Software reuse refers to the development of new software systems with the likelihood of completely or partially using existing components or resources with or without modification. Reusability is the measure of the ease with which previously acquired concepts and objects can be used in new contexts. It is a promising strategy for improvements in software quality, productivity and maintainability as it provides for cost effective, reliable (with the consideration that prior testing and use has eliminated bugs) and accelerated (reduced time to market) development of the software products. In this paper we present an efficient automation model for the identification and evaluation of reusable software components to measure the reusability levels (high, medium or low) of procedure oriented Java based (object oriented) software systems. The presented model uses a metric framework for the functional analysis of the Object oriented software components that target essential attributes of reusability analysis also taking into consideration Maintainability Index to account for partial reuse. Further machine learning algorithm LMNN is explored to establish relationships between the functional attributes. The model works at functional level rather than at structural level. The system is implemented as a tool in Java and the performance of the automation tool developed is recorded using criteria like precision, recall, accuracy and error rate. The results gathered indicate that the model can be effectively used as an efficient, accurate, fast and economic model for the identification of procedure based reusable components from the existing inventory of software resources.

The Joint Probabilistic Data Association (JPDA) solves single sensor multi-target tracking in clutter, but it cannot be used directly in multi-sensor multi-target tracking (MMT) and has high computational complexity with the number of targets and the number of returns. This paper presents a hybrid method to implement MMT by combing Maximum Likelihood Estimation (MLE) with Adaptive Neuro-Fuzzy Inference System (ANFIS). The MLE is applied to classify the same source observations at one time into the same set, then the cheap JPDA(CJPDA) approach is used to calculate the data association probability, and ANFIS is used to realize the MMT. The computer simulations indicate that this scheme achieves MMT perfectly with higher precision and easy realization.

This work employed Artificial Neural Networks and Decision Trees data analysis techniques to discover new knowledge from historical data about accidents in one of Nigeria’s busiest roads in order to reduce carnage on our highways. Data of accidents records on the first 40 kilometres from Ibadan to Lagos were collected from Nigeria Road Safety Corps. The data were organized into continuous and categorical data. The continuous data were analysed using Artificial Neural Networks technique and the categorical data were also analysed using Decision Trees technique .Sensitivity analysis was performed and irrelevant inputs were eliminated. The performance measures used to determine the performance of the techniques include Mean Absolute Error (MAE), Confusion Matrix, Accuracy Rate, True Positive, False Positive and Percentage correctly classified instances. Experimental results reveal that, between the machines learning paradigms considered, Decision Tree approach outperformed the Artificial Neural Network with a lower error rate and higher accuracy rate. Our research analysis also shows that, the three most important causes of accident are Tyre burst, loss of control and over speeding.

The image recognition of mobile robot is to extract the effective target information. The essence of image extraction is image segmentation. By extracting and distinguishing planar objects and three-dimensional objects, we propose two new algorithms. The color image is extracted by using CIE Lab Space. Then we propose a comparison method through the collection of two image samples. According to the principle of geometric distortion in the geometric space, we can easily distinguish the planar object in the environment. Therefore, Experimental results show that the combination of these two methods is accurate and fast in the color image recognition.

Implicit Generalized Predictive Control (IGPC) algorithm can directly identify controller parameters without the need of solving Diophantine equation, thus can reduce the on-line algorithm computation time. In order to improve IGPC performance and extend its application, modified Particle Swarm Optimization (PSO) algorithm is introduced into IGPC rolling horizon optimization, combined with general IGPC gradient optimization method under unconstrained condition, a new hybrid optimization method is obtained, this modified IGPC can be used to both of the non-constraint industry process control and the constraint industry process control. Aiming at the superheated steam temperature control of sub-critical 600MW boiler, a new cascade compound control strategy that combines an outer loop IGPC master adjuster and an inner loop PID auxiliary adjuster is adopted. Finally the simulation results have shown that the proposed method can constrain the control action, prevent dramatic change of the input signal, thus can achieve good static and dynamic performances.

The advancements of Information Technology have led to many developments that make life easier and faster with high reachability and efficiency. There is a drastic improvement in the area of distributed applications with the advent of mobile agent technology over the usual client/server framework. Applying mobile agent technology in the area of distributed applications improves the performance and quality of service. Mobile agent technologies in particular have taken a prominent place in handling effective road and vehicle traffic (VANET). This paper highlights on the integration of previously defined MATLB, PCM and MSA Agent and proposes an approach for reducing the size of the mobile agent that helps in data collection of vehicles in the VANET for effective and efficient traffic control. This paper is an advancement of agent load shedding algorithm and an attempt to optimize the size reduction process. This reduction in size of mobile agents will enhance the performance of VANET making the agents more acceptable by the hosts and correspondingly building an effective co-operative vehicular network.

Nowadays, using of e-learning has a special place in organizations and universities. Understanding the efficiency and effectiveness of this type of education, scientific and professional assemblies try to provide effective tools and strategies to operate this kind of training better. E-Learning management system as one of the basic requirements of the system plays a special role in this field. Therefore all companies are looking for a system that meets their needs in the field of e-learning. Standards of content and structure of e-learning must be set so that access to possibilities such as content reuse or gathering or discriminating subject from various sources at different times is possible. This paper reviews and compares some of the most important standards in the field of e-learning.

Segmentation of a word into characters is one of the important challenges in optical character recognition. This is even more challenging when we segment characters in an offline handwritten document. Touching characters make this problem more complex. In this paper, we have applied water reservoir based technique for identification and segmentation of touching characters in handwritten Gurmukhi words. Touching characters are segmented based on reservoir base area points. We could achieve 93.51% accuracy for character segmentation with this method. If the characters are neither broken nor overlapping, then this technique shall produce even better results.

Service Orientation Architecture is playing a vital role in the middleware and enterprise organizations through its key design principles and there are many benefits of it as well. Loose Coupling between services is one of its design principles that help system or architecture to maintain its efficiency because services are less dependent on each other. Stopping of one service could not affect other service and system but there is a fact that services have less knowledge of other services that’s why services cannot be able to properly communicate with each other and Service Oriented Architecture is totally based on communicative messages between services in order to perform their functionality. This paper provides a basic solution to control the messaging criteria between services. This paper holds the brief description of messaging criteria of services in service oriented architecture by defining the process of message that fulfills the guaranteed delivery of message to its other end while communicating or asking for a service.

The Proportional Integral Derivative (PID) Fuzzy Sliding Mode Controller (FSMC) is the most widely used control strategy in the Industry (control of robotic arm). The popularity of PID FSMC controllers can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. The process of setting of PID FSMC controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing. Biologically inspired evolutionary strategies have gained importance over other strategies because of their consistent performance over wide range of process models and their flexibility. This paper analyses the modified PID FSMC controllers based on minimum rule base for flexible robot manipulator system and test the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.