Predicting Fish Habitat in the Persian Gulf Using Artificial Intelligence

Tao Hai; Jincheng Zhou; Hoorieh Ahmadi; Ayibatonbo Ebiare Ekiye; Yangping Wei; Celestine Iwendi; Zakaria Boulouard

doi:10.1007/978-3-031-37164-6_22

Predicting Fish Habitat in the Persian Gulf Using Artificial Intelligence

Tao Hai
, Jincheng Zhou
, Hoorieh Ahmadi
, Ayibatonbo Ebiare Ekiye
, Yangping Wei
, Celestine Iwendi
, Zakaria Boulouard

Qiannan Normal College for Nationalities
Nanchang Institute of Science and Technology
University of Bolton
University of Hassan II Casablanca

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The Persian Gulf is one of the most important habitats in the Middle East. It can be extremely beneficial to aquatic species’ survival and environmental preservation to continuous monitoring and collect data about aquatic animals, their habitats, and behaviours. Finding a novel and suitable method to carry out accurate and automatic monitoring with low timing and low cost for monitoring aquatic species’ behaviour in this high potential area is helpful. To predict fish habitat in Persian Gulf Convolutional Neural Network method and Naïve Bayes algorithm are used. Deep learning convolutional neural network technology is mostly used for data science classification and recognition because of its exceptional accuracy and to solve search and optimization issues, the Naïve Bayes algorithm is employed. Results indicate for predicting fish habitat in the Persian Gulf, the accuracy of the Convolutional Neural Network algorithm and the Naïve Bayes algorithms is 97.32% and 95.47%, respectively. With p = 0.025 (p0.05), there is a substantial difference between the Naïve Bayes method and the Convolutional Neural Network algorithm. Therefore, The Convolutional Neural Network method seems to be more accurate than the Naïve Bayes method at predicting fish habitat in the Persian Gulf.

Original language	English
Title of host publication	Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions
Editors	Celestine Iwendi, Zakaria Boulouard, Natalia Kryvinska
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	309-319
Number of pages	11
ISBN (Print)	9783031371639
DOIs	https://doi.org/10.1007/978-3-031-37164-6_22
State	Published - 2023
Externally published	Yes
Event	International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023 - Bolton, United Kingdom Duration: 24 Feb 2023 → 25 Feb 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	735 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023
Country/Territory	United Kingdom
City	Bolton
Period	24/02/23 → 25/02/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 15 Life on Land

Keywords

Convolutional neural network algorithm
Deep learning
Fish habitat
Naïve bayes
Novel approach

Access to Document

10.1007/978-3-031-37164-6_22

Cite this

Hai, T., Zhou, J., Ahmadi, H., Ekiye, A. E., Wei, Y., Iwendi, C., & Boulouard, Z. (2023). Predicting Fish Habitat in the Persian Gulf Using Artificial Intelligence. In C. Iwendi, Z. Boulouard, & N. Kryvinska (Eds.), Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions (pp. 309-319). (Lecture Notes in Networks and Systems; Vol. 735 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-37164-6_22

Hai, Tao ; Zhou, Jincheng ; Ahmadi, Hoorieh et al. / Predicting Fish Habitat in the Persian Gulf Using Artificial Intelligence. Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions. editor / Celestine Iwendi ; Zakaria Boulouard ; Natalia Kryvinska. Springer Science and Business Media Deutschland GmbH, 2023. pp. 309-319 (Lecture Notes in Networks and Systems).

@inproceedings{eec7c558dcdf4134badb1d6a56db92ad,

title = "Predicting Fish Habitat in the Persian Gulf Using Artificial Intelligence",

abstract = "The Persian Gulf is one of the most important habitats in the Middle East. It can be extremely beneficial to aquatic species{\textquoteright} survival and environmental preservation to continuous monitoring and collect data about aquatic animals, their habitats, and behaviours. Finding a novel and suitable method to carry out accurate and automatic monitoring with low timing and low cost for monitoring aquatic species{\textquoteright} behaviour in this high potential area is helpful. To predict fish habitat in Persian Gulf Convolutional Neural Network method and Na{\"i}ve Bayes algorithm are used. Deep learning convolutional neural network technology is mostly used for data science classification and recognition because of its exceptional accuracy and to solve search and optimization issues, the Na{\"i}ve Bayes algorithm is employed. Results indicate for predicting fish habitat in the Persian Gulf, the accuracy of the Convolutional Neural Network algorithm and the Na{\"i}ve Bayes algorithms is 97.32\% and 95.47\%, respectively. With p = 0.025 (p0.05), there is a substantial difference between the Na{\"i}ve Bayes method and the Convolutional Neural Network algorithm. Therefore, The Convolutional Neural Network method seems to be more accurate than the Na{\"i}ve Bayes method at predicting fish habitat in the Persian Gulf.",

keywords = "Convolutional neural network algorithm, Deep learning, Fish habitat, Na{\"i}ve bayes, Novel approach",

