Who Cited It

State-of-the-art in artificial neural network applications: A survey

2018 · Heliyon · 3,071 citations · 0 from inside this corpus

Oludare Isaac Abiodun, Aman Jantan low, Abiodun Esther Omolara, Kemi Victoria Dada low, Nachaat Mohamed, Humaira Arshad

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.

State-of-the-art in artificial neural network applications: A survey (2018)State-of-the-art in artificia…Learning from Imbalanced Data (2009)Learning from Imbalanced DataIdentification and control of dynamical systems using neural networks (1990)Identification and control of…Training feedforward networks with the Marquardt algorithm (1994)Training feedforward networks…Learning Deep Architectures for AI (2009)Learning Deep Architectures f…Neural Networks And Learning Machines (2010)Neural Networks And Learning …Introduction to the theory of neural computation (1994)Introduction to the theory of…Learning Deep Architectures for AI (2009)Learning Deep Architectures f…A survey of deep neural network architectures and their applications (2016)A survey of deep neural netwo…A fuzzy K-nearest neighbor algorithm (1985)A fuzzy K-nearest neighbor al…Advances in Neural Information Processing Systems 20 (2008)Advances in Neural Informatio…Kalman Filtering and Neural Networks (2001)Kalman Filtering and Neural N…Introduction to the Theory of Neural Computation (2018)Introduction to the Theory of…Neural-network-based fuzzy logic control and decision system (1991)Neural-network-based fuzzy lo…
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What this paper cites, inside the corpus

PaperYearCited
Learning from Imbalanced Data200910,209
Identification and control of dynamical systems using neural networks19908,037
Training feedforward networks with the Marquardt algorithm19947,739
Learning Deep Architectures for AI20096,951
Neural Networks And Learning Machines20106,571
Introduction to the theory of neural computation19946,461
Learning Deep Architectures for AI20095,077
A survey of deep neural network architectures and their applications20163,285
A fuzzy K-nearest neighbor algorithm19852,679
Advances in Neural Information Processing Systems 2020082,600
Kalman Filtering and Neural Networks20012,404
Introduction to the Theory of Neural Computation20181,789
Neural-network-based fuzzy logic control and decision system19911,485

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DOI · Open access copy · OpenAlex record

Topics

Neural Networks and ApplicationsComputer Science
Machine Learning and ELMComputer Science
Anomaly Detection Techniques and ApplicationsComputer Science

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