The Use of Artificial Intelligence Technologies in The Biological Sciences

Aamir, M., Rahman, Z., Dayo, Z. A., Abro, W. A., Uddin, M. I., Khan, I., ... & Hu, Z. (2022). A deep learning approach for brain tumor classification using MRI images. Computers and Electrical Engineering, 101, 108105.

Akman, O., Eaton, C. D., Hrozencik, D. Jenkins, K. P., & Thompson, K. V. (2020). Building community-based approaches to systemic reform in mathematical biology education. Bulletin of Mathematical Biology, 82(109). https://doi.org/10.1007/s11538-020-00781-4

Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hasoul, S., Ben-Ari, R., & Barkan, E. (2016). A region based convolutional network for tumor detection and classification in breast mammography. In Deep Learning and Data Labeling for Medical Applications (pp. 197- 205). Springer, Cham.

Al Rahhal, M. M., Bazi, Y., AlHichri, H., Alajlan, N., Melgani, F., & Yager, R. R. (2016). Deep learning approach for active classification of electrocardiogram signals. Information Sciences, 345, 340-354.

Alexe, B., Deselaers, T., & Ferrari, V. (2012). Measuring the objectness of image windows. IEEE transactions on pattern analysis and machine intelligence, 34(11), 2189-2202

An, X., Kuang, D., Guo, X., Zhao, Y., & He, L. (2014). A deep learning method for classification of EEG data based on motor imagery. In International Conference on Intelligent Computing (pp. 203-210). Springer, Cham.

Asgari, E., & Mofrad, M. R. (2015). Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one, 10(11), e0141287.

Ayaz, M., Sharma, T., & Rao, S. H. (2023). Disruptive artificial intelligence (AI) use-cases in insurance. In AIP Conference Proceedings (Vol. 2782, No. 1). AIP Publishing.

Baltruschat, I. M., Nickisch, H., Grass, M., Knopp, T., & Saalbach, A. (2018). Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification. arXiv preprint arXiv:1803.02315.

Bar-Cohen, Y. (2003). Actuation Of Biologically Inspired Intelligent Robotics Using Artificial Muscles. Industrial Robot, 30 (4), 331-337. https://doi.org/10.1108/01439910310479702

Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019). Reinforcement learning, fast and slow. Trends in cognitive sciences, 23(5), 408-422.

Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical science, 16(3), 199-231.

Cao, Z., Simon, T., Wei, S. E., & Sheikh, Y. (2017, July). Realtime multi-person 2d pose estimation using part affinity fields. In CVPR (Vol. 1, No. 2, p. 7).

Chen, C. L., Mahjoubfar, A., Tai, L. C., Blaby, I. K., Huang, A., Niazi, K. R., & Jalali, B. (2016). Deep learning in label-free cell classification. Scientific reports, 6, 21471

Cheng, M. M., Zhang, Z., Lin, W. Y., & Torr, P. (2014). BING: Binarized normed gradients for objectness estimation at 300fps. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3286-3293).

Collins, F. S., Green, E. D., Guttmacher, A. E., & Guyer, M. S. (2003). A vision for the future of genomics research. Nature, 422(6934), Article 6934. https://doi.org/10.1038/nature01626

Cruz-Roa, A. A., Ovalle, J. E. A., Madabhushi, A., & Osorio, F. A. G. (2013). A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. In International Conference on Medical Image Computing and Computer-Assisted Intervention(pp. 403-410). Springer, Berlin, Heidelberg

Cunningham, P., Cord, M., & Delany, S. J. (2008). Supervised learning. In Machine learning techniques for multimedia: case studies on organization and retrieval (pp. 21-49). Berlin, Heidelberg: Springer Berlin Heidelberg.

D’Agostino, N., Bentley, A., & Chen, C. (Eds.). (2023). Genome wide association studies and genomic selection for crop improvement in the era of big data. Frontiers Media SA.

Dike, H. U., Zhou, Y., Deveerasetty, K. K., & Wu, Q. (2018, October). Unsupervised learning based on artificial neural network: A review. In 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS) (pp. 322-327). IEEE.

