Dear Ahmed A. Mawgoud

Complex machine learning problems are usually a challenge of Data availability, complexity (mainly cardinality), and drift. Thus, (i) transfer learning and generative modeling are used to overcome problems of lack of data and introduce additional information from pre-trained models. Meanwhile, (ii) deep representations provide a more meaningful feature spaces of data dealing with the issues of cardinality (i.e. samples with different labels have similar instances). Finally, (iii) adaptive learning (dynamic programming, e.g., gated units, recurrent layers, forgetting mechanism, etc.) is used to overcome data drift (data change) and adapt the model to the rapidly changing data characteristics over time. So mainly, if you want to improve the capability of machine learning models in any problem, you should target some of the challenges separately or all of them together. In this case, I can propose recurrent expansion with multiple repeats (REMR), a representation learning algorithm of our design capable of producing a deeper representations by merging ordinary deep networks themselves for multiple rounds. learn more about REMR at:

1. Berghout, T.; Benbouzid, M.; Ferrag, M.A. Deep Learning with Recurrent Expansion for Electricity Theft Detection in Smart Grids. In Proceedings of the IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society; IEEE: Brussels (Belgium), 2022; pp. 1–6.

2. Berghout, T.; Benbouzid, M.; Bentrcia, T.; Amirat, Y.; Mouss, L. Exposing Deep Representations to a Recurrent Expansion with Multiple Repeats for Fuel Cells Time Series Prognosis. Entropy 2022, 24, 1009, doi:10.3390/e24071009.

3. Berghout, T.; Benbouzid, M.; Amirat, Y. Improving Small-scale Machine Learning with Recurrent Expansion for Fuel Cells Time Series Prognosis. In Proceedings of the IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society; IEEE, 2022; pp. 1–5.

Use CNN based Neural Network to train you model .

To improve the accuracy of object detection in underwater environments using deep learning, there are several techniques that can be utilized. Data augmentation involves creating additional training data by applying transformations such as cropping, rotating, and flipping images, which can help to reduce overfitting and improve the model's generalization performance. Transfer learning involves using a pre-trained model as a starting point and fine-tuning it on new data, which can be useful when there is a limited amount of training data available. Ensemble learning involves training multiple models and combining their predictions, which can often lead to improved performance. Adaptive learning rates can improve the model's convergence and reduce the risk of overfitting, while regularization involves adding constraints to the model to prevent overfitting. Some common regularization techniques include dropout, weight decay, and early stopping.

I lt is better to do by CNN deep learning algorithms with best parameters with considering overfiting and underfiting.

Deep learning can be used to improve the accuracy of object detection in UAVs for shark spotting and surveillance in a variety of ways. Firstly, deep learning algorithms can be used to identify and classify different shark species and size ranges in the captured images, allowing for more accurate identification and tracking of sharks in the area. Secondly, deep learning can be used to determine the position and orientation of the shark, enabling more precise tracking and surveillance of the area. Finally, deep learning can be used to identify and classify other objects in the environment, such as other marine animals, boats, and buoys, to help the UAV avoid contact with these objects.