Dependency of the learning technique on the problem nature | ||
| International Journal of Theoretical and Applied Research | ||
| Volume 2, Issue 1, June 2023, Pages 77-84 PDF (331.82 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijtar.2023.142717.1010 | ||
| Authors | ||
| Amira Salah Elkhateeb* 1; Hend Salah Mancy2; Mervat zaki3; Kamal Eldahshan4 | ||
| 1Dept. of Mathematics, Computer Science Division, Faculty of Science, Tanta University | ||
| 2Dept. of Mathematics, Computer science Division, Faculty of Science (Girls), Al-Azhar University, Cairo, Egypt | ||
| 3Dept. of Mathematics, Faculty of Science (Girls), Al-Azhar University, Cairo, Egypt | ||
| 4Dept. of Mathematics, Computer science Division, Faculty of Science, Al-Azhar University, Cairo, Egypt. | ||
| Abstract | ||
| Indicators Perception and prediction based upon available datasets has recently gained an increasing importance. Artificial intelligence (AI) is the backbone of perception and prediction; learning techniques are being used by most of the researchers to achieve these goals while ontologies are being used to collect, represent, understand and use input data. Using a comprehensive ontology can improve the process of incrementally learning a visual concept detection model. The problematic nature may be in healthcare, Transportation and etc. Applying AI on different environmental sectors like solar irradiation, Agriculture, water domain and other natural disasters have been increased in recent years due to weather changes and human activities. Achieving high accuracy and high efficiency have always been challenges for researchers for faster natural disaster management or natural phenomena exploitation in economy development. With inflating data, there is direction to deep learning models and hybrid methods that enhance the outcome. This paper reviews on how artificial intelligence applied in different environmental applications and development stages of AI models until now. It shows advantages and disadvantages of each model and providing appropriate recommendations for each application to achieve the best forecasting. | ||
| Keywords | ||
| Artificial intelligence; ontology; Machine learning; deep learning, Environmental applications | ||
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