A lightweight hybrid model for accurate ammonia prediction in pig houses by Jacqueline Musabimana, Qiuju Xie, Hong Zhou, Ping Zheng, Honggui Liu, Tiemin Ma, Jiming Liu

“…The model replaces feedforward networks with separable convolutional layers to capture local and spatial dependencies more efficiently, as well as reduce computational complexity. …”
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An intelligent optimized object detection system for disabled people using advanced deep learning models with optimization algorithm by Marwa Obayya, Fahd N. Al-Wesabi, Menwa Alshammeri, Huda G. Iskandar

“…Furthermore, the MobileNetV3 model is utilized for the feature extraction process. The temporal convolutional network (TCN) model is implemented for classification. …”
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Enhancing LoRa-Based Outdoor Localization Accuracy Using Machine Learning by Nur Kelesoglu, Marzena Halama, Anna Strzoda

“…We further propose a Hybrid Model that combines convolutional feature extraction with gradient-boosted regression. …”
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Deep Learning-Based Sign Language Recognition Using Efficient Multi-Feature Attention Mechanism by Esma Yenisari, Sirma Yavuz

“…Subsequently, dataset-specific contextual features are extracted utilizing distinct network types; spatial dependencies are modeled via Convolutional Neural Networks (CNNs), whereas temporal dynamics are learned through Recurrent Neural Networks (RNNs). …”
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GazeMap: Dual-Pathway CNN Approach for Diagnosing Alzheimer’s Disease from Gaze and Head Movements by Hyuntaek Jung, Shinwoo Ham, Hyunyoung Kil, Jung Eun Shin, Eun Yi Kim

“…This study proposes a novel AD detection framework integrating gaze and head movement analysis via a dual-pathway convolutional neural network (CNN). Unlike conventional methods relying on linguistic, speech, or neuroimaging data, our approach leverages non-invasive video-based tracking, offering a more accessible and cost-effective solution to early AD detection. …”
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Personalizing Seizure Detection for Individual Patients by Optimal Selection of EEG Signals by Rosanna Ferrara, Martino Giaquinto, Gennaro Percannella, Leonardo Rundo, Alessia Saggese

“…The system uses an efficient Convolutional Neural Network that processes data from just two channels. …”
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Reducing lead requirements for wearable ECG: Chest lead reconstruction with 1D-CNN and Bi-LSTM by Kazuki Hebiguchi, Hiroyoshi Togo, Akimasa Hirata

“…Our preprocessing and network architecture effectively capture both spatial and temporal features. …”
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Optimizing deep learning models for glaucoma screening with vision transformers for resource efficiency and the pie augmentation method. by Sirikorn Sangchocanonta, Pakinee Pooprasert, Nichapa Lerthirunvibul, Kanyarak Patchimnan, Phongphan Phienphanich, Adirek Munthuli, Sujittra Puangarom, Rath Itthipanichpong, Kitiya Ratanawongphaibul, Sunee Chansangpetch, Anita Manassakorn, Visanee Tantisevi, Prin Rojanapongpun, Charturong Tantibundhit

“…To tackle the resource and time challenges in glaucoma screening with convolutional neural network (CNN), we chose the Data-efficient image Transformers (DeiT), a vision transformer, known for its reduced computational demands, with preprocessing time decreased by a factor of 10. …”
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Design of a Portable Biofeedback System for Monitoring Femoral Load During Partial Weight-Bearing Walking by Tao Ma, Tianyang Fan, Xun Xu, Tao Sun

“…Utilizing data collected from 12 participants, a physics-informed temporal convolutional network (PITCN) method was proposed to estimate the internal femoral loading. …”
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Unified Deep Learning Model for Global Prediction of Aboveground Biomass, Canopy Height, and Cover from High-Resolution, Multi-Sensor Satellite Imagery by Manuel Weber, Carly Beneke, Clyde Wheeler

“…The model architecture is a custom Feature Pyramid Network consisting of an encoder, decoder, and multiple prediction heads, all based on convolutional neural networks. …”
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An Improved Phase Space Reconstruction Method-Based Hybrid Model for Chaotic Traffic Flow Prediction by Yue Hou, Da Li, Di Zhang, Zhiyuan Deng

