Comparative Study and Utilization of Best Deep Learning  Algorithms for the Image Processing

Kanakam Siva Rama Prasad; P Edukondalu; G Subba Rao

doi:10.55524/

Authors

Kanakam Siva Rama Prasad Head & Professor, Department of Artifical Intelligence & Data Science, Pace Institute of Technology and Sciences, Ongole, Andhra Pradesh, India Author
P Edukondalu Associate Professor, Department of Artifical Intelligence & Data Science, Pace Institute of Technology and Sciences, Ongole, Andhra Pradesh, India Author
G Subba Rao Assistant Professor, Department of Artifical Intelligence & Data Science, Pace Institute of Technology and Sciences, Ongole, Andhra Pradesh, India Author

DOI:

https://doi.org/10.55524/

Keywords:

Deep learning, machine learning, convolutional neural networks (CNN) recurrent neural networks (RNN), autoencoder (DAE), deep belief networks (DBNs), long short-term memory (LSTM), review, survey, state of the art

Abstract

Deep learning has gained immense popularity in scientific computing, and its algorithms are widely used in complex problem-solving industries. Every deep learning algorithm use different types of neural networks to perform indented tasks. Deep learning (DL) algorithms have emerged from different machine learning and soft computing methodologies. Since then, a number of deep learning (DL) algorithms have been recently introduced in the scientific community and applied in various application fields. Today, the use of DLs has become indispensable due to their intelligence, effective learning, accuracy and reliability in model creation.

However, a comprehensive list of DL algorithms has not yet been presented in the scientific literature. This article lists the most popular DL algorithms and their application areas. Deep learning uses ANN artificial neural networks to perform convoluted calculations on huge amounts of data. It is a type of machine learning based on the structure and function of the human brain. Deep learning algorithms train machines by learning from examples. Industries such as healthcare, e-commerce, entertainment and advertising often use deep learning.

Downloads

Download data is not yet available.

References

A.Thilagavathy, K.Vijaya Kanth, 2013, “ An Efficient Implementation of Multi Layer Perceptron Neural Network for Signal Processing” , International Journal of Engineering Research & Technology (IJERT) ICSEM – 2013 (Volume 1 – Issue 06)

“A Survey on Image Processing using CNN in Deep Learning” Bhavesh Patil, Mrunali Ghate, Poonam Shinare, Ajay Patil. International Research Journal of Engineering and Technology (IRJET)

Issam Hammad, Kamal El-Sankary, and Jason Gu. " A Comparative Study on Machine Learning Algorithms for the Control of a Wall Following Robot." 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2019. (Pages: 2995 – 3000)

Naskath, J., Sivakamasundari, G. & Begum, A.A.S. A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN. Wireless Pers Commun (2022). https://doi.org/10.1007/s11277-022-10079-4

Ahsan, M. M., Alam, T. E., Trafalis, T., & Huebner, P. (2020). Deep MLP-CNN model using mixed-data to distinguish between COVID-19 and non-COVID-19 patients. Symmetry, 12(9),

https://doi.org/10.3390/sym12091526

Desai, M., & Shah, M. (2021). An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical eHealth, 4, 1– 11. https://doi.org/10.1016/j.ceh.2020.11.002

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Ye., Omran Al-Shamma, J., Santamaría, M. A., Fadhel, M.A.- A., & Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data.

Singh, J., & Banerjee, R. (2019). A Study on Single and Multi layer Perceptron Neural Network. 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), 35-40.