Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Karaşan, Ali | Kahraman, Cengiz

Article Type: Research Article

Abstract: Location selection for a freight village is extremely important since it has strong impacts on quality of life such as reduction of traffic congestion, reduction of carbon emission, and effective use of lands. The inclusion of such criteria in a location selection requires the fuzzy sets to be used in the decision making methodology. In this paper, we propose a novel integrated fuzzy decision model for the location selection of freight villages. In this integrated methodology, we use DEMATEL for determining the most effective criteria and their inner and outer dependencies; ANP for weighting the determined criteria; and TOPSIS for …finding the best location alternative. The proposed model is applied to a case study for the city of Istanbul in Turkey. Show more

Keywords: Intuitionistic fuzzy sets, DEMATEL, ANP, TOPSIS, freight village

DOI: 10.3233/JIFS-17169

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2017

Authors: Daneshpayeh, Roohallah | Borumand Saeid, Arsham | Mirvakili, Saeed

Article Type: Research Article

Abstract: In this paper, we intend to introduce the notion of the radical of a filter in a pseudo BL-algebra and express its characteristics and properties. We define dense, infinitesimal, nilpotent, and unity elements in a pseudo BL-algebra, and then, investigate the relationship between these elements and the radical in a pseudo BL-algebra. Our study revealed significant results in this regard.

Keywords: Pseudo BL-algebra, (Prime, Maximal) filter, Radical, (Dense, Infinitesimal, Nilpotent, Unity) element

DOI: 10.3233/JIFS-181039

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2018

Authors: Tan, Yao | Shum, Hubert P. H. | Chao, Fei | Vijayakumar, V. | Yang, Longzhi

Article Type: Research Article

Abstract: Fuzzy inference systems have been successfully applied to many real-world applications. Traditional fuzzy inference systems are only applicable to problems with dense rule bases covering the entire problem domains, whilst fuzzy rule interpolation (FRI) works with sparse rule bases that do not cover certain inputs. Thanks to its ability to work with a rule base with less number of rules, FRI approaches have been utilised as a means to reduce system complexity for complex fuzzy models. This is implemented by removing the rules that can be approximated by their neighbours. Most of the existing fuzzy rule base generation and simplification …approaches only target dense rule bases for traditional fuzzy inference systems. This paper proposes a new sparse fuzzy rule base generation method to support FRI. In particular, this approach uses curvature values to identify important rules that cannot be accurately approximated by their neighbouring ones for initialising a compact rule base. The initialised rule base is then optimised using an optimisation algorithm by fine-tuning the membership functions of the involved fuzzy sets. Experiments with a simulation model and a real-world application demonstrate the working principle and the actual performance of the proposed system, with results comparable to the traditional methods using rule bases with more rules. Show more

Keywords: Fuzzy inference, fuzzy interpolation, sparse rule base generation, curvature

DOI: 10.3233/JIFS-169978

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2018

Authors: Dorfeshan, Y. | Mousavi, S. Meysam

Article Type: Research Article

Abstract:  Uncertainty plays an important role in project decision-making problems that involve incomplete and imperfect information of real-world situations. To completely considering the uncertainty of decision-making methods, Pythagorean fuzzy sets (PFSs) are used. PFSs in comparisons with classic fuzzy sets provide degrees of membership, non-membership and hesitancy, and in comparisons with intuitionistic fuzzy sets (IFSs), they prepare the larger space to explain the agreement, disagreement and hesitancy grades. In this paper, to tackle the uncertainty of real-world projects and determine the critical path of projects by considering efficient criteria, such as time, cost, risk, quality and safety, a new group decision …methodology is extended based on concepts of technique for order of preference by similarity to ideal solution (TOPSIS) and complex proportional assessment (COPRAS) methods under PFSs. Furthermore, a new modified version of the proposed methodology is used to specify the weight of each expert. Finally, a case study from the literature, concerning workflow schema of marble processing plants project, is presented to better express the capability of the proposed methodology. Show more

Keywords: Project critical path, marble processing plant, Pythagorean fuzzy sets (PFSs), group decision-making methodology, decision makers’ weights, TOPSIS, COPRAS

DOI: 10.3233/JIFS-172252

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2018

Authors: Akhtar, Nadeem | Sufyan Beg, M M

Article Type: Research Article

Abstract: Finding coherent topics in Twitter data is difficult task because of the sparseness and informal language. Tweets also provide rich contextual and auxiliary metadata which can be used to supervise the topic modeling to get more coherent topics. In this paper, a novel topic model is proposed which extends Author Topic Model for twitter. Standard Author Topic Model cannot be used on Twitter data as every tweet has exactly one author. The proposed User Graph Topic Model (UGTM) considers the semantic relationships among tweet users based on the contextual information like hashtags, user mentions and replies to make a user …graph. Related users of author of a tweet are found and used in tweet generation process. Related user information from the user graph is used to obtain the dirichlet prior for user generation. Empirical results show that the proposed UGTM outperforms standard Author Topic Model (ATM) on experimental data. Show more

Keywords: Topic models, Latent Dirichlet Allocation, user graph

DOI: 10.3233/JIFS-169934

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2018

Authors: Mahata, Animesh | Mondal, Sankar Prasad | Alam, Shariful | Chakraborty, Avishek | Dey, Sujit Kumar

Article Type: Research Article

Abstract: In this paper we study the mathematical model for diabetes in fuzzy environment. For real situation we notice that some data cannot be precisely known. For that reason we have to take the parameters as imprecise parameter. In this paper we take fuzzy number as the imprecise parameter. The diabetes model is formed by the concept of system of linear differential equation whose initial condition are fuzzy number. The solution procedure for solving governing fuzzy differential equations i.e., the fuzzy model is taken as generalized Hukuhara derivative approach. To show the biological importance the stability analysis of the model is …done in fuzzy environment. The numerical results found and are illustrated through computer simulation which is followed by discussions and conclusions in an elegant way. Show more

DOI: 10.3233/JIFS-171571

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2018

Authors: Wang, Shengnan

Article Type: Research Article

Abstract:  Smart education is one of the main applications of Smart cities which requires artificial intelligence-based models during the education process. Optimizing the educational contents to match the student’s capabilities and backgrounds is one of the core tasks in the smart educations. To improve the optimization of an education system, a data mining algorithm is used to classify and summarize the education data and improve the accuracy of teaching content. An optimization and updating scheme is put forward from the algorithm flow and evaluation model, and an adequate evaluation model is established by effectively evaluating the developing state of wisdom education. …In the test of intelligent education model, the efficiency of AI education and the data accuracy of the data mining algorithm are tested. The test results show that the integration of AI can improve the efficiency of learning and teaching, and make the teaching content more accurate, which is worth to further promoting. Show more

Keywords: AI technology, intelligent education mode, practicality, data mining

DOI: 10.3233/JIFS-179058

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2019

Authors: Tian, Shuicheng | Tang, Kai | Yang, Pengfei | Jia, Aifang | Hailey, Melvin,

Article Type: Research Article

Abstract:  To build a safety behavior evaluation system in network management, the cloud computing model is introduced in this study for parametric study. First, the development model of cloud computing management is expounded, a parametric model of safety behavior coefficient is constructed by using a cloud computing platform. Then, an optimization and update scheme is put forward from the algorithm flow and evaluation model, and an adequate evaluation model is established by effectively evaluating the state of security management. Finally, the parameter data is refined through USES data mining algorithm to ensure the reasonable calculation of the data. In the test …of the parameter model, the simulation is carried out by adopting the cloud computing platform. The test results prove that the application of the parameter model can guarantee the reasonable management of safety behavior, the error in the process of safety behavior management can be reduced, and the accuracy of control can be enhanced. The parameter model is worthy of further application and extension. Show more

