Journal of Intelligent & Fuzzy Systems - Volume 32, issue 4

Authors: El-Alfy, El-Sayed M.

Article Type: Research Article

Abstract: Computer-based testing systems take advantage of the interaction between computers and individuals to sequentially customize the presented test items to the test-taker’s ability estimate. Administering such sequential adaptive tests has many benefits including personalized tests, accurate measurement, item security, and substantial cost reduction. However, the design of such intelligent tests is a complex process and it is important to explore the impact of various parameters and options on the performance before switching from traditional tests in a particular environment. Although Monte Carlo simulation is a typical tool for achieving this purpose, it depends on generating pseudo-random samples, which may fail …to effectively represent the environment under study and thus incorrect inferences can be drawn. This paper presents a comprehensive case study to evaluate and compare the performance of a number of sequential adaptive testing procedures but using post-hoc simulation, where items of a real conventional test are re-administered adaptively. The comparisons are based on the number of administered items, standard error of measurement, item exposure rates, and correlation between adaptive and non-adaptive estimates. It is found that the results varies based on the settings. However, Bayesian estimation with adaptive item selection can lead to greater savings in terms of the number of test items without jeopardizing the estimated ability. It also has the lowest average exposure rate for each item. Show more

Keywords: Latent trait modeling and estimation, adaptive testing, sequential testing, maximum-likelihood estimation, Bayesian estimation, real-data simulation, item response theory, human-computer interaction

DOI: 10.3233/JIFS-169241

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2977-2986, 2017

Authors: Sharma, Chhavi | Bedi, Punam

Article Type: Research Article

Abstract: With the enormous growth in the volume of online data, users are flooded with a gigantic amount of information. This has made the task of Recommender systems (RSs) even more engrossing. Research in RSs has been revolving around newer concepts like social factors, context of the user and the groups they belong to. This paper presents the design and development of a Community based Collaborative Filtering Recommender System (CCFRS). Louvain method of community detection has been applied to discover communities in the dataset. The method of generating recommendations is based on the proposed idea of Item Frequency-Inverse Community Frequency (IF-ICF) …score of each item in the target user’s community. IF scores help finding the set of items which are unique to a particular community. ICF values are inversely proportional to the number of communities in which an item has been rated. It is used to calculate the uniqueness of the item across the communities. The IF-ICF scores of the items are further employed to find the prediction scores of items unseen by the user in order to present a set of top ‘n’ recommendations to the user. A prototype of the system is developed using Java and experimental analysis has been carried out for the domain of books. Show more

Keywords: Recommender Systems (RSs), community detection, Louvain method, IF-ICF

DOI: 10.3233/JIFS-169242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2987-2995, 2017

Authors: Gautam, Anjali | Bedi, Punam

Article Type: Research Article

Abstract: Proliferation of information is a major confront faced by e-commerce industry. To ease the customers from this information proliferation, Recommender Systems (RS) were introduced. To improve the computational time of a RS for large scale data, the process of recommendation can be implemented on a scalable, fault tolerant and a distributed processing framework. This paper proposes a Content-Based RS implemented on scalable, fault tolerant and distributed framework of Hadoop Map Reduce. To generate recommendations with improved computational time, the proposed technique of Map Reduce Content-Based Recommendation (MRCBR) is implemented using Hadoop Map Reduce which follows the traditional process of content-based …recommendation. MRCBR technique comprises of user profiling and document feature extraction which uses the vector space model followed by computing similarity to generate recommendation for the target user. Recommendations generated for the target user is a set of Top N documents. The proposed technique of recommendation is executed on a cluster of Hadoop and is tested for News dataset. News items are collected using RSS feeds and are stored in MongoDB. Computational time of MRCBR is evaluated with a Speedup factor and performance is evaluated with the standard evaluation metric of Precision, Recall and F-Measure. Show more

Keywords: Content-based RS, vector space model, distributed computing, Hadoop Map Reduce

