Journal of Intelligent & Fuzzy Systems - Volume 34, issue 3

Authors: Tripathi, Diwakar | Edla, Damodar Reddy | Cheruku, Ramalingaswamy

Article Type: Research Article

Abstract: Credit scoring is a procedure to estimate the risk related with credit products which is calculated using applicants’ credentials and applicants’ historical data. However, the data may have some redundant and irrelevant information and features, which lead to lower accuracy on the credit scoring model. So, by eliminating the redundant features can resolve the problem of credit scoring dataset. In this work, we have proposed a hybrid credit scoring model based on dimensionality reduction by Neighborhood Rough Set (NRS) algorithm and layered ensemble classification with weighted voting approach to improve the classification performance. For classifiers’ raking, we have proposed a …novel classifier ranking algorithm as an underlying model for representing ranks of the classifiers based on classifier accuracy. It is used on seven heterogeneous classifiers for finding the ranks of those classifiers. Further five best ranking classifiers are used as base classifier in layered ensemble framework. Results of the ensemble frameworks (Majority Voting (MV), Weighted Voting (WV), Layered Majority Voting (LMV), Layered Weighted Voting (LWV)) with all features and after feature reduction by various existing feature selection algorithms are compared in terms of accuracy, sensitivity, specificity and G -measure. Further, results of ensemble frameworks with NRS are also compared in terms of ROC curve analysis. The experimental outcomes reveal the success of proposed methods in two benchmarked credit scoring (Australian credit scoring and German loan approval) datasets obtained from UCI repository. Show more

Keywords: Weighted voting, classification, feature selection, ensemble learning, credit scoring

DOI: 10.3233/JIFS-169449

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1543-1549, 2018

Authors: Singh, Shivkaran | Anand Kumar, M. | Soman, K.P.

Article Type: Research Article

Abstract: Neural machine translation is an approach to learn automatic translation using a large, single neural network. It models the whole translation process in an end-to-end manner without requiring any additional components as in statistical machine translation systems. Neural machine translation has achieved promising translation performances. It has become the conventional approach in machine translation research nowadays. In this work, we applied neural machine translation for English-Punjabi language pair. In particular, attention based mechanism was used for developing the machine translation system. We also developed the parallel corpus for English-Punjabi language pair. As of now, we are releasing version-1 of the …corpus and it is freely available for any non-commercial research. To the best of author’s knowledge, there is no relevant literature on neural/statistical machine translation implementation for English-Punjabi language pair as of this writing. To evaluate the system, BLEU evaluation metric was used. To quantify system’s performance, the results obtained were further compared with existing systems such as AnglaMT and Google Translate. The BLEU score of the developed system exceeds both of these systems marginally. Show more

Keywords: Neural machine translation, recurrent neural network, long short term memory, English Punjabi machine translation

DOI: 10.3233/JIFS-169450

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1551-1559, 2018

Authors: Mohanapriya, N. | Kousalya, G. | Balakrishnan, P. | Pethuru Raj, C.

Article Type: Research Article

Abstract: Cloud computing offers utility-based IT services on-demand to the users on a pay-per-use-basis. The cloud centers consist of physical machines (PMs) with virtual machines (VMs). These data centers consume a large amount of energy due to the improper resource utilization and lack of efficient scheduling algorithms to perform the task-resource mapping. These issues lead to huge energy consumption along with high maintenance costs and carbon emissions. In this paper, a Power Efficient Scheduling and VM Consolidation (PESVMC) algorithm is proposed to address these issues and the associated challenges. The numerous existing research works concentrated on the application of energy management …techniques to hardware level support for the reduction of energy consumption. The proposed algorithm emphasizes on the software level by taking the flexibility of the virtualization technology and it consists of two phases, VM Scheduling phase, and VM Consolidation phase. In the Scheduling phase, the tasks with maximum runtime are allocated to VM, which is expected to consume minimal energy. In the VM Consolidation phase, overloaded and underloaded hosts are determined based on the double-threshold scheme. Further, Live Migration technique is applied for migrating the VMs from over-utilized or underutilized hosts to other hosts with the normal utilization. A power efficient utilization factor is introduced to determine the underloaded hosts. This utilization factor is proven to reduce the number of migrations, which can cause additional energy consumption. Energy efficient Scheduling combined with VM Consolidation is successful in maximizing the resource utilization and minimizing the energy consumption. The experimental evaluation is performed using WorkflowSim and the proposed algorithm achieves significant energy conservation and resource utilization. Show more

