The first phase of reverse engineering of web-oriented applications is the extraction of concepts hidden in HTML pages including tables, lists and forms, or marked in XML documents. In this paper, we present an approach to index semantically these two sources of information (HTML page and XML document) using on the one hand, domain ontology to validate the extracted concepts and on the other hand the similarity measurement between ontology concepts with the aim of enrichment the index. This approach will be conceived in three steps (modeling, attaching and Enrichment) and thereafter, it will be realized and implemented by examples. The obtained results lead to better re-engineering of web applications and subsequently a distinguished improvement in the web structuring.

Software reusability is very important and crucial attribute to evaluate the system software. Due to incremental growth of software development, the software reusability comes under attention of many researcher and practitioner. It is pretty easier to reuse the software than developing the new software. Software reusability reduces the development time, cost and effort of software product. Software reusability define the depth to which a module can be reused again with very little or no modification. However the prediction of this quality attribute is cumbersome process. Aspect oriented software development is new approach that introduce the concerns to overcome the issues with modular programming and object oriented programming. However many researcher worked on accessing the software reusability on object oriented system but the software reusability of aspect oriented system is not completely explored. This paper explores the various metric that affects the reusability of aspect oriented software and estimate it using fuzzy logic approach.

The dynamic nature of social network and the influence it has on the provision of immediate solutions to a simple task made their usage prominent and dependable. Whether it is a task of getting a solution to a trivial problem or buying a gadget online or any other task that involves collaborative effort, interacting with people across the globe, the immediate elucidation that comes into anyone’s mind is the social network. Question Answer systems, Feedback systems, Recommender systems, Reviewer Systems are some of the frequently needed applications that are used by people for taking a decision on performing a day to day task. Experts are needed to maintain such systems which will be helpful for the overall development of the web communities. Finding an expert who can do justice for a question involving multiple domain knowledge is a difficult task. This paper deal with an expert finding approach that involves extraction of expertise that is hidden in the profile documents and publications of a researcher who is a member of academic social network. Keywords extracted from an expert’s profile are correlated against index terms of the domain of expertise and the experts are ranked in the respective domains. This approach emphasizes on text mining to retrieve prominent keywords from publications of a researcher to identify his expertise and visualizes the result after statistical analysis.

Mobile Computing (MC) provides multi services and a lot of advantages for millions of users across the world over the internet. Millions of business customers have leveraged cloud computing services through mobile devices to get what is called Mobile Cloud Computing (MCC). MCC aims at using cloud computing techniques for storage and processing of data on mobile devices, thereby reducing their limitations. This paper proposes architecture for a Swarm Intelligence Based Mobile Cloud Computing Model (SIBMCCM). A model that uses a proposed Parallel Particle Swarm Optimization (PPSO) algorithm to enhance the access time for the mobile cloud computing services which support different E Commerce models and to better secure the communication through the mobile cloud and the mobile commerce transactions.

Health Ecosystem is derisory in techniques to haul out the information from the database because of the lack of effective scrutiny tool to discern concealed relationships and trends in them. By applying the data mining techniques, precious knowledge can be excerpted from the health care system. Extracted knowledge can be applied for the accurate diagnosis of disease and proper treatment. Heart disease is a group of condition affecting the structure and functions of the heart and has many root causes. Heart disease is the leading cause of death in all over the world in recent years. Researchers have developed many data mining techniques for diagnosing heart disease. This paper proposes a technique of preprocessing the data set and using Particle Swarm Optimization (PCO) algorithm for Feature Reduction. After applying the PCO, the accuracy for prediction is tested. It is observed from the experiments, a potential result of 83% accuracy in the prediction. The performance of PCO algorithm is then compared with Ant Colony Optimization (ACO) algorithm. The experimental results show that the accuracy obtained from PCO is better than ACO. The performance measures are based on Accuracy, Sensitivity and Specificity. The other measures such as Kappa statistic, Mean Absolute Error, Root Mean Squared Error, True Positive Rate are also taken for evaluation. As future direction of this paper, a hybrid technique which combines PCO with Rough Set theory is suggested.

