Advancing smart communities with a deep learning framework for sustainable resource management. by Yongyan Zhao

“…The framework leverages long short-term memory (LSTM) networks for temporal data, convolutional neural networks (CNNs) for spatial analysis, and autoencoders for anomaly detection. …”
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An enhanced deep learning-based feature extraction framework for moving object detection by Upasana Panigrahi, Prabodh Kumar Sahoo, Manoj Kumar Panda, Aswini Kumar Samantaray, Ganapati Panda

“…The designed Multi-Scale Feature Pooling Framework (MSFP) guarantees the preservation of multi-scale and multi-dimensional features across different scales. …”
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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. …”
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A Novel Foundation Model-Based Framework for Multimodal Retinal Age Prediction by Christopher Nielsen, Matthias Wilms, Nils D. Forkert

“…Traditionally, machine learning predictions of biological retinal age utilize convolutional neural network (CNN) architectures and data from color fundus photography (CFP). …”
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Hybrid CNN-Ensemble Framework for Intelligent Optical Fiber Fault Detection and Diagnosis by Salem Titouni, Idris Messaoudene, Yassine Himeur, Omar Alnaseri, Farouk Chetouah, Boualem Hammache, Massinissa Belazzoug, Shadi Atalla, Wathiq Mansoor

“…This paper introduces a novel Hybrid CNN-Ensemble framework, combining convolutional neural networks (CNNs) for deep feature extraction with ensemble learning techniques including XGBoost, Support Vector Machines (SVM), and Random Forest (RF). …”
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FRORS: An Effective Fine-Grained Retrieval Framework for Optical Remote Sensing Images by Yong-Qiang Mao, Zhizhuo Jiang, Yu Liu, Yiming Zhang, Kehan Qi, Hanbo Bi, You He

“…With the rapid development of convolutional neural networks (CNN) in the field of remote sensing, it has become possible for remote sensing image retrieval tasks to move toward more fine-grained classes. …”
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Federated and ensemble learning framework with optimized feature selection for heart disease detection by Olfa Hrizi, Karim Gasmi, Abdulrahman Alyami, Adel Alkhalil, Ibrahim Alrashdi, Ali Alqazzaz, Lassaad Ben Ammar, Manel Mrabet, Alameen E.M. Abdalrahman, Samia Yahyaoui

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Beyond Handcrafted Features: A Deep Learning Framework for Optical Flow and SLAM by Kamran Kazi, Arbab Nighat Kalhoro, Farida Memon, Azam Rafique Memon, Muddesar Iqbal

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Deep Neural Framework With Visual Attention and Global Context for Predicting Image Aesthetics by Yifei Xu, Nuo Zhang, Pingping Wei, Genan Sang, Li Li, Feng Yuan

“…Specifically, the framework can be any state-of-the-art convolution classification network compatible with visual attention. …”
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Advanced lightweight deep learning vision framework for efficient pavement damage identification by Shuai Dong, Yunlong Wang, Jin Cao, Jia Ma, Yang Chen, Xin Kang

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The Reliability of Diagnosing Schizophrenia Using the GRU Layer in Conjunction with EEG Rhythms by Pankaj Kumar Sahu, Karan Jain

“…Results: The RDCGRU framework performs efficiently with alpha-EEG rhythm (88.06%) and harshly with delta-EEG rhythm (60.05%). …”
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Improved crop row detection by employing attention-based vision transformers and convolutional neural networks with integrated depth modeling for precise spatial accuracy by Hassan Afzaal, Derek Rude, Aitazaz A. Farooque, Gurjit S. Randhawa, Arnold W. Schumann, Nicholas Krouglicof

“…The proposed framework employs the latest attention and convolution-based encoders, such as ConvFormer, CAFormer, Swin Transformer, and ConvNextV2, in precisely identifying crop rows across varied and challenging agricultural environments. …”
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Research on an Intelligent Sedimentary Microfacies Recognition Method Based on Convolutional Neural Networks Within the Sequence Stratigraphy of Well Logging Curve Image Groups by Xinyi Yuan, Xidong Wang, Shutian Wang, Feng Tian, Zichun Yang

“…This study focuses on the Lower Cretaceous Yaojia Formation Member 1 (K2y1) in the satellite oilfield of the Songliao Basin, integrating sequence stratigraphy into a machine learning framework to propose an innovative convolutional neural network (CNN)-based facies recognition method using log-curve image groups by graphically transforming five log curves and establishing a CNN model that correlates log responses with microfacies. …”
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Radar-Based Damage Detection in a Wind Turbine Blade Using Convolutional Neural Networks: A Proof-of-Concept Under Fatigue Loading by Erik Streser, Sercan Alipek, Manuel Rao, Jonas Simon, Jochen Moll, Peter Kraemer, Viktor Krozer

“…This paper reports a convolutional neural network (CNN)-based damage detection approach for radar-based structural health monitoring of wind turbine blades. …”
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Melt Density Monitoring of Extruder Extrusion Process Based on Multi-source Data Fusion and Convolutional Long Short-term Memory Neural Network by Binbin ZHANG, Zhuyun CHEN, Fei ZHANG, Gang JIN

“…Ensuring precise control over melt density is imperative for achieving desired product characteristics and maintaining process stability in polymer blending operations.Methods The research proposes a novel methodological framework that integrates multi-source data fusion with a convolutional long short-term memory (LSTM) neural network architecture to address this challenge. …”
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A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment by Jinyin Bai, Wei Zhu, Shuhong Liu, Chenhao Ye, Peng Zheng, Xiangchen Wang

“…First, preprocessed industrial operation data are fed into the model, and hyperparameter optimization is conducted using the Optuna framework to improve training efficiency and generalization capability. …”
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A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI by Sayyed Shahid Hussain, Pir Masoom Shah, Hussain Dawood, Xu Degang, Ahmad Alshamayleh, Muhammad Adnan Khan, Taher M. Ghazal

“…The proposed model comprises three primary blocks: Transformer-block, convolutional block, and dense-block. The input image is processed concurrently by the cosine transformer and convolutional block. …”
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A Framework for Breast Cancer Classification with Deep Features and Modified Grey Wolf Optimization by Fathimathul Rajeena P.P, Sara Tehsin

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Optimized Attention Enhanced Temporal Graph Convolutional Network Espoused Research of Intelligent Customer Service System based on Natural Language Processing Technology by Zhifeng Wei, Hongyan Wang, Qiang Xu, Yi Qu, Wei Xing

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Personalized trajectory inference framework integrating driving behavior recognition and temporal dependency learning. by Jinhao Yang, Junwen Cao, Mingyu Fang

“…Comparative analysis with baseline models (LSTM, Social-LSTM, Social-Velocity-LSTM, Convolutional-Social-LSTM) reveals particularly enhanced accuracy in long-term predictions. …”
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