Keywords: Workflow scheduling, energy efficiency, VM consolidation

DOI: 10.3233/JIFS-169451

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1561-1572, 2018

Authors: Jain, Mohit | Maurya, Shubham | Rani, Asha | Singh, Vijander

Article Type: Research Article

Abstract: This paper presents, a novel nature-inspired optimization paradigm, named as owl search algorithm (OSA) for solving global optimization problems. The OSA is a population based technique based on the hunting mechanism of the owls in dark. The proposed method is validated on commonly used benchmark problems in the field of optimization. The results obtained by OSA are compared with the results of six state-of-the-art optimization algorithms. Simulation results reveal that OSA provides promising results as compared to the existing optimization algorithms. Moreover, to show the efficacy of the proposed OSA, it is used to design two degree of freedom PI …(OSA-2PI) controller for temperature control of a real-time heat flow experiment (HFE). Experimental results demonstrate that OSA-2PI controller is more precise for temperature control of HFE in comparison to the conventional PI controller. Show more

Keywords: Nature-inspired algorithm, unconstrained optimization, two degree of freedom PI controller, Heat flow experiment

DOI: 10.3233/JIFS-169452

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1573-1582, 2018

Authors: Shukla, Alok Kumar | Singh, Pradeep | Vardhan, Manu

Article Type: Research Article

Abstract: In the recent era, evolutionary meta-heuristic algorithms is popular research area in engineering and scientific field. One of the intelligent evolutionary meta-heuristic algorithms is Teaching Learning Based Optimization (TLBO). The basic TLBO algorithm follows the isolated learning strategy for the whole population. This invariable learning strategy may cause the misconception of knowledge for a specific learner, which makes it unable to deal with different complex situations. For solving the complex non-linear optimization problems, local optimum frequently happens in the generating process. To resolve these kinds of problem, this paper introduces Neighbour based TLBO (NTLBO) and differential mutation. The concept of …neighbour learning and differential mutation is introduced to improve the convergence solution after each run of experiment. Neighbour learning method maintains the explorative and exploitation search of the population and discourages the premature convergence. The efficiency of the proposed algorithm is evaluated on eight benchmark functions of Congress on Evolutionary Computation (CEC) 2006. The proposed NTLBO present extensive comparative study with the state-of-the-art forms of the meta-heuristic algorithms for standard benchmark functions. The result shows that the proposed NTLBO gives the superior performance over recent meta-heuristic algorithms. Show more

Keywords: Differential mutation, explorative and exploitation, meta-heuristic, neighbour learning, Teaching learning-based optimization

DOI: 10.3233/JIFS-169453

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1583-1594, 2018

Authors: Sreedhar, K.C. | Suresh Kumar, N.

Article Type: Research Article

Abstract: Because of its increasing usage, internet has become an integral component of our daily lives. In this paradigm, users can share their perceptions and collaborate with others easily through social communities. The e-healthcare community service is particularly recommended by individual patients who are remotely located, have embarrassing medical conditions, or have caretaker responsibilities that may prohibit them from obtaining satisfactory face-to-face medical and emotional support. However, participation in such online social collaborations may be constrained due to cultural and language barriers. This paper proposes a privacy-preserving collaborative e-healthcare system that connects and integrates patients or caretakers into different groups. This …system helps them to chat with other patients with similar problems, understand their feelings, and much more. However, patients’ private and sensitive information cannot be disclosed to anyone at any point of time. The recommended model uses a special technique, k-centroid multi-view point similarity algorithm, to cluster e-profiles based on their similarities. Finally, a distributed hashing technique is used to encrypt the clustered profiles to persevere patients’ personal information. The suggested framework is compared with well-known privacy-preserving clustering algorithms to compute accuracy and latency by using popular similarity measures. Show more

Keywords: e-profile, healthcare, k-centroid, symptoms, disease, cluster

DOI: 10.3233/JIFS-169454

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1595-1607, 2018

Authors: Mathew, Terry Jacob | Sherly, Elizabeth | Alcantud, José Carlos R.

