Journal of Intelligent & Fuzzy Systems - Volume 36, issue 3

Authors: Akhtar, Nadeem | Beg, M.M. Sufyan

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. 36, no. 3, pp. 2229-2240, 2019

Authors: Pragadeesh, C. | Jeyaraj, Rohana | Siranjeevi, K. | Abishek, R. | Jeyakumar, G.

Article Type: Research Article

Abstract: Research has proved that DNA Microarray data containing gene expression profiles are potentially excellent diagnostic tools in the medical industry. A persistent problem with regard to accessible microarray datasets is that the number of samples are much lesser than the number of features that are present. Thus, in order to extract accurate information from the dataset, one must use a robust technique. Feature selection (FS ) has proved to be an effective way by which irrelevant and noisy data can be discarded. In FS , relevant features are picked, and result in commendable classification accuracy. This paper proposes a …model that employs a compounded hybrid feature selection technique (Filter + Wrapper) to classify microarray cancer data. Initially, a filter method called Information Gain (IG ) to eliminate redundant features that will not contribute significantly to the final classification is used. Following to that, an evolutionary computing technique (micro Genetic Algorithm (mGA )) to find the best minimal subset of required features is employed. Then the features are classified using a traditional Support Vector Classifier and also cross validated to obtain high classification accuracy, using a minimal number of features. The complexity of the model is reduced significantly by adding mGA , as opposed to already existing models that use various other feature selection algorithms. Show more

Keywords: Genetic algorithm, feature selection, microarray, hybrid methods, classification

DOI: 10.3233/JIFS-169935

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2241-2246, 2019

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

Article Type: Research Article

Abstract: In the context of optimal subset selection, hybrid feature selection approaches play a significant role that has been the topic of a substantial number of studies because of the growing need for data mining applications. In feature subset selection (FSS) problem; there are two significant shortcomings that need to be addressed: At first, finding a suitable filter method that can be reasonably fast and energetically computed for large volume of data, and second, an efficient wrapper strategy that can discriminate the samples over the entire search space in a considerable amount of time. After a study of the shortcomings of …individual feature selection methods (filter or wrapper), this paper investigated a new hybrid feature selection approach with conjunction of filter and wrapper method that can take benefit of both ways for a classification problem. The proposed hybrid uses the filter method as conditional mutual information maximization and wrapper method as genetic algorithm to enhance the overall classification performance and speed up the search process to identify the essential features. The proposed method is known as FWFSS. To get rid of meaningless features and determine the biomarkers, wrapper method as genetic algorithm uses the naïve Bayes (NB) classifier as a fitness function. The proposed method is verified on the University of California, Irvine (UCI) repository, and microarray datasets. From experimental study, it is observed that our approach outperforms convenient methods regarding classification accuracy, the number of optimal features reported in the literature. Show more

Keywords: Data mining, genetic algorithm, conditional mutual information maximization, feature selection

DOI: 10.3233/JIFS-169936

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2247-2259, 2019

Authors: Singh, Namrata | Singh, Pradeep

Article Type: Research Article

Abstract: Breakthrough classification performances have been achieved by utilizing ensemble techniques in machine learning and data mining. Bagging is one such ensemble technique that has outperformed single models in obtaining higher predictive performances. This paper proposes an ensemble technique by utilizing the basic bootstrap aggregating technique on hybridization of two base learners namely Naïve Bayes (NB) and Decision Tree (DT). Before induction of the DT, NB algorithm is employed for eliminating mislabeled or contradictory instances from the training set. Consequently, bagging approach is applied on hybrid NBDT as the base learner. The resultant Bagged Naïve Bayes-Decision Tree (BNBDT) algorithm is then …used for improving the classification accuracy of various multi-class problems. This algorithm iteratively trains the base learner from random samples of the training set, and then performs majority voting of their predictions. The proposed algorithm is compared with both ensemble and single classification techniques such as Random Forest, Bagged NB, Bagged DT, NB, and DT. Experimental results over 52 UCI data sets with bag size 100 demonstrate that the proposed algorithm significantly outperforms the existing algorithms. Show more

Keywords: Bagging, naïve bayes, decision tree, classification, multi-class problems, machine learning, hybrid learner

DOI: 10.3233/JIFS-169937

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2261-2271, 2019

Authors: Panjwani, Bharti | Mohan, Vijay | Rani, Asha | Singh, Vijander

Article Type: Research Article

Abstract:  This article presents optimal drug scheduling in chemotherapeutic treatment for eradication of cancerous cells while maintaining tolerable toxicity for the complete period of treatment. For this purpose a cascade control technique is designed wherein individual 2DOF FOPID controllers are employed to regulate drug concentration and toxicity. Conventional schemes fail to address the needs of divergent objectives of cancer chemotherapy which motivates the authors to employ a multi-objective optimization technique, NSGA-II to optimally tune the controller parameters. 2DOF FOPID, its integer order counterpart and PID control schemes are tested on cancer patient model for comparative analysis. The performance of proposed controller …is evaluated on the basis of number of cancer cells and normal cells remaining at the end of treatment. Further robustness of the controller is analysed for parametric uncertainty in patient model and disturbance in infusion pump which affects the input drug dosages. The results reveal that proposed control scheme provides optimal drug scheduling and is significantly robust in the presence of uncertainty and disturbances. Show more

Keywords: Two degree of freedom-fractional order PID controller, cancer chemotherapy, non-dominated sorting genetic algorithm II, robustness testing

DOI: 10.3233/JIFS-169938

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2273-2284, 2019

Authors: Naser, Husain | Awad, Wasan S. | El-Alfy, El-Sayed M.

