Inclusive Indigenous Simple Programming Language and Interpreter Written in Chichewa (Chich++)
Codesplain: AI Powered Documentation App
Unified Banking Interface using Machine Learning and Blockchain for Credit Scoring and Smart Contracts
Virtual Credit and Debit Card Issuing System
Unveiling the Recent Trends in Biomedical Artificial Intelligence Research
Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
Ensuring Software Quality in Engineering Environments
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
Software environment is a testing environment where in the different test cases are executed to check the correctness, reliability and performance of the developed framework. In manual testing functionalities of a specific version of the application is tested manually through specific test cases. Test automation is an important technique for testing large scale applications using an automation tool. This paper deals with the different test automation frameworks, also discuss how to develop a framework using coded UI and Page Object Model for windows based application.
Now-a-days, every organization and institution provides security for their users. There are different systems are available for providing security and identifying of employees of the desired organization. No one is stick on one system for providing security. Every system having some disadvantages. Compared to the other systems most of the confidential organizations uses the biometric system for providing security and recognition. This paper depicts about the biometric system clearly with many types, steps involved in this system, advantages and disadvantages and finally applications.
Bad smells are not uncommon in software systems. Such problems arise as a result of incomplete, inconsistent or incorrect requirements followed, accordingly, by bad design decisions which travel to the construction phase ending up with malfunctioning software. Such problems are expected to be handled and resolved during the evolution of the software which may result in more complicated systems that are difficult to maintain, and the software starts aging. Various tools are available to help in uncovering, analyzing and visualizing various bad smells. Once the bad smells are uncovered, a remedial action should be taken such as refactoring. One of the new tools to detect and measure a big number of bad smells is Designite. In this paper, we use Designite to analyze six open source systems and see if bad smells are resolved while software is evolving or systems keep stinking. We found that software quality, in terms of resolving bad smells, gained less focus as the software evolves on the expense of focusing on adaptive and corrective actions and that would keep the software stinking. We also discussed some recommendations on how to reduce bad smells during the software process and some other recommendations to enhance the Designite tool.
The main objective of the proposed Top Hill method is to decrease the time of insertion. Sorting is done by using the pivot method of quick sort, binary searching, and doubly linked list. By using all these method in a combination, the proposed method can sort real time data and increase the size of the list with the worst case of O(n).
A new algorithm for tracking humans in surveillance videos is introduced in this paper. The human head and foot point are first identified by using color and shape based human detection algorithm. These annotations are taken as input for the proposed tracking algorithm. The number of past pixels taken for modelling the present pixel in Gaussian Mixture Model (GMM) is modified in this algorithm. The Weighted Running Window (WRW) is used in choosing the past pixels. The number of past pixels is limited and also more weightages is given to the immediate past pixel, thereby reducing the time taken for tracking. Different tracking parameters are used for comparing the proposed algorithm with other existing algorithm. Performance results show that the proposed algorithm is out performing when compared with the other existing algorithms for human tracking. Performance Evaluation of Tracking and Surveillance (PETS) 2009 View 1 dataset is taken for conducting the experiment.