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

Authors: Dhanya, N.M. | Kousalya, G. | Balakrishnan, P.

Article Type: Research Article

Abstract: Due to the advancement of mobile technology, a large number of computationally intensive applications are created for smart phones. But the limitations of battery and processing power of smart phones are making it inferior to laptops and desktop computers. Mobile Cloud Offloading (MCO) allows the smart phones to offload computationally intensive tasks to the cloud, making it more effective in terms of energy, speed or both. Increased networking capacity due to the availability of high speed Wi-Fi and cellular connections like 3G/4G makes offloading more efficient. Even then, the choice of offloading is not always advisable, because of the highly …dynamic context information of mobile devices and clouds. In this paper, we propose a dynamic decision making system, which will decide whether to offload or do the tasks locally, depending on the current context information and the optimization choice of the user. Metrics are developed for time, energy and combination of time and energy to assess the proposed system. A test bed is implemented and the results are showing improvements from the currently existing methods. Show more

Keywords: Mobile cloud, context aware offloading, decision making, application partitioning, offloading prediction

DOI: 10.3233/JIFS-169251

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3081-3089, 2017

Authors: Mathaba, Sizakele | Adigun, Matthew | Oladosu, John | Oki, Okikayode

Article Type: Research Article

Abstract: Radio Frequency Identification (RFID) uses sensors to enable communication among things or objects in what is called Internet of Things (IoT) technology. Web 2.0 tools, on the other hand, are used on electronic devices (phones, PDAs, computers, etc.) to transmit data contents over the internet. In this study, we used a synergy of both technologies to enhance inventory control. We proposed software architecture which combined RFID and Web 2.0 tool advantages. The proposed architecture was used to develop an inventory management software prototype focused on enterprises in developing countries in Africa, specifically South Africa. The inventory management prototype was developed …and was able to detect misplaced products and low stock levels, and send notification on Twitter to update inventory managers on mobile phone. Scalability measurements of the software were taken to validate the performance of the software prototype. The findings show that the system scaled reliably with increasing numbers of items read. The contribution of this work was compared to existing literature and our findings are presented in this paper. Real- life evaluation for a specific industry will be necessary to further reveal what improvements would be required to make this architecture more relevant. Behavioural study of users will also be required to further determine the economic and social benefits of this approach. Show more

Keywords: Internet of Things (IoT), Radio Frequency Identifier (RFID), inventory management, software architecture, social network

DOI: 10.3233/JIFS-169252

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3091-3101, 2017

Authors: Oki, Olukayode A. | Olwal, Thomas O. | Mudali, Pragasen | Adigun, Matthew

Article Type: Research Article

Abstract: Spectrum decision is the capability of Secondary Users to choose the best accessible spectrum band to satisfy a user’s Quality of Service (QoS) requirements. Spectrum decision comprises three primary functions; spectrum characterization, spectrum selection and dynamic reconfiguration of cognitive radio. The study of dynamic reconfiguration of transceiver parameters in spectrum decision making has been motivated because of its importance to the realization of efficient spectrum utilization and management in distributed mobile cognitive radio networks. Spectrum decision making in a distributed cognitive radio network is crucial, so as to ensure that an appropriate frequency and channel bandwidth are selected to meet …the QoS requirements of different types of applications and to maintain the spectrum quality. In attempting to address the issue of dynamic reconfiguration of transceiver parameters in decision making for cognitive radio networks, different approaches can be found in the literature. However, due to some of the challenges associated with these approaches such as high computational complexity, ambiguity, non-repeatability and non-deplorability of these classical approaches, researchers are still trying to explore other techniques that will be less ambiguous, more efficient, more understandable and easier to deploy in a highly dynamic environment like distributed cognitive radio networks. Hence, this paper reviews the existing approaches, identifies the challenges and proposes a biologically inspired optimal foraging approach to address the decision making problem and other problems relating to the existing approaches. Show more

Keywords: Cognitive radio, distributed, foraging, spectrum, reconfiguration

DOI: 10.3233/JIFS-169253

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3103-3110, 2017

Authors: Rajarajeswari, PL. | Karthikeyan, N.K.

Article Type: Research Article

Abstract: The lifetime of a Wireless Sensor Network (WSN) depends on the efficiency of the Cluster Head (CH) selection techniques that address most of the significant issues related to network management. The existing energy based CH selection mechanisms consider that all the participating sensors are trustworthy. Conversely, the trust-based CH selection schemes assume that the sensor nodes are energy efficient. But, these assumptions of energy factor or trust assessment made by the CH selection mechanisms may not be true and the Residual Energy (RE) of the sensors may not be the sole factor to identify an effective CH in a WSN. …Hence, this paper presents hybrid integrated energy and trust assessment based forecasting model known as Hyper-geometric Energy Factor based Semi-Markov Prediction Mechanism (HEFSPM) for effective CH election so as to improve the lifetime of the network. From the simulation results, it is inferred that HEFSPM is superior in improving the lifetime of the network to a maximum extent of 22% than the existing CH election mechanisms considered for investigation. Show more

Keywords: Semi-markov process, Cluster Head, Hyper-geometric distribution, energy, trust assessment, prediction probability

DOI: 10.3233/JIFS-169254

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3111-3120, 2017

Authors: Kalra, Bhawna | Sharma, J.B.