Article Type: Research Article

Abstract: The diagnostic prediction models in medical sciences are more relevant today than ever before. The nature and type of the data do have a profound impact on the prediction output. As the nature of data changes, the choice of intelligent methods also has to be altered adaptively to attain promising results. A highly customised data oriented model which encompasses multi-dimensional information can aid and improve the prediction process. This paper proposes an adaptive soft set based intelligent system which is designed to receive a set of input parameters related to any disease and generates the risk percentage of the patient. …The system produces soft sets with the given inputs by fuzzification; followed by rule generation. The rules are analysed to obtain the risk percentage and based on its intensity, the system proceeds with the disease diagnosis. Four different approaches are introduced in this study to enhance the risk prediction accuracy, namely subset of parameters method, adaptive selection of analysis metrics, weighted rules method and the unique set method. The best model is acquired from these approaches in an adaptive fashion by the algorithm. Our method of risk prediction is applied for prostate cancer detection as a case study and we provide exhaustive comparison of the different approaches employed within the algorithm. The results prove that this synergistic approach gives better prediction results than the existing methods. The combination of unique set and weighted approach gave the best predictive solution for the proposed system. Show more

Keywords: Soft set, fuzzy set, adaptive decision making, risk prediction

DOI: 10.3233/JIFS-169455

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1609-1618, 2018

Authors: Harikumar, Sandhya | Akhil, A.S.

Article Type: Research Article

Abstract: High-dimensional data analysis is quite inevitable due to emerging technologies in various domains such as finance, healthcare, genomics and signal processing. Though data sets generated in these domains are high-dimensional, intrinsic dimensions that provide meaningful information are often much smaller. Conventionally, unsupervised clustering methods known as subspace clustering are utilized for finding clusters in different subspaces of high dimensional data, by identifying relevant features, irrespective of labels associated with each instance. Available label information, if incorporated in clustering algorithm, can bias the algorithm towards solutions more consistent with our knowledge, leading to improved cluster quality. Therefore, an Information Gain based …Semi-supervised- subspace Clustering (IGSC) is proposed that identifies a subset of important attributes based on the known label for each data instance. The information about the labels associated with data sets is integrated with the search strategy for subspaces to leverage them into a model based clustering approach. Our experimentation on 13 real world labeled data sets proves the feasibility of IGSC and we validate the clusters obtained, using an improvised Davies Bouldin Index (DBI) for semi-supervised clusters. Show more

Keywords: Subspace clustering, semi-supervised, information gain, entropy

DOI: 10.3233/JIFS-169456

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1619-1629, 2018

Authors: Dai, Yinglong | Wang, Guojun | Li, Kuan-Ching

Article Type: Research Article

Abstract: Deep Neural Networks (DNNs) have powerful recognition abilities to classify different objects. Although the models of DNNs can reach very high accuracy even beyond human level, they are regarded as black boxes that are absent of interpretability. In the training process of DNNs, abstract features can be automatically extracted from high-dimensional data, such as images. However, the extracted features are usually mapped into a representation space that is not aligned with human knowledge. In some cases, the interpretability is necessary, e.g. medical diagnoses. For the purpose of aligning the representation space with human knowledge, this paper proposes a kind of …DNNs, termed as Conceptual Alignment Deep Neural Networks (CADNNs), which can produce interpretable representations in the hidden layers. In CADNNs, some hidden neurons are selected as conceptual neurons to extract the human-formed concepts, while other hidden neurons, called free neurons, can be trained freely. All hidden neurons will contribute to the final classification results. Experiments demonstrate that the CADNNs can keep up with the accuracy of DNNs, even though CADNNs have extra constraints of conceptual neurons. Experiments also reveal that the free neurons could learn some concepts aligned with human knowledge in some cases. Show more

Keywords: Deep neural networks, conceptual alignment, interpretability, supervised learning, representation learning

DOI: 10.3233/JIFS-169457

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1631-1642, 2018

Authors: Gayathri, R.G. | Nair, Jyothisha J.

Article Type: Research Article

Abstract: Computing the all pair shortest paths in a graph is a widely used solution, but a time-consuming process too. The popularly used conventional algorithms rely solely on the computing capability of the CPU, but fail to meet the demand of real-time processing and mostly do not scale well for larger data. In this paper, we propose the ex-FTCD (extending Full Transitive Closure with Dijkstra’s) algorithm for finding the all pair shortest path by merging the features of the greedy technique in Dijkstra’s single source shortest path method and the transitive closure property. Experiments show that the process improves computing speed …and is more scalable. We re-designed the algorithm for the parallel execution and implemented it in mapreduce on Hadoop that supports the conventional map/reduce jobs. This work also includes the implementation on Spark that supports the in-memory computational capability which uses Random Access Memory for computations. The experiments show that the numbers of iterations are relatively small for even large networks. Show more

Keywords: All pair shortest path, transitive closure, distributed computing, greedy approach

DOI: 10.3233/JIFS-169458

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1643-1652, 2018