Electric Vehicle charging infrastructure is crucial to the sustainable energy transition but it is also susceptible to cyberattacks. Protection against possible cyberattacks, such as Distributed Denial of Service (DDoS) assaults, has become crucial with the growth of electric vehicle supply equipment (EVSE) networks. The extensive Electric Vehicle Supply Equipment (EVSE) dataset, which includes high-performance computing (HPC) statistics, system performance indicators, and network traffic data, is used to analyze the proposed approach, and to create a strong anomaly detection framework by utilising deep learning techniques, to analyse host-based data, such as kernel eventlogs and HPC data. LSTM based model is used for sequence-based learning, analysing high-granularity event sequences to identify anomalous patterns, and putting in place a preprocessing pipeline that guarantees dependable data input for the model are some of the main contributions.

With advancement in AI technology, it is very crucial to detect ship images for naval surveillance and maritime security. So, to address challenges of the accurate ship detection capability we can use the emerging technology of deep learning for object detection models, particularly You Only Look Once (YOLO). It achieves very high performance in real time scenario which results in significant popularity of the YOLO for object detection. In this we focused on ship detection using different architectures of YOLO to apply for the aerial ship images detection. We have trained the different versions of YOLO models, evaluated and compared their performances on same aerial dataset. Major performance metrics, namely class loss for detecting the object class, distribution focal loss for the correct identifying of the location of bounding box have been investigated. The results show that the version-10 of YOLO models improves both computational efficiency and the accuracy of detection.

The accurate and consistent detection of ocean eddies significantly improves the monitoring of ocean surface dynamics and the identification of regional hydrographic and biological characteristics. The study of marine ecosystems and climate change requires an understanding of ocean eddies. Data from multi-satellite altimeters, which track sea surface height, are used in eddy detection. Altimeter measurements provide an accurate representation of the sea surface height. The existing deep learning-based eddy detection approaches suffer from high model and computational complexity. The fact that there are eddies of different diameters makes eddy identification more challenging. In this paper, the detection of ocean eddies using a dual encoder and decoder architecture is proposed to address these inadequacies. An attention mechanism is developed to comprehend the pixel-level context of the semantic segmentation. A series connection of separable convolutions is proposed to adequately describe the context of multi-scale fusion. Further, the tracking of eddies is also proposed using a novel tracking method. The experimental outcomes demonstrate that the proposed approach achieved mean intersection of union score, F-beta score, and mean pixel accuracy of 89.98 %, 94.47%, 95.13% and 89.66%, 94.54%, 95.51% on the Southern Atlantic Ocean and the South China Sea datasets.

A circular movement of ocean water known as eddy is crucial for moving various ocean elements across the ocean. They are essential to the biota and circulation of the ocean. The detection of ocean eddies has significant advantages for the research of marine biological habitats and climate change. The evolution of oceanic remote sensing technologies enables their identification in sea surface height images. Deep learning techniques employed for eddy detection are still in their infancy. This paper proposes a deep convolutional neural network model for automated semantic segmentation and identification of ocean eddies at the pixel level. Semantic segmentation requires an understanding of context efficiently for pixel-level recognition. The attention mechanism is proposed to capture contextual information to tackle this challenge. The self-attention process is used in the suggested attention mechanism to define the semantic relationship between any two pixels. A novel module termed the series atrous spatial pyramid module is proposed as an alternative to the multi-scale fusion model currently in use to capture the multi-scale context of feature maps. Further, a new feature enhancing block that cascades encoder outputs with the decoder is also proposed. The experimental findings demonstrate that the proposed architecture has obtained mean pixel accuracy, F-beta score, mean intersection of union score of 94.52%, 94.45%, 88.64% on the Southern Atlantic Ocean dataset and 94.97%, 94.90%, 88.67% on the South China Sea dataset respectively which is better when compared to existing state-of-the-art techniques.

An eddy is a circular movement of ocean water that carries several ocean elements across the ocean. The study of marine biological environments and climate change benefits from the identification of ocean eddies. Eddy detection is one of the most active fields in physical oceanography research. Despite recent advancements, deep convolution neural networks for eddy detection are still in their infancy. The wide range of eddies' sizes and shapes makes automatic eddy segmentation challenging. Through the creation of a dense forecast, UNet solves this problem. The network architecture, however, is immensely intricate. In this study, a dilated convolution U-Net is proposed for the semantic segmentation of ocean eddies. This method reduces architectural complexity without compromising performance. Further, a new attention approach is proposed to enhance the decoder features before cascading with the encoded feature map. The experimental results demonstrate that the proposed architecture outperforms the state-of-the art techniques for eddy detection.

Ocean eddies have a significant effect on the maritime environment. They are necessary for carrying a variety of ocean traces across the ocean. Although deep learning algorithms for detecting eddies are a relatively new trend, it is still in their infancy. In this letter, a deep learning method for ocean eddy identification based on semantic segmentation is proposed. In semantic segmentation, understanding the context efficiently for pixel-level recognition is crucial. Two attention modules are proposed to tackle this problem. The proposed work consists of VGG16-based U-Net architecture with two attention modules to show a contextual correlation in the channel and spatial dimensions. Every pixel or channel adapts to include context from every other pixel or channel based on their correlations. Furthermore, a new residual path is proposed to replace the conventional skip connection between encoder and decoder modules. The findings of the experiments show that adopting an attention-based deep framework and new residual path improves the model performance over the existing state-of-the-art techniques.

Ocean eddies have a significant effect on the maritime environment. They are necessary for carrying a variety of ocean traces across the ocean. Eddy detection is one of the most active fields of physical oceanographic research. Although it is a new trend, using deep learning algorithms to find eddies is still in its early stages. The different sizes and shapes of eddies make automatic eddy segmentation challenging. U-Net makes a dense prediction to solve this problem. However, the network architecture is very intricate. In this paper, a dilated convolution U-Net is developed for the semantic segmentation of ocean eddies using sea surface height data. This technique decreases architectural complexity without sacrificing performance. Further, a new residual path is proposed to cascade encoder outputs with the decoder. The experimental results demonstrate that the proposed architecture outperforms the existing deep learning techniques for eddy detection.

Ocean eddies are a common occurrence in ocean water circulation. They have an enormous impact on the marine ecosystem. One of the most active study topics in physical oceanography is ocean eddy detection. Although using deep learning algorithms to detect eddies is a recent trend, it is still in its infancy. In this paper, an attention mechanism-based ocean eddy detection approach using deep learning is proposed. Attention mechanism has spatial and channel attention modules that are cascaded to convolution blocks-based encoder model to simulate spatial and channel semantic interdependencies. In the spatial attention module, the feature at each point is aggregated selectively by the sum of the features at all positions. The channel attention module aggregates related data from all channel maps to selectively highlight interdependent channel maps. The original feature map and the feature map obtained through the attention mechanism are appended to enhance the feature representation further, resulting in more accurate segmentation results. The findings of the experiments show that adopting an attention-based deep framework improves eddy recognition accuracy significantly.