Article Type: Research Article

Abstract: In multi carrier OFDM systems parameters like speed, throughput and hardware area can be improved by using efficient Fast Fourier Transform approach. In this paper an area efficient and high speed 32 bit floating point FFT processor for OFDM using Vedic multiplication process is presented. Proposed FFT processor is based on memory based architecture and utilizing Urdhavatiraykbhyam sutra for Vedic multiplication. As the number of inbuilt multipliers available in FPGAs are limited, hence external multiplication module are required in the multicarrier OFDM systems, in order to reduce the complexity of FPGA implementation. By the use of Vedic multiplication process in …FFT of OFDM high throughput with smaller area can be achieved. Simulation results explain that the proposed scheme is having high speed and throughput. Show more

Keywords: FFT, OFDM, Vedic multiplication

DOI: 10.3233/JIFS-169255

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3121-3128, 2017

Authors: Bhullar, Rohit K. | Pawar, Lokesh | Bajaj, Rohit | Manocha, Amit K.

Article Type: Research Article

Abstract: Parallel Processing has been a widely studied field, used and implemented in computational systems. Many different types of topologies of processors have been implemented and their performance has been analyzed. The processor technology keeps evolving so their computational capability must be utilized accordingly when employed in parallel systems. In this article, new intra-parallel processor architectures (segmented/heterogeneous) has been used and an intelligent co-operative protocol has been implemented to optimally utilize the parallel components of the parallel processor design. More precisely a friendship based intelligent load balancing strategy has been designed and implemented to maximally utilize the parallel processor, which takes …care of overloading and starvation problems and makes intelligent decisions regarding job scheduling. Context switching policies must also be designed carefully to stop performance degradation and with intelligent techniques this switching time can be reduced considerably. Work proposed in this article performs and executes load stability with feasible priori information about processors utilization, depending upon and based on this metric value the entire process space is partitioned among different categories. Based on the load status and state of affairs, processors are categorized and labeled and a suitable set out of those is figured-out that act as buddy for others and handles incoming process queue for overloaded processors. Further history and statistics of each processors is maintained and is utilized to make intelligent future scheduling decisions. Show more

Keywords: Intelligent systems, parallel environment, intra-processor parallelism, load stability, job scheduling strategy

DOI: 10.3233/JIFS-169256

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3129-3142, 2017

Authors: Soumya, T. | Thampi, Sabu M.

Article Type: Research Article

Abstract: The night video fusion algorithms integrate the visuals captured by a security surveillance camera, which in turn improve the visual perception. The recent development in night fusion research focused on fusing both illuminated and non-illuminated areas simultaneously however, the natural color of the light area may be lost. Moreover, the contrast of the illuminated regions decreases because of the dark pixels surrounding those regions. Hence, the color and contrast should be improved to obtain the actual color of the illuminated regions. We propose a fuzzy inference system based wavelet fusion to enhance the light regions of a nonuniform illuminated night …video surveillance system. To include spatial and temporal variations of the illuminated regions, a spatio-temporal illumination approach is used. A contribution index of the illuminated regions is generated using a fuzzy membership function. Subsequently, the stationary wavelets are used to decompose high-frequency and low-frequency coefficients of both night and day background frames for frame fusion. The contribution index selects the illuminated regions presented in these wavelet coefficients for fusion. Finally, the inverse wavelet transform is applied to reconstruct the illumination enhanced frame. The proposed approach effectively highlights the illuminated regions and provides a better visual perception. Show more

Keywords: Night video surveillance, stationary wavelet transform, frame fusion, fuzzy inference system

DOI: 10.3233/JIFS-169257

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3143-3149, 2017

Authors: Vishnu Pradeep, V. | Sowmya, V. | Soman, K.P.

