State-of-the-art in artificial neural network applications: A survey
| Autor: | Humaira Arshad, Aman Jantan, Kemi Victoria Dada, Abiodun Esther Omolara, Nachaat AbdElatif Mohamed, Oludare Isaac Abiodun |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
020209 energy Computer Science::Neural and Evolutionary Computation 02 engineering and technology Machine learning computer.software_genre Article Taxonomy (general) Convergence (routing) 0202 electrical engineering electronic engineering information engineering lcsh:Social sciences (General) lcsh:Science (General) Focus (computing) Multidisciplinary Artificial neural network business.industry Feed forward Fault tolerance Scalability lcsh:H1-99 020201 artificial intelligence & image processing Artificial intelligence State (computer science) business computer lcsh:Q1-390 |
| Zdroj: | Heliyon Heliyon, Vol 4, Iss 11, Pp e00938-(2018) |
| ISSN: | 2405-8440 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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