The demand for global software development is growing. The nonavailability of software experts at one place or a country is the reason for the increase in the scope of global software development. Software developers who are located in different parts of the world with diversified skills necessary for a successful completion of a project play a critical role in the field of software development. Using the skills and expertise of software developers around the world, one could get any component developed or any IT-related issue resolved. The best software skills and tools are dispersed across the globe, but to integrate these skills and tools together and make them work for solving real world problems is a challenging task. The discipline of risk management gives the alternative strategies to manage risks that the software experts are facing in today’s world of competitiveness. This research is an effort to predict risks related to time, cost, and resources those are faced by distributed teams in global software development environment. To examine the relative effect of these factors, in this research, neural network approaches like Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient have been implemented to predict the responses of risks related to project time, cost, and resources involved in global software development. Comparative analysis of these three algorithms is also performed to determine the highest accuracy algorithms. The findings of this study proved that Bayesian Regularization performed very well in terms of the MSE (validation) criterion as compared with the Levenberg–Marquardt and Scaled Conjugate Gradient approaches.

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Background: The skeletal and soft tissue analysis of the face in total is a very important parameter of diagnosis. Readily available and non expensive imaging software are available which can be used for cephalometric analysis for hard and soft tissues of the face to make an appropriate treatment plan. Aims: The purpose of this study was to compare the accuracy of linear and angular measurements between the digital software IMAGE J, ICY and manual tracing. Objective: The purpose of this study was to compare angular and linear measurements obtained through manual and digital cephalometric tracings using IMAGE J and ICY software with lateral cephalometric radiographs. Material and Methods: The sample consisted of 50 lateral cephalometric radiographs. One properly trained and calibrated examiner performed the 50 manual tracing and then the same radiograph was traced digitally on a digital software. Five angular measurements SNA (Sella, nasion, A point), SNB Sella, nasion, B point), ANB (A point, nasion, B point), W angle, ULA (Upper lip angle) and three linear measurements ULT (upper lip thickness), projection of upper lip to TVL (true vertical line) & WITS appraisal were traced the conventional lateral cephalogram of 50 participants were obtained. Manual tracing was done and hard tissue landmark including the above mentioned angular and linear variables were marked. In a similar way a soft copy of the above 50 radiograph was obtained and uploaded in the digital imaging software IMAGE J and ICY. Results: SNA, SNB, ANB, W angle, WITS appraisal, upper lip thickness (ULT) upper lip angle, (ULA), projection of labrale superioris to TVL showed statistically significant values when measured manually and by digital software methods. Values measured by software methods had less errors. Conclusion: The results show statistically significant values between manual and digital tracing. The actual variation lies in identification of landmarks and their measurements. landmarks are identified better in software methods than manual methods and also there was a difference in measurements of angular and linear values of soft and hard tissue landmarks.

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