system under partial shading condition. The proposed control scheme overcomes the difficulties occurring in traditional MPPT algorithms such as difficulty in attaining global maximum power under partial shading condition and incapability of handling oscillations in power at maximum power point. The algorithm is an amalgamation of IMVO and direct duty cycle control approach. The wormhole existence probability and time distance ratio are considered to be adaptive in improved MVO so as to ensure precise exploration and exploitation. In …this work multi crystal solar panel, KC130GT by M/S Kyocera, is analyzed for dynamic profiles of irradiance. Traditional P&O MPPT and improved particle swarm optimization MPPT (IPSO MPPT) are also designed for comparative analysis. The suggested IMVO MPPT proves to be superior in terms of power tracking performance, average efficiency and convergence capability as compared to other designed controllers. Show more

Keywords: Solar PV system, MPPT, multi verse optimization, improved multi verse optimization

DOI: 10.3233/JIFS-179749

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6715-6726, 2020

Authors: Nangrani, S.P. | Joshi, K.D.

Article Type: Research Article

Abstract: Engineering systems are nowadays expanding beyond expected limits and their complexity is also increasing. One of the largest integrated system is power system. Some well-designed power system experience strange situation and suffer through chaos owing to weak dynamic performance. Stability issues also haunt power system in such cases. Loss of stability investigation in power system leads to evidence of chaos as intermediate state quite often. Black swan theory tells us to be ready for unseen unruly behavior at any time. Noah and Joseph effects are also surfacing in almost every large expanding power system from different parts of world. Complexity …of system component behavior and complex stability boundaries pose a threat and ready to push power system where chaos is prevalent. It is debatable to see whether chaos leads to instability. This paper closely summarizes the chaos studies in light of reported research and advocates strongly the inclusion of advancement in chaos theory for detailed investigation post disturbance. This paper deals with a comprehensive review of strange behavior of nonlinear dynamic system and relevance of such studies for future anomalous behavior in the light of complexity science applied to engineering disasters such as blackouts. It is targeted to provide direction for futuristic complexity arising due to working on the brink of instability for economic reasons and to have certain preparation before inevitable blackouts, disasters and failures. A classified list of more than 50 relevant research publications is also given for quick reference. Show more

Keywords: Lyapunov exponent (LE), single machine infinite bus (SMIB), dynamic stability assessment (DSA)

DOI: 10.3233/JIFS-179750

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6727-6737, 2020