Keywords: Cloud computing, safety management, safety behavior coefficient, optimization decision

DOI: 10.3233/JIFS-179060

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2019

Authors: Krishna, P. Murali | Reddy, R. Pradeep | Narayanan, Veena | Lalitha, S. | Gupta, Deepa

Article Type: Research Article

Abstract: This paper presents a technique to detect the six affective states of individual using audio cues. Bi-spectral features extracted from entire speech signal and voiced part of speech are used to create feature vectors. For classification K-Nearest Neighbor (KNN) and Simple Logistic Classifiers (SL) are used. eNTERFACE audio-visual emotional speech corpus that consists of six archetypal affective states: Fear, Anger, Disgust, Sad, Happy, and Surprise is considered. The performance of the system is analyzed based on features obtained from voiced part of speech and features obtained from the entire speech signal. The work proposed is first of its kind …in affect computation, where a compact 13-dimensional Bi-spectral features extracted from the voiced speech segments is able to yield promising performance. A considerable improvement of 8.46% – 27.6% recognition rate is achieved with the proposed methodology compared to the existing approaches using emotion samples from the same speech corpus adding novelty to the proposed work. Show more

Keywords: Bi-spectral, voiced speech, affective state recognition

DOI: 10.3233/JIFS-169926

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2018

Authors: Kumar, Aiswarya S. | Nair, Jyothisha J.

Article Type: Research Article

Abstract: Deep neural networks have dramatically gained immense potential in almost every field like computer vision, natural language processing, biomedical informatics etc. Among these networks, autoencoders are popular in performing dimensionality reduction task, while learning a representation for an unlabeled dataset. A usual way of dealing with such networks is to pre-train them in a layer-wise fashion, and consequently fine-tune the whole stack in a supervised manner. In this paper, a pair-wise training strategy is proposed to determine optimum model parameters by reducing training time as well as the complexity of training a convolutional autoencoder without compromising its accuracy. The proposed …approach works in a fully unsupervised manner and has been tested on datasets like MNIST, CIFAR10, and CIFAR100 and it shows that the training time has improved by an average of 25% on these three datasets. Show more

Keywords: Deep learning, convolutional autoencoder, pair-wise training

DOI: 10.3233/JIFS-169910

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2018

Authors: Yuguang, Zhong | Fan, Yang | Feng, Liu

Article Type: Research Article

Abstract: A new artificial bee colony algorithm called modified artificial bee colony algorithm (MABC) is presented to solve the multi-objective fuzzy flexible job-shop scheduling problem (MFFJSP) in this paper. The objectives of MFFJSP are to minimize the maximum fuzzy completion time (fuzzy makespan), maximize the weighted agreement index and minimize the maximum fuzzy machine workload. The three-point satisfaction-degree model is adopted to calculate the agreement index and this model can indicate the degree of satisfaction between the due date and the processing time. An effective local search operator based on variable neighborhood search (VNS) and crossover operator are embedded in this …algorithm for obtaining good searching performance. In order to make the novel algorithm valid, we texted it, five benchmark instances and a practical case for the sake of effectiveness. Then, the performance of the proposed MABC has been compared with other existing algorithms to prove the superiority of this algorithm. In the end, the Taguchi method is used to investigate the impact of three key parameters from the MABC. Show more

Keywords: Artificial bee colony algorithm, agreement index, variable neighborhood search, multi-objective fuzzy flexible job-shop scheduling problem

DOI: 10.3233/JIFS-181152

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2018

Authors: Zadehparizi, Fatemeh | Jam, Shahrokh

Article Type: Research Article

Abstract: In this paper, a new chaotic teaching learning based optimization (CTLBO) is proposed. TLBO is a rather newly proposed population-based algorithm. This algorithm has no control parameters for the tuning and has a simple structure. We improve its performance by chaotic maps. First, the presented CTLBO is tested on nine unimodal/multimodal benchmark functions. Then, chaotic sequences are applied as vectors with different initial values for design of a frequency reconfigurable antenna (FRA) as a practical example. Comparisons of the performance of this algorithm with those of the basic TLBO, genetic algorithm and particle swarm optimization show the ability of this …algorithm in design of FRAs in terms of faster convergence and better performance. A prototype of the optimized antenna with CTLBO algorithm is fabricated and the simulation and measurement results agree suitably. Show more

Keywords: Teaching learning based optimization, Chaos theory, Frequency reconfigurable antenna.

DOI: 10.3233/JIFS-171420

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2018

Authors: Ignacio, Algredo-Badillo | Luis Alberto, Morales-Rosales | Carlos Arturo, Hernandez-Gracidas | Juan Crescenciano, Cruz-Victoria | Daniel, Pacheco-Bautista | Miguel, Morales-Sandoval

Article Type: Research Article

Abstract:  Object detection is a technologically challenging issue, which is useful for safety in outdoor environments, where this object, frequently, represents an obstacle that must be avoided. Although several object detection methods have been developed in recent years, they usually tend to produce poor results in outdoor environments, being mainly affected by sunlight, light intensity, shadows, and limited computational resources. This open problem is the main motivation for exploring the challenge of developing low-cost object detection solutions, with the characteristic of being easily adaptable and having low power requirements, such as the ones needed in on-board obstacle detection systems in automobiles. …In this work, we present a trade-off analysis of several architectures using an FPGA-based design that implements ANNs (FPGA-ANN) for outdoor obstacle detection, focused in road safety. The analyzed FPGA-ANN architectures merge outdoor data gathered by a Kinect sensor, images and infrared data, to construct an outdoor environment model for object detection, which allows to detect if there is an obstacle in the near surroundings of a vehicle. Show more

Keywords: Obstacle detection, artificial neural networks, FPGA implementation, architecture trade-off analysis, road safety

DOI: 10.3233/JIFS-169997

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2018

Authors: Tarek, Zahraa | AL-Rahmawy, Mohammed | Tolba, Ahmed

Article Type: Research Article

Abstract:  Traffic congestion is a big problem that influences the traffic flow in big cities, so better control of the traffic signals is always searched to solve this type of traffic problems. Fog computing is one of the most efficient paradigms for traffic system control as it enables connecting and analyzing big traffic data to help the control of traffic signals in the appropriate time. There are different optimization methods, which can be used to control traffic signal; one of these is Particle Swarm Optimization (PSO ) algorithm, and there is correlation between PSO parameters (particle velocity, position) and traffic …parameters (vehicle speed and location). Roundabouts with traffic signals is one of the modern roads infrastructures used to reduce traffic jam. Our objective is to minimize the average delay time in order to decrease the traffic congestion. This paper presents a control strategy called COTSD-PSO for optimizing traffic signaling based on PSO combined with three sub-controllers; this strategy depends on traffic control rules. These sub-controllers are PSO-Jump , PSO-Turn and PSO-Mix depend on two parameters; extension time and urgency degree for the different phases in the traffic cycle. PSO algorithm is applied to optimize the control of the traffic signal network for roundabouts model on fog computing environments using real data from Taif streets in KSA country. The PSO simulation results show that the PSO-Mix has the fastest convergence rate for the optimal solution and the best performance in minimizing the average delay time compared with the other combinations. Show more

