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Paper ID:IJSREM61925

Neural Networks: Intelligent Systems for Learning and Prediction

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM61925

Volume:

10 |

4 |

April 2026

Pages:

1-2

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MD

Mahee Dave

Department of Computer Science

Gandhinagar University, Gandhinagar, Gujarat, India.

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HP

Hani Patel

Department of Computer Science

Gandhinagar University, Gandhinagar, Gujarat, India.

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Abstract

Neural Networks have become a key technology in Artificial Intelligence for solving complex problems such as pattern recognition, prediction, and decision-making. Inspired by the human brain, they learn from data and improve performance over time. This paper presents an overview of neural network architecture, types (feedforward, convolutional, and recurrent), and training methods, including backpropagation and optimisation techniques. It also highlights applications in healthcare, finance, natural language processing, and computer vision. Advantages such as high accuracy and automation are discussed, along with challenges such as computational cost, overfitting, and lack of interpretability. Future advancements in explainable AI and efficient models are also considered.

KEYWORDS

Keywords: Neural Networks Artificial Intelligence Deep Learning Pattern Recognition Machine Learning.

Cite This Article

Mahee Dave, Hani Patel, "Neural Networks: Intelligent Systems for Learning and Prediction," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-2, April 2026.

Full Article/Conclusion

Neural Networks are a key technology in Artificial Intelligence, enabling intelligent systems and improving decision-making across industries. They have revolutionised the way machines learn and interact with data by enabling them to analyse complex patterns and make accurate predictions. Their application in fields such as healthcare, finance, transportation, and communication has significantly enhanced efficiency, accuracy, and automation. Furthermore, neural networks continue to evolve with advancements in deep learning, making them more powerful and capable of solving real-world problems.

References

LeCun, Y., Bengio, Y., & Hinton, G. (2015).  Deep Learning. Nature, 521(7553), 436–444.Goodfellow, I., Bengio, Y., & Courville, A. (2016).  Deep Learning. MIT Press.Haykin, S. (2009).  Neural Networks and Learning Machines (3rd ed.). Pearson Education.Schmidhuber, J. (2015).  Deep Learning in Neural Networks: An Overview. Neural Networks, 61, 85–117.Krizhevsky, A., Sutskever, I., & Hinton, G. (2012).  ImageNet Classification with Deep Convolutional Neural Networks.  Advances in Neural Information Processing Systems (NeurIPS).Hochreiter, S., & Schmidhuber, J. (1997).  Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.Vaswani, A., et al. (2017).  Attention Is All You Need.  Advances in Neural Information Processing Systems (NeurIPS).

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Publication Date

5/1/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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