Deep Learning for Sea Turtle Classification: A Bibliometric Analysis Using VOSviewer
Keywords:
Technology, Information System, Computer Science
Abstract
Deep learning in the context of sea turtles constitutes a significant area of exploration within marine science. Consequently, the aim of this study is to perform a bibliometric analysis focusing on the subject of sea turtle deep learning, leveraging mapping analysis through the utilization of VOSviewer software. For this research, we employed a bibliometric and descriptive quantitative approach. The data was acquired by conducting a search on Google Scholar using the keyword "Sea Turtle Deep Learning," which yielded a total of 880 articles published between 2018 and 2023. Notably, only 19 of these articles were directly relevant to the research topic. The findings underscore the diversity of research outcomes in the realm of sea turtle deep learning over this time span. In conclusion, this investigation underscores the significance of conducting bibliometric analyses, particularly within the domain of sea turtle deep learning, and serves as a valuable reference for future research endeavours in defining research themes
References
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Balakrishnan, G., Velavan, R., Batoo, K. M., & Raslan, E. H. (2020). Microstructure, optical and photocatalytic properties of MgO nanoparticles. Results in Physics, 16, pp. 103013.
Bidwe, R. V., Mishra, S., Patil, S., Shaw, K., Vora, D. R., Kotecha, K., & Zope, B. (2022). Deep Learning approaches for video compression: A bibliometric analysis. Big Data and Cognitive Computing, 6(2), 44.
Blumenthal, J. M., Austin, T. J., Bothwell, J. B., Broderick, A. C., Ebanks-Petrie, G., Olynik, J. R., Orr, M. F., Solomon, J. L., & Witt, M. J. (2009). Effects of local ecological knowledge and training on the accuracy of sea turtle nesting surveys. Conservation Biology, 23(5), 1130–1141.
Buhrmester, V., Münch, D., & Arens, M. (2021). Analysis of explainers of black box Deep Neural Networks for Computer Vision: A survey. Machine Learning and Knowledge Extraction, 3(4), 966–989.
Butticè, V., & Ughetto, E. (2023). What, where, who, and how? A bibliometric study of crowdfunding research. IEEE Transactions on Engineering Management, 70(9), 3078– 3099.
Casale, P., Margaritoulis, D., Broderick, A. C., Freggi, D., Giovannotti, M., Godley, B. J., Hochscheid, S., Luschi, P., Mazaris, A. D., & Panagopoulou, A. (2013). Sea turtle conservation in italy: Assessing the needs of the loggerhead turtle (Caretta caretta) in the mediterranean sea. Aquatic Conservation: Marine and Freshwater Ecosystems, 23(5), 675–684.
Chatterjee, A., Narahari, K. N., Joshi, M., & Agrawal, P. (2019). SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text. Proceedings of the 13th International Workshop on Semantic Evaluation.
Coulibaly, S., Kamsu-Foguem, B., Kamissoko, D., & Traore, D. (2022). Deep learning for precision agriculture: A bibliometric analysis. Intelligent Systems with Applications, 16(200102), 200102.
Cramer, E. Y., Ray, E. L., Lopez, V. K., Bracher, J., Brennen, A., Castro Rivadeneira, A. J., Gerding, A., Gneiting, T., House, K. H., Huang, Y., Jayawardena, D., Kanji, A. H., Khandelwal, A., Le, K., Mühlemann, A., Niemi, J., Shah, A., Stark, A., Wang, Y., … Reich, N. G. (2022). Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proceedings of the National Academy of Sciences of the United States of America, 119(15), e2113561119.
Craswell, N., Croft, W. B., de Rijke, M., Guo, J., & Mitra, B. (2018). Neural information retrieval: introduction to the special issue. Information Retrieval, 21(2–3), 107–110.
Degrave, J., Hermans, M., Dambre, J., & Wyffels, F. (2019). A differentiable physics engine for deep learning in robotics. Frontiers in Neurorobotics, 13, 6.
Dobrucka, R. (2018). Synthesis of MgO nanoparticles using Artemisia abrotanum herba extract and their antioxidant and photocatalytic properties. Iranian Journal of Science and Technology, Transactions A: Science, 42(2), pp. 547-555.
