State-of-the-art in artificial neural network applications: A survey
Oludare Isaac Abioduna b✉, Aman Jantanb✉, Abiodun Esther Omolarac, Kemi Victoria Dadad, Nachaat AbdElatif Mohamede, Humaira Arshadf
- aDepartment of Computer Science, Bingham University, Karu, Nigeria
- bSchool of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia
- cDepartment of Computer Science, Ahmadu Bello University, Zaria, Nigeria
- dDepartment of Mathematical Sciences, Nasarawa State University, Keffi, Nigeria
- eCalifornia University, USA
- fDepartment of Computer Science and Information Technology, Islamia University of Bahawalpur, Pakistan
- ✉Corresponding author
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Abstract
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.
TL;DRThe study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems and proposed feedforwardand feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. (Semantic Scholar)