DOI: 10.3233/JIFS-169243

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 2997-3008, 2017

Authors: Singh, Harpreet | Kaur, Manpreet | Kaur, Parminder

Article Type: Research Article

Abstract: As the size of websites continues to grow, current research focuses on the development of intelligent websites which facilitate the browsing by providing a navigation aid to the website users. Web page recommendation systems provide suggestions to the website users about the webpages that may be of concern to them by evaluating the collective navigation behavior of previous website users. The main motive of this study was to explore the utilization of partially ordered sequential rules (POSR) in making future predictions for website users. Sequential rules provide the association between the events that occur in a particular sequence. In this …paper, two sequential rule mining algorithms, namely TRuleGrowth and CMRules have been separately used to generate sequential rules. Then the sequential rules were used to make predictions about the future interests of the users regarding webpages. The experimental results on a real life dataset have revealed that the rules generated by TRuleGrowth algorithm were able to make predictions with higher accuracy than those generated by CMRules algorithm. Show more

Keywords: Sequential rule mining, partially ordered sequential rules, recommendation system

DOI: 10.3233/JIFS-169244

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3009-3015, 2017

Authors: Nangrani, S.P. | Bhat, S.S.

Article Type: Research Article

Abstract: This paper presents a novel Perturb-Boost Fuzzy Logic Controller for controlling the instability of nonlinear dynamical system behavior. Several applications can make use of the small perturbation technique discussed in the paper related to industrial control, mechanical nonlinear systems, electrical systems and other systems governed by nonlinear differential equations. This paper presents the power system as an application for use of novel controller to control voltage instability problem. The power system is an electro-mechanical nonlinear dynamical system, and is described by a combination of electrical and mechanical parameter based differential equations together. The power system faces problems related to voltage …instability and chaos. Voltage instability exists in almost every power system for a specific set of mechanical power, electrical loading and initial conditions. Voltage instability can be controlled by injecting a small amount of reactive power using a power electronic device called a Static Volt Ampere Reactive Compensator. The amount of reactive power to be injected is trivial for different types and sizes of the power system. To control voltage instability, reactive power in a power system needs to be boosted. Proposed controller output decides amount of reactive power which perturbs the system equation to the stable operating point. The proposed Perturb-Boost Fuzzy Logic Controller differs from conventional controllers due to its single shot boost action, which perturbs system dynamics in such a way as to push it to safe zones of voltage stability. This paper analyzes the performance of the proposed controller to control the voltage instability for the generalized three node power system benchmark model. Reactive power to be injected is momentary due to single shot boost action. Time to reach instability gets delayed by approximately fifteen seconds using proposed controller for the benchmark model. Mitigation of voltage collapse is discussed in view of simulation results using proposed novel controller. Show more

Keywords: Fuzzy logic controller, chaos, nonlinear dynamical systems, nonlinear systems, power system stability

DOI: 10.3233/JIFS-169245

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3017-3029, 2017

Authors: Sheik Mohammed, S. | Devaraj, D. | Imthias Ahamed, T.P.

Article Type: Research Article

Abstract: Optimization of fuzzy Maximum Power Point Tracking (MPPT ) controller using Learning Automata (LA ) algorithm is proposed in this paper. The optimal duty cycle of the DC-DC converter circuit is obtained using LA for various environmental conditions through learning process. The fuzzy MPPT controller is developed using the information collected by LA through the learning process. The proposed model is developed and tested using MATLAB for standard test conditions of PV, constant temperature and varying irradiation level, constant irradiation and varying temperature level, and varying temperature and varying irradiation level. The results obtained using the proposed fuzzy MPPT are …compared with the conventional Perturb and Observe (P&O) MPPT and variable step size Fuzzy MPPT based PV system. The experimental set up is developed and the test is conducted under different conditions for the solar PV system with P&O MPPT and the proposed LA Fuzzy MPPT. The results show that the proposed LA based Fuzzy MPPT method is more accurate and its tracking response is faster. Show more

Keywords: Learning Automata, Pursuit Algorithm, Maximum Power Point Tracking, Fuzzy, photovoltaic, MATLAB

DOI: 10.3233/JIFS-169246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3031-3041, 2017

Authors: Malik, Hasmat | Sharma, Rajneesh

Article Type: Research Article

Abstract: In the presented work, an intelligent model for fault classification of a transmission line is proposed. Ten different types of faults (LAG, LBG, LCG, LABG, LBCG, LCAG, LAB, LBC, LCA and LABC) have been considered along with one healthy condition on a simulated transmission line system. Post fault current signatures have been used for feature extraction for further study. Empirical Mode Decomposition (EMD) method is used to decompose post fault current signals into Intrinsic Mode Functions (IMFs). These IMFs are used as input variables to an artificial neural network (ANN) based intelligent fault classification model. Relief Attribute Evaluator with Ranker …search method is used to select the most relevant input variables for fault classification of a three-phase transmission line. Proposed approach is able to select most relevant input variables and gives better result than other combinations. Ours is a first attempt at using EMD for feature selection in fault classification of transmission lines. Show more

