Accepted Papers
- Formal Methods for Commercial Applications Issues vs. Solutions
Saiqa Bibi, Saira Mazhar and Nasir Mehmood Minhas, University Institute of Information Technology, PakistanABSTRACT
Many software engineering researchers have consummated to use formal methods to win best quality of software. It is advocated that in 21st Century, most of software will be developed with benefits of Formal Methods. Academia has also acknowledged that the custom of Formal Methods in development of Software will be helpful for Industry to encounter its goals by inducing better software process and increasing the Software Quality. The benefits include faults found in earlier stage of Software development, automating checking the certain properties and minimizing rework. In spite of their recognition in academic world and these claimed advantages, formal methods are still not widely used by commercial software industry. In this paper we have identified issues in use of formal methods for commercial applications and devised strategies to overcome these difficulties which will provide motivations to use formal methods for commercial applications. - Qos Management In Distributed Rtdbms Using Imprecise Computation Technique
Malek Ben Salem, Emna Bouazizi, Issam Hamdi and Rafik Bouaziz, Sfax University, TunisiaABSTRACT
In our days, there are large amounts of real-time data which is used by geographically distributed realtime applications. Using Distributed Real-Time Database Management Systems (DRTDBMS) is more and more needed to manage the large amount of real-time data while meeting the stringent temporal requirements in real-time applications. In the presence of unpredictable workload for user transactions between different nodes, providing Quality of Service (QoS) guarantees in DRTDBMSs is a challenging task. Many QoS based approaches are presented in same research works. Feedback Scheduling based approaches can manage the unpredictable workload variations. Furthermore, the imprecise computation technique has been defined as a technique to improve the QoS in centralized RTDBMS. In this paper, we propose to apply the imprecise computation technique in a distributed feedback control scheduling architecture to improve the QoS in DRTDBMS. Then, our goal is to ensure a better quality of the service by increasing the committed transactions number before their deadline with an amount of faults tolerance on data and transactions causing an acceptable of inaccurate results and using three replication data policies. - Partial Orders Embedding is NP-complete
Dariusz Kalocinski, Warsaw University, PolandABSTRACT
Following Barwise, we consider examples of natural language sentences that seem to express that there is an embedding of one partial order into the other. We prove NP-completeness of two versions of partial orders embedding problem. We show that the task of computing the truth value of such sentences in finite models is NP-complete. - Acceleration Base Particle Swarm Optimization (Apso) for RNA Secondary Structure Prediction
Shikha Agrawal and Jitendra Agrawal, Rajiv Gandhi Proudyogiki Vishwavidhyalaya, IndiaABSTRACT
RNA Secondary Structure prediction is one of the most significant research areas in bioinformatics. Many research works have been developed in the area of RNA secondary structure prediction with optimization methods. But there are certain drawbacks still prevailing in the existing optimization methods. To avoid such drawbacks in the existing methods, we have proposed an Acceleration base Particle Swarm Optimization (APSO) algorithm for finding minimum free energy of RNA secondary structures. The experimental result shows that our proposed APSO algorithm efficiently locates the RNA structures. Furthermore, the performance of our proposed APSO algorithm is evaluated by invoking eight benchmark functions and also compared with Genetic Algorithm (GA) and standard Particle Swarm Optimization (PSO). The test result shows that the APSO is efficient both in test benchmark functions and prediction model and better than other algorithms. - Teaching Learning Based Optimization (TLBO) Based Improved Iris Recognition System
Shraddha Sharma, Shikha Agrawal and Sanjay Silakari, Rajiv Gandhi Proudyogiki Vishwavidhyalaya, IndiaABSTRACT
Optimization technique plays an major role in the iris system. The optimized feature gives an optimized template for matching process and these optimized template increases the identification rate of iris system. In this paper, we proposed an Teaching Learning Based Optimization (TLBO) based iris recognition system in which feature extraction phase of iris recognition system is optimized by using TLBO. The process of feature extraction is performed by texture feature extraction Gabor wavelet transform technique. TLBO is than applied on these features. Teaching learning based optimization algorithm acquired the feature of iris image as a student and generates the optimized feature template as a teacher. The process of optimization designed the fitness constraints function for the selection of feature in student to teacher The proposed algorithm compared with other iris recognition methods such as the Hough transform function and Genetic Algorithm. Experimental results when applied to CASIA dataset shows superior performance with better recognition rate of proposed algorithm as compared to Genetic Algorithm and Hough transform. - Power Efficient And Reliable Topology Control and Maintenance ForWireless Sensor Networks
Jisha Shanavas and Simi S, Amrita School of Engineering, IndiaABSTRACT
In Wireless Sensor networks, the network topology changes over time due to the varying environmental and link characteristics. Distributed topology control of nodes in dynamic networks is a major factor that affects the connectivity and lifetime of the network. Nodes in networks like wireless sensor network have limited resources. Topology control algorithms are helpful to improve the energy utilization, reduce interference between nodes and extend lifetime of the networks operating on battery power. This paper proposes a strategy for topology control and maintenance based on the dynamic network link characteristics. The algorithm calculates the bounds on the number of neighbors per node which helps to reduce power consumption and interference effects. Also the algorithm ensures strong connectivity in the network so that reachability between any two nodes in the network is guaranteed. Analysis and simulation results illustrate the correctness and effectiveness of our proposed algorithm. - A Cooperative Method to Improve Segmentation of Brain MR Images
Lamiche Chaabane and Moussaoui Abdelouahab, university of M’sila, AlgeriaABSTRACT
In this paper , we present a fully unsupervised segmentation process of magnetic resonance image (MRI) of the brain using a data fusion technique and some of ideas of the possibility theory context. The fusion methodology is decomposed into three fundamental phases. We modeling information coming from T2 and PD weighted images in a common framework, in this step an hybridization between FCM and PCM algorithms is retained. In the second phase an operator of fusion is used to combine then these information. Finally, an image of fusion is generated when a decision rule is applied. Some results are presented and discussed using a set of simulated MR image. - Auto Claim Fraud Detection using Multi Classifier System
Luis Alexandre Rodrigues and Nizam Omar, Mackenzie University, BrazilABSTRACT
Through a cost matrix and a combination of classifiers, this work identifies the most economical model to perform the detection of suspected cases of fraud in a dataset of automobile claims. The experiments performed by this work show that working more deeply in sampled data in the training phase and test phase of each classifier is possible obtain a more economic model than other model presented in the literature.