Eaton, C. D., LaMar, M. D., & McCarthyc M. L. (2020). 21st century reform efforts in undergraduate quantitative biology education: Conversations, initiatives, and curriculum change in the United States of America. Letters in Biomathematics, 7(1), 55-66

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115.

Fakoor, R., Ladhak, F., Nazi, A., & Huber, M. (2011). Using deep learning to enhance cancer diagnosis and classification. In Proceedings of the International Conference on Machine Learning (Vol. 28).

Fu, H., Xu, Y., Wong, D. W. K., & Liu, J. (2016). Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on (pp. 698-701).

Garabaghi, F. H., Benzer, R., Benzer, S., & Günal, A. Ç. (2022). Effect of polynomial, radial basis, and Pearson VII function kernels in support vector machine algorithm for classification of crayfish. Ecological Informatics, 72, 101911.

Garcia, J., & Fernández, F. (2015). A comprehensive survey on safe reinforcement learning. Journal of Machine Learning Research, 16(1), 1437-1480.

Gaur, L., Bhatia, U., Jhanjhi, N. Z., Muhammad, G., & Masud, M. (2023). Medical image-based detection of COVID-19 using deep convolution neural networks. Multimedia systems, 29(3), 1729-1738.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

Han, M., Wu, H., Chen, Z., Li, M., & Zhang, X. (2023). A survey of multi-label classification based on supervised and semi-supervised learning. International Journal of Machine Learning and Cybernetics, 14(3), 697-724.

Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.

Hua, K. L., Hsu, C. H., Hidayati, S. C., Cheng, W. H., & Chen, Y. J. (2015). Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets and therapy, 8.

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Jafari, M. H., Nasr-Esfahani, E., Karimi, N., Soroushmehr, S. M., Samavi, S., & Najarian, K. (2016). Extraction of skin lesions from nondermoscopic images using deep learning. arXiv preprint arXiv:1609.02374.

James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). Unsupervised learning. In An Introduction to Statistical Learning: with Applications in Python (pp. 503-556). Cham: Springer International Publishing.

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

Khanafer, M., & Shirmohammadi, S. (2020). Applied AI in instrumentation and measurement: The deep learning revolution. IEEE Instrumentation & Measurement Magazine, 23(6), 10-17.

Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778-782.

Li, Y., Sixou, B., & Peyrin, F. (2021). A review of the deep learning methods for medical images super resolution problems. Irbm, 42(2), 120-133.

Liu, B., & Liu, B. (2011). Supervised learning. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, 63-132.

Liu, M., Hu, L., Tang, Y., Wang, C., He, Y., Zeng, C., ... & Huo, W. (2022). A deep learning method for breast cancer classification in the pathology images. IEEE Journal of Biomedical and Health Informatics, 26(10), 5025-5032.

Mariani, M. M., Machado, I., Magrelli, V., & Dwivedi, Y. K. (2023). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122, 102623.

Mhlanga, D. (2023). Artificial intelligence and machine learning for energy consumption and production in emerging markets: a review. Energies, 16(2), 745.

Mittal, S., Srivastava, S., & Jayanth, J. P. (2022). A survey of deep learning techniques for underwater image classification. IEEE Transactions on Neural Networks and Learning Systems.

Moein, M. M., Saradar, A., Rahmati, K., Mousavinejad, S. H. G., Bristow, J., Aramali, V., & Karakouzian, M. (2023). Predictive models for concrete properties using machine learning and deep learning approaches: A review. Journal of Building Engineering, 63, 105444.

Murugappan, V., & Sabeenian, R. S. (2017). Texture based medical image classification by using multi-scale gabor rotation-invariant local binary pattern (MGRLBP). Cluster Computing, 1-14.

Nasteski, V. (2017). An overview of the supervised machine learning methods. Horizons. b, 4, 51-62.

