P A College of Engineering

About: P A College of Engineering is a based out in . It is known for research contribution in the topics: Dihedral angle & Ring (chemistry). The organization has 298 authors who have published 594 publications receiving 4888 citations.

4-[(1E)-Benzylideneamino]-3-methyl-2,4-dihydro-1H-1,2,4-triazole-5-thione.

Abstract: The acyclic C=N double bond of 4-[(1E)-benzylideneamino]-3-methyl-2,4-dihydro-1H-1,2,4-triazole-5-thione, C10H10N4S, is trans configured. The mol. is almost planar. The angle between the 2 rings is just 10.25(7)°. The crystal packing is stabilized by N-H···S H bonds and I€-I€ interactions. [on SciFinder(R)]

A looking-out portal (LOP) approach to enhance qualitative aspects of bandwidth utilisation in academic networks

Abstract: Campuses of educational institutions periodically need to increase network bandwidth to keep up with increased demand and this decision is based on the quantitative aspects of the network bandwidth utilisation. The qualitative utilisation of the bandwidth is seldom looked into. Improving the qualitative utilisation of the bandwidth may not even necessitate a network upgrade. Although blacklist-based access control techniques help to a certain degree, the findings of this research indicate otherwise. A multi-tier, whitelist-based, looking-out portal (LOP) approach is presented that promises to improve the qualitative utilisation of the network while positively impacting pertinent resource identification and location of sources on the internet. The authors draw on their years of experience serving as students and staff in various campuses of universities and colleges in various countries while making recommendations.

Notice of Violation of IEEE Publication Principles A framework for medical image classification using soft set

Abstract: Notice of Violation of IEEE Publication Principles "A Framework for Medical Image Classification Using Soft Set" by N.K. Anitha, G. Keerthika, M.Maheswari, J. Praveena in the Proceedings of the 2nd International Conference on Current Trends in Engineering and Technology (ICCTET), July 2014, pp. 268-272 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper is a duplication of the original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: "A Framework for Medical Images Classification Using Soft Set" by Saima Anwar Lashari, Rosziati Ibraham in Procedia Technology 11, 2013, pp. 548-556 Medical image classification is a significant research area that receives growing attention from both the research community and medicine industry. It addresses the problem of diagnosis, analysis and teaching purposes in medicine. For these several medical imaging modalities and applications based on data mining techniques have been proposed and developed. Thus, the primary objective of medical image classification is not only to achieve good accuracy but to understand which parts of anatomy are affected by the disease to help clinicians in early diagnosis of the pathology and in learning the progression of a disease. This furnishes motivation from the advancement in data mining techniques and particularly in soft set, to propose a classification algorithm based on the notions of soft set theory. As a result, a new framework for medical imaging classification consisting of six phases namely: data acquisition, data pre-processing, data partition, soft set classifier, data analysis and performance evolution is presented.

Video retrieval: An accurate approach based on Kirsch descriptor

Abstract: In this paper, a video retrieval model is developed based on Kirsch local descriptor. In the first stage, the input video is segmented into shots and keyframes are extracted. In the next stage, local descriptors are extracted from each keyframe and clustered into k clusters using k-means clustering procedure. Given a query frame, the local descriptors are extracted from it in a similar manner, and then compared with the descriptors of the database video using k-nearest neighbor search algorithm to find the matching keyframe. Experiments have been performed on the TRECVID video segments to demonstrate the performance of the proposed approach for video retrieval applications.

2-(4-Methyl­benzoyl­oxymeth­yl)-5-(5-methyl-2,4-dioxo-1,2,3,4-tetra­hydro­pyrimidin-1-yl)tetra­hydro­furan-3-yl 4-methyl­benzoate

Abstract: The title compound, C26H26N2O7, is a thiamidine derivative. Geometric parameters are in the usual ranges. The crystal packing is stabilized by a classical N - H center dot center dot center dot O hydrogen bond, several weak C - H center dot center dot center dot O hydrogen bonds and a pi - pi stacking interaction.