Journal of Intelligent & Fuzzy Systems - Volume 37, issue 5

Authors: Fayaz, Muhammad | Ullah, Israr | Shah, Abdul Salam | Kim, DoHyuen

Article Type: Research Article

Abstract: Intelligent optimized energy management and prediction model in electric vehicles received attraction of the researchers in the last couple of years. Several techniques and models have been proposed in the literature for optimized energy management and control, but the trade-off between occupant comfort index and the energy consumption is still a significant challenge to the research community. In this paper, we have proposed a model based on learning to optimization and learning to control for user comfort maximization and efficient energy consumption. The proposed model is comprised of three layers; prediction module, learning to optimization module and learning to control …module. In the prediction module, we have used the Kalman filter for noise removal and prediction of environmental parameters. In learning to optimization module, the bat algorithm has been used for user comfort maximization and energy consumption minimization. Furthermore, we have used the learning module with optimization module in order to tune the user preferences parameters in the comfort index formula used in the bat optimization algorithm. Likewise, the learning module has been used with the conventional fuzzy logic controller in order to improve its performance. In the conventional fuzzy logic controller, the membership functions boundaries are usually determined through hit and trial method, and once the membership functions are determined, they remain fixed for the entire process. In the learning to control module, the membership functions tuning is carried out. The membership functions are continuously tuned to get effective results. Experimental results indicate that the proposed method performs better as compared to the conventional methods and achieves improved user comfort with reduced energy consumption. Show more

Keywords: Energy optimization, energy consumption, user comfort, bat algorithm, electric vehicles, learning to control

DOI: 10.3233/JIFS-190095

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6683-6706, 2019

Authors: Saritha, S. | Santhosh Kumar, G.

Article Type: Research Article

Abstract: The spatial colocation problem is totally different from the traditional association rule problem, as it operates on spatial data and not on conventional transaction data. In this work, a spatial colocation mining framework is proposed that mines spatial colocation of image-objects present in images using a tensor factorization approach. The framework takes in image data directly, tensorize it and perform the mining task, thus eliminating the need of converting into a transaction based approach. An interestingness measure called, spatial dominance is also proposed in this work. This measure is an indicator of the prevalence of the mined colocation pattern. Algorithms …are designed in this framework, first to map the classified pixels as members of image-objects, which is a pre-stage before mining and second to find spatial colocation patterns. Experiment results are provided to show the strength of the spatial colocation mining algorithm. Show more

Keywords: Data mining, spatial colocation, tensors, image-objects

DOI: 10.3233/JIFS-190122

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6707-6716, 2019

Authors: Ziari, Shokrollah | Bica, Alexandru Mihai

Article Type: Research Article

Abstract: In this paper, an iterative numerical method has been developed to solve nonlinear fuzzy Volterra integral equations based on three-point quadrature formula. The error estimation of the method is obtained based on Lipschitz condition and in order to confirm the yielded theoretical results, we perform the iterative method on some numerical examples.

Keywords: Nonlinear fuzzy Volterra-Hammerstein integral equations, Iterative numerical method, L-Lipschitz fuzzy functions

DOI: 10.3233/JIFS-190149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6717-6729, 2019

Authors: de Jesús Rubio, José | Garcia, Enrique | Ochoa, Genaro | Elias, Israel | Cruz, David Ricardo | Balcazar, Ricardo | Lopez, Jesus | Novoa, Juan Francisco

Article Type: Research Article

Abstract:  An unscented Kalman filter can be applied for the experimental learning of the solar dryer for oranges drying and the greenhouse for crop growth to know better the processes and to improve their performances. The contributions of this document are: a) an unscented Kalman filter is designed for the learning of nonlinear functions, b) the unscented Kalman filter is applied for the experimental learning of the two mentioned processes.

Keywords: Unscented Kalman filter, greenhouse, solar dryer, experimental learning

DOI: 10.3233/JIFS-190216

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6731-6741, 2019

Authors: Poornappriya, T.S. | Durairaj, M.