Ng, J. Y. H., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., & Toderici, G. (2015). Beyond short snippets: Deep networks for video classification. In Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on (pp. 4694-4702)

Nguyen, A., Clune, J., Bengio, Y., Dosovitskiy, A., & Yosinski, J. (2017). Plug & play generative networks: Conditional iterative generation of images in latent space. In Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on (pp. 3510- 3520)

Nian, R., Liu, J., & Huang, B. (2020). A review on reinforcement learning: Introduction and applications in industrial process control. Computers & Chemical Engineering, 139, 106886.

Ong, P., Tan, Y. K., Lai, K. H., & Sia, C. K. (2023). A deep convolutional neural network for vibration-based health-monitoring of rotating machinery. Decision Analytics Journal, 7, 100219.

Patange, G. S., & Pandya, A. B. (2023). How artificial intelligence and machine learning assist in industry 4.0 for mechanical engineers. Materials Today: Proceedings, 72, 622-625.

Pauly, D., & Morgan, G. R. (Eds.). (1987). Length-based methods in fisheries research (Vol. 13). WorldFish.

Pichler, M., & Hartig, F. (2023). Machine learning and deep learning—A review for ecologists. Methods in Ecology and Evolution, 14(4), 994-1016.

Piczak, K. J. (2015, September). Environmental sound classification with convolutional neural networks. In Machine Learning for Signal Processing (MLSP), 2015 IEEE 25th International Workshop on (pp. 1-6).

Prudencio, R. F., Maximo, M. R., & Colombini, E. L. (2023). A survey on offline reinforcement learning: Taxonomy, review, and open problems. IEEE Transactions on Neural Networks and Learning Systems.

Rich, E., & Knight, K. (2009). Artificial intelligence, Third Edition, Ed. New Delhi: McGraw-Hill,

Ricker, W. E. 1975. Computation and interpretation of biological statistics of fish populations. Bulletin of the Fisheries Research Board of Canada.

Shah, H. M., Gardas, B. B., Narwane, V. S., & Mehta, H. S. (2023). The contemporary state of big data analytics and artificial intelligence towards intelligent supply chain risk management: a comprehensive review. Kybernetes, 52(5), 1643-1697.

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Aamir, M., Rahman, Z., Dayo, Z. A., Abro, W. A., Uddin, M. I., Khan, I., ... & Hu, Z. (2022). A deep learning approach for brain tumor classification using MRI images. Computers and Electrical Engineering, 101, 108105.

Akman, O., Eaton, C. D., Hrozencik, D. Jenkins, K. P., & Thompson, K. V. (2020). Building community-based approaches to systemic reform in mathematical biology education. Bulletin of Mathematical Biology, 82(109). https://doi.org/10.1007/s11538-020-00781-4

Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hasoul, S., Ben-Ari, R., & Barkan, E. (2016). A region based convolutional network for tumor detection and classification in breast mammography. In Deep Learning and Data Labeling for Medical Applications (pp. 197- 205). Springer, Cham.

Al Rahhal, M. M., Bazi, Y., AlHichri, H., Alajlan, N., Melgani, F., & Yager, R. R. (2016). Deep learning approach for active classification of electrocardiogram signals. Information Sciences, 345, 340-354.

Alexe, B., Deselaers, T., & Ferrari, V. (2012). Measuring the objectness of image windows. IEEE transactions on pattern analysis and machine intelligence, 34(11), 2189-2202

An, X., Kuang, D., Guo, X., Zhao, Y., & He, L. (2014). A deep learning method for classification of EEG data based on motor imagery. In International Conference on Intelligent Computing (pp. 203-210). Springer, Cham.

Asgari, E., & Mofrad, M. R. (2015). Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one, 10(11), e0141287.

Ayaz, M., Sharma, T., & Rao, S. H. (2023). Disruptive artificial intelligence (AI) use-cases in insurance. In AIP Conference Proceedings (Vol. 2782, No. 1). AIP Publishing.

Baltruschat, I. M., Nickisch, H., Grass, M., Knopp, T., & Saalbach, A. (2018). Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification. arXiv preprint arXiv:1803.02315.

Bar-Cohen, Y. (2003). Actuation Of Biologically Inspired Intelligent Robotics Using Artificial Muscles. Industrial Robot, 30 (4), 331-337. https://doi.org/10.1108/01439910310479702

Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019). Reinforcement learning, fast and slow. Trends in cognitive sciences, 23(5), 408-422.

Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical science, 16(3), 199-231.

Cao, Z., Simon, T., Wei, S. E., & Sheikh, Y. (2017, July). Realtime multi-person 2d pose estimation using part affinity fields. In CVPR (Vol. 1, No. 2, p. 7).

Chen, C. L., Mahjoubfar, A., Tai, L. C., Blaby, I. K., Huang, A., Niazi, K. R., & Jalali, B. (2016). Deep learning in label-free cell classification. Scientific reports, 6, 21471

Cheng, M. M., Zhang, Z., Lin, W. Y., & Torr, P. (2014). BING: Binarized normed gradients for objectness estimation at 300fps. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3286-3293).

Collins, F. S., Green, E. D., Guttmacher, A. E., & Guyer, M. S. (2003). A vision for the future of genomics research. Nature, 422(6934), Article 6934. https://doi.org/10.1038/nature01626

Cruz-Roa, A. A., Ovalle, J. E. A., Madabhushi, A., & Osorio, F. A. G. (2013). A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. In International Conference on Medical Image Computing and Computer-Assisted Intervention(pp. 403-410). Springer, Berlin, Heidelberg

Cunningham, P., Cord, M., & Delany, S. J. (2008). Supervised learning. In Machine learning techniques for multimedia: case studies on organization and retrieval (pp. 21-49). Berlin, Heidelberg: Springer Berlin Heidelberg.

D’Agostino, N., Bentley, A., & Chen, C. (Eds.). (2023). Genome wide association studies and genomic selection for crop improvement in the era of big data. Frontiers Media SA.

Dike, H. U., Zhou, Y., Deveerasetty, K. K., & Wu, Q. (2018, October). Unsupervised learning based on artificial neural network: A review. In 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS) (pp. 322-327). IEEE.

Eaton, C. D., LaMar, M. D., & McCarthyc M. L. (2020). 21st century reform efforts in undergraduate quantitative biology education: Conversations, initiatives, and curriculum change in the United States of America. Letters in Biomathematics, 7(1), 55-66

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115.

Fakoor, R., Ladhak, F., Nazi, A., & Huber, M. (2011). Using deep learning to enhance cancer diagnosis and classification. In Proceedings of the International Conference on Machine Learning (Vol. 28).

Fu, H., Xu, Y., Wong, D. W. K., & Liu, J. (2016). Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on (pp. 698-701).

Garabaghi, F. H., Benzer, R., Benzer, S., & Günal, A. Ç. (2022). Effect of polynomial, radial basis, and Pearson VII function kernels in support vector machine algorithm for classification of crayfish. Ecological Informatics, 72, 101911.

Garcia, J., & Fernández, F. (2015). A comprehensive survey on safe reinforcement learning. Journal of Machine Learning Research, 16(1), 1437-1480.

Gaur, L., Bhatia, U., Jhanjhi, N. Z., Muhammad, G., & Masud, M. (2023). Medical image-based detection of COVID-19 using deep convolution neural networks. Multimedia systems, 29(3), 1729-1738.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

Han, M., Wu, H., Chen, Z., Li, M., & Zhang, X. (2023). A survey of multi-label classification based on supervised and semi-supervised learning. International Journal of Machine Learning and Cybernetics, 14(3), 697-724.

Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.

Hua, K. L., Hsu, C. H., Hidayati, S. C., Cheng, W. H., & Chen, Y. J. (2015). Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets and therapy, 8.

IBM Artificial Intelligence (IBM-AI). (2023). Featured topics. Available online: https://www.ibm.com/cloud/learn/what-is-artificialintelligence (accessed on 03 December.

Jafari, M. H., Nasr-Esfahani, E., Karimi, N., Soroushmehr, S. M., Samavi, S., & Najarian, K. (2016). Extraction of skin lesions from nondermoscopic images using deep learning. arXiv preprint arXiv:1609.02374.