Keywords: Fog computing, traffic control, particle swarm optimization, roundabout, extension time, urgency degree, delay time

DOI: 10.3233/JIFS-18077

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2018

Authors: Ozkok, Beyza Ahlatcioglu

Article Type: Research Article

Abstract: Linear Programming (LP) is an optimization problem, which deals with finding optimal solutions under set of linear inequality or linear equality constraints. Fuzzy Linear Programming (FLP) and Fuzzy Linear Programming problems (FFLP) have gained great importance in recent years due to the uncertainties that may arise in the parameters and variables of the problems. In this paper we provided an extension to Ozkok et al. [Ozkok, B. A., Albayrak, I., Kocken, H. G., & Ahlatcioglu, M. (2016). An approach for finding fuzzy optimal and approximate fuzzy optimal solution of fully fuzzy linear programming problems with mixed constraints. Journal of Intelligent …& Fuzzy Systems, 31(1), 623-632.] to find fuzzy optimal and approximate fuzzy optimal solution of FFLP with trapezoidal fuzzy numbers. Show more

Keywords: Fully fuzzy linear programming problem (FFLP), trapezoidal fuzzy numbers, ranking function, infeasibility

DOI: 10.3233/JIFS-18016

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2018

Authors: Majumdar, A. | Laskar, N.M. | Biswas, A. | Sood, S.K. | Baishnab, K.L.

Article Type: Research Article

Abstract: With the advent of IoT, cloud/fog based healthcare systems have become a growing trend in modern healthcare systems. These systems comprise of smart sensors, which on integration with medical devices, generate heterogeneous medical big data that can be used in diagnosis of various diseases. However, there is a continuous flow of large quantity of data in such a systems, due to which it may face many difficulties. Among various pre-requisites for proper functioning of these systems, lifetime is a vital factor. Keeping in view these aspects, the use of new hybrid whale-PSO algorithm (HWPSO) in clustering has been proposed for …prolonging the network lifetime by preserving the power of network edge devices. In addition to this, a novel fitness function with a set of relevant criteria of edge devices such as energy factor, average intra-cluster distance, average distance to cluster leader over data analytics center, average sleeping time, and computational load has been taken into account in the selection of cluster leader. The cluster leader is responsible for managing intra-cluster and inter-cluster data communication. Show more

Keywords: e-Healthcare, fog computing, particle swarm optimization, whale optimization

DOI: 10.3233/JIFS-169957

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2018

Authors: Amiripalli, Shanmuk Srinvas | Bobba, Veeramallu

Article Type: Research Article

Abstract: In the year 2015 per person is having 3.47 devices that will be around 25 billion devices, In future years 2020 expected devices per person are 6.58 which is around 50 billion devices. From this statistic, major problems for IoT devices are scalability and survivability was identified. To address these problems a novel architecture design was proposed named as TGO (Trimet graph optimization) topology for Wireless sensor networks and IoT. As the number of devices increases, scalability increases leads to decrease of Quality of service (QoS). In this paper graph based solution was given for scalability and survivability using TGO …topology. In Scalability model an algorithm and flowchart was proposed. Experiments are conducted on Star of star, TGO of TGO, Wheel of wheel networks. In analysis part we observed TGO consumes less power than star and wheel networks. For parameters like received packets, lost packets, hops etc. TGO lies between star and wheel. In Survivable model Hexagon and TGO are 2 edge survivable networks are compared, whereTGO shows better performance in all parameter. Show more

Keywords: Wireless sensor network, peer to peer network, topology, graph optimization, quality of service

DOI: 10.3233/JIFS-169954

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2018

Authors: Jeong, Sang-Ki | Choi, Hyeung-Sik | Kang, Jin-Il | Oh, Ji-Youn | Kim, Seo-Kang | Minh Nhat, Thieu Quang

Article Type: Research Article

Abstract: The underwater glider (UG) is an underwater exploration equipment that propels itself with very low power consumption by converting the vertical motion into a horizontal motion using a buoyancy control device and a wing. To enhance gliding performance of the UG, a hybrid underwater glider (HUG) has been developed to compensate for the disadvantages of slow speed and horizontal movement and limited navigation accuracy of the UG by attaching propellant to the UG [15 ]. In this paper, the structure, control system and control algorithm of the HUG are presented. Also, for the developed HUG, motion performance of the …HUG is simulated by computer. To realize precise navigation of the developed HUG, an attitude reference system (ARS) composed of a ring laser gyroscope(RLG) and a geomagnetic sensor is developed with the Extended Kalman Filter (EKF) algorithm. To control the HUG, the six degrees of freedom equations of the HUG with the hydrodynamic force coefficient were studied. A control algorithm based on the neural network was proposed to reduce tracking error of the HUG. To validate the proposed control algorithm, a computer simulation using Matlab / Simulink was performed [13 ]. Show more

Keywords: Hybrid underwater glider, ARS (Attitude Reference System), underwater navigation, neural network self- tuning -PID, neural network - PID parallel controller

DOI: 10.3233/JIFS-169881

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2018

Authors: Srinivasan, Sundar | ShivaKumar, K.B.

Article Type: Research Article

Abstract: A cognitive radio (CR) can be programmed and configured dynamically to use best wireless channels. Such a radio automatically detects available channels in wireless spectrum, and then accordingly changes its transmission. The CR system consists of primary user or licensed user and secondary user or unlicensed user. The security attacks such as active attack and passive attack are identified between primary user and secondary user and packet loss occurs during packet transmission. The security problem occurring while transmission of signal between primary user and secondary user is rectified by using a hybrid RSA (Riverest, Shaimer and Adleman) and HMAC (Hash …Message Authentication Code) algorithms where former is used for key generation and latter is used for tag generation which is sent along with signal. Additionally packet loss incurred in system incurs is reduced with aid of Markov Chain Model during transmission. The comparison results provided showefficiency of the proposed algorithm in cognitive radio system in terms of parameters such as throughput, encryption time, decryption time, Packet Delivery Ratio and energy consumption. Show more

Keywords: Cognitive radio, RSA (Riverest, Shaimer and Adleman), HMAC (Hash Message Authentication Code), Markov Chain Model, active attack, passive attack

DOI: 10.3233/JIFS-169999

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2018

Authors: Sinha, Rupesh Kumar | Sahu, S.S.

Article Type: Research Article

Abstract:  Cryptography is the most peculiar way to secure data and most of the encryption algorithms are mainly used for textual data and not suitable for transmission data such as images. It is seen that the generation of secure key in Image cryptography has been a challenging task in the way of providing secured key generation for the transmitted data. In order to aid secured key generation in this context, an optimized secret key generation based on Chebyshev polynomial with Adaptive Firefly (FF) optimization technique is proposed. The optimized key is utilized with process of shuffling, diffusion, and swapping to get …a better encrypted image. At the receiver end, reverse process is applied with optimized key to retrieve the original input image. The efficiency of our proposed method is assessed by the exhaustive experimental study. The results show that the proposed methodology provided correlation coefficient of 0.21, Number of Pixels Change Rate (NPCR) of 0.996, Unified Average Changing Intensity (UACI) of 0.3346 and Information Entropy of 7.995 as compared with the existing methods. Show more

Keywords: Encrypted image, DWT, Chebyshev polynomial, optimized secret key, Adaptive firefly (FF) optimization algorithm

DOI: 10.3233/JIFS-169998

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2018

Authors: Kulkarni, Swati V. | Dhage, Sudhir N.