Essien, E. R., Atasie, V. N., Okeafor, A. O., & Nwude, D. O. (2020). Biogenic synthesis of magnesium oxide nanoparticles using Manihot esculenta (Crantz) leaf extract. International Nano Letters, 10(1), pp. 43-48.
Fang, F., Ventre, C., Basios, M., Kanthan, L., Martinez-Rego, D., Wu, F., & Li, L. (2022). Cryptocurrency trading: a comprehensive survey. Financial Innovation, 8(1).
Faurina, R., Wijanarko, A., Heryuanti, A. F., Ishak, S. I., & Agustian, I. (2023). Comparative study of ensemble deep learning models to determine the classification of turtle species. Computer Science and Information Technologies, 4(1), 24–32.
Garrett, D. E. (2012). Potash: deposits, processing, properties and uses. Springer Science & Business Media.
Husaeni, D. F., & Nandiyanto, A. B. D. (2023). Mapping visualization analysis of computer science research data in 2017-2021 on the google scholar database with vosviewer”. International Journal of Informatics, Information System and Computer Engineering (INJIISCOM), 3(1), 1–18.
Jensen, M. P., Allen, C. D., Eguchi, T., Bell, I. P., LaCasella, E. L., Hilton, W. A., Hof, C. A. M., & Dutton, P. H. (2018). Environmental warming and feminization of one of the largest sea turtle populations in the world. Current Biology: CB, 28(1), 154-159.e4.
Khairi, S., Bakar, M., Alias, M., Bakar, S., Liong, C.-Y., Rosli, N., & Farid, M. (2021). Deep learning on histopathology images for breast cancer classification: A bibliometric analysis. Healthcare (Basel, Switzerland), 10(1), 10.
Kittinger, J. N., Houtan, K. S. V., McClenachan, L. E., & Lawrence, A. L. (2013). Using historical data to assess the biogeography of population recovery. Ecography, 36(8), 868- 872.
Liu, J., Wang, M., Bao, L., Li, X., Sun, J., & Ming, Y. (2020). Classification and recognition of turtle images based on convolutional neural network. IOP Conference Series. Materials Science and Engineering, 782(5), 052044.
Lorus, J., & Milne, M. J. (2018). Handbook of turtles: The turtles of the United States, Canada, and Baja California. Archie Carr. Ithaca, N. y.: Comstock pub., Cornell univ. Press, 1952. 542 pp. $7.50. Science (New York, N.Y.), 116(3007), 181–181.
Marcus, G. (2018). Deep learning: A critical appraisal. In arXiv [cs.AI]. http://arxiv.org/abs/1801.00631
Mguni, L. L., Mukenga, M., Jalama, K., & Meijboom, R. (2013). Effect of calcination temperature and MgO crystallite size on MgO/TiO2 catalyst system for soybean oil transesterification. Catalysis Communications, 34, pp. 52-57.
Min, K., Cuiffi, J. D., & Mathers, R. T. (2020). Ranking environmental degradation trends of plastic marine debris based on physical properties and molecular structure. Nature Communications, 11(1), 727.
Nandatamadini, F., Karina, S., Nandiyanto, A. B. D., & Ragadhita, R. (2019). Feasibility study based on economic perspective of cobalt nanoparticle synthesis with chemical reduction method. Cakra Kimia (Indonesian E-Journal of Applied Chemistry), 7(1), pp. 61-68.
Nandiyanto, A. B. D. (2018). Cost analysis and economic evaluation for the fabrication of activated carbon and silica particles from rice straw waste. Journal of Engineering Science and Technology, 13(6), pp. 1523-1539.
Nassar, M. Y., Mohamed, T. Y., Ahmed, I. S., & Samir, I. (2017). MgO nanostructure via a sol-gel combustion synthesis method using different fuels: an efficient nano-adsorbent for the removal of some anionic textile dyes. Journal of Molecular Liquids, 225, pp. 730-740.
Papafitsoros, K., Adam, L., Čermák, V., & Picek, L. (2022). SeaTurtleID: A novel long- span dataset highlighting the importance of timestamps in wildlife re-identification.
Prasanth, R., Kumar, S. D., Jayalakshmi, A., Singaravelu, G., Govindaraju, K., & Kumar, V. G. (2019). Green synthesis of magnesium oxide nanoparticles and their antibacterial activity. Indian Journal of Geo Marine Sciences, 48(08), pp. 1210-1215.