James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). Unsupervised learning. In An Introduction to Statistical Learning: with Applications in Python (pp. 503-556). Cham: Springer International Publishing.

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

Khanafer, M., & Shirmohammadi, S. (2020). Applied AI in instrumentation and measurement: The deep learning revolution. IEEE Instrumentation & Measurement Magazine, 23(6), 10-17.

Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778-782.

Li, Y., Sixou, B., & Peyrin, F. (2021). A review of the deep learning methods for medical images super resolution problems. Irbm, 42(2), 120-133.

Liu, B., & Liu, B. (2011). Supervised learning. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, 63-132.

Liu, M., Hu, L., Tang, Y., Wang, C., He, Y., Zeng, C., ... & Huo, W. (2022). A deep learning method for breast cancer classification in the pathology images. IEEE Journal of Biomedical and Health Informatics, 26(10), 5025-5032.

Mariani, M. M., Machado, I., Magrelli, V., & Dwivedi, Y. K. (2023). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122, 102623.

Mhlanga, D. (2023). Artificial intelligence and machine learning for energy consumption and production in emerging markets: a review. Energies, 16(2), 745.

Mittal, S., Srivastava, S., & Jayanth, J. P. (2022). A survey of deep learning techniques for underwater image classification. IEEE Transactions on Neural Networks and Learning Systems.

Moein, M. M., Saradar, A., Rahmati, K., Mousavinejad, S. H. G., Bristow, J., Aramali, V., & Karakouzian, M. (2023). Predictive models for concrete properties using machine learning and deep learning approaches: A review. Journal of Building Engineering, 63, 105444.

Murugappan, V., & Sabeenian, R. S. (2017). Texture based medical image classification by using multi-scale gabor rotation-invariant local binary pattern (MGRLBP). Cluster Computing, 1-14.

Nasteski, V. (2017). An overview of the supervised machine learning methods. Horizons. b, 4, 51-62.

Ng, J. Y. H., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., & Toderici, G. (2015). Beyond short snippets: Deep networks for video classification. In Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on (pp. 4694-4702)

Nguyen, A., Clune, J., Bengio, Y., Dosovitskiy, A., & Yosinski, J. (2017). Plug & play generative networks: Conditional iterative generation of images in latent space. In Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on (pp. 3510- 3520)

Nian, R., Liu, J., & Huang, B. (2020). A review on reinforcement learning: Introduction and applications in industrial process control. Computers & Chemical Engineering, 139, 106886.

Ong, P., Tan, Y. K., Lai, K. H., & Sia, C. K. (2023). A deep convolutional neural network for vibration-based health-monitoring of rotating machinery. Decision Analytics Journal, 7, 100219.

Patange, G. S., & Pandya, A. B. (2023). How artificial intelligence and machine learning assist in industry 4.0 for mechanical engineers. Materials Today: Proceedings, 72, 622-625.

Pauly, D., & Morgan, G. R. (Eds.). (1987). Length-based methods in fisheries research (Vol. 13). WorldFish.

Pichler, M., & Hartig, F. (2023). Machine learning and deep learning—A review for ecologists. Methods in Ecology and Evolution, 14(4), 994-1016.

Piczak, K. J. (2015, September). Environmental sound classification with convolutional neural networks. In Machine Learning for Signal Processing (MLSP), 2015 IEEE 25th International Workshop on (pp. 1-6).

Prudencio, R. F., Maximo, M. R., & Colombini, E. L. (2023). A survey on offline reinforcement learning: Taxonomy, review, and open problems. IEEE Transactions on Neural Networks and Learning Systems.

Rich, E., & Knight, K. (2009). Artificial intelligence, Third Edition, Ed. New Delhi: McGraw-Hill,

Ricker, W. E. 1975. Computation and interpretation of biological statistics of fish populations. Bulletin of the Fisheries Research Board of Canada.

Shah, H. M., Gardas, B. B., Narwane, V. S., & Mehta, H. S. (2023). The contemporary state of big data analytics and artificial intelligence towards intelligent supply chain risk management: a comprehensive review. Kybernetes, 52(5), 1643-1697.

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