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International Journal of Advanced Networking and Applications

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Shehata, Naglaa Saeed

Big Data with Column Oriented NOSQL Database to Overcome the Drawbacks of Relational Databases

Algorithms of Deep Learning:Convolutional Neural Network Role with Colon Cancer Disease

Abstract Views :146 | PDF Views:1

Authors

Naglaa Saeed Shehata ¹, Mona Nasr ², Laila El Fangary ¹, Laila Abd EL Hamid ¹

Affiliations
1 Information Systems Department ,Faculty of Computers and Artificial Intelligence, Helwan University, EG
2 Information Systems Department, Faculty of Computers and Artificial Intelligence, Helwan University, EG

Source

International Journal of Advanced Networking and Applications, Vol 13, No 1 (2021), Pagination: 4827-4832

Abstract

The world's third most serious and lethal cancer rankings are colon cancer. Like cancer, the most important stage of early diagnosis is. Deep learning has become a leading learning tool for object detection and its successes in advancing the analysis of medical images have attracted attention. Convolutionary neural networks (CNNs), which play an indispensable role in the detection and potential early diagnose of colon cancer, are the most popular method of deep learning algorithms for this purpose. In this article we hope to take a look at the progress of colonic cancer analysis by studying profound learning practices. This study provides an overview of popular profound study algorithms used in analysis of colon cancer. All studies in the fields of colon cancer, including detection, classification as well as segmentation and survival prediction, will then be collected. Finally, we will conclude the work by summarizing the latest deep learning practices in analysis of colon cancer, a critical examination of the challenges and proposals for future research.

Keywords

Deep Learning, Colon Cancer, Medical Image Analysis, Convolutional Neural Networks.

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References

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M. Abdellatif, M. Salah, and N. Saeed, “ScienceDirect Overcoming business process reengineering obstacles using ontology-based knowledge map methodology,” Futur. Comput. Informatics J., vol. 3, no. 1, pp. 7–28, 2018.

N.Shehata and A.Abed, “Big Data with Column Oriented NOSQL Database to Overcome the Drawbacks of Relational Databases,” Int. J. Adv. Netw. Appl., vol. 4428, pp. 4423–4428, 2020.

Deep Neural Networks for Weather Forecasting

Abstract Views :44 | PDF Views:1

Authors

Naglaa Saeed Shehata ¹, Mohamed Saeed Shehata ²

Affiliations
1 Faculty of Computers & Artificial Intelligence, Helwan University, Cairo, EG
2 Faculty of engineering, Banha university, EG

Source

International Journal of Advanced Networking and Applications, Vol 15, No 2 (2023), Pagination: 5915-5923

Abstract

Numerical Weather Prediction focuses on taking current observations of weather and processing these data with computer models to forecast the future state of weather and is used to produce shortand medium-range weather forecasts from 10-15 days of the state of the atmosphere. A weather satellite is a type of satellite that is primarily used to monitor the weather and climate of the Earth. Electromagnetic radiation is energy emitted by all matter above absolute zero temperature ex., visible light, infrared light, heat, microwaves, and radio and television waves and Electromagnetic radiation is absorbed mainly by several gases in the Earth's atmosphere, among the most important being water vapor, carbon dioxide, and ozone. The paper proposed methodology involves training deep neural networks to take reanalysis weather data at a given point in time as input, and then produce reanalysis weather data at a future point in time as output. We used a single point in time for both input and output

Keywords

weather prediction, computer models ,Electromagnetic radiation

Full Text

References

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