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.
Highlights
In recent times artificial neural networks (ANNs) has become popular and helpful model for classification, clustering, pattern recognition and prediction in many disciplines
The great potential of ANNs is the high-speed processing provided in a massive parallel implementation and this has heightened the need for research in this domain [4]
The results indicate that the selected research articles are recent because they were majorly published between the year 2009e2018 and focuses on developmental and technological issues regarding ANN
Summary
In recent times artificial neural networks (ANNs) has become popular and helpful model for classification, clustering, pattern recognition and prediction in many disciplines. ANNs are mostly used for universal function approximation in numerical paradigms because of their excellent properties of self-learning, adaptivity, fault tolerance, nonlinearity, and advancement in input to an output mapping [5] These data analysis factors give more reason why ANNs are effective, efficient and successful in providing a high level of capability in handling complex and noncomplex problems in many spheres of life. Transportation, computer security, banking, insurance, properties management, marketing, energy, and those challenges that cannot be solve by the computational ability of traditional procedures and conventional mathematics Despite these extensive applications of ANNs, there is an increasing need to address the problem of adopting a systematic approach in ANNs development phase to improve its performance. A synthetic machine might still be out of reach, but systems like ANNs that help improve people’s lives are here today
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