Publications
An Evaluation of Machine Learning and Big Data Analytics Performance in Cloud Computing and Computer Vision
Jun 11, 2023Journal International Journal on Recent and Innovation Trends in Computing and Communication
Publisher Auricle Global Society of Education and Research
DOI https://doi.org/10.17762/ijritcc.v11i6.7144
Issue 6
Volume 11
Although cloud computing is receiving a lot of attention, security remains a significant barrier to its general adoption. Cloud service users frequently worry about data loss, security risks, and availability issues. Because of the accessibility and openness of the huge volume of data amassed by sensors and the web throughout recent years, computer applications have seen a remarkable change from straightforward data processing to machine learning. Two widely used technologies, Big Data and Cloud computing, are the focus of worry in the IT industry. Enormous data sets are put away, handled, and broke down under the possibility of "Big Data." Then again, cloud computing centers around giving the framework to make such systems conceivable in a period and cash saving way. The objective of the review is to survey the Big Data Analytics and Machine learning ideal models for use in cloud computing and computer vision. The programmed data examination of enormous data sets and the production of models for the wide connections between data are the center highlights of machine learning (ML). The usefulness of machine learning-based strategies for identifying threats in a cloud computing environment is surveyed and compared in this research. © 2023 Authors. All rights reserved.
Building an Intelligent System to Distinguish Russian Printed Letters using Artificial Neural Networks
Dec 1, 2011Journal AL-Rafidain Journal of Computer Sciences and Mathematics
Publisher Auricle Global Society of Education and Research
DOI https://doi.org/10.33899/csmj.2011.163655
Issue 2
Volume 8
In this research, an intelligent computer system is designed for recognizing printed Russian letters by extracting features of the letter by finding the Eigen values which then used for training and testing the artificial neural network used in this work namely, Elman NN. This network is used as a tool for decision making. Data is entered using a flatbed scanner which results in high extensity, fineness and homogeneous BMP extension images. The programs are implemented by Matlab language, the software include image enhancement techniques, image segmentation, resize the segmented image and features extraction dependent on Eigen values .These values are then used to train and test the Elman Neural Network. In this work the pass ratio of recognition up to 90 % .
تمييز الحروف العبرية باستخدام الشبكات العصبية الاصطناعية
Mar 15, 2010Journal Journal of Prospective Researches
Publisher كلية الحدباء الجامعة
Volume 29,30
In this research, an intelligent computer system has been designed for recognizing printed Hebrew letters by extracting features of the letter by finding the Eigen values which have been used then for training and testing the artificial neural network used in this work namely, Elman NN. This network is used as a tool for decision making. Data were entered using a flatbed scanner which results high extensity, fineness and homogeneous BMP extension images. The programs were implemented by Matlab language, the software include image enhancement techniques, image segmentation, resize the segmented image and features extraction dependent on eigen values and document the final result by visual basic.net because Matlab doesn't have the ability to print the Hebrew letter as a final result.
Survey of Computer Network Traffic Analysis Using Artificial Intelligence Algorithms
Dec 7, 2024Journal International Research Journal of Innovations in Engineering and Technology
Publisher IRJIET (International Research Journal of Innovations in Engineering and Technology)
DOI http://dx.doi.org/10.47001/irjiet/2024.812002
Issue 12
Volume 8
This paper introduces a literature review and experimental scenario for network construction; the paper analyzes the network traffic analysis status with artificial intelligence algorithms such as machine learning (ML), ensemble learning, and deep learning for study about analysis in traffic, cybersecurity, balanced loading of network and prediction the traffic moving. The technical aspects of AI are used to analyze and detect attacks or conjunctions for large amounts of network data, thereby detecting anomalies or malicious activities that affect networks. Also, when training deep learning such as convolution neural networks (CNN) or recurrent neural networks (RNN) for datasets (historical data), learn benign network behavior as well as anomalies that may result from malicious activities. The paper introduces how AI technology is used to detect security threats and analyze networks on an outstanding basis