AI-enhanced real-time monitoring of marine pollution: part 1-A state-of-the-art and scoping review by Navya Prakash, Navya Prakash, Oliver Zielinski, Oliver Zielinski

“…This review synthesizes 53 recent studies on Artificial Intelligence applications in marine pollution detection, focusing on different model architectures, sensing technologies and preprocessing methods. …”
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Detecting Anomalies in Hydraulically Adjusted Servomotors Based on a Multi-Scale One-Dimensional Residual Neural Network and GA-SVDD by Xukang Yang, Anqi Jiang, Wanlu Jiang, Yonghui Zhao, Enyu Tang, Zhiqian Qi

“…Firstly, the multi-scale features of the vibration signals of the hydraulically adjusted servomotor were extracted and fused using one-dimensional convolutional blocks with three different scales to construct a multi-scale one-dimensional residual neural network binary classification model capable of recognizing normal and abnormal states. …”
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Dynamic spatiotemporal graph network for traffic accident risk prediction by Pengcheng Zhang, Wen Yi, Yongze Song, Penggao Yan, Peng Wu, Ammar Shemery, Keith Hampson, Albert P. C. Chan

“…Our model uses channel-wise convolutional neural networks to detect spatial accident patterns across weekly, daily, and hourly time scales with automatic weight learning, simultaneously employing graph convolutional networks to process road network features, population feature while integrating external data like weather and dates. …”
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LPFFNet: Lightweight Prior Feature Fusion Network for SAR Ship Detection by Xiaozhen Ren, Peiyuan Zhou, Xiaqiong Fan, Chengguo Feng, Peng Li

“…In addition, the enhanced ghost convolution (EGConv) is used to generate more reliable gradient information. …”
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Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification by V. Hnatushenko, V. Hnatushenko, V. Hnatushenko, D. Soldatenko

“…In this paper, we proposed convolution neural networks for semantic segmentation, where sample imbalance is considered based on a particular loss function coupled with data augmentation. …”
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Starting driving style recognition of electric city bus based on deep learning and CAN data by Dengfeng Zhao, Zhijun Fu, Chaohui Liu, Junjian Hou, Shesen Dong, Yudong Zhong

“…The starting driving style recognition method based on Convolutional Neural Network (CNN) model is constructed. …”
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Multi-Head Graph Attention Adversarial Autoencoder Network for Unsupervised Change Detection Using Heterogeneous Remote Sensing Images by Meng Jia, Xiangyu Lou, Zhiqiang Zhao, Xiaofeng Lu, Zhenghao Shi

“…Heterogeneous remote sensing images, acquired from different sensors, exhibit significant variations in data structure, resolution, and radiometric characteristics. …”
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MLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detection by Jianhua Ye, Pan Li, Yunda Zhang, Ze Guo, Shoujin Zeng, Youji Zhan

“…The over-parameterized convolutional layers enhance the interactive ability of feature representation and context information. …”
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Calibrating calving parameterizations using graph neural network emulators: application to Helheim Glacier, East Greenland by Y. Koo, Y. Koo, Y. Koo, G. Cheng, M. Morlighem, M. Rahnemoonfar, M. Rahnemoonfar

“…In this study, we adopt three standard graph neural network (GNN) architectures, including graph convolutional network, graph attention network, and equivariant graph convolutional network (EGCN), to develop surrogate models for finite-element simulations from the Ice-sheet and Sea-level System Model. …”
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Outdoor Dataset for Flying a UAV at an Appropriate Altitude by Theyab Alotaibi, Kamal Jambi, Maher Khemakhem, Fathy Eassa, Farid Bourennani

“…Eleven experiments performed with the Gazebo simulator using a drone and a convolution neural network (CNN) proved the database’s effectiveness in avoiding different types of obstacles while maintaining an appropriate altitude and the drone’s ability to navigate in a 3D environment.…”
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Computer Vision-Based Lane Detection and Detection of Vehicle, Traffic Sign, Pedestrian Using YOLOv5 by Raşit Köker, Osman Eldoğan, Gülyeter Öztürk

