LLM-Augmented Linear Transformer–CNN for Enhanced Stock Price Prediction by Lei Zhou, Yuqi Zhang, Jian Yu, Guiling Wang, Zhizhong Liu, Sira Yongchareon, Nancy Wang

“…In this study, we propose a novel hybrid deep learning framework that integrates a large language model (LLM), a Linear Transformer (LT), and a Convolutional Neural Network (CNN) to enhance stock price prediction using solely historical market data. …”
Get full text

Novel Deep Learning Framework for Evaporator Tube Leakage Estimation in Supercharged Boiler by Yulong Xue, Dongliang Li, Yu Song, Shaojun Xia, Jingxing Wu

“…To address these issues, this study proposes a novel deep learning framework (LSTM-CNN–attention), combining a Long Short-Term Memory (LSTM) network with a dual-pathway spatial feature extraction structure (ACNN) that includes an attention mechanism(attention) and a 1D convolutional neural network (1D-CNN) parallel pathway. …”
Get full text

Employing sentinel-2 time-series and noisy data quality control enhance crop classification in arid environments: A comparison of machine learning and deep learning methods by Zahra Mohammadi Mobarakeh, Saeid Pourmanafi, Mohsen Ahmadi

“…For crop classification, we used four machine learning and deep learning methods including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and Temporal Convolutional Neural Network (TCNN), and compared their results before and after quality control measures. …”
Get full text

Research on channel estimation based on joint perception and deep enhancement learning in complex communication scenarios by Xin Liu, Shanghong Zhao, Yanxia Liang, Shahid Karim

“…In this article, we address the intelligent, reflective surface (IRS)-assisted channel estimation problem and propose an intelligent channel estimation model based on the fusion of convolutional neural network (CNN) and gated recurrent unit (GRU) row features, utilizing the reinforcement learning Deep Deterministic Policy Gradient (DDPG) strategy for Channel Reconstruction Prediction and Generation Network (CRPG-Net). …”
Get full text

Infrared image super resolution with structure prior from uncooled infrared readout circuit by Kai Che, Jian Lv, Jia Wei, Jiayuan Gong, Longcheng Que, Yun Zhou

“…We propose an efficient Row-Column Transformer Block (RCTB) that splits features into rows and columns to effectively capture the spatio-temporal correlation between row-column pixels. Acknowledging the continuity of temperature information within the image and the correlation between adjacent pixel regions, we develop a Compact Convolution Block (CCB) that incorporates a U-shape Spatial Channel Attention Block (USCAB) to extract local features before the RCTB. …”
Get full text

Differentiating localized autoimmune pancreatitis and pancreatic ductal adenocarcinoma using endoscopic ultrasound images with deep learning by Hitomi Nakamura, Motohisa Fukuda, Akiko Matsuda, Naohiko Makino, Hirohito Kimura, Yu Ohtaki, Yoshihito Nawa, Soushi Oyama, Yuya Suzuki, Toshikazu Kobayashi, Tetsuya Ishizawa, Yasuharu Kakizaki, Yoshiyuki Ueno

“…We applied transfer learning to a convolutional neural network called ResNet152, together with our innovative imaging method contributing to data augmentation and temporal data process. …”
Get full text

Improved Connected-Mode Discontinuous Reception (C-DRX) Power Saving and Delay Reduction Using Ensemble-Based Traffic Prediction by Ji-Hee Yu, Yoon-Ju Choi, Seung-Hwan Seo, Seong-Gyun Choi, Hye-Yoon Jeong, Ja-Eun Kim, Myung-Sun Baek, Young-Hwan You, Hyoung-Kyu Song

“…To address this issue, this paper presents an ensemble model combining random forest (RF) and a temporal convolutional network (TCN) to predict traffic occurrences and adjust C-DRX activation timing. …”
Get full text

A multi-scale cross-dimension interaction approach with adaptive dilated TCN for RUL prediction by Zhe Lu, Bing Li, Changyu Fu, Liang Xu, Bai Jiang, Zelong Li, Junbao Wu, Siye Jia

“…To address the aforementioned issues, this paper proposes an Adaptive Dilated Temporal Convolutional Network (AD-TCN) approach, incorporating a Multi-Scale Cross-Dimension Interaction Module (MSCDIM) to enhance feature extraction and interaction. …”
Get full text

Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention Framework by Md. Shadman Abid, Razzaqul Ahshan, Mohammed Al-Abri, Rashid Al Abri

“…In this context, a novel robust deep learning model, termed the Convolutional Neural Network-Bidirectional Long Short-Term Memory model with spatiotemporal attention mechanism (CNN-BiLSTM-STA), is developed in this study. …”
Get full text

BiDGCNLLM: A Graph–Language Model for Drone State Forecasting and Separation in Urban Air Mobility Using Digital Twin-Augmented Remote ID Data by Zhang Wen, Junjie Zhao, An Zhang, Wenhao Bi, Boyu Kuang, Yu Su, Ruixin Wang