Article Type: Research Article

Abstract:   The Credit Score is the most fascinating three digit number associated with an individual or an organization as it figures out what loans you will qualify for and the interest rate you will pay. Current credit scoring system is based on the financial history of individual or organization. This work illustrates a new credit scoring system which incorporates Legacy credit score and emotional/social credit score. The legacy credit score is based on the financial history of an individual. The emotional/social credit score is based on analysis and study of social media and other web interaction. The new system called …information trustworthiness is developed to improve the precision of social media data when compared with data from reliable sources. Finally, the proper fractions of legacy credit score and emotional/social credit score are added to get Advanced Credit Score. This score is more precise than Legacy credit score as it also incorporates personality traits of an individual which have a high impact on one’s financial behavior. However, the accuracy of the Advanced Credit Score is dependent on the fractions of legacy credit score and emotional/social credit score selected. The advance scoring system can be effectively used to distinguish people who defaulted many times and who never used loans or services like credit cards which are otherwise not possible using legacy financial credit scoring system. Show more

Keywords: Credit score, Naive Bayes, CRISP-DM, data mining, Multilayer Perceptron, Random Forest, Random Tree

DOI: 10.3233/JIFS-169948

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2018

Authors: George, Koshy | Vishnukumar, S.

Article Type: Research Article

Abstract:  The need for understanding the terrain or conditions of large areas aerially has gained prominence as the aerial images provide a near clear coverage of the area under study. Individual image provides just a portion of the area, thus to understand the whole area, mosaicking or stitching of these images is needed. Image mosaicking aids in providing with a ”Big Picture” as an outcome by joining the images taken during the flight. In this paper we propose a method which aims at generating a seamless aerial mosaick using only the images captured by the UAV as input. This involves identifying …candidate images from the images captured by the UAV periodically during its flight and stitching the images together. This method evaluates various feature descriptors and feature matching techniques that can be integrated into the mosaicking system. The proposed work is a hybrid approach that uses the Scale Invariant Feature Transform (SIFT) for feature extraction and the key features are matched using the Fast Library for Approximate Nearest Neighbors (FLANN). RANdom Sample Consensus (RANSAC), is used for the removal of features that are redundant or act as outliner, providing candidates for Homography estimation. This is followed by image stitching that involves the use of Multi-Band Blending to produce a visually seamless mosaick. The results obtained were evaluated for quality using Universal Quality index Measure (QIM) and is found to be perfect. Show more

Keywords: Mosaicking, UAV, SIFT, FLANN, RANSAC, Homography, Multi-band Blending

DOI: 10.3233/JIFS-169919

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2018

Authors: Bansod, Suprit D. | Nandedkar, Abhijeet V.

Article Type: Research Article

Abstract:  Anomaly detection from crowd is a widely addressed problem in the field of computer vision. It is an essential part of video surveillance and security. In surveillance videos, very little information about anomalous behaviors is available, so it becomes difficult to identify such activities. In this work, transfer learning technique is used to train the network. A convolutional neural network (CNN) based VGG16 pre-trained model is used to learn spatial level appearance features for anomalous and normal patterns. Two approaches are explored to detect anomalies, i) homogeneous approach and ii) hybrid approach. In homogeneous approach, pre-trained network is used to …fine-tune CNN for each dataset, while testing, single dataset is considered. Whereas, in hybrid approach, pre-trained network is used to fine-tune CNN on one dataset and it is further used to fine-tune another dataset. The performance of proposed system is verified on standard benchmark datasets such as UCSD and UMN available for anomaly detection, also the results of proposed system are compared with existing deep learning approaches. Show more

Keywords: Anomaly detection, surveillance, transfer learning, CNN, fine-tune

DOI: 10.3233/JIFS-169908

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2018

Authors: Jangiti, Saikishor | Sri Ram, E. | Ravi, Logesh | Sriram, V.S. Shankar

Article Type: Research Article

Abstract:  With the advent of cloud computing, a cost-effective and reliable choice to employ IT infrastructure, the cyber-physical systems (CPS) are transforming into loosely coupled cloud and fog CPS. The sensor information from physical processes at CPS is continuously processed by fog computing nodes and is forwarded for advanced data analytics offered as a service from the cloud. The computation offloaded by fog devices are initiated as Virtual Machines (VMs) in the cloud data center. The effective placement of these VMs into minimum Physical Machines (PMs) involves economic and environmental issues. Recent research works signify the use of First-Fit Decreasing (FFD) …based heuristic techniques to address this NP-Hard problem as a vector bin-packing problem. In this research work, we present a set of hybrid heuristics and an ensemble heuristic to improve the solution quality. The simulation results show that the proposed heuristics are highly scalable and economical in comparison with the individual heuristic-based approaches. Show more

Keywords: cyber-physical systems, fog computing, cloud computing, virtual machine placement, first-fit decreasing

DOI: 10.3233/JIFS-179004

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2018

Authors: Ramachandran, Sumalatha | Palivela, Lakshmi Harika | Giridharan, C.

Article Type: Research Article

Abstract:  The importance of the surveillance is increasing every day. Surveillance is monitoring of activities, behavior and other changing information. An intelligent automatic system to detect behavior of the human is very important in public places. For this necessity, a framework is proposed to detect suspicious human behavior as well as tracking of human who is doing some unusual activity such as fighting and threatening actions and also distinguishing the human normal activities from the suspicious behavior. The human activity is recognized by extracting the features using the convolution neural network (CNN) on the extracted optical flow slices and pre-training the …activities based on the real-time activities. The obtained learned feature creates a score for each input which is used to predict the type of activity and it is classified using multi-class support vector machine (MSVM). This improved design will provide better surveillance system than existing. Such system can be used in public places like shopping mall, railway station or in a closed environment such as ATM where security is the prime concern. The performance of the system is evaluated, by using different standard datasets having different objects and achieved 95% performance as explained in experimental analysis. Show more

Keywords: Suspicious activity detection, optical flow, convolutional neural networks, support vector machine, multi-class SVM

DOI: 10.3233/JIFS-179003

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2018

Authors: Karaşan, Ali | Boltürk, Eda | Kahraman, Cengiz

Article Type: Research Article

Abstract: Combinative Distance-based Assessment (CODAS) method aims at selecting the best alternative having the largest distance to the negative-ideal point based on the Euclidean distance as the primary and the Taxicab distance as the secondary measure. Uncertainty is generally an unavoidable issue in real life problems, which can be tackled by various approaches that may be stochastic or fuzzy. Neutrosophic sets theory is an ultimate extension of ordinary fuzzy sets and neutrosophic sets are the superset of intuitionistic fuzzy sets. This paper develops a novel interval-valued neutrosophic CODAS method and presents comparative analyses with crisp and ordinary fuzzy CODAS methods. Together …with comparative analyses, a sensitivity analysis is also given for the selection problem among wind energy plant locations. Show more

Keywords: CODAS, interval-valued neutrosophic fuzzy, renewable energy, decision making, location selection

