Data and AI - Math bibliographies - in Harvard style

In-text: (Accenture, 2021)

Your Bibliography: Accenture, 2021. How to create unparalleled workstation security | Accenture. [online] Accenture.com. Available at: <https://www.accenture.com/us-en/blogs/how-accenture-does-it/how-to-create-unparalleled-workstation-security> [Accessed 26 August 2022].

In-text: (Alippi, Disabato and Roveri, 2018)

Your Bibliography: Alippi, C., Disabato, S. and Roveri, M., 2018. Moving Convolutional Neural Networks to Embedded Systems: The AlexNet and VGG-16 Case. 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), [online] pp.212-223. Available at: <https://ieeexplore.ieee.org/abstract/document/8480072> [Accessed 26 August 2022].

In-text: (Brownlee, 2019)

Your Bibliography: Brownlee, J., 2019. A Gentle Introduction to Batch Normalization for Deep Neural Networks. [online] Machine Learning Mastery. Available at: <https://machinelearningmastery.com/batch-normalization-for-training-of-deep-neural-networks/> [Accessed 28 August 2022].

In-text: (Chatterjee, 2018)

Your Bibliography: Chatterjee, S., 2018. Deep learning unbalanced training data?Solve it like this.. [online] Towards Data Science. Available at: <https://towardsdatascience.com/deep-learning-unbalanced-training-data-solve-it-like-this-6c528e9efea6> [Accessed 28 August 2022].

In-text: (Chen, Deng and Wang, 2022)

Your Bibliography: Chen, Y., Deng, J. and Wang, T., 2022. Skin Cancer Diagnosis and Medical Service System Based on Deep Learning Models. 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI), [online] pp.367-371. Available at: <https://ieeexplore.ieee.org/abstract/document/9750739> [Accessed 28 August 2022].

In-text: (Gupta, 2021)

Your Bibliography: Gupta, A., 2021. A Comprehensive Guide on Deep Learning Optimizers. [online] Analytics Vidhya. Available at: <https://www.analyticsvidhya.com/blog/2021/10/a-comprehensive-guide-on-deep-learning-optimizers/> [Accessed 28 August 2022].

In-text: (Han, Liu and Fan, 2018)

Your Bibliography: Han, D., Liu, Q. and Fan, W., 2018. A new image classification method using CNN transfer learning and web data augmentation. Expert Systems with Applications, [online] 95, pp.43-56. Available at: <https://www.sciencedirect.com/science/article/abs/pii/S0957417417307844> [Accessed 27 August 2022].

In-text: (Jogin et al., 2018)

Your Bibliography: Jogin, M., Madhulika, M., Divya, G., Meghana, R. and Apoorva, S., 2018. Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning. 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information &amp; Communication Technology (RTEICT), [online] Available at: <https://ieeexplore.ieee.org/abstract/document/9012507/citations?tabFilter=papers#citations> [Accessed 26 August 2022].

In-text: (Liu, Yokoyama, Fu and Yamamoto, 2022)

Your Bibliography: Liu, P., Yokoyama, T., Fu, W. and Yamamoto, M., 2022. Statistical Analysis of Medium‐Scale Traveling Ionospheric Disturbances Over Japan Based on Deep Learning Instance Segmentation. Space Weather, [online] 20(7). Available at: <https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022SW003151> [Accessed 28 August 2022].

In-text: (Luo et al., 2018)

Your Bibliography: Luo, C., Li, X., Wang, L., He, J., Li, D. and Zhou, J., 2018. How Does the Data set Affect CNN-based Image Classification Performance?. 2018 5th International Conference on Systems and Informatics (ICSAI), [online] pp.361-366. Available at: <https://ieeexplore.ieee.org/abstract/document/8599448> [Accessed 26 August 2022].

In-text: (Montserrat, Lin, Allebach and Delp, 2017)

Your Bibliography: Montserrat, D., Lin, Q., Allebach, J. and Delp, E., 2017. Training Object Detection And Recognition CNN Models Using Data Augmentation. Electronic Imaging, [online] 29(10), pp.27-36. Available at: <https://www.semanticscholar.org/paper/Training-Object-Detection-And-Recognition-CNN-Using-Montserrat-Lin/a3f4a5ba0777e2e0386d6df6aa23399e7e14a202> [Accessed 29 August 2022].