author = "Tao Hai and Jincheng Zhou and Hoorieh Ahmadi and Ekiye, \{Ayibatonbo Ebiare\} and Yangping Wei and Celestine Iwendi and Zakaria Boulouard",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023 ; Conference date: 24-02-2023 Through 25-02-2023",

year = "2023",

doi = "10.1007/978-3-031-37164-6\_22",

language = "English",

isbn = "9783031371639",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "309--319",

editor = "Celestine Iwendi and Zakaria Boulouard and Natalia Kryvinska",

booktitle = "Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions",

address = "Germany",

}

Hai, T, Zhou, J, Ahmadi, H, Ekiye, AE, Wei, Y, Iwendi, C & Boulouard, Z 2023, Predicting Fish Habitat in the Persian Gulf Using Artificial Intelligence. in C Iwendi, Z Boulouard & N Kryvinska (eds), Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions. Lecture Notes in Networks and Systems, vol. 735 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 309-319, International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023, Bolton, United Kingdom, 24/02/23. https://doi.org/10.1007/978-3-031-37164-6_22

Predicting Fish Habitat in the Persian Gulf Using Artificial Intelligence. / Hai, Tao; Zhou, Jincheng; Ahmadi, Hoorieh et al.
Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions. ed. / Celestine Iwendi; Zakaria Boulouard; Natalia Kryvinska. Springer Science and Business Media Deutschland GmbH, 2023. p. 309-319 (Lecture Notes in Networks and Systems; Vol. 735 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Predicting Fish Habitat in the Persian Gulf Using Artificial Intelligence

AU - Hai, Tao

AU - Zhou, Jincheng

AU - Ahmadi, Hoorieh

AU - Ekiye, Ayibatonbo Ebiare

AU - Wei, Yangping

AU - Iwendi, Celestine

AU - Boulouard, Zakaria

PY - 2023

Y1 - 2023

N2 - The Persian Gulf is one of the most important habitats in the Middle East. It can be extremely beneficial to aquatic species’ survival and environmental preservation to continuous monitoring and collect data about aquatic animals, their habitats, and behaviours. Finding a novel and suitable method to carry out accurate and automatic monitoring with low timing and low cost for monitoring aquatic species’ behaviour in this high potential area is helpful. To predict fish habitat in Persian Gulf Convolutional Neural Network method and Naïve Bayes algorithm are used. Deep learning convolutional neural network technology is mostly used for data science classification and recognition because of its exceptional accuracy and to solve search and optimization issues, the Naïve Bayes algorithm is employed. Results indicate for predicting fish habitat in the Persian Gulf, the accuracy of the Convolutional Neural Network algorithm and the Naïve Bayes algorithms is 97.32% and 95.47%, respectively. With p = 0.025 (p0.05), there is a substantial difference between the Naïve Bayes method and the Convolutional Neural Network algorithm. Therefore, The Convolutional Neural Network method seems to be more accurate than the Naïve Bayes method at predicting fish habitat in the Persian Gulf.

AB - The Persian Gulf is one of the most important habitats in the Middle East. It can be extremely beneficial to aquatic species’ survival and environmental preservation to continuous monitoring and collect data about aquatic animals, their habitats, and behaviours. Finding a novel and suitable method to carry out accurate and automatic monitoring with low timing and low cost for monitoring aquatic species’ behaviour in this high potential area is helpful. To predict fish habitat in Persian Gulf Convolutional Neural Network method and Naïve Bayes algorithm are used. Deep learning convolutional neural network technology is mostly used for data science classification and recognition because of its exceptional accuracy and to solve search and optimization issues, the Naïve Bayes algorithm is employed. Results indicate for predicting fish habitat in the Persian Gulf, the accuracy of the Convolutional Neural Network algorithm and the Naïve Bayes algorithms is 97.32% and 95.47%, respectively. With p = 0.025 (p0.05), there is a substantial difference between the Naïve Bayes method and the Convolutional Neural Network algorithm. Therefore, The Convolutional Neural Network method seems to be more accurate than the Naïve Bayes method at predicting fish habitat in the Persian Gulf.

KW - Convolutional neural network algorithm

KW - Deep learning

KW - Fish habitat

KW - Naïve bayes

KW - Novel approach

UR - https://www.scopus.com/pages/publications/85174483817

U2 - 10.1007/978-3-031-37164-6_22

DO - 10.1007/978-3-031-37164-6_22

M3 - Conference contribution

AN - SCOPUS:85174483817

SN - 9783031371639

T3 - Lecture Notes in Networks and Systems

SP - 309

EP - 319

BT - Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions

A2 - Iwendi, Celestine

A2 - Boulouard, Zakaria

A2 - Kryvinska, Natalia

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023

Y2 - 24 February 2023 through 25 February 2023

ER -

Hai T, Zhou J, Ahmadi H, Ekiye AE, Wei Y, Iwendi C et al. Predicting Fish Habitat in the Persian Gulf Using Artificial Intelligence. In Iwendi C, Boulouard Z, Kryvinska N, editors, Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions. Springer Science and Business Media Deutschland GmbH. 2023. p. 309-319. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-37164-6_22

Predicting Fish Habitat in the Persian Gulf Using Artificial Intelligence

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this