DOI: 10.3233/JIFS-181255

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2018

Authors: Laskar, Mohammad Azharuddin | Laskar, Rabul Hussain

Article Type: Research Article

Abstract: In recent times, Dynamic Time Warping (DTW) based template matching systems have again come to the forefront in the field of text-dependent speaker verification. Its integration with the latest technology, like i-vector/Probabilistic Linear Discriminant Analysis (PLDA) and Deep Neural Network (DNN), has resulted in significant improvement in the performance of the systems. DTW algorithm time aligns two templates and gives a similarity score based on the optimal warping path. It however weighs all the local distances equally, along the optimal path. In this paper, we propose complementing the DTW based text dependent speaker verification systems with local scores derived from …the vicinity of speaker-identity-rich regions. The vowel regions are used to determine portions along the warping path that are more important in terms of speaker discriminating information content. Two systems, namely the DTW/ Mel-frequency Cepstral Coefficients (MFCC) system and the online i-vector/PLDA/DTW system have been extended to incorporate the knowledge of specific regions of interest. The results have been evaluated on Part 1 of RSR2015 database. Relative improvements of upto 11.85% and 49.41% are observed for the extended systems based on MFCC and i-vector respectively. Show more

Keywords: DTW, vowel regions, online i-vector, text-dependent speaker verification

DOI: 10.3233/JIFS-169927

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2018

Authors: Vijayakumar, K. | Rajesh, K. | Vishnuvardhanan, G. | Kannan, S.

Article Type: Research Article

Abstract:  The Distributed Generation (DG) systems are highly useful in recent days for increasing the penetration of renewable energy, in which the design of grid connected inverters is one of the demanding and challenging task. For this reason, different controller strategies are developed in the traditional works for controlling the inverters with increased efficiency. But, it has the major limitations of increased computational complexity, steady state error and reduced compensation capability. To solve these issues, this research work aims to design a new controller by implementing a novel Monkey King Evolution Algorithm (MKEA) for grid connected converters. The motive of this …work is to increase the overall effectiveness of the power system by controlling the inverter without affecting its output. Also, it aims to provide a secure and convenient controller for the power converters. Here, the information that is obtained from the system which includes real power, distorted power due to load, reactive power of load, and apparent power of inverter are taken as the input. Later, the four numbers of monkeys are initialized, which evaluates the best solution based on these parameters. Sequentially, the monkey king obtains the best solutions from the monkeys, using which the most suitable and best solution for taking the decision is selected. Based on this, the reference current is generated by performing the voltage regulation, and abc to dq0 transformation processes. During simulation, the efficiency of the controller is analyzed by using the measures of phase voltage, phase current, active power, reactive power, apparent power, grid voltage, and output voltage. The Total Harmonic Distortion (THD) is effectively reduced by using the MKEA based controller design. Extensive simulation and experimental results are presented to validate the effectiveness of the proposed controller and control strategy. Show more

Keywords: Grid connected inverters, Distributed Generation System, Monkey King Evolution Algorithm (MKEA), Photovoltaic (PV) System, controller design, reference current generation

DOI: 10.3233/JIFS-179000

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2018

Authors: Priyanga, S. | Gauthama Raman, M.R. | Jagtap, Sujeet S. | Aswin, N. | Kirthivasan, Kannan | Shankar Sriram, V.S.

Article Type: Research Article

Abstract: Despite the increasing awareness of cyber-attacks against Critical Infrastructure (CI), safeguarding the Supervisory Control and Data Acquisition (SCADA) systems remains inadequate. For this purpose, designing an efficient SCADA Intrusion Detection System (IDS) becomes a significant research topic of the researchers to counter cyber-attacks. Most of the existing works present several statistical and machine learning approaches to prevent the SCADA network from the cyber-attacks. Whereas, these approaches failed to concern the most common challenge, “Curse of dimensionality”. This scenario accentuates the necessity of an efficient feature selection algorithm in SCADA IDS where it identifies the relevant features and eliminates the redundant …features without any loss of information. Hence, this paper proposes a novel filter-based feature selection approach for the identification of informative features based on Rough Set Theory and Hyper-clique based Binary Whale Optimization Algorithm (RST-HCBWoA). Experiments were carried out by Power system attack dataset and the performance of RST-HCBWoA was evaluated in terms of reduct size, precision, recall, classification accuracy, and time complexity. Show more

Keywords: SCADA, intrusion detection system, Rough Set Theory (RST), hyperclique property, feature selection

DOI: 10.3233/JIFS-169960

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2018

Authors: Dixit, Veer Sain | Jain, Parul

Article Type: Research Article

Abstract:  Context aware recommender system has become an area of rigorous research attributing to incorporate context features, thereby increases accuracy while making recommendations. Most of the researches have proved neighborhood based collaborative filtering to be one of the most efficient mechanisms in recommender systems because of its simplicity, intuitiveness and wide usage in commercial domains. However, the basic challenges observed in this area include sparsity of data, scalability and utilization of contexts effectively. In this study, a novel framework is proposed to generate recommendations independently of the count and type of context dimensions, hence pertinent for real life recommender systems. In …the framework, we have used k -prototype clustering technique to group contextually similar users to get a reduced and effective set. Additionally, particle swarm optimization technique is applied on the closest cluster to find the contribution of different context features to control data sparsity problem. Also, the proposed framework employs an improved similarity measure which considers contextual condition of the user. The results came from the series of experiments using two context enriched datasets showcasing that the proposed framework increases the accuracy of recommendations over other techniques from the same domain without consuming extra cost in terms of time. Show more

Keywords: Collaborative filtering, unsupervised learning, particle swarm optimization, euclidean distance, context aware recommendations

DOI: 10.3233/JIFS-179001

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2018

Authors: Hasnat, Abul | Barman, Dibyendu | Sarkar, Suchintya

Article Type: Research Article

Abstract:  Shared visual cryptography is a method to protect image-based secrets where an image is kept as multiple shares having less computational decoding process. Steganography is a technique to hide secret data in some carrier like-audio, image etc. Steganography technique is categorized into four categories. i) Spatial Domain Technique- Image pixel values are converted into binary and some of the binary values changed to hide secret data. ii) Transform Domain Technique- the message is hidden in cover image and then it is transformed in the frequency domain. iii) Distortion Technique-information is stored by changing the value of the pixel. iv)Visual Cryptography …Technique-Image is broken into two or more parts called shares. This article proposes a hybrid visual crypto-steganography approach which exploits the advantages of both approaches to protect image based secret in communication. Most of the visual cryptography is applied on black and white images but the proposed method can be applied directly on color images having three channels. This method does not change the image size. Also an exact replica of original image can be reconstructed therefore this process does not result in image quality degradation. This article proposes novel color image share cryptography where seven shares are generated from one color image (correlated/de-correlated color space). These shares are sent to the receiver and original image is reconstructed using all those shares. Share generation and image reconstruction is based on simple operation like pixel shuffling, reversing binary string of the image information, ratio of pixel intensity values. Row key matrix and column key matrix are generated using random function. Pixel positions are shuffled using these two key matrixes. These seven shares namely Row Key, Column Key, Remainder matrix, Quotient matrix, R ratio matrix, G ratio matrix and B ratio matrix are generated. Then Row key matrix, Column key matrix, Remainder matrix, Quotient matrix and three ratio matrices are hidden into separate cover images by LSB encoding technique and sent over the network. Receiver can reconstruct the image if all shares are available only. The proposed method is applied on standard images in the literature and images captured using standard digital camera. Comparison study with existing methods shows that the proposed method performs better in terms of NIST metrics. The method has many applications in the area of visual cryptography, shared cryptography, image based authentication etc. Show more

