IJISA Vol. 9, No. 8, Aug. 2017
Cover page and Table of Contents: PDF (size: 946KB)
The weight updates are required for group decision-making which has similar parameters used by the decision maker (DM). Each DM as the stakeholder may have similar or different parameters in selecting parameters. Therefore, we have to accommodate the interests of all decision makers (DMs) to obtain alternative decisions. DM who has selected the parameters inputs the initial weight (W_Pi) based on the classical methods, and then recalculates to obtain the updated weights (W_j) until the final weight (W_j^i) is obtained for the alternative of group decision-making (GDM). The initial weight uses a weighting directly or multi criteria decision-making (MCDM). This method aims to provide the fairness for all DMs who have different knowledge in determining the value of the weights and the selection parameters. In order to obtain alternative decisions, we used technique for order preference by similarity to ideal solution (TOPSIS) method to update weight. In this paper, the alternative output of the decisions is applied in two stages: the decisions of each DM and the group, where this output consists of four types of alternatives. Based on the proposed method, the result of GDM shows that the third alternative is recommended in decision-making. This method is effectively performed in decision-making which has different parameters and weights of each DM to support group decision.[...] Read more.
This work falls within the framework of the Arabic natural language processing. We are interested in parsing Arabic texts. Existing parsers generate parse trees that give an idea about the structure of the sentence without considering the syntactic functions specific to the Arabic language. Thus, the results are still insufficient in terms of syntactic information. The system we have developed in this article takes into consideration all these syntactic functions. This system begins with a morphological analysis in the context. Then, it uses a CFG grammar to extract the phrases and ends by exploiting the formalism of unification grammar and traditional grammar to combine these phrases and generate the final sentence structure.[...] Read more.
In recent times enumerable number of clustering algorithms have been developed whose main function is to make sets of objects having almost the same features. But due to the presence of categorical data values, these algorithms face a challenge in their implementation. Also some algorithms which are able to take care of categorical data are not able to process uncertainty in the values and so have stability issues. Thus handling categorical data along with uncertainty has been made necessary owing to such difficulties. So, in 2007 MMR algorithm was developed which was based on basic rough set theory. MMeR was proposed in 2009 which surpassed the results of MMR in taking care of categorical data and it could also handle heterogeneous values as well. SDR and SSDR were postulated in 2011 which were able to handle hybrid data. These two showed more accuracy when compared to MMR and MMeR. In this paper, we further make improvements and conceptualize an algorithm, which we call MMeMeR or Min-Mean-Mean-Roughness. It takes care of uncertainty and also handles heterogeneous data. Standard data sets have been used to gauge its effectiveness over the other methods.[...] Read more.
The proposed method of graphical data protection is a combined crypto-steganographic method. It is based on a bit values transformation according to both a certain Boolean function and a specific scheme of correspondence between MSB and LSB. The scheme of correspondence is considered as a secret key. The proposed method should be used for protection of large amounts of secret graphical data.[...] Read more.
In today's age of digital technology and intelligent systems, home automation has become one of the fastest developing technology in the world as more and more people begin to see the idea of remotely monitoring and controlling their home appliances more as a necessity rather than a luxury. This paper presents the design and development of a smart home system that allows control of home appliances using both Bluetooth and GSM technology. The use of multiple control mediums gives more robustness to the system as appliance control and monitoring is made cheaper and possible regardless of the distance from which control is being effected. The system is controlled using a dedicated android based application which ensures convenience and ease of use. In addition, it is equipped with a security feature which is activated when the user is away from home. This enables the user to detect intrusion while the user is away.[...] Read more.
In this paper considered the problem of reducing the dimension of the feature space using nonlinear mapping the object description on numerical axis. To reduce the dimensionality of space used by rules agglomerative hierarchical grouping of different - type (nominal and quantitative) features. Groups do not intersect with each other and their number is unknown in advance. The elements of each group are mapped on the numerical axis to form a latent feature. The set of latent features would be sorted by the informativeness in the process of hierarchical grouping. A visual representation of objects obtained by this set or subset is used as a tool for extracting hidden regularities in the databases. The criterion for evaluating the compactness of the class objects is based on analyzing the structure of their connectivity. For the analysis used an algorithm partitioning into disjoint classes the representatives of the group on defining subsets of boundary objects. The execution of algorithm provides uniqueness of the number of groups and their member objects in it.
The uniqueness property is used to calculate the compactness measure of the training samples. The value of compactness is measured with dimensionless quantities in the interval of [0, 1]. There is a need to apply of dimensionless quantities for estimating the structure of feature space. Such a need exists at comparing the different metrics, normalization methods and data transformation, selection and removing the noise objects.
Combinatorial Interaction Testing (CIT) is a cost effective testing technique that aims to detect interaction faults generated as a result of interaction between components or parameters in a software system. CIT requires the generation of effective test sets that cover all possible t-way (t denotes the strength of testing) interactions between parameters. Covering array (CA) and mixed covering array (MCA) are often used to represent test sets. This paper presents a hybrid algorithm that integrates artificial bee colony algorithm (ABC) and harmony search algorithm (HS) to construct CAs for testing all 2-way interactions (pair-wise testing) in software systems. The performance of the proposed hybrid algorithm ABCHS-CAG is compared and analyzed by performing experiments on a set of benchmark problems on pair-wise testing. The results show that ABCHS-CAG generates smaller CAs than its greedy counterparts whereas its performance is comparable to the existing state-of-the-art meta-heuristic algorithms.[...] Read more.
Higher Order Neural Networks (HONN) are characterized with fast learning abilities, stronger approximation, greater storage capacity, higher fault tolerance capability and powerful mapping of single layer trainable weights. Since higher order terms are introduced, they provide nonlinear decision boundaries, hence offering better classification capability as compared to linear neuron. Nature-inspired optimization algorithms are capable of searching better than gradient descent-based search techniques. This paper develops some hybrid models by considering four HONNs such as Pi-Sigma, Sigma-Pi, Jordan Pi-Sigma neural network and Functional link artificial neural network as the base model. The optimal parameters of these neural nets are searched by a Particle swarm optimization, and a Genetic Algorithm. The models are employed to capture the extreme volatility, nonlinearity and uncertainty associated with stock data. Performance of these hybrid models is evaluated through prediction of one-step-ahead exchange rates of some real stock market. The efficiency of the models is compared with that of a Radial basis functional neural network, a multilayer perceptron, and a multi linear regression method and established their superiority. Friedman’s test and Nemenyi post-hoc test are conducted for statistical significance of the results.[...] Read more.