In-text: (Panthakkan, Anzar, Mansoori and Ahmad, 2020)

Your Bibliography: Panthakkan, A., Anzar, S., Mansoori, S. and Ahmad, H., 2020. Accurate Prediction of COVID-19 (+) Using AI Deep VGG16 Model. 2020 3rd International Conference on Signal Processing and Information Security (ICSPIS), [online] pp.1-4. Available at: <https://ieeexplore.ieee.org/abstract/document/9340145> [Accessed 30 August 2022].

In-text: (Raghu et al., 2020)

Your Bibliography: Raghu, S., Sriraam, N., Temel, Y., Rao, S. and Kubben, P., 2020. EEG based multi-class seizure type classification using convolutional neural network and transfer learning. Neural Networks, [online] 124, pp.202-212. Available at: <https://www.sciencedirect.com/science/article/abs/pii/S0893608020300198> [Accessed 28 August 2022].

In-text: (Tammina, 2019)

Your Bibliography: Tammina, S., 2019. Transfer learning using VGG-16 with Deep Convolutional Neural Network for Classifying Images. International Journal of Scientific and Research Publications (IJSRP), [online] 9(10), p.p9420. Available at: <https://www.researchgate.net/profile/Srikanth-Tammina/publication/337105858_Transfer_learning_using_VGG-16_with_Deep_Convolutional_Neural_Network_for_Classifying_Images/links/5dc94c3ca6fdcc57503e6ad9/Transfer-learning-using-VGG-16-with-Deep-Convolutional-Neural-Network-for-Classifying-Images.pdf?_sg%5B0%5D=started_experiment_milestone&origin=journalDetail> [Accessed 29 August 2022].

In-text: (TensorFlow, 2022)

Your Bibliography: TensorFlow, 2022. tf.image.flip_left_right  |  TensorFlow v2.9.1. [online] TensorFlow.com. Available at: <https://www.tensorflow.org/api_docs/python/tf/image/flip_left_right> [Accessed 27 August 2022].

In-text: (TensorFlow, 2022)

Your Bibliography: TensorFlow, 2022. tf.keras.layers.RandomCrop  |  TensorFlow v2.9.1. [online] TensorFlow. Available at: <https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomCrop> [Accessed 27 August 2022].

In-text: (TensorFlow, 2022)

Your Bibliography: TensorFlow, 2022. tf.keras.layers.RandomRotation  |  TensorFlow v2.9.1. [online] TensorFlow.com. Available at: <https://www.tensorflow.org/api_docs/python/tf/keras/layers/RandomRotation> [Accessed 27 August 2022].

In-text: (Thanapol et al., 2020)

Your Bibliography: Thanapol, P., Lavangnananda, K., Bouvry, P., Pinel, F. and Leprevost, F., 2020. Reducing Overfitting and Improving Generalization in Training Convolutional Neural Network (CNN) under Limited Sample Sizes in Image Recognition. 2020 - 5th International Conference on Information Technology (InCIT), [online] pp.300-305. Available at: <https://ieeexplore.ieee.org/abstract/document/9310787> [Accessed 28 August 2022].

In-text: (Yang et al., 2021)

Your Bibliography: Yang, H., Ni, J., Gao, J., Han, Z. and Luan, T., 2021. A novel method for peanut variety identification and classification by Improved VGG16. Scientific Reports, [online] 11(1). Available at: <https://www.researchgate.net/publication/353679755_A_novel_method_for_peanut_variety_identification_and_classification_by_Improved_VGG16> [Accessed 30 August 2022].

In-text: (Zhang, 2020)

Your Bibliography: Zhang, J., 2020. Optimisation Algorithm — Adaptive Moment Estimation(Adam). [online] Towards Data Science. Available at: <https://towardsdatascience.com/optimisation-algorithm-adaptive-moment-estimation-adam-92144d75e232> [Accessed 28 August 2022].