Keywords: Binary image, Cryptography, GCD, image decryption, image encryption, image security, quotient, remainder, shared visual cryptography, steganography

DOI: 10.3233/JIFS-179002

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2018

Authors: Basjaruddin, Noor Cholis | Sutjiredjeki, Ediana | Akbar, Hilman Wahyu Caesar

Article Type: Research Article

Abstract: An electronic glove is a sensor-equipped glove for detecting changes in the motion or the bending of a finger. The function of an electronic glove can replace the function of a remote control, especially for people with disabilities. In this study, we designed and realized a glove equipped with a flex sensor on each finger to detect the bending of the finger and used a mobile robot as the plant of the electronic glove. The system works by detecting finger-bending in the right hand and processing it into the commands stop, forward, backward, forward left and forward right. The flex …sensor resistance value caused by the finger-bending is used as an input for fuzzy logic control and produces the output to control the mobile robot. The flex sensor is capable of varying values between normal conditions of 27.2 kΩ up to a maximum of 46 kΩ . The lowest speed of the mobile robot is 0.75 m/s, with a maximum speed of 2.17 m/s. The commands from the electronic glove are sent to the mobile robot wirelessly with a maximum distance between electronic glove and the mobile robot of 200 m in an obstacle-free situation. Based on the experimental results, 100% of the orders were executed according to the design. The use of fuzzy logic ensures that every bending change is accompanied by a change in speed, so that mobile robot movements become smoother. Show more

Keywords: Electronic glove, flex sensor, fuzzy logic, mobile robot

DOI: 10.3233/JIFS-18968

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2018

Authors: Ton-That, An H. | Cao, Nhan T.

Article Type: Research Article

Abstract:  This paper introduces a fuzzy approach for classifying speech emotions in which a fuzzy inference system based on fuzzy associative memory (FAM-FIS) is used for recognizing speech emotions. Experiments on two databases of emotion speech Emo-DB in German and SAVEE in English, and feature of Mel-Frequency Cepstral Coefficients (MFCC) showed that the accuracy rates of the fuzzy inference system are better than that of Bayes and Support Vector Machine (SVM) on same kind of features and databases. Namely, with MFCC feature and 19 dimensions, Emo-DB is 74.31% and SAVEE is 97.29%.

Keywords: Speech emotion recognition, fuzzy inference systems, fuzzy associative memory, pattern recognition, fuzzy approach

DOI: 10.3233/JIFS-18594

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2018

Authors: Ji, Jinchao | Chen, Yongbing | Feng, Guozhong | Zhao, Xiaowei | He, Fei

Article Type: Research Article

Abstract: Data objects with both numeric and categorical attributes are prevalent in many real-world applications. However, most of the partitional clustering algorithms dealing with such data may trap into local optima. To further promote the performance, a novel clustering algorithm, called ABC-K-Prototypes (Artificial Bee Colony clustering based on K-Prototypes), is presented on the basis of the K-Prototypes algorithm, the search strategy of the artificial bee colony, and the chaos theory. In the presented approach, the one-step k-prototypes procedure is first given, and then this procedure is combined with the search strategy of the artificial bee colony to address the mixed numeric …and categorical data. In the search process of scout bees, the chaotic map is utilized to generate chaotic sequences for substituting the random numbers. To accelerate the convergence of the ABC-K-Prototypes algorithm, the multi-source search is adopted in the search process of scout bees. Finally, the performance of the ABC-K-Prototypes algorithm is demonstrated by a series of experiments on mixed numeric and categorical data in comparison with that of the other popular algorithms. Show more

Keywords: Clustering, numeric attribute, categorical attribute, mixed data, artificial bee colony

DOI: 10.3233/JIFS-18146

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2018

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

Article Type: Research Article

DOI: 10.3233/JIFS-169905

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-6, 2018

Authors: Mohan, Jesna | Nair, Madhu S.

Article Type: Research Article

Abstract:  The ever growing video data over the internet has raised the challenge of efficient storage and retrieval of multimedia data. Video Summarization is one of the solutions to the problem which extracts interesting parts of a video. These summaries capture the essential content of the original video and enable the viewers to pick out interesting videos from huge video repositories by viewing its compact representation. To address the challenge of managing the video data, a new method for static video summarization is proposed here which is capable of describing input videos using a precise yet meaningful subset of frames. The …method utilizes HOG descriptors of Gabor maps of input frames. A high-level representation of HOG descriptors is created using sparse auto-encoders (SAE). The final summary of the video is formed using candidate frames obtained after K-means clustering of these feature vectors. The summarization step is preceded by redundancy elimination to overcome computational burden. The method provides better results compared to the other state-of-the-art video summarization methods. Show more

Keywords: Video Summarization, Gabor maps, Histogram of Oriented Gradients (HOG), Sparse autoencoder (SAE), K-means clusterings

DOI: 10.3233/JIFS-169906

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2018

Authors: Zhu, Xuhui | Ni, Zhiwei | Ni, Liping | Jin, Feifei | Cheng, Meiying | Li, Jingming

Article Type: Research Article

Abstract:  Ensemble pruning is usually used to improve classification ability of an ensemble using less number of classifiers, and it is an NP-hard problem. Existing ensemble pruning approaches always find the optimal sub-ensemble using diversity of classifiers or running heuristic search algorithms separately. Diversity and accuracy of classifiers are widely recognized as two important properties of an ensemble. The increase of the diversity of classifiers must lead to the decrease of the average accuracy of the whole classifiers, and vice versa, so there is a tradeoff between diversity and accuracy of classifiers. Finding the tradeoff is the key to a successful …ensemble. Heuristic algorithms have good results when it comes to finding the tradeoff, but it is unfeasible to do an exhaustive search. Hence, we propose a Spread Binary Artificial Fish swarm algorithm combined with a Double-fault measure for Ensemble Pruning (SBAFDEP) using a combination of diversity measures and heuristic algorithms. First, the classifiers in an initial pool are pre-pruned using a double-fault measure, which significantly alleviates the computational complexity of ensemble pruning. Second, the final ensemble is efficiently assembled from the retaining classifiers after pre-pruning using the proposed Spread Binary Artificial Fish Swarm Algorithm (SBAFSA). Simulation and experiment results on 25 UCI datasets show that SBAFDEP performs better than other state-of-the-art pruning approaches. It provides a novel research idea for ensemble pruning. Show more

Keywords: Artificial fish swarm algorithm, spread behavior, double-fault measure, diversity, ensemble pruning

DOI: 10.3233/JIFS-169993

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2018

Authors: Vijayan, Vineetha | Sherly, Elizabeth

Article Type: Research Article

Abstract:  One of the major issues of road accidents all over the world is drowsiness state of the driver. It is a complex phenomenon to measure a driver’s consciousness in a direct manner. This work proposes with three deep neural architecture for learning facial features which consists of 68 attributes from the RGB video input of a driver. The experimentation is conducted by three different CNN models such as ResNet50, VGG16 and InceptionV3. These three networks are combined for representation learning which then put together the features to form a feature fused architecture(FFA). The trained features as well as facial movements …such as eye blinking, yawning and head swaying are again trained with a softmax classifier to classify the drowsiness state of driver. Out of the three networks and FFA, InceptionV3 shows 78% accuracy. Show more

Keywords: Convolutional neural networks, drowsiness, deep learning

DOI: 10.3233/JIFS-169909

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2018

Authors: Mohan, Vysakh S. | Vinayakumar, R. | Sowmya, V. | Soman, K.P.

