Machine learning to predict killer whale (Orcinus orca) behaviors using partially labeled vocalization data by Sophia Sandholm

“…Despite that, with a careful combination of recent machine learning techniques, including a ResNet-34 convolutional neural network and a custom loss function designed for partially labeled learning, a 96.1% general behavior label classification accuracy on previously unheard segments is achieved. …”
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Traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories. by Hao Li, Linbing Chen

“…To address this, this paper proposes a deep learning model that combines Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Graph Neural Networks (GNN) for traffic accident risk prediction using vehicle spatiotemporal trajectory data. …”
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Wave Downscaling Approach with TCN model, Case Study in Bengkulu, Indonesia by Dio Navialdy, Didit Adytia

“…In this paper, to downscale global wave height data into a high-resolution local wave height with less computation resources, we propose a machine learning-based approach to downscaling using the Temporal Convolutional Network (TCN) model. To train the model, we obtain the wave dataset using the SWAN model in a local domain. …”
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Helium Speech Recognition Method Based on Spectrogram with Deep Learning by Yonghong Chen, Shibing Zhang, Dongmei Li

“…Then, we combine a deep fully convolutional neural network with connectionist temporal classification (CTC) to form an acoustic model, in which the spectrogram features of helium speech are used as an input to convert speech signals into phonetic sequences. …”
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MobilitApp: A Deep Learning-Based Tool for Transport Mode Detection to Support Sustainable Urban Mobility by Gerard Caravaca Ibanez, Luis J. de la Cruz Llopis, Adrian Catalin Diaconeasa, Alberto Bazan Guillen, Monica Aguilar Igartua

“…Our approach leverages a hierarchical model combining convolutional neural networks (CNNs) for feature extraction and long short-term memory (LSTM) layers for temporal processing, enhanced by skip connections. …”
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The application of suitable sports games for junior high school students based on deep learning and artificial intelligence by Xueyan Ji, Shamsulariffin Bin Samsudin, Muhammad Zarif Bin Hassan, Noor Hamzani Farizan, Yubin Yuan, Wang Chen

“…This study intends to develop a Spatial Temporal-Graph Convolutional Network (ST-GCN) action detection algorithm based on the MediaPipe framework. …”
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Amazonian manatee critical habitat revealed by artificial intelligence‐based passive acoustic techniques by Florence Erbs, Mike van derSchaar, Miriam Marmontel, Marina Gaona, Emiliano Ramalho, Michel André

“…A Convolutional Neural Network model successfully detected Amazonian manatee vocalizations with a 0.98 average precision on training data. …”
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Interpretable Machine Learning for Multi-Crop Yield Prediction in Semi-Arid Regions: A Hierarchical Approach to Handle Climate Data Sparsity by Rachid Ed-daoudi, M’barek El Haloui

“…The framework integrates Random Forest (RF) techniques for crop-specific modeling, exploiting their robustness to nonlinear relationships in small datasets, while long short term memory (LSTM) networks extract long-term climate trends, and Convolutional Neural Network (TCNN) with dilated kernels capture multi-scale temporal dependencies, creating a complementary hierarchy that leverages the strengths of each approach. …”
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Research on Data Repair of Pile-Type Adjustable Wind Turbine Foundation Monitoring Based on FST-ATTNet by WEI Huanwei, ZHAO Jizhang, ZHENG Xiao, TAN Fang, LIU Cong

“…In the spatial domain, the Temporal Convolutional Network (TCN) models long-range dependencies by expanding causal convolutions, thereby capturing local and global spatial relationships. …”
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An Uncertainty-Aware Lane Change Motion Planning Algorithm Based on Probabilistic Trajectory Prediction Distribution by Zhiqiang Zhang, Lei Zhang, Mingqiang Wang, Cong Wang, Zhenpo Wang

