The need to accurately estimate time and cost for effective planning of software projects is becoming crucial driven by the escalating demands of the software market. Several models proposed in the history of Software Engineering discipline to estimate time, costs associated with planning and managing software projects as Line of Code (LOC), Function Point (FP) and Constructive Cost Model (COCOMO). This paper focuses upon the COCOMO Model. It is further consisted of its two sub models called COCOMO I and COCOMO II. The primary objective of this research is to use an appropriate case study to evaluate the accuracy of the sub models COCOMO I and II and ascertain the variation of the realistic resource effort, staff and time. The findings to date show that the Application Composition Model of COCOMO II is more accurate in determining time and cost for the successful conclusion of a software project than the other two COCOMO I and II Models for a similar application for example Task Manager.

The prediction and diagnosis of Tuberculosis survivability has been a challenging research problem for many researchers. Since the early dates of the related research, much advancement has been recorded in several related fields. For instance, thanks to innovative biomedical technologies, better explanatory prognostic factors are being measured and recorded; thanks to low cost computer hardware and software technologies, high volume better quality data is being collected and stored automatically; and finally thanks to better analytical methods, those voluminous data is being processed effectively and efficiently. Tuberculosis is one of the leading diseases for all people in developed countries including India. It is the most common cause of death in human being. The high incidence of Tuberculosis in all people has increased significantly in the last years. In this paper we have discussed various data mining approaches that have been utilized for Tuberculosis diagnosis and prognosis. This study paper summarizes various review and technical articles on Tuberculosis diagnosis and prognosis also we focus on current research being carried out using the data mining techniques to enhance the Tuberculosis diagnosis and prognosis. Here, we took advantage of those available technological advancements to develop the best prediction model for Tuberculosis survivability.

Reuse of existing software components is the main goal of Component-Based Software Engineering. Different organizations designed different web-services as the most famous type of component. Adaptation is a critical issue when building new applications by reusing existing services. How to adapt these services to work correctly is the main goal of most of the proposed models and techniques in software component filed. Behavioral mismatches are one of the adaptation problems. Different solutions have been written to address this problem like adapter and some other techniques. Most of the papers focused on how to create the adapter to overcome the incompatibility problem. In this paper, the authors provide a simple integrated tool that can solves ordering mismatching. The results are concluded using a survey from thirty one respondents. The proposed solution needs further validation by conducting a case study.

This paper presents a new context-based solution for Web services discovery. The service description includes an enriched representation, in order to make more efficient the discovery and selection stages.
Our approach gives, services publishing and searching, another dimension. Services context-based selection uses a new quantitative similarity measure to calculate the correspondence degree between the client and the services contexts in order to provide users with appropriate services according to their contexts.

We propose in this paper a method to match silhouettes. Silhouettes are described with a text-based representation. An iterative process is used to reduce descriptors. When the size of a little part is negligible in relation with sizes of main parts, that little part will be considered as noisy and will be suppressed from the initial textual descriptor. After the reduction process, the descriptors can be compared in order to perform the matching process.

The analysis of text in the form of tweets, chat or posts can be an interesting as well as challenging area of research. In this paper, such an analysis provides information about the human behavior as positive, negative or neutral. For simplicity, tweets from social networking site, Twitter, are extracted for analyzing human personality. Various concepts from natural language processing, text mining and neural networks are used to establish the final outcome of the application. For analyzing text, Neural Networks are implemented which are so modeled that they predict the Human behavior as positive, negative or neutral based on extracted and preprocessed data. Using Neural Networks, the particular pattern is identified and weights are provided to words based on the extracted pattern.Neural networks have an added advantage of adaptive learning. This application can be immensely useful for politics, medical science, sports, matrimonial purposes etc.The results so obtained are quite promising.

The Proportional Integral Derivative (PID) Fuzzy hybrid (switching mode computed torque sliding mode) Controller is presented in this research. The popularity of PID FHC 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 FHC 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 manual tuning techniques and compares the same with Gradient Descent tuning methods for tuning PID FHC controllers for flexible robot manipulator system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.

The obstacle avoidance is currently treated by methods that fall into two broad categories: global and local approach. This paper considers the obstacles whose velocity and direction cannot be easily predicted. Such obstacle is called transient obstacle. To avoid such kind of obstacle, we introduce a local path planning method for a robotics by using cellular automaton approach. The cellular automaton was combined with Dijkstra shortest path algorithm as global path planning to obtain a path for mobile robot to be able to avoid transient obstacle along the path. Using the proposed method, a scene in a typical corridor has been created. Moreover, this paper also evaluated two kinds of obstacle avoidance motion. First, the robot uses the “stop and go” method, which is the robot decreases its speed while encounter a transient obstacle. The second one is detour method, in which the robot makes a detour motion to avoid a transient obstacle. To coupe the drawbacks of local path planning, this paper also propose the enhancement of detour method. The simulation results show that in dynamic environment with transient obstacles, the “stop and go” method produces minimal collision with shortest-distance path. While, using the detour method generates minimal collision with time-minimal navigation path.