Article Type: Research Article

Abstract: The prompt enhancement of Telecom turned to be a vibrant and economical industry, which comprises an intrinsically great perspective for customer churn, requiring exact churn prediction models. In recent times, there has been phenomenal responsiveness in the development of feature selection methods for a large number of datasets. Through this research work, a High Relevancy and Low Redundancy (HRLR) approach by consuming Vague Set (VS) has proposed for selecting the subset of features from the features set. This proposed method is based on the Minimum Redundancy and Maximum Relevancy (MRMR) approach by using Vague Set. The proposed HRLR-VS method is …based on the filtered approach feature selection, where the features are selected only when the measure of feature-class relevancy is maximized and a measure of feature-feature redundancy is minimized. The collaboration of similarity measures and ranking algorithms are prepared by utilizing the vital notions of Vague Sets information energies by Information Gain, Gain Ratio, and Chi-Square methods. The projected approach has been employed with the Particle Swarm Optimization for probing the best feature subset. Further, it measures the efficacy of the projected approach HRHL-VS for telecommunication dataset. The performance metrics like Accuracy, Kappa Statistics, True Positive Rate, Precision, F-Measure, Recall, MAE, RRSE, RMSE and RAE are considered in this paper for evaluating the proposed HRLR-VS method. The proposed HRRL-VS method has compared with existing literature approaches like mRMR and FCBF. From the result obtained in this paper, the proposed HRLR-VS method better results in all aspects for selecting the feature subset in telecommunication dataset. Show more

Keywords: Feature Selection, Vague Set, Information Gain, Gain Ratio, Chi-Square, Particle Swarm Optimization, Euclidean Distance, Cosine Similarity, Pearson’s Correlation Coefficient

DOI: 10.3233/JIFS-190242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6743-6760, 2019

Authors: Zerhari, Btissam | Lahcen, Ayoub Ait | Mouline, Salma

Article Type: Research Article

Abstract: Attribute and class noises are the two important sources of Corruptions (noise) contained in real-world datasets which may deteriorate data interpretation and accuracy. Class noise has potentially serious negative impacts compared to attribute noise, however, the existing major class noise detection methods are not able to address this problem efficiently. To overcome issues related to detection and the elimination of class noise, we suggest a new noise filtering approach able to identify and remove class noise, called Multi-Iterative Partitioning Class Noise Filter (MIPCNF). Since there is no single filter that consistently outperforms its counterparts in all database types and in …different levels of noise, our approach relies on an algorithm in which several rounds of class noise detection are performed on different partitions of the data using several classifiers. Therefore, we use different filtering strategies: iterative noise filter, partitioning filter and ensemble-based filter. The experimental results, on 14 real-world datasets, and statistical analysis, show that our method is not only overcoming the higher noise but also over-performing latest class noise detection and elimination strategies in different levels of noise. Show more

Keywords: Class noise, Noise Detection, Noise Elimination, Partitioning Filter, Large Data

DOI: 10.3233/JIFS-190261

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6761-6772, 2019

Authors: Maheshwari, Karan | Joseph Raj, Alex Noel | Mahesh, Vijayalakshmi G.V. | Zhuang, Zhemin | Rufus, Elizabeth | Shivakumara, Palaiahnakote | Naik, Ganesh R.

Article Type: Research Article

Abstract: In today’s world, there have been lots of unique optical character recognition systems. One drawback of these systems is that they cannot work effectively on natural scene images where the text is not only subject to different orientations, lightning, and background but can be of multiple scripts as well. The paper, proposes a state of the art algorithm to detect texts of different dialects and orientations in an image. The whole text detection pipeline is divided into two parts. First, extraction of probable text regions in an image is performed based on a combination of statistical filters, which results in …a high recall. These regions are then fed to an Artificial Neural Networks (ANN) based classifier which classifies whether the proposed regions are text or non-text, which increases the overall precision. The validity of the algorithm is verified on the most challenging bilingual text detection dataset MSRA-TD500 and a promising F1 score of 0.67 is reported. Show more

Keywords: Text detection, entropy and variance filters, invariant moments, artificial neural networks, bilingual text detector

DOI: 10.3233/JIFS-190339

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6773-6784, 2019

Authors: Vanitha, V. | Krishnan, P.