Article Type: Research Article

Abstract: Remote sensing satellites are proficient in taking earth images across various regions in visible part of electromagnetic spectrum. The images can be panchromatic image of a single band, multispectral image of three to seven different bands, and hyperspectral image taken from about 220 contiguous spectral bands. These images are used together or on its own, depending on the significance and usage of the preferred application. Pan-sharpening is one method which is used to improve the quality of a low resolution multispectral image by fusion with a high resolution panchromatic image. This paper proposes a method based on M-band wavelets for …the pan-sharpening of a low resolution multispectral image. The method tries to improve the spatial characteristics while preserving the spectral quality of the data. The proposed technique uses weighted fusion rule and average fusion rule. The data used for the experiment were acquired by high resolution optical imagers onboard QuickBird, WorldView-3, WorldView-2 and GeoEye-1. A comparison with existing fusion techniques is done based on image quality metrics and visual interpretation. The experimental results and analysis suggests that the proposed pan-sharpening technique outperforms other compared pre-existing pan-sharpening methods. Show more

Keywords: M-band wavelets, pan sharpening, average fusion rule, weighted fusion rule, image quality metrics

DOI: 10.3233/JIFS-169258

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3151-3158, 2017

Authors: Kaur, Arshvir | Sood, Nitakshi | Aggarwal, Naveen | Vij, Dinesh | Sachdeva, Bhavdeep

Article Type: Research Article

Abstract: Traffic congestion occurs when the number of the vehicles increases more than the existing space of the road. This deleterious problem is increasing at an alarming rate in the whole world. For any effective Intelligent Transportation System, early detection of traffic congestion is very important to take corrective action. Several techniques have been developed to detect traffic congestion, most of which are infrastructure based. Even though these techniques are widely used, but they have many downsides as well. They require large capital input for installation as well as for maintenance. In this paper, we propose an efficient and cost-effective method …using smartphones to determine the traffic state of the road. The acoustic data collected from commuter’s smartphone is segmented into fixed size frames. Various time and frequency based features such as (MFCC, Delta & Delta-Delta, ZCR, STE, and RMS) are extracted from each frame and used for detecting traffic state as ’busy street’ or ’quiet street’. We have compared the accuracy of two classifiers Support Vector Machines and Neural Network by using acoustic data collected from 320 different recording sessions. Experiments have shown that feature set having features MFCC, STE and RMS, results in better classification accuracy of 91.8% with Neural Network and 93% with SVM. Furthermore, various relevant factors affecting the classification accuracy are also tested like frame size, window functions, overlapping size and different combination of features. The frame size of 8192 and hamming window function proved to be more efficient than others. Show more

Keywords: Acoustic signal, traffic state, temporal features, spectral features, support vector machine, Neural Network

DOI: 10.3233/JIFS-169259

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3159-3166, 2017

Authors: Dewasthale, Mugdha | Kharadkar, R.D.

Article Type: Research Article

Abstract: Least Mean Square (LMS) and Normalized-Least Mean Square (NLMS) algorithms are very popular and frequently used algorithms for noise cancellation in speech. But selecting the step size for updating the weight of adaptive filter is the big issue in LMS and NLMS algorithms. So as to meet disagreeing requirements of quick convergence and less MSE, step size needs to be correctly controlled. Along with step size, length of adaptive filter also plays major role in the effective noise cancellation. These two factors greatly affect the performance of the ANC. To get the best possible solution, a variety of trials of …filter length and step size are required. The main motivation behind the development of proposed High Performance Self Tuning (HPST) adaptive filter algorithm is to adaptively determine the step size. The selection of length of adaptive filter is based on the distance between two microphones in the ANC system. The proposed algorithm works very well, as shown in the experiments which are carried out on NOIZEUS speech corpus as well as actually recorded noisy speech signals. Results indicate that proposed algorithm is superior to referred algorithms in terms of Mean- Square- Error (MSE), Peak- Signal to Noise ratio (PSNR), convergence time and complexity. Show more

Keywords: Convergence time, complexity, filter length, LMS, MSE, NLMS, step size, PSNR

DOI: 10.3233/JIFS-169260

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3167-3176, 2017

Authors: Vidyadharan, Divya S. | Thampi, Sabu M.

Article Type: Research Article

Abstract: Detecting forged digital image has been an active research area in recent times. Tampering introduces artifacts within images that differentiate tampered images from authentic images. Forgery detection techniques try to identify these artifacts by analyzing differences in the texture properties of the image. In this paper, we propose a multi-texture description based method to detect tampering. Different texture descriptors considered are Local Binary Pattern, Local Phase Quantization, Binary Statistical Image Features and Binary Gabor Pattern. The method captures subtle texture variations at different scales and orientation using Steerable Pyramid Transform (SPT) decomposition of image. The different texture descriptors extracted from …each subband image after SPT decomposition is combined to form the multi-texture representation. Then, ReliefF feature selection method is applied on this high dimensional multi-texture representation to generate a compact representation. This compact multi-texture representation is classified using Random Forest classifier. We have evaluated the performance of individual texture descriptors and multiple textures in detecting image forgery. Experimental results show that the compact multi-texture description has improved detection accuracy. Show more