Solomon, T. (2020). … of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score.
Stephen, O., Sain, M., Maduh, U. J., & Jeong, D.-U. (2019). An efficient deep learning approach to pneumonia classification in healthcare. Journal of Healthcare Engineering, 2019, 4180949.
Wallace, B. P., DiMatteo, A. D., Hurley, B. J., Finkbeiner, E. M., Bolten, A. B., Chaloupka, M. Y., Hutchinson, B. J., Abreu-Grobois, F. A., Amorocho, D., Bjorndal, K. A., Bourjea, J., Bowen, B. W., Dueñas, R. B., Casale, P., Choudhury, B. C., Costa, A., Dutton, P. H., Fallabrino, A., Girard, A., … Mast, R. B. (2010). Regional management units for marine turtles: A novel framework for prioritizing conservation and research across multiple scales. PloS One, 5(12), e15465.
Wang, H., Ge, S., Xing, E. P., & Lipton, Z. C. (2019). Learning robust global representations by penalizing local predictive power. In arXiv [cs.CV]. http://arxiv.org/abs/1905.13549
Wang, Y., Liu, S., Afzal, N., Rastegar-Mojarad, M., Wang, L., Shen, F., Kingsbury, P., & Liu, H. (2018). A comparison of word embeddings for the biomedical natural language processing. Journal of Biomedical Informatics, 87, 12–20.
Weinstein, B. G. (2018). A computer vision for animal ecology. The Journal of Animal Ecology, 87(3), 533–545.
Zhang, H., Hu, J., Xie, J., Wang, S., & Cao, Y. (2019). A solid-state chemical method for synthesizing MgO nanoparticles with superior adsorption properties. RSC advances, 9(4), pp. 2011-2017.
Zhao, Zhou, Li, Cao, He, Yu, Li, Elvidge, Cheng, & Zhou. (2019). Applications of satellite remote sensing of nighttime light observations: Advances, challenges, and perspectives. Remote Sensing, 11(17), 1971.
Alswaidan, N., & Menai, M. E. B. (2020). A survey of state-of-the-art approaches for emotion recognition in text. Knowledge and Information Systems, 62(8), 2937–2987.
Arruda, H., Silva, E. R., Lessa, M., Proença Jr, D., & Bartholo, R. (2022). VOSviewer and bibliometrix. Journal of the Medical Library Association: JMLA, 110(3), 392.
Athalye, A., Engstrom, L., Ilyas, A., & Kwok, K. (2017). Synthesizing robust adversarial examples. In arXiv [cs.CV]. http://arxiv.org/abs/1707.07397
Attal, Z., & Direkoglu, C. (2020). Sea turtle species classification for environmental research and conservation. In Advances in Intelligent Systems and Computing (pp. 580– 587). Springer International Publishing.
Balakrishnan, G., Velavan, R., Batoo, K. M., & Raslan, E. H. (2020). Microstructure, optical and photocatalytic properties of MgO nanoparticles. Results in Physics, 16, pp. 103013.
Bidwe, R. V., Mishra, S., Patil, S., Shaw, K., Vora, D. R., Kotecha, K., & Zope, B. (2022). Deep Learning approaches for video compression: A bibliometric analysis. Big Data and Cognitive Computing, 6(2), 44.
Blumenthal, J. M., Austin, T. J., Bothwell, J. B., Broderick, A. C., Ebanks-Petrie, G., Olynik, J. R., Orr, M. F., Solomon, J. L., & Witt, M. J. (2009). Effects of local ecological knowledge and training on the accuracy of sea turtle nesting surveys. Conservation Biology, 23(5), 1130–1141.
Buhrmester, V., Münch, D., & Arens, M. (2021). Analysis of explainers of black box Deep Neural Networks for Computer Vision: A survey. Machine Learning and Knowledge Extraction, 3(4), 966–989.
Butticè, V., & Ughetto, E. (2023). What, where, who, and how? A bibliometric study of crowdfunding research. IEEE Transactions on Engineering Management, 70(9), 3078– 3099.
