State-of-the-art in artificial neural network applications: A survey
This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study present...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2018-11-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844018332067 |
| _version_ | 1818849075453231104 |
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| author | Oludare Isaac Abiodun Aman Jantan Abiodun Esther Omolara Kemi Victoria Dada Nachaat AbdElatif Mohamed Humaira Arshad |
| author_facet | Oludare Isaac Abiodun Aman Jantan Abiodun Esther Omolara Kemi Victoria Dada Nachaat AbdElatif Mohamed Humaira Arshad |
| author_sort | Oludare Isaac Abiodun |
| collection | DOAJ |
| description | This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application. |
| first_indexed | 2024-12-19T06:27:28Z |
| format | Article |
| id | doaj.art-9881137d1f8648ac840b5fd0012fb4ed |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-12-19T06:27:28Z |
| publishDate | 2018-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-9881137d1f8648ac840b5fd0012fb4ed2022-12-21T20:32:30ZengElsevierHeliyon2405-84402018-11-01411e00938State-of-the-art in artificial neural network applications: A surveyOludare Isaac Abiodun0Aman Jantan1Abiodun Esther Omolara2Kemi Victoria Dada3Nachaat AbdElatif Mohamed4Humaira Arshad5School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia; Department of Computer Science, Bingham University, Karu, Nigeria; Corresponding author.School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia; Corresponding author.Department of Computer Science, Ahmadu Bello University, Zaria, NigeriaDepartment of Mathematical Sciences, Nasarawa State University, Keffi, NigeriaCalifornia University, USADepartment of Computer Science and Information Technology, Islamia University of Bahawalpur, PakistanThis is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.http://www.sciencedirect.com/science/article/pii/S2405844018332067Computer science |
| spellingShingle | Oludare Isaac Abiodun Aman Jantan Abiodun Esther Omolara Kemi Victoria Dada Nachaat AbdElatif Mohamed Humaira Arshad State-of-the-art in artificial neural network applications: A survey Heliyon Computer science |
| title | State-of-the-art in artificial neural network applications: A survey |
| title_full | State-of-the-art in artificial neural network applications: A survey |
| title_fullStr | State-of-the-art in artificial neural network applications: A survey |
| title_full_unstemmed | State-of-the-art in artificial neural network applications: A survey |
| title_short | State-of-the-art in artificial neural network applications: A survey |
| title_sort | state of the art in artificial neural network applications a survey |
| topic | Computer science |
| url | http://www.sciencedirect.com/science/article/pii/S2405844018332067 |
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