Article Type: Research Article

Abstract:  Deep Rectified System for High-speed Tracking in Images (DRSHTI) is a unified open-source web portal developed for object detection in images. It aims to be a platform for the end user, where he/she can perform object detection on images without going through the hassles of debugging countless lines of code or setting up the right environment to perform computer vision tasks. By making the platform open-source, this work targets beginners in computer vision to form a basic understanding of object detection as an artificial intelligence task. This is made possible by releasing source codes, tools and tutorials on its usage …via GitHub. This open-source portal offers two detection pipelines based on Faster-RCNN – a model to detect ground vehicles in aerial images and a model to detect everyday objects in 37 different classes in normal images. The former model is trained on VEDAI dataset, which gave 98.6% accuracy during testing and is offered as proof-of-concept that showcases the models ability to perform small target detections, but the latter model is trained on the PASCAL VOC dataset. Making the project open-source also aims at bringing in more development and tweaking to the existing vehicle detection module. The web portal can be accessed via https://drshti.github.io , where user can upload images and get annotations on objects present in it. Tutorials and source codes can be found at https://github.com/vyzboy92/Object-Detection-Net . Show more

DOI: 10.3233/JIFS-169907

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2018

Authors: Chhabra, Rishu | Krishna, C. Rama | Verma, Seema

Article Type: Research Article

Abstract:  Intelligent Transportation Systems (ITS) aim at reducing the risks associated with the transportation system as road accidents are becoming one of the primary causes of death in developing countries. Monitoring of driver behavior is one of the key areas of ITS and assists in vehicle safety systems. It has gained importance in order to reduce traffic accidents and ensure the safety of all the road users, from the drivers to the pedestrians. In this work, we present a context-aware system that considers the vehicle, driver and the environment for driver behavior classification as a safe or fatigue or unsafe driver …(representing any other unsafe driving behavior like a drunk driver, reckless driver etc.) using a Dynamic Bayesian Network (DBN). We have designed a questionnaire to obtain the influencing factors that decide safe, unsafe and fatigue driving behavior. The collected data has been analyzed using Statistical Package for Social Sciences (SPSS). It has been observed that several techniques in the past have been proposed for driver behavior classification or detection; which either use specialized sensors or hardware devices, inbuilt smartphone sensors (like a gyroscope, accelerometer, magnetometer and GPS etc.), complex sensor fusion algorithms and techniques to detect driver behavior. The novelty of our work lies in designing and developing a context-aware system based on Android smartphone; that considers the complete driving context (driver, vehicle and surrounding environment) and classifies the driver behavior using a DBN. In order to identify driver fatigue, results from the designed questionnaire and previous research studies have been used without the need for special hardware devices. A DBN that combines all the contextual information has been created using GeNIe Modeler. Learning of DBN has been carried out using the Expec-tation–Maximization (EM) algorithm. The real-time data for DBN learning and testing has been collected on Chandigarh-Patiala National Highway, India using an Android smartphone. The proposed system yields an overall classification accuracy of 80–83%.The focus of this paper is to develop a cost-effective context-aware driver behavior classification system, to promote ITS in developing countries. Show more

Keywords: DBN, driving behavior, intelligent transportation systems, sensors, smartphone

DOI: 10.3233/JIFS-169995

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2018

Authors: Chopra, Parul | Agarwal, Shivangi | Rani, Asha | Singh, Vijander

Article Type: Research Article

Abstract: A myoelectric prosthetic limb can be directed by sEMG signals from amputee’s residual muscles. The capability of such prosthetic hand may be enhanced by classifying additional hand motion commands. As the amputee’s residual muscles are limited and it is essential to come up with the ways to identify as many hand motion directions as possible with sEMG signals recognized by few sensors. Recent algorithms for pattern recognition in sEMG signals are tested with limited recognition patterns and inconsistent classification accuracy. The proper choice of denoising algorithm has intense effect on classification rates. Therefore FIR-median hybrid (FMH) filter, and discrete wavelet …transform (DWT) denoising methods are used in this work for filtering sEMG signals. Five time domain features are used for classification of motions and four different physical activities are classified using ANN. It is observed from the results that FMH filter removes noise more effectively as compared to DWT which improves the classification accuracy. Show more

Keywords: sEMG, DWT, FMH, LM-Backpropogation algorithm, physical action classification, myoelectric prosthetic limb

DOI: 10.3233/JIFS-169924

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2018

Authors: Khan, Munna | Reza, Md Qaiser | Salhan, Ashok Kumar | Sirdeshmukh, Shaila P.S.M.A.

Article Type: Research Article

Abstract:  The acoustic resonance spectroscopy is an accurate, precise, inexpensive, and non-destructive method for identification and quantification of materials. The acoustics based inspection methods used for classification of materials in the field of food, security, and healthcare is constrained by expensive instrumentation, complicated transducer coupling, etc. Hence, a simple, inexpensive, and portable system has been devised that acquires data quickly and classifies the materials. It has two piezoelectric transducers glued to both ends of the V-shaped quartz tube, one acting as a transmitter and another as a receiver. The transmitter generates vibration by white noise excitation. The receiver detects the resultant …signal after interaction with samples and recorded the acoustic signal with the help of a laptop and software. From analysis of power spectrum of signals acquired from each of the samples, seven resonant peaks were obtained. PCA analysis was carried out by selecting only two principal components as feature vectors for classification. The overall accuracy of the classifiers: LDA and Naive Bayes were 98.91% and 96.83% respectively. The classification accuracy of LDA for distilled water, sugar solution, and salt solution were found to be 100%, 98.5%, and 98.25% respectively, while the accuracy of the Naive Bayes classifier was 94%, 98.5%, and 98% respectively. The results show that the classification accuracy of LDA is better than Naive Bayes classifier. The datasets of the developed simple system show a significant capability in the classification of materials. Show more

Keywords: Acoustic resonance spectroscopy (ARS), acoustic signature, principal component analysis (PCA), linear discriminant analysis (LDA)

DOI: 10.3233/JIFS-169994

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2018

Authors: Pradhan, Buddhadeb | Vijayakumar, V. | Hui, Nirmal Baran | Sinha Roy, Diptendu

Article Type: Research Article

Abstract:  Navigation of multiple robots is a challenging task, particularly for many robots, since individual gains may more often than not adversely affect global gain. This paper investigates the problem of multiple robots moving towards individual goals within a common workspace without colliding amongst themselves. Two solutions for coordination namely Fuzzy Logic Controller (FLC) and Genetic Algorithm based FLC (GA-FLC) have been employed and the efficacy of cooperation strategies have been compared with their non-cooperative counterparts as well as with the fundamental potential field method (PFM). Proposed coordination schemes are verified through simulations. A total of 100 scenarios are considered varying …the number of robots (8, 12, 16 and 20). The obtained results show the efficacy of the proposed schemes. Show more

Keywords: Multi-agent systems, motion planning, coordination

DOI: 10.3233/JIFS-169996

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2018

Authors: Yuan, Xiaohui | Elhoseny, Mohamed

Article Type: Research Article

Keywords: Smart network applications, scientific computing and data management, data-driven service management, spatial data techniques, data processing in smart city applications

