Detecting Distributed Predicates Using Genetic Algorithms

Detection of distributed predicates is one of the techniques that have been used in the literature to improve the dependability of distributed programs. This technique (sometimes referred to as runtime verification) is used to verify that a given run of a distributed program satisfies certain properties (specified as predicates). In general, the detection of a distributed predicate can incur significant overhead due to the existence of multiple processes running concurrently. Several techniques have been introduced in the literature to efficiently detect distributed predicates. However, most of these techniques work efficiently for certain classes of predicates, like conjunctive predicates. In this paper, the authors have presented a technique based on genetic algorithms to efficiently detect distributed predicates under the possibly modality. The authors have used JGAP (Java Genetic Algorithms Package) to implement the algorithm and conducted several experiments to demonstrate its effectiveness.

An Intelligent and Dynamic Decision Support System for Nonlinear Environments

Nonlinear time series systems are high dimensional and chaotic in nature. Since, the design of a dynamic and efficient decision making system is a challenging task, a Support Vector Machine (SVM) based model is proposed to predict the future event of a nonlinear time series environment. This model is a non-parametric model that uses the inherent structure of the data for forecasting. The Hybrid Dimensionality Reduction (HDR) and Extended Hybrid Dimensionality Reduction (EHDR) techniques are proposed to represent the time series data and to reduce the dimensionality and control noise besides subsequencing the time series data. The proposed SVM based model using EHDR is compared with the models using Symbolic Aggregate approXimation (SAX), HDR, SVM using Kernel Principal Component Analysis(KPCA) and SVM using varying tube size values for historical data on different financial instruments. The experimental results have proved that the prediction accuracy of the proposed model is better compared with other models taken for the experimentation.

An Intelligent Particle Swarm Optimization for Fuzzy Based Heterogeneous Radio Access Technology (RAT) Selection

The future wireless networks are heterogeneous in nature where different Radio Access Technologies (RATs) coexist in the same coverage area. The user terminal has to select the best access technology among Wireless Local Area Network (WLAN), Wireless Wide Area Network (WWAN), and Universal Mobile Telecommunication Systems (UMTS) etc. at its current location. Thus, selecting the appropriate RAT and cell becomes a complex problem in heterogeneous network due to number of variables involved in the selection process. The main objective of this work in the heterogeneous networks is to maximize the percentage of satisfied users who are assigned to the networks. Henceforth, this paper presents an innovative mechanism for the selection of heterogeneous networks such as WWAN and WLAN. The performance of the proposed algorithm is evaluated using 1000 datasets. From the simulation results, it is found that the proposed algorithm gives highest probability for mobile user satisfaction than the existing methods.

A Modified Watershed Segmentation Method to Segment Renal Calculi in Ultrasound Kidney Images

Segmentation of stones from abdominal ultrasound images is a unique challenge to the researchers because these images have heavy speckle noise and attenuated artifacts. In the previous renal calculi segmentation method, the stones were segmented from the medical ultra sound kidney stone images using Adaptive Neuro Fuzzy Inference System (ANFIS). But, the method lacks in sensitivity and specificity measures. The segmentation method is inadequate in its performance in terms of these two parameters. So, to avoid these drawbacks, a new segmentation method is proposed in this paper. Here, new region indicators and new modified watershed transformation is utilized. The proposed method is comprised of four major processes, namely, preprocessing, determination of outer and inner region indictors, modified watershed segmentation with ANFIS performance. The method is implemented and the results are analyzed in terms of various statistical performance measures. The results show the effectiveness of proposed segmentation method in segmenting the kidney stones and the achieved improvement in sensitivity and specificity measures. Furthermore, the performance of the proposed technique is evaluated by comparing with the other segmentation methods.