1) IRAJ is moving to next issue from 16th April 2015.
2) IRAJ Management has issued a thank you note to all the editors for their constant endeavours for enhancement for IRAJ.
3) Chief editor of IRAJ has achieved UGC Net qualification.
|VOLUME 14, ISSUE 1, 1 October, 2018 To 31 December, 2018|
|VOLUME 13, ISSUE 1, 1 April, 2017 To 30 June, 2017|
|VOLUME 12, ISSUE 1, 1 January, 2017 To 31 March, 2017|
|VOLUME 11, ISSUE 1, 16 October, 2016 To 31 December, 2016|
|VOLUME 9, ISSUE 1, 15 April, 2016 To 14 June, 2016|
|VOLUME 8, ISSUE 1, 15 January, 2016 To 14 April, 2016|
|VOLUME 7, ISSUE 1, 15 October, 2015 To 14 January, 2016|
|VOLUME 6, ISSUE 1, 15 July, 2015 To 14 October, 2015|
|VOLUME 5, ISSUE 1, 16 April, 2015 To 15 July, 2015|
|VOLUME 4, ISSUE 1, 16 January, 2015 To 15 April, 2015|
|VOLUME 2, ISSUE 1, 16 August, 2014 To 15 November, 2014|
|VOLUME 1, ISSUE 1, 15 June, 2014 To 15 August, 2014|
Image processing has been topics of research from past and researchers have set mile stones in image processing. The major challenges in image processing has been to detect the particular object in images, matching images, handling distorted images along with the accuracy of the image processing algorithms and their performance. Recent innovations in machine learning has opened new prospects in image processing which is not only helpful in increasing the accuracy and performance of image processing but have been used to reduce the complexities in implementation of complexity of the algorithms quickly. In this work a survey of these techniques is being proposed to measure and list some of the latest techniques and algorithms using machine learning techniques in image processing.
Virtual Machine migration has been a challenge in the cloud computing and cloud environment from the very first implementations of the cloud. The users may need to create thousands of the virtual machines and apply unlimited loads on them. This has facilitated the users to great extent by pay per use and optimal resource utilization for what they pay. But along with cloud server managers are facing the same advantages as challenges as the server on which load is increased has to be eased by migrating some or more virtual machines to other servers which are respectively vacant. This not only requires migrating without shutdown challenge but also requires evaluating such virtual machines proactively. For this proactive evaluation many researchers have proposed various algorithms and still require continuous researches for enhancing the performance of such systems. In this paper, such methods are being discussed and the challenges in them are being shown.