Article Type: Research Article

Abstract: An e-learning system offering a personalised learning path will be vastly appealing to the learners. Adaptive techniques when employed in e-learning can sustain the interest and motivation of the learners and help them to complete the enrolled courses successfully. In addition, it would improve their performance and thus, enhance the overall learning experience. Personalisation takes into consideration the characteristics of the individual learner and the diversity in his/her needs. The main challenge is finding a match between these individual characteristics and the sequence of the learning content. It is a complex task to implement as it involves selection of the …appropriate material from a vast amount of the available learning materials. It is a challenge to perform this process manually as it requires both technical savvy and pedagogical skills. In this paper, a stigmergy model is proposed, which was applied to build a customised learning path. The aim was to provide personalisation that satisfied the needs of an individual in a widely heterogeneous e-learning environment. Compared with the traditional teaching method, this tailored learning path, generated using the proposed approach, shows promise and was found to enhance the performance of the learners. Show more

Keywords: Learning path, learning content sequence, personalised E-learning, ant colony optimisation, curriculum sequencing

DOI: 10.3233/JIFS-190349

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6785-6800, 2019

Authors: Ajeena Beegom, A.S. | Rajasree, M.S.

Article Type: Research Article

Abstract: Scientific workflow applications include a set of tasks, which have complex inter dependencies with each other, along with a large number of parallel tasks. The problem of scheduling such application tasks involves careful decisions on determining the sequence in which it can be processed, causing high impact on the cost of execution and makespan (execution time), when executed on a cloud computing system. Achieving optimal schedule, which can optimize both of these objectives while keeping the dependencies between tasks intact is a real challenge. In this work, a non-dominated sorting based particle swarm optimization approach to find an optimal schedule …for workflow applications in cloud computing systems is proposed. A graph is used to represent tasks in the workflow and the dependencies among tasks. The optimization problem is modelled using integer programming formulation, subject to capacity and dependency constraints among tasks and Virtual Machines (VM). Simulation studies and result comparison with other representative algorithms in the literature shows that the proposed algorithm is promising. Show more

Keywords: Cloud computing, workflow scheduling, non-dominated sorting, particle swarm optimization, pareto-optimality

DOI: 10.3233/JIFS-190355

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6801-6813, 2019

Authors: Naz, Farah | Kamran, Muhammad | Mehmood, Waqar | Khan, Wilayat | Alkatheiri, Mohammed Saeed | Alghamdi, Ahmed S. | Alshdadi, Abdulrahman A.

Article Type: Research Article

Abstract: The figurative language involving sarcasm on social networks is evolving the way how the humans use computers to communicate. Consequently, artificial intelligence techniques are applied in various scenarios to make the social networking more intelligent - for instance, identification of figurative language. Identifying both literal and non-literal meaning is not easy for a machine and it is hard even for people. Therefore, novel and exact frameworks ready to identify figurative languages are important. In sarcasm detection, this is even more challenging because sarcasm changes the polarity of an evidently positive or negative expression into its inverse. To maintain a …distance for a sarcastic message being comprehended in its unintended actual meaning, in micro-blogging sites, for example messages on Twitter, sarcasm is frequently set apart with a hashtag for example, ’#sarcastic’, '#sarcasm', ’#not’ etc. Moreover, the customer reviews may also contain some element of sarcasm. To contribute to this area, we gathered the data of tweets and reviews from Twitter, thesarcasmdetector.com, and Kaggle and proposed a mechanism for detecting sarcasm automatically using a classifier. A detailed experimental study was also conducted to evaluate the proposed mechanism. The results of this study were quite promising and proved the effectiveness of our approach. Show more

Keywords: Computational semantics, sarcasm detection, intelligent social networking, understanding uncertainty

DOI: 10.3233/JIFS-190596

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6815-6828, 2019