“…The right and left lanes within the driving area of the vehicle are identified, and the drivable area of the vehicle is highlighted with a different color. To detect traffic signs, pedestrians, cars, and bicycles around the vehicle, we utilize the YOLOv5 model, which is based on Convolutional Neural Networks. …”
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Dynamic Cascade Detector for Storage Tanks and Ships in Optical Remote Sensing Images by Tong Wang, Bingxin Liu, Peng Chen

“…Some studies have shown that different stages should have different Intersections of Union (IoU) thresholds to distinguish positive and negative samples because each stage has different IoU distributions. …”
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A method for identifying gully-type debris flows based on adaptive multi-scale feature extraction by Qiuyu Liu, Ting Wang, Zhijie Zheng, Baoyun Wang

“…Integrated into the feature layer of Convolutional Neural Network (CNN) to solve the multi-scale feature extraction problem of gullies. …”
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An Intrusion Detection System over the IoT Data Streams Using eXplainable Artificial Intelligence (XAI) by Adel Alabbadi, Fuad Bajaber

“…DL models are needed to train enormous amounts of data and produce promising results. Three different DL models, i.e., customized 1-D convolutional neural networks (1-D CNNs), deep neural networks (DNNs), and pre-trained model TabNet, are proposed. …”
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Flexible integration of spatial and expression information for precise spot embedding via ZINB-based graph-enhanced autoencoder by Jiacheng Leng, Jiating Yu, Ling-Yun Wu, Hongyang Chen

“…However, most domain identification methods do not adequately integrate expression and spatial information to flexibly identify different types of domains. To address these issues, we introduce Spot2vector, a computational framework that leverages a graph-enhanced autoencoder integrating zero-inflated negative binomial distribution modeling, combining both graph convolutional networks and graph attention networks to extract the latent embeddings of spots. …”
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Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism by Yang Chen, Zeyang Tang, Yibo Cui, Wei Rao, Yiwen Li

“…The experimental results indicate that the proposed model has a more powerful comprehensive ability to capture spatiotemporal relationships and improve accuracy and stability in different prediction steps.…”
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A single flow detection enabled method for DDoS attacks in IoT based on traffic feature reconstruction and mapping by Lixia XIE, Bingdi YUAN, Hongyu YANG, Ze HU, Xiang CHENG, Liang ZHANG

“…To address the slow response time of existing detection modules to Internet of things (IoT) distributed denial of service (DDoS) attacks, their low feature differentiation, and poor detection performance, a single flow detection enabled method based on traffic feature reconstruction and mapping (SFDTFRM) was proposed.Firstly, SFDTFRM employed a queue to store previously arrived flow based on the first in, first out rule.Secondly, to address the issue of similarity between normal communication traffic of IoT devices and DDoS attack traffic, a multidimensional reconstruction neural network model more lightweight compared to the baseline model and a function mapping method were proposed.The modified model loss function was utilized to reconstruct the quantitative feature matrix of the queue according to the corresponding index, and transformed into a mapping feature matrix through the function mapping method, enhancing the differences between different types of traffic, including normal communication traffic of IoT devices and DDoS attack traffic.Finally, the frequency information was extracted using a text convolutional network and information entropy calculation and the machine learning classifier was employed for DDoS attack traffic detection.The experimental results on two benchmark datasets show that SFDTFRM can effectively detect different DDoS attacks, and the average metrics value of SFDTFRM is a maximum of 12.01% higher than other existing methods.…”
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An applied noise model for scintillation-based CCD detectors in transmission electron microscopy by Christian Zietlow, Jörg K. N. Lindner

“…Thus, this paper aims to give an insight into the different noise contributions occurring on such detectors, into their underlying statistics and their correlation. …”
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Crack Detection and Evolution Law for Rock Mass under SHPB Impact Tests by Xie Beijing, Dihao Ai, Yu Yang

“…Secondly, a deep convolution network model named CrackSHPB was designed based on a deep learning algorithm. …”
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Comparative Analysis of Hybrid Deep Learning Models for Electricity Load Forecasting During Extreme Weather by Altan Unlu, Malaquias Peña

“…This research is divided into two case studies that analyze different combined DL model architectures. Case Study 1 conducts CNN-Recurrent (RNN, LSTM, GRU, BiRNN, BiGRU, and BiLSTM) models with fully connected dense layers, which combine convolution and recurrent neural networks to capture both spatial and temporal dependencies in the data. …”
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