A novel method for distracted driving behaviors recognition with hybrid CNN-BiLSTM-AM model by Dengfeng Zhao, Haojie Li, Zhijun Fu, Bao Ma, Fang Zhou, Chaohui Liu, Wenbin He

“…The proposed framework consists of hybrid convolutional neural network and bidirectional long short term memory network to extract multi-scale spatiotemporal features of high-dimensional distracted behavior data. …”
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A Hybrid MLP and CNN Architecture for Sequential Recommendation by Li Yuan, Xue-Yi Zhao

“…In this context, we propose a novel multi-interest sequential recommendation framework that effectively captures both long-term and short-term user interests. …”
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Improving thermal state preparation of Sachdev–Ye–Kitaev model with reinforcement learning on quantum hardware by Akash Kundu

“…We demonstrate the effectiveness of the RL framework in both noiseless and noisy quantum hardware environments, maintaining high accuracy in thermal state preparation. …”
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A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm by Zhixin Xia, Zhangqi Zheng, Lexin Bai, Xiaolei Yang, Yongshan Liu

“…The algorithm can obtain hypergraphs without knowing the attribute information of hypergraph nodes and combines the graph convolutional network (GCN) framework to capture node feature information for link prediction. …”
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GAT-Enhanced YOLOv8_L with Dilated Encoder for Multi-Scale Space Object Detection by Haifeng Zhang, Han Ai, Donglin Xue, Zeyu He, Haoran Zhu, Delian Liu, Jianzhong Cao, Chao Mei

“…In this paper, a multi-scale feature fusion framework based on an improved version of YOLOv8_L is proposed. …”
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A modified deep neural network enables identification of foliage under complex background by Xiaolong Zhu, Junhao Zuo, Honge Ren

“…For the sake of enhancing the identification ability of current network and meeting the needs of the high accuracy of distinguishing similar small objects (foliage) in the complex scenes, this paper proposes a modified region-based fully convolutional network which adopts Inception V3 accompanying with residual connection as the main framework. …”
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Neural network-based forecasting and uncertainty analysis of new power generation capacity of electric energy by Xingyu Dou, Zehan Cui

“…To solve this, we propose a framework combining an optimized multiscale convolutional neural network (MSCNN) and long - short - term memory network (LSTM). …”
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AutoWindLoc: Precise Localization of Wind Turbines in High-Resolution Orthophotos for Enhanced Registers by J. Middendorf, A. Kelm, S. Frintrop

“…The paper proposes a novel framework for automatically detecting wind turbines in orthophotos, transferring this information to a database, and linking detected turbines to an existing registry to minimize location inaccuracy. …”
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Using a Solution Construction Algorithm for Cyclic Shift Network Coding under Multicast Network to the Transformation of Musical Performance Styles by Xiuqin Wang, Jun Geng, Zhiyuan Li

“…This paper presents a theoretical framework of the circular shift network coding system through the study of nonmultiple clustered interval music performance style conversion and the analysis of music conversion by using circular shift topology, and a series of basic research results of circular shift network coding is obtained under this framework. …”
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Combining Deep Learning and Street View Images for Urban Building Color Research by Wenjing Li, Qian Ma, Zhiyong Lin

“…The framework is composed of two phases: “deep learning” and “quantitative analysis.” …”
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Integrating Machine Learning and IoT for Effective Plant Disease Management by Bhoi Manjulata, Dubey Ahilya

“…Then, this paper presents an innovative framework that utilizes ML and IoT technologies to improve the crop health and yield. …”
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Deep context-attentive transformer transfer learning for financial forecasting by Ling Feng, Ananta Sinchai

“…A transfer learning framework is incorporated to enhance generalization across markets through pretraining, encoder freezing, and fine-tuning. …”
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Low-Power Branch CNN Hardware Accelerator with Early Exit for UAV Disaster Detection Using 16 nm CMOS Technology by Yu-Pei Liang, Wen-Chin Chao, Ching-Che Chung

“…This paper presents a disaster detection framework based on aerial imagery, utilizing a Branch Convolutional Neural Network (B-CNN) to enhance feature learning efficiency. …”
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GOMFuNet: A Geometric Orthogonal Multimodal Fusion Network for Enhanced Prediction Reliability by Yi Guo, Rui Zhong

“…This paper introduces the Geometric Orthogonal Multimodal Fusion Network (GOMFuNet), a novel mathematical framework designed to address these challenges. GOMFuNet synergistically combines two core mathematical principles: (1) It utilizes geometric deep learning, specifically Graph Convolutional Networks (GCNs), within its Cross-Modal Label Fusion Module (CLFM) to perform fusion in a high-level semantic label space, thereby preserving inter-sample topological relationships and enhancing robustness to inconsistencies. (2) It incorporates a novel Label Confidence Learning Module (LCLM) derived from optimization theory, which explicitly enhances prediction reliability by enforcing mathematical orthogonality among the predicted class probability vectors, directly minimizing output uncertainty. …”
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Unsupervised Anomaly Detection for Volcanic Deformation in InSAR Imagery by Robert Popescu, Nantheera Anantrasirichai, Juliet Biggs

“…We test three different state‐of‐the‐art architectures, one convolutional neural network Patch Distribution Modeling (PaDiM) and two generative models (GANomaly and Denoising diffusion probabilistic models (DDPM)). …”
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Deep learning with ensemble-based hybrid AI model for bipolar and unipolar depression detection using demographic and behavioral based on time-series data by Naga Raju Kanchapogu, Sachi Nandan Mohanty

“…Machine learning (ML) and deep learning (DL) offer automated approaches to detect depression using behavioral and demographic data.Methods This study proposes a hybrid AI framework combining structured demographic features with synthetic actigraph time-series data. …”
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Deep Neural Networks for Accurate Depth Estimation with Latent Space Features by Siddiqui Muhammad Yasir, Hyunsik Ahn

“…In response to these challenges, this study introduces a novel depth estimation framework that leverages latent space features within a deep convolutional neural network to enhance the precision of monocular depth maps. …”
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Attention-fused residual transformer CNN for robust lower limb movement recognition by A. Anitha, D. Jeraldin Auxillia

“…To address these challenges, a new framework that combines an Attention-Fused Residual-Transformer Convolutional Neural Network (AF-RT-CNN) is proposed. …”
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A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching by Chengyao Liu, Fei Dong, Kunpeng Ge, Yuanyuan Tian

“…To overcome these problems, by integrating the superiority of deep learning method and feature-based transfer learning method, this work proposes an innovative cross-domain fault diagnosis framework based on deep transfer convolutional neural network and supervised joint matching. …”
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Autoencoder-Augmented Graph Neural Networks for Accurate and Scalable Structure Recognition in Analog/Mixed-Signal Schematics by Mohamed Salem, Witesyavwirwa Vianney Kambale, Ali Deeb, Sergii Tkachov, Anjeza Karaj, Joachim Pichler, Manuel Ludwig Lexer, Kyandoghere Kyamakya

“…In this work, a novel framework has been proposed that combines the generative augmentation capabilities of convolutional Autoencoders with the structural analysis power of Graph Convolutional Networks (GCNs). …”
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