“…Using Remote ID data, we propose BiDGCNLLM, a hybrid prediction framework that integrates a Bidirectional Graph Convolutional Network (BiGCN) with Dynamic Edge Weighting and a reprogrammed Large Language Model (LLM, Qwen2.5–0.5B) to capture spatial dependencies and temporal patterns in drone speed trajectories. …”
Get full text

A Multi-Scale Deep Learning Framework Combining MobileViT-ECA and LSTM for Accurate ECG Analysis by Abduljabbar S. Ba Mahel, Mehdhar S. A. M. Al-Gaashani, Reem Ibrahim Alkanhel, Dina S. M. Hassan, Mohammed Saleh Ali Muthanna, Ammar Muthanna, Ahmed Aziz

“…The proposed model utilizes a hybrid framework that combines standard and dilated convolutional networks, advanced attention mechanisms, and temporal sequence learning to address the complexities of ECG data. …”
Get full text

Intelligent Fault Warning Method for Wind Turbine Gear Transmission System Driven by Digital Twin and Multi-Source Data Fusion by Tiantian Xu, Xuedong Zhang, Wenlei Sun

“…At the algorithmic level, a CNN-LSTM-Attention fault prediction model is proposed, which innovatively integrates the spatial feature extraction capabilities of a convolutional neural network (CNN), the temporal modeling advantages of long short-term memory (LSTM), and the key information-focusing characteristics of an attention mechanism. …”
Get full text

Handwritten Text Recognition for Documentary Medieval Manuscripts by Sergio Torres Aguilar, Vincent Jolivet

“…The architecture of the models is based on a Convolutional Recurrent Neural Network (CRNN) coupled with a Connectionist Temporal Classification (CTC) loss. …”
Get full text

Diagnosis of Alzheimer's disease using non-linear features of ERP signals through a hybrid attention-based CNN-LSTM model by Elias Mazrooei Rad, Sayyed Majid Mazinani, Seyyed Ali Zendehbad

“…In this study, a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model is proposed for the diagnosis of Alzheimer’s disease (AD) from the Event-Related Potential (ERP) signals obtained from the Electroencephalogram (EEG) data. …”
Get full text

Integrating Copula-Based Random Forest and Deep Learning Approaches for Analyzing Heterogeneous Treatment Effects in Survival Analysis by Jong-Min Kim

“…This paper presents deep learning models—specifically, Long Short-Term Memory (LSTM) networks and hybrid Convolutional Neural Network–LSTM (CNN-LSTM) with a Copula-Based Random Forest (CBRF) model to estimate Heterogeneous Treatment Effects (HTEs) in survival analysis. …”
Get full text

1D-CNN and A Weight-Based Balancing Technique for Improved Detection of Non-Technical Losses in Power Distribution Systems by Rajitroj Nattaya, Seepromting Katsarin, Chatthaworn Rongrit

“…This paper presents a novel approach combining a weight-based balancing technique with a one-dimensional convolutional neural network (1D-CNN). Load profiles were collected from the smart meter database of the Provincial Electricity Authority (PEA) in Khon Kaen, Thailand, covering seasonal variations—winter, summer, and rainy seasons—to ensure comprehensive pattern representation. …”
Get full text

Energy-Efficient Fall-Detection System Using LoRa and Hybrid Algorithms by Manny Villa, Eduardo Casilari

“…This study introduces a hybrid system that integrates a threshold-based model for preliminary detection with a deep learning-based approach that combines a CNN (Convolutional Neural Network) for spatial feature extraction with a LSTM (Long Short-Term Memory) model for temporal pattern recognition, aimed at improving classification accuracy. …”
Get full text

A multimodal deep learning architecture for predicting interstitial glucose for effective type 2 diabetes management by Muhammad Salman Haleem, Daphne Katsarou, Eleni I. Georga, George E. Dafoulas, Alexandra Bargiota, Laura Lopez-Perez, Miguel Rujas, Giuseppe Fico, Leandro Pecchia, Dimitrios Fotiadis, Gatekeeper Consortium

“…The CGM time series were processed using a stacked Convolutional Neural Network (CNN) and a Bidirectional Long Short-Term Memory (BiLSTM) network followed by an attention mechanism. …”
Get full text

A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting by Pan Wu, Zilin Huang, Yuzhuang Pian, Lunhui Xu, Jinlong Li, Kaixun Chen

“…Results show that the proposed method outperforms other deep learning algorithms (such as recurrent neural network (RNN) and convolutional neural network (CNN)) in terms of both calculating efficiency and prediction accuracy. …”
Get full text

A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted Living by Fahmid Al Farid, Ahsanul Bari, Abu Saleh Musa Miah, Sarina Mansor, Jia Uddin, S. Prabha Kumaresan

“…Furthermore, we explore a wide range of machine learning and deep learning models—including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCNs), and Graph Convolutional Networks (GCNs)—along with lightweight transfer learning methods suitable for environments with limited computational resources. …”
Get full text