Keywords: Image forgery detection, multi-texture description, image tampering detection

DOI: 10.3233/JIFS-169261

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3177-3188, 2017

Authors: Sarkhel, Ritesh | Chowdhury, Tithi Mitra | Das, Mayuk | Das, Nibaran | Nasipuri, Mita

Article Type: Research Article

Abstract: Evolutionary Algorithms (EA) are robust optimization approaches which have been successfully applied to a wide range of problems. However, these well-established metaheuristic strategies are computationally expensive because of their slow convergence rate. Opposition Based Learning (OBL) theory has managed to alleviate this problem to some extent. Through simultaneous consideration of estimates and counter estimates of a candidate solution within a definite search space, better approximation of the candidate solution can be achieved. Although it addresses the slow convergence rate to some extent, it is far from alleviating it completely. The present work proposes a novel approach towards improving the performance …of OBL theory by allowing the exploration of a larger search space when computing the candidate solution. Instead of considering all the components of the candidate solution simultaneously, the proposed method considers each of component individually and attempts to find the best possible combination by using a metaheuristic technique. In the present work, this improved Opposition learning theory has been integrated with the classical HS algorithm, to accelerate its convergence rate. A comparative analysis of the proposed method against classical Opposition Based Learning has been performed on a comprehensive set of benchmark functions to prove its superior performance. Show more

Keywords: Evolutionary Algorithms, Opposition Based Learning, Harmony Search algorithm, optimization

DOI: 10.3233/JIFS-169262

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3189-3199, 2017

Authors: Tsuya, Kohei | Takaya, Mayumi | Yamamura, Akihiro

Article Type: Research Article

Abstract: The firefly algorithm is applied to the uncapacitated facility location problem which is a well known optimization problem. The light absorption coefficient parameter γ of the firefly algorithm is examined to obtain better performance and suitable values of γ are explored for the uncapacitated facility location problem. Effectiveness of local search in the firefly algorithm is also investigated. In addition, the firefly algorithm equipped with local search is compared with the artificial bee colony algorithm with respect to average relative percent error and hit to optimum rate.

Keywords: Metaheuristic, swarm intelligence, firefly algorithm, artificial bee colony algorithm, uncapacitated facility location problem

DOI: 10.3233/JIFS-169263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3201-3208, 2017

Authors: Nair, Jyothisha J. | Thomas, Susanna

Article Type: Research Article

Abstract: Graphs are considered to be one of the best studied data structures in discrete mathematics and computer science. Hence, data mining on graphs has become quite popular in the past few years. The problem of finding frequent itemsets in conventional data mining on transactional databases, thus transformed to the discovery of subgraphs that frequently occur in the graph dataset containing either single graph or multiple graphs. Most of the existing algorithms in the field of frequent subgraph discovery adopts an Apriori approach based on generation of candidate set and test approach. The problem with this approach is the costlier candidate …set generation, particularly when there exist more number of large subgraphs. The research goals in frequent subgraph discovery are to evolve (i) mechanisms that can effectively generate candidate subgraphs excluding duplicates and (ii) mechanisms that find best processing techniques that generate only necessary candidate subgraphs in order to discover the useful and desired frequent subgraphs. In this paper, a two phase approach is proposed by integrating Apriori algorithm on graphs to frequent subgraph (FS) tree to discover frequent subgraphs in graph datasets. Show more

Keywords: Frequent subgraph mining, Apriori, graph mining, frequent subgraph tree

DOI: 10.3233/JIFS-169264

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3209-3219, 2017

Authors: Abhishek, S.N. | Balakirithikaa, R.B. | Madhan, C. | Vasudevan, Shriram K.

Article Type: Research Article

Abstract: Life is nothing less than a hell without any entertainment in it. Thanks to mobile phones that let us entertain ourselves on the go. Mobile phones that are being launched nowadays, come with super impressive features that revolve around entertainment. Mobile manufacturers know it pretty well that entertainment has become an indispensable part of human life in the current era. This is the reason why mobile phones are nothing but a complete portable entertainment package. The main source of portable entertainment is music. A very common but irritating problem faced by the youngsters of this era is missing their favorite …beat, or pausing a song frequently while conversing with someone. This seems to be negligible, but irritates most as they have to rewind the song or even restart it from beginning for the single beat. So think about a system that stops playing when the buds are taken off, and automatically continues when it is placed back. This seems to be simple but it is not so. This will change the whole experience of enjoying media, making a new mile stone in the entertainment world. This system will bring a new generation of media players that not only allows us to listen to our favorite music whenever we want but also allows automatic access without having to unlock our phones every now and then for the same. Show more

Keywords: Earphone, intelligent earphones, auto pause, auto play, ear plug controller

DOI: 10.3233/JIFS-169265

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3221-3228, 2017