DOI: 10.3233/JIFS-179057

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-5, 2018

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

Article Type: Research Article

Abstract:  This paper proposes Multi verse optimization (MVO) based MPPT controller to mitigate the possibility of losing tracking direction in solar photovoltaic system under variable irradiance. Generally conventional Perturb & Observe and hill climbing MPPT techniques are used due to effortless implementation. However, these techniques are not capable of handling oscillations in power at MPP and exhibit drift under variable irradiance conditions which leads to power loss. Therefore a hybrid of standard MVO and direct duty cycle control is proposed to minimize the inadequacies occurring in conventional controllers. Three cases i.e. constant irradiance, rapid and step changes in irradiance are …considered for the analysis. The supremacy of proposed method is justified by comparing it with traditional P&O, Particle swarm optimization (PSO) based MPPT and Grey wolf optimization (GWO) based MPPT techniques. It is observed from the results that MVO based MPPT controller is capable of avoiding drift and offers fast convergence. Therefore proposed controller outperforms in terms of tracking efficiency, settling time, peak overshoot, and integral absolute error. Show more

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

DOI: 10.3233/JIFS-169929

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2018

Authors: Santhi, B. | Brindha, G.R.

Article Type: Research Article

Abstract: The exponential growth of Internet through sharing text content necessitates the analysis to convert them into useful information. The research areas such as Web mining, Opinion mining and Text mining focus on studies namely content mining, statistical analysis, prediction, and classification. Multinomial Naïve Bayes (MNB), the state of art of Bayesian classifier is the fastest and simplest text classifier. The objective of the proposed study is to enhance the classification by substituting the conditional probability of existing MNB with probability based frequency computation. A new combination that consists of Pointwise Mutual Information (PMI) and different normalized Term Frequency (TF) is …used for computing the conditional probability. The new combinations provide weight to the words based on the information gain carried by the words related to the document that belongs to a class. The robustness of Similarity based Enhanced Conditional Probability MNB (SECP-MNB) is reflected in classification accuracy measurement. Show more

Keywords: Text classification, conditional probability, multinomial Naïve Bayes, machine learning, pointwise mutual information

DOI: 10.3233/JIFS-181009

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2018

Authors: Dhanya, D. | Arivudainambi, D.

Article Type: Research Article

Abstract:  Even though, cloud computing reduces the operating cost by enabling adaptation of virtual machines, it has suffered in selection of optimal virtual machine due to shortage of resource or resource wastage, sudden changes in requirement so it requires optimal resource allocation. Resource allocation is the process of providing services and storage space to the particular task requested by the users. This is one of the important challenges in cloud computing environment and has variant level of issues like scheduling task, computational performance, reallocation, response time and cost efficiency. In this research work we introduce a three-phase scheduling method based on …memory, energy and QOS in order to overcome the above issues which also yield low energy consumption, maximum storage and the high level Quality of Service (QoS). Biggest Memory First and Biggest Access First is introduced with NUMA scheduler and cache scheduler for memory scheduling and the optimal VM resulting from the three phases of scheduling is determined by Grey Wolf Optimization (GWO) algorithm. To carry the security level of optimized VMs, Streamline Security and Introspection security analysis are exhausted for detecting the malware VMs which results the secured and efficient VMs for further resource allocation. Our proposed methodology is implemented using the Cloud Sim tool and the experimental result shows the efficiency of our proposed method in terms of security, time consumption, and cost. Show more

Keywords: Quality of service (QoS), Biggest Access First (BAF), Biggest Memory First (BMF), Grey Wolf Optimization (GWO), streamline security, introspection security analysis

DOI: 10.3233/JIFS-169989

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2018

Authors: Raghav, R.S. | Dhavachelvan, P.

Article Type: Research Article

Abstract:  The Severe acute respiratory syndrome coronavirus (SARS-CoV) are deadly infectious disease which can easily transmit and causes severe problems in humans. It is known as a coronavirus and referred as a common form of virus that naturally causes upper-respiratory tract illnesses and the symptoms are hard to identify. It is important to recognize the patient and providing them with suitable action with constant intensive care. Healthcare amenities is constructed on fog and big-data based system and it is integrated with cyber-physical system. The role of Cyber physical system in health care domain is to fetch deep insights about the nature …of disease and carry the monitoring process with early detection of infected users. The objective is to identify occurrence of SARS at initial stage. In proposed system, resemblance factor is evaluated from the extracted keywords. In order to identify the difference between SARS affected and others, the proposed scheme fetches the inputs from user’s displayed in the form of text. It is passed to deep recurrent neural network (RNN) model. It extracts useful information from the raw information given by the user. The J48graft algorithm is used to carry the classification based on the type of infection and symptoms of each user. The data is stored in the bigdata layer (mongoDB) and it detects the infected area by using the geospatial feature in mongo dB. The methodology is framed in the proposed model to prevent the spread of disease to other users. In case of any abnormality the generation of alert process is done instantaneously and directed on user’s mobile from fog layer. The final experimental outcome reveals information about the performance of proposed system in terms of Success rate, failure rate, latency and accuracy %. It shows that the proposed algorithm gives high level of accuracy when it is compared with other primitive methods. Show more

Keywords: Cyber physical systems, deep recurrent neural network, J48graft, bigdata analytics, fog layer, mongo dB

DOI: 10.3233/JIFS-169992

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2018

Authors: Arunkumari, T. | Indragandhi, V.

Article Type: Research Article

Abstract:  In this manuscript a DC non-isolated converter model with high static voltage gain module is presented. The proposed converter has the feature of stable frequency and stable output voltage. It also achieves high voltage conversion, high efficiency, low voltage stress and less switching loss. The voltage tripler technique is implemented in the proposed model. The designed converter model attains high static gain with reduced duty cycle. The proposed single switch converter is controlled by fuzzy-PI controlled technique. The working process of the converter under Continuous Conduction Mode (CCM) is explained. The 30 V input source is boosted up to 400 V. The …simulation of the presented converter is done with MATLAB simulink. The hardware prototype is also tested and results are analysed. Show more

Keywords: Continuous conduction mode, duty-cycle, high voltage gain, fuzzy-PI, voltage tripler

DOI: 10.3233/JIFS-169975

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2018

Authors: Chain, Kai | Kuo, Wen-Chung

Article Type: Research Article

Abstract: In this paper, a novel multisignature scheme based on chaotic maps is proposed. The purpose of multisignatures is to manage the complexities of a document signed by numerous signatories. The chaotic system is an encryption method proposed in recent years that has received widespread attention in the field of cryptography. This paper first examines the methods of multisignatures and chaotic maps, and then proposes a multisignature scheme that was designed using chaotic maps. During the processing of a multisignature scheme, to authenticate the precision of the received document, the signatory must first obtain the signed document, and then utilize the …characteristics of chaotic maps to recover the original document. If the recovered document is meaningful, this indicates that no errors exist in the signature, thus the signatures are valid and the document can be signed. If the recovered document is not sufficiently clear, signing is denied. At present, because no relevant literature exists that has proposed utilizing chaotic maps to establish a multisignature system; this paper aims to develop a novel research method. Show more

Keywords: Cryptography, multisignature, digital signature, chaotic maps

DOI: 10.3233/JIFS-169877

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2018