“…This paper presents an overall risk assessment method for the host vehicle to achieve efficient motion planning considering uncertainties of the predicted driving behaviors of surrounding vehicles. A Social Temporal Convolutional Long Short-Term Memory network is constructed to capture the interactive characteristics among the host and surrounding vehicles and to predict the statistical distribution of the trajectory prediction uncertainty in the prediction horizon. …”
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Physics-Informed Learning Framework for Lower Limb Kinematic Prediction With Sparse Sensors and Its Application in Chronic Stroke by Yan Guo, Yusuke Sekiguchi, Wen Zeng, Satoru Ebihara, Dai Owaki, Mitsuhiro Hayashibe

“…This study proposes a physics-informed learning framework utilizing a temporal convolutional network (TCN) for lower-limb kinematics prediction, significantly reducing sensor count to only two IMUs. …”
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A Novel Technique for Facial Recognition Based on the GSO-CNN Deep Learning Algorithm by Rana H. Al-Abboodi, Ayad A. Al-Ani

“…The GSO output is fed into the deep convolutional neural network (DCNN) that effectively trains the captured face images. …”
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Improving Wind Turbine Gearbox Reliability: A Hybrid Deep Learning Approach Using Bispectrum Image Analysis by Muhammad Zacky Asy'Ari, Jun Kondoh, Benyamin Kusumoputro

“…This study explores the potential of bispectrum image analysis combined with Convolutional Neural Network (CNN), CNN-Long Short-Term Memory (LSTM), and CNN-Bidirectional LSTM networks for enhanced fault detection and predictive maintenance for wind turbine systems. …”
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Mapping herbaceous wetlands using combined phenological and hydrological features from time-series Sentinel-1/2 imagery by Zhaolong Yang, Xiaodong Na

“…A deep learning algorithm (temporal convolutional neural network (TempCNN)) and the CVHIs dataset were used to map wetlands in the Zhalong National Nature Reserve in China. …”
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Prediction of COVID-19 cases by multifactor driven long short-term memory (LSTM) model by Yanwen Shao, Tsz Kin Wan, Kei Hang Katie Chan

“…Then, we use a long-time span data to predict the future COVID-19 new cases by training a long short-term memory (LSTM) model, a support vector regressor (SVR) and a temporal convolutional network (TCN), among which LSTM possessed the best performance and offered a good generalization ability. …”
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Task-Related EEG as a Biomarker for Preclinical Alzheimer’s Disease: An Explainable Deep Learning Approach by Ziyang Li, Hong Wang, Lei Li

“…An interpretable deep learning framework—Interpretable Convolutional Neural Network (InterpretableCNN)—was utilized to identify AD-related EEG features. …”
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Using artificial intelligence tools to predict and alleviate poverty by Ghada Alturif, Wafaa Saleh, Alaa A. El-Bary, Radwa Ahmed Osman

“…A one-dimensional Convolutional Neural Network (1D-CNN) is used in the model to examine and extract patterns from historical socio-economic data from 2000 to 2022. …”
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TDNN achitecture with efficient channel attention and improved residual blocks for accurate speaker recognition by Wenzao Li, Sai Yao, Bing Wan, Linsong Xiao, Chengyu Hou, Yanchuan Zhong, Wengang Zhou

“…Abstract In recent years, with the advancement of deep learning, Convolutional Neural Networks (CNNs) have been widely applied in speaker recognition, making CNN-based speaker embedding learning the predominant method for speaker verification. …”
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An ensemble of deep representation learning with metaheuristic optimisation algorithm for critical health monitoring using internet of medical things by Mai Alduailij

“…The classification process utilizes ensemble models, including the Temporal Convolutional Network (TCN), the Attention-based Bidirectional Gated Recurrent Unit (A-BiGRU), and the Hybrid Deep Belief Network (HDBN) techniques. …”
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Multi-User Activity Recognition Using Plot Images Based on Ambiental Sensors by Anca Roxana Alexan, Alexandru Iulian Alexan, Stefan Oniga

“…In the second stage, the generated data are provided to a sequential convolutional neural network, which predicts the 16 activities developed by two users. …”
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