Article Type: Research Article

Abstract: This paper presents a deterministic algorithm for approximating the solution of the Symmetric Traveling Salesman Problem (STSP) using a multi perfect matching and partitioning technique. Initially, we find the minimum cost collection of sub-tours that cover all cities, such that each sub-tour consists of at least four edges. The obtained solution is then partitioned into k branches, where k is the length of the smallest sub-tour in the resulting solution. The algorithm solves the sub-problems in parallel and selects the sub-problem with the minimum resulting cost to be partitioned further. The algorithm converges when a complete cycle without …sub-tours is found. The performance of the proposed algorithm is evaluated and compared with the optimal values obtained by some well-known algorithms for solving STSP using 24 instances from the TSPLIB online library. The results of the experiments carried out in this study show that our approach yields optimum or near-optimum solutions in polynomial execution time. Show more

Keywords: Traveling Salesman Problem, Symmetric TSP, approximation algorithms, combinatorial optimization

DOI: 10.3233/JIFS169939

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2285-2295, 2019

Authors: Anusuya Ilamathi, V.S. | Vimala, J. | Davvaz, Bijan

Article Type: Research Article

Abstract:  Residuated lattices are algebraic frameworks with crucial bond to mathematical logic. A multiset is a collection that bearing repetition of objects in it. In this paper, the notion of multisets is applied to filters of residuated lattices and introduced the new concept of multiset filters. The relation between multiset filters and their n-level sets is showed and some principal characterizations of multiset filter are discussed. Furthermore, as an application of the proposed concept, a decision making problem is presented.

Keywords: Multiset, multiset filter, residuated lattices, decision making problem

DOI: 10.3233/JIFS-169940

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2297-2305, 2019

Authors: Pandipriya, AR. | Vimala, J. | Anusuya Ilamathi, VS.

Article Type: Research Article

Abstract:  In 2018, we presented the structure of lattice on one of the efficient hybrid models interval-valued hesitant fuzzy soft set. As a result of this intention, the new idealogy of lattice on IVHFSS was introduced with vital properties and its real life application was examined. In this current work, we instigated how the idea of homomorphism and isomorphism on L - IVHFSS is working and few concomitant theorems are proved.

Keywords: L-, L-

DOI: 10.3233/JIFS-169941

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2307-2310, 2019

Authors: Rajavel, Rajkumar | Iyer, Kanagachidambaresan | Maheswar, R. | Jayarajan, P. | Udaiyakumar, R.

Article Type: Research Article

Abstract:  Future cloud computing creates a new trend of opting service over the internet through some intelligent third-party broker. In cloud market, both consumer and provider compete with each other against the conflicting requirements, and the competition among cloud providers to trade their services to potential consumers of cloud market. There is an increasing need for automated negotiation framework to quickly reach agreement in competitive cloud market which can provide maximum utility value and success rate among the negotiating parties. Researchers develop various behavioral learning negotiation strategies (such as market driven) in the existing negotiation frameworks for maximizing either the choice …of utility value or success rate of parties. Moreover these strategies can be applicable to the environment, where the opponent’s behaviors are predictable or precisely known. It may be daunting to apply in the dynamically varying competitive cloud market. So, the proposed Adaptive Neuro-Fuzzy Behavioral Learning (ANFBL) strategy can be applicable, where the opponent’s behavior is partially and imprecisely known. Therefore, the proposed strategy can maximize both utility value and success rate without compromising either choice. An extensive simulation is conducted to evaluate the efficiency of strategies which shows that proposed strategy achieve higher utility and higher success rate than existing learning approach, without any negotiation conflict among the parties. Show more

Keywords: Cloud computing, automated negotiation framework, fuzzy behavioral learning, adaptive neuro-fuzzy

DOI: 10.3233/JIFS-169942

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2311-2322, 2019

Authors: Sabeena Begam, S. | Vimala, J.

Article Type: Research Article

Abstract:  Molodtsov instigated the concept of soft set theory as a generic mathematical tool for dealing with uncertainty. Yong Yang et.al propounded the idea of multi-fuzzy soft set and investigated its application in decision making problems. The main objective of this paper is to derive the notion of lattice approach on multi-fuzzy soft set and analyse its application using forecasting process.

Keywords: Soft set, fuzzy soft set, multi-fuzzy soft set, lattice ordered multi-fuzzy soft set

DOI: 10.3233/JIFS-169943

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 3, pp. 2323-2331, 2019