Keywords: Empirical mode decomposition, artificial neural network, transmission line, fault diagnosis, feature selection

DOI: 10.3233/JIFS-169247

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3043-3050, 2017

Authors: Raval, P.D. | Pandya, A.S.

Article Type: Research Article

Abstract: The paper presents a novel idea of protection of the multi-terminal Extra High Voltage (EHV) transmission line having multiple Series compensation. A statistical learning perspective for improved classification of faults using Artificial Neural Networks (ANN) has been proposed. The protective scheme uses single end cur-rent data of three phases of line to detect and classify faults. A Multiresolution Analysis (MRA) wavelet transform is employed to decompose the signals acquired and further processed to extract statistical features. The statistical features learning algorithm utilizes a set of ANN structures with a different combination of Neural Network parameters to determine the best ANN …topology for Classifier. The algorithm generates different fault patterns arising out of different fault scenarios and altering system parameters in the test system. The features are selected based on ANOVA F-test statistics to determine relevance and improve classification accuracy. The features thus selected from fault patterns are given to the Hybrid Wavelet-ANN structure. The ANN once trained on a part of data set is later tested on the other part of unseen patterns and further validated on rest of the patterns. To provide a comparative Support Vector Machine Classifier is used to classify the fault patterns. A 5 fold cross validation is used on the data set to check the accuracy of SVM. It is shown that the proposed method using Pattern Recognition using Hybrid structure provides a high accuracy with reliability in identifying and classifying fault patterns as opposed to SVM. Show more

Keywords: Series compensation, multi-resolution analysis (MRA), artificial neural network, SVM, feature selection

DOI: 10.3233/JIFS-169248

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3051-3058, 2017

Authors: Sreedhanya, L.R. | Varghese, Abi | Nair, Madhu S. | Wilscy, M.

Article Type: Research Article

Abstract: Based on a flame image processing technology, a fuzzy based temperature monitoring system in a rotary kiln was reported. In this paper, we propose a Fuzzy based flame analysis, which consider Red, Green and Blue intensity planes, to measure the temperature from the flame image. The proposed approach integrates RGB intensity as fuzzified input variables, temperature as defuzzified output variables and fuzzy inference rules based Mamdani models. Based on the color characteristics of burning flame, temperature of different flame zones are located using a fuzzy logic controller. The temperature level at hotspot area is the highest and through the fuzzy …analysis we were able to identify hotspot area from the flame image. In order to evaluate the performance of the proposed method, quantitative metric such as f-measure has been used and it was found that the f-measure metric yields high accuracy for the hotspot area. The visual inspection of the results along with the f-measure values showed the superiority of our work. Experimental results indicate that the proposed approach can be applied to a high resolution video flame image. Show more

Keywords: Temperature mapping, flame image analysis, fuzzy inference system, rotary kiln, Mamdani model

DOI: 10.3233/JIFS-169249

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3059-3067, 2017

Authors: Chen, Xin | Yang, Pengfei | Qiu, Tie | Yin, Hao | Ji, Jianwei

Article Type: Research Article

Abstract: With the development of network technology, the Internet portals have been constantly changing. However, such portals cannot meet the requirements of Internet of Everything (IoE) communication between people and things or between things themselves. Inspired by the ideal of container, Mobile Cross-platform Application Development Framework (MCADF) and Platform as a Service (PaaS), we designed a “Cloud + Container” portal platform for IoE. The cloud provides application development, testing, deployment, operation, management, and other functions. It is also responsible for data storage, management and analysis. The terminal of user container is used to carry and manage mass applications and IoE data, …and provide user access entry. In this paper, we first introduce the background of IoE and related problems, then give the detailed design of the platform. Finally, we evaluate the performance of the platform comparing with other platforms. Show more

Keywords: IoE, container, MCADF, PaaS, cloud platform

DOI: 10.3233/JIFS-169250

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3069-3080, 2017