There are many Geo-Location techniques proposed in cellular networks. They are mainly classified based on the parameters used to extract location information. In this study it is tried to have a new look to these positioning methods and to classify them differently regardless of parameters type. We classified these techniques base on mathematical algorithms which is used to derive location information of users in the network. Such algorithms are divided into three main subclasses in here, estimation theory based (MUSIC, ESPIRIT), Meta-heuristic (Genetic, PSO...) and filtering approaches (Kalman, Particle, Grid, MH ). The proofs and details of how to apply techniques are presented and the simulation results are given.

Emotion plays a significant role in human perception and decision making whereas, prosodic features plays a crucial role in recognizing the emotion from speech utterance. This paper introduces the speech emotion corpus recorded in the provincial languages of Pakistan: Urdu, Balochi, Pashto Sindhi and Punjabi having four different emotions (Anger, Happiness, Neutral and Sad). The objective of this paper is to analyze the impact of prosodic feature (pitch) on learning classifiers (adaboostM1, classification via regression, decision stump, J48) in comparison with other prosodic features (intensity and formant) in term of classification accuracy using speech emotion corpus recorded in the provincial languages of Pakistan. Experimental framework evaluated four different classifiers with the possible combinations of prosodic features with and without pitch. An experimental study shows that the prosodic feature (pitch) plays a vital role in providing the significant classification accuracy as compared to prosodic features excluding pitch. The classification accuracy for formant and intensity either individually or with any combination excluding pitch are found to be approximately 20%. Whereas, pitch gives classification accuracy of around 40%.

In this research, intelligent adaptive backstepping control is presented as robust control for continuum robot. The first objective in this research is design a Proportional-Derivative (PD) fuzzy system to compensate the system model uncertainties. The second objective is focused on the design tuning gain adaptive methodology according to high quality partly nonlinear methodology. Conventional backstepping controller is one of the important robust controllers especially to control of continuum robot manipulator. The fuzzy controller is used in this method to system compensation. In real time to increase the system robust fuzzy logic theory is applied to backstepping controller. To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. This method is applied to continuum robot manipulator to have the best performance.

Cloud Computing is an innovation ideas that helps in reducing the computing cost. Cloud Computing offers better computing through improved utilization and reduced administration and infrastructure costs. Cloud computing is the long-held dream of computing as a utility. Cloud Computing is the combination of Software as a Service (SaaS) and Utility Computing. Cloud computing shares characteristics with autonomic computing, peer to peer, grid computing, client server model, mainframe computer and utility computing. It has various open source resources which gives different platform for better computing utilization. Cloud computing are managed by Cloud Management tools, loaded and tested by various other software testing tools. Cloud computing modelling and simulation is done by CloudSim or SPECI or GroundSim or DCSim on the basis of testing benchmark. The application of Cloud Computing is discussed.

One of the most important phenomena of some systems is chaos which is caused by nonlinear dynamics. In this paper, the new 3 dimensional chaotic system is firstly investigated and then utilizing an intelligent controller which based on brain emotional learning (BELBIC), this new chaotic system is synchronized. The BELBIC consists of reward signal which accept positive values. Improper selection of the parameters causes an improper behavior which may cause serious problems such as instability of system. It is needed to optimize these parameters. Genetic Algorithm (GA), Cuckoo Optimization Algorithm (COA), Particle Swarm Optimization Algorithm (PSO) and Imperialist Competitive Algorithm (ICA) are used to compute the optimal parameters for the reward signal of BELBIC. These algorithms can select appropriate and optimal values for the parameters. These minimize the Cost Function, so the optimal values for the parameters will be founded. Selected cost function is defined to minimizing the least square errors. Cost function enforce the system errors to decay to zero rapidly. Numerical simulation results are presented to show the effectiveness of the proposed method.