Casale, P., Margaritoulis, D., Broderick, A. C., Freggi, D., Giovannotti, M., Godley, B. J., Hochscheid, S., Luschi, P., Mazaris, A. D., & Panagopoulou, A. (2013). Sea turtle conservation in italy: Assessing the needs of the loggerhead turtle (Caretta caretta) in the mediterranean sea. Aquatic Conservation: Marine and Freshwater Ecosystems, 23(5), 675–684.
Chatterjee, A., Narahari, K. N., Joshi, M., & Agrawal, P. (2019). SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text. Proceedings of the 13th International Workshop on Semantic Evaluation.
Coulibaly, S., Kamsu-Foguem, B., Kamissoko, D., & Traore, D. (2022). Deep learning for precision agriculture: A bibliometric analysis. Intelligent Systems with Applications, 16(200102), 200102.
Cramer, E. Y., Ray, E. L., Lopez, V. K., Bracher, J., Brennen, A., Castro Rivadeneira, A. J., Gerding, A., Gneiting, T., House, K. H., Huang, Y., Jayawardena, D., Kanji, A. H., Khandelwal, A., Le, K., Mühlemann, A., Niemi, J., Shah, A., Stark, A., Wang, Y., … Reich, N. G. (2022). Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proceedings of the National Academy of Sciences of the United States of America, 119(15), e2113561119.
Craswell, N., Croft, W. B., de Rijke, M., Guo, J., & Mitra, B. (2018). Neural information retrieval: introduction to the special issue. Information Retrieval, 21(2–3), 107–110.
Degrave, J., Hermans, M., Dambre, J., & Wyffels, F. (2019). A differentiable physics engine for deep learning in robotics. Frontiers in Neurorobotics, 13, 6.
Dobrucka, R. (2018). Synthesis of MgO nanoparticles using Artemisia abrotanum herba extract and their antioxidant and photocatalytic properties. Iranian Journal of Science and Technology, Transactions A: Science, 42(2), pp. 547-555.
Essien, E. R., Atasie, V. N., Okeafor, A. O., & Nwude, D. O. (2020). Biogenic synthesis of magnesium oxide nanoparticles using Manihot esculenta (Crantz) leaf extract. International Nano Letters, 10(1), pp. 43-48.
Fang, F., Ventre, C., Basios, M., Kanthan, L., Martinez-Rego, D., Wu, F., & Li, L. (2022). Cryptocurrency trading: a comprehensive survey. Financial Innovation, 8(1).
Faurina, R., Wijanarko, A., Heryuanti, A. F., Ishak, S. I., & Agustian, I. (2023). Comparative study of ensemble deep learning models to determine the classification of turtle species. Computer Science and Information Technologies, 4(1), 24–32.
Garrett, D. E. (2012). Potash: deposits, processing, properties and uses. Springer Science & Business Media.
Husaeni, D. F., & Nandiyanto, A. B. D. (2023). Mapping visualization analysis of computer science research data in 2017-2021 on the google scholar database with vosviewer”. International Journal of Informatics, Information System and Computer Engineering (INJIISCOM), 3(1), 1–18.
Jensen, M. P., Allen, C. D., Eguchi, T., Bell, I. P., LaCasella, E. L., Hilton, W. A., Hof, C. A. M., & Dutton, P. H. (2018). Environmental warming and feminization of one of the largest sea turtle populations in the world. Current Biology: CB, 28(1), 154-159.e4.
Khairi, S., Bakar, M., Alias, M., Bakar, S., Liong, C.-Y., Rosli, N., & Farid, M. (2021). Deep learning on histopathology images for breast cancer classification: A bibliometric analysis. Healthcare (Basel, Switzerland), 10(1), 10.
Kittinger, J. N., Houtan, K. S. V., McClenachan, L. E., & Lawrence, A. L. (2013). Using historical data to assess the biogeography of population recovery. Ecography, 36(8), 868- 872.
Liu, J., Wang, M., Bao, L., Li, X., Sun, J., & Ming, Y. (2020). Classification and recognition of turtle images based on convolutional neural network. IOP Conference Series. Materials Science and Engineering, 782(5), 052044.
Lorus, J., & Milne, M. J. (2018). Handbook of turtles: The turtles of the United States, Canada, and Baja California. Archie Carr. Ithaca, N. y.: Comstock pub., Cornell univ. Press, 1952. 542 pp. $7.50. Science (New York, N.Y.), 116(3007), 181–181.
