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

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

doi:https://doi.org/10.1016/j.heliyon.2018.e00938

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.

13 of 13 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.

this paper works it cites works citing it node size = global citations · hover for the full title

Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

State-of-the-art in artificial neural network applications: A survey→Learning from Imbalanced DataLegacy edge — source detail unavailable
State-of-the-art in artificial neural network applications: A survey→Identification and control of dynamical systems using neural networksLegacy edge — source detail unavailable
State-of-the-art in artificial neural network applications: A survey→Training feedforward networks with the Marquardt algorithmLegacy edge — source detail unavailable
State-of-the-art in artificial neural network applications: A survey→Learning Deep Architectures for AILegacy edge — source detail unavailable
State-of-the-art in artificial neural network applications: A survey→Neural Networks And Learning MachinesLegacy edge — source detail unavailable
State-of-the-art in artificial neural network applications: A survey→Introduction to the theory of neural computationLegacy edge — source detail unavailable
State-of-the-art in artificial neural network applications: A survey→Learning Deep Architectures for AILegacy edge — source detail unavailable
State-of-the-art in artificial neural network applications: A survey→A survey of deep neural network architectures and their applicationsLegacy edge — source detail unavailable
State-of-the-art in artificial neural network applications: A survey→A fuzzy K-nearest neighbor algorithmLegacy edge — source detail unavailable
State-of-the-art in artificial neural network applications: A survey→Advances in Neural Information Processing Systems 20Legacy edge — source detail unavailable
State-of-the-art in artificial neural network applications: A survey→Kalman Filtering and Neural NetworksLegacy edge — source detail unavailable
State-of-the-art in artificial neural network applications: A survey→Introduction to the Theory of Neural ComputationLegacy edge — source detail unavailable
State-of-the-art in artificial neural network applications: A survey→Neural-network-based fuzzy logic control and decision systemLegacy edge — source detail unavailable
Kalman Filtering and Neural Networks→Introduction to the theory of neural computationLegacy edge — source detail unavailable
A survey of deep neural network architectures and their applications→Learning Deep Architectures for AILegacy edge — source detail unavailable
A survey of deep neural network architectures and their applications→Learning Deep Architectures for AILegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
Learning from Imbalanced Data	2009	10,209
Identification and control of dynamical systems using neural networks	1990	8,037
Training feedforward networks with the Marquardt algorithm	1994	7,739
Learning Deep Architectures for AI	2009	6,951
Neural Networks And Learning Machines	2010	6,571
Introduction to the theory of neural computation	1994	6,461
Learning Deep Architectures for AI	2009	5,077
A survey of deep neural network architectures and their applications	2016	3,285
A fuzzy K-nearest neighbor algorithm	1985	2,679
Advances in Neural Information Processing Systems 20	2008	2,600
Kalman Filtering and Neural Networks	2001	2,404
Introduction to the Theory of Neural Computation	2018	1,789
Neural-network-based fuzzy logic control and decision system	1991	1,485

Links

Download BibTeX · Download RIS · DOI · Open access copy · OpenAlex record

Topics

Neural Networks and Applications	Computer Science
Machine Learning and ELM	Computer Science
Anomaly Detection Techniques and Applications	Computer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

supports6 author record(s) attached.
supports269 reference(s) recorded.
supportsThe DOI's year agrees with the publication year.
supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:47+00:00.

sha256 a08467ae9504f237…