“…Secondly, to address the problem of insufficient learning ability of traditional convolutional combinatorial modeling for complex phase space laws of chaotic traffic flow, the high-dimensional phase space features are extracted using the layer-by-layer pretraining mechanism of convolutional deep belief networks (CDBNs), and the temporal features are extracted by combining with long short-term memory (LSTM). …”
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ClassRoom-Crowd: A Comprehensive Dataset for Classroom Crowd Counting and Cross-Domain Baseline Analysis by Wenqian Jiang, Xiaohua Huang, Qun Zhao, Sheng Liu

“…Current methods predominantly employ convolutional neural networks, which require large datasets for training. …”
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A novel EEG artifact removal algorithm based on an advanced attention mechanism by Rui Jiang, Shen Tong, Jiawei Wu, Haowei Hu, Ran Zhang, Heng Wang, Yan Zhao, Weixin Zhu, Shuyan Li, Xiao Zhang

“…Therefore, this study proposes CLEnet by integrating dual-scale CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory), and incorporating an improved EMA-1D (One-Dimensional Efficient Multi-Scale Attention Mechanism). …”
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Deepfake Image Forensics for Privacy Protection and Authenticity Using Deep Learning by Saud Sohail, Syed Muhammad Sajjad, Adeel Zafar, Zafar Iqbal, Zia Muhammad, Muhammad Kazim

“…Key approaches include the use of CNNs, RNNs, and hybrid models like CNN-LSTM, CNN-GRU, and temporal convolutional networks (TCNs) to capture both spatial and temporal features during the detection of deepfake videos and images. …”
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Robust Anomaly Detection of Multivariate Time Series Data via Adversarial Graph Attention BiGRU by Yajing Xing, Jinbiao Tan, Rui Zhang, Jiafu Wan

“…Hence, this paper proposes a robust multivariate temporal data anomaly detection method based on graph attention for training convolutional neural networks (PGAT-BiGRU-NRA). …”
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RUL Prediction of DC Contactor Using CNN-LSTM With Channel Attention and Fusion of Dual Aggregated Features by Sai Wang, Yuanfeng Zhang, Hao Huang, Yun Shi, Jianfei Si

“…Challenges arise due to high-dimensional operational data, difficulty fusing spatial-temporal features, and noisy environments. This paper proposes a novel deep learning model called DAF-CA-CNN-LSTM, which integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), a Channel Attention (CA) mechanism, and a Dual Aggregated Features (DAF) strategy. …”
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Fusion of Recurrence Plots and Gramian Angular Fields with Bayesian Optimization for Enhanced Time-Series Classification by Maria Mariani, Prince Appiah, Osei Tweneboah

“…Experiments on seven univariate datasets show that our method significantly outperforms traditional classifiers such as one-nearest neighbor with Dynamic Time Warping, Shapelet Transform, and RP-based convolutional neural networks. For multivariate tasks, the proposed fusion model achieves macro F1 scores of 91.55% on the UCI Human Activity Recognition dataset and 98.95% on the UCI Room Occupancy Estimation dataset, outperforming standard deep learning baselines. …”
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On the added value of sequential deep learning for the upscaling of evapotranspiration by B. Kraft, B. Kraft, B. Kraft, J. A. Nelson, S. Walther, F. Gans, U. Weber, G. Duveiller, M. Reichstein, W. Zhang, M. Rußwurm, D. Tuia, M. Körner, Z. Hamdi, M. Jung

“…., non-sequential) models (extreme gradient boosting (XGBoost) and a fully connected neural network (FCN)) with sequential models (a long short-term memory (LSTM) model and a temporal convolutional network (TCN)) for the modeling and upscaling of ET. …”
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Pose estimation for health data analysis: advancing AI in neuroscience and psychology by Juan Yu, Daoyu Zhu

“…The framework integrates multi-modal data sources and applies temporal graph convolutional networks, ensuring both scalability and adaptability to diverse tasks. …”
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Hybrid deep learning for IoT-based health monitoring with physiological event extraction by Sivanagaraju Vallabhuni, Kumar Debasis

“…However, the present-day models have failed to effectively encompass spatial-temporal data samples. Methods This paper presents a novel hybrid machine-learning model by amalgamating Convolutional Neural Networks (CNNs) with Long Short-Term Memory models (LSTMs) to boost prediction accuracy. …”
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