Marcus, G. (2018). Deep learning: A critical appraisal. In arXiv [cs.AI]. http://arxiv.org/abs/1801.00631
Mguni, L. L., Mukenga, M., Jalama, K., & Meijboom, R. (2013). Effect of calcination temperature and MgO crystallite size on MgO/TiO2 catalyst system for soybean oil transesterification. Catalysis Communications, 34, pp. 52-57.
Min, K., Cuiffi, J. D., & Mathers, R. T. (2020). Ranking environmental degradation trends of plastic marine debris based on physical properties and molecular structure. Nature Communications, 11(1), 727.
Nandatamadini, F., Karina, S., Nandiyanto, A. B. D., & Ragadhita, R. (2019). Feasibility study based on economic perspective of cobalt nanoparticle synthesis with chemical reduction method. Cakra Kimia (Indonesian E-Journal of Applied Chemistry), 7(1), pp. 61-68.
Nandiyanto, A. B. D. (2018). Cost analysis and economic evaluation for the fabrication of activated carbon and silica particles from rice straw waste. Journal of Engineering Science and Technology, 13(6), pp. 1523-1539.
Nassar, M. Y., Mohamed, T. Y., Ahmed, I. S., & Samir, I. (2017). MgO nanostructure via a sol-gel combustion synthesis method using different fuels: an efficient nano-adsorbent for the removal of some anionic textile dyes. Journal of Molecular Liquids, 225, pp. 730-740.
Papafitsoros, K., Adam, L., Čermák, V., & Picek, L. (2022). SeaTurtleID: A novel long- span dataset highlighting the importance of timestamps in wildlife re-identification.
Prasanth, R., Kumar, S. D., Jayalakshmi, A., Singaravelu, G., Govindaraju, K., & Kumar, V. G. (2019). Green synthesis of magnesium oxide nanoparticles and their antibacterial activity. Indian Journal of Geo Marine Sciences, 48(08), pp. 1210-1215.
Solomon, T. (2020). … of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score.
Stephen, O., Sain, M., Maduh, U. J., & Jeong, D.-U. (2019). An efficient deep learning approach to pneumonia classification in healthcare. Journal of Healthcare Engineering, 2019, 4180949.
Wallace, B. P., DiMatteo, A. D., Hurley, B. J., Finkbeiner, E. M., Bolten, A. B., Chaloupka, M. Y., Hutchinson, B. J., Abreu-Grobois, F. A., Amorocho, D., Bjorndal, K. A., Bourjea, J., Bowen, B. W., Dueñas, R. B., Casale, P., Choudhury, B. C., Costa, A., Dutton, P. H., Fallabrino, A., Girard, A., … Mast, R. B. (2010). Regional management units for marine turtles: A novel framework for prioritizing conservation and research across multiple scales. PloS One, 5(12), e15465.
Wang, H., Ge, S., Xing, E. P., & Lipton, Z. C. (2019). Learning robust global representations by penalizing local predictive power. In arXiv [cs.CV]. http://arxiv.org/abs/1905.13549
Wang, Y., Liu, S., Afzal, N., Rastegar-Mojarad, M., Wang, L., Shen, F., Kingsbury, P., & Liu, H. (2018). A comparison of word embeddings for the biomedical natural language processing. Journal of Biomedical Informatics, 87, 12–20.
Weinstein, B. G. (2018). A computer vision for animal ecology. The Journal of Animal Ecology, 87(3), 533–545.
Zhang, H., Hu, J., Xie, J., Wang, S., & Cao, Y. (2019). A solid-state chemical method for synthesizing MgO nanoparticles with superior adsorption properties. RSC advances, 9(4), pp. 2011-2017.
Zhao, Zhou, Li, Cao, He, Yu, Li, Elvidge, Cheng, & Zhou. (2019). Applications of satellite remote sensing of nighttime light observations: Advances, challenges, and perspectives. Remote Sensing, 11(17), 1971.
Published
2024-03-11
How to Cite
[1]
S. NurFarahim, A. Shabil, and N. Sabrina Balqis, “Deep Learning for Sea Turtle Classification: A Bibliometric Analysis Using VOSviewer”, INJIISCOM, vol. 5, no. 1, pp. 88-101, Mar. 2024.
Section
Articles
