American Journal of Computer Science and Engineering Survey (AJCSES) is a peer review open access journal publishing the state of the art research in computer science and engineering survey.
American Journal of Computer Science and Engineering Survey (AJCSES) is devoted to the publication referred papers on cutting-edge research in all the scientific areas of Computer Engineering and novel insights into its technology. Journal intends to provide its researchers, practitioners and academics the latest and remarkable researches made by different scientists and industrial experts by providing free access to the published articles.
Manuscripts elucidating research surveys, technical reports, overviews, latest innovations and advancements in applied computer science and allied sciences are solicited.
Subjects covered include:
Robotics and application
Neural Networks and Biomedical Simulations
Microprocessors and microcontrollers
Assembly language programming
Computational biology and bioinformatics
Computer algorithm design and analysis
Data Base Management & Information Retrievals
Systems & Computer Architecture
Geographical Information Systems/ Global Navigation Satellite Systems (GIS/GNSS)
Soft Computing (AI, Neural Networks, Fuzzy Systems, etc.)
Web and internet computing
The journal is indexed in: SAO/NASA Astrophysics Data System (ADS), American National Engineering Database-DDL (Digital Data link), arXiv (arXiv.org) Cornell University Library, UCSF Library, Cite Factor, American Standards for Journals and Research, Google Scholar, Open Academic Journal Index, Indian Science, Cite Seer, Science Central (Directory of Science), International Education Standards, Information systems journal, iSEEK
Send your manuscripts as an e-mail attachment to the Editorial Office at [email protected]
Author(s): Abdalla Ye, Iqbal Mt, Shehata M
Image forgery detection approaches are varied and serve same objectives. However, the difference in image properties causes some limitations of most of these approaches. Integrate multiple forensic ap ... Read More
Author(s): Safa Ibrahim Adi and Mohammed Aldasht
Feature selection in high-dimensional datasets is con-sidered to be a complex and time-consuming problem. To enhance the accuracy of classification and reduce the execution time, Parallel Evolutionary ... Read More
All Published work is licensed under a Creative Commons Attribution 4.0 International License
Copyright © 2018 All rights reserved. iMedPub LTD Last revised : August 13, 2018