We found 15 results that contain "computer"

Edited: The Agile Alliance defines 12 lightness principles for those who need to attain agility:

Our highest priority is to satisfy the client through early and continuous delivery of valuable computer software.
Welcome dynamic necessities, even late in development. Agile Processes harness modification for the customer’s competitive advantage.
Deliver operating computer software often, from a pair of weeks to a couple of months, with a preference to the shorter timescale.
Business individuals and developers should work along daily throughout the project.
The build comes around actuated people. offer them the setting and support they have, and trust them to urge the task done.
the foremost economical and effective methodology of conveyancing info to and among a development team is face-to-face speech.
Working with computer software is the primary life of progress.
Agile processes promote property development. The sponsors, developers, and users will be able to maintain a relentless pace indefinitely.
Continuous attention to technical excellence and smart style enhances nimbleness.
Simplicity—the art of maximizing the number of work not done—is essential.
the most effective architectures, necessities, and styles emerge from self–organizing groups.
At regular intervals, the team reflects on a way to become simpler, then tunes and adjusts its behavior consequently.

Digital image processing deals with manipulation of digital images through a digital computer. It is a subfield of signals and systems but focus particularly on images. DIP focuses on developing a computer system that is able to perform processing on an image. The input of that system is a digital image and the system process that image using efficient algorithms, and gives an image as an output. The most common example is Adobe Photoshop. It is one of the widely used application for processing digital images

Machine-generated data is information automatically generated by a computer process, application, or other mechanism without the active intervention of a human. While the term dates back over fifty years,[1] there is some current indecision as to the scope of the term. Monash Research's Curt Monash defines it as "data that was produced entirely by machines OR data that is more about observing humans than recording their choices."[2] Meanwhile, Daniel Abadi, CS Professor at Yale, proposes a narrower definition, "Machine-generated data is data that is generated as a result of a decision of an independent computational agent or a measurement of an event that is not caused by a human action."[3] Regardless of definition differences, both exclude data manually entered by a person.[4] Machine-generated data crosses all industry sectors. Often and increasingly, humans are unaware their actions are generating the data.[

Education is the key to everything that is good in our world today. Advances in computers, information technology, math, medicine, psychology, engineering, and every other discipline would be impossible if education didn't help us build on the advances of the great minds that came before us.
In fact, it is essential that as a society that we keep learning new things. Education is not only about the past and present, but it is also the key to the future. It will help discipline our children for the intellectual challenges of the rest of the 21st century.

Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, speech recognition, problem-solving, learning, and planning.

Embedded systems make the environment alive with little computations and automated processes, from computerized cooking appliances to lighting and plumbing fixtures to window blinds to automobile braking systems to greeting cards. The expected difference in the future is the addition of networked communications that will allow many of these embedded computations to coordinate with each other and with the user

Big data is a collection of large datasets that cannot be processed using traditional computing techniques. Testing of these datasets involves various tools, techniques and frameworks to process. Big data relates to data creation, storage, retrieval and analysis that is remarkable in terms of volume, variety, and velocity. You can learn more about Big Data, Hadoop and Mapreduce here In this tutorial we will learn, Testing Big Data application is more a verification of its data processing rather than testing the individual features of the software product. When it comes to Big data testing, performance and functional testing are the key. In Big data testing QA engineers verify the successful processing of terabytes of data using commodity cluster and other supportive components. It demands a high level of testing skills as the processing is very fast. Processing may be of three types Along with this, data quality is also an important factor in big data testing. Before testing the application, it is necessary to check the quality of data and should be considered as a part of database testing. It involves checking various characteristics like conformity, accuracy, duplication, consistency, validity, data completeness, etc.