Multiclass Incremental Learning for Fault Diagnosis in Induction Motors Using Fine-Tuning with a Memory of Exemplars and Nearest Centroid Classifier by Magdiel Jiménez-Guarneros, Jonas Grande-Barreto, Jose de Jesus Rangel-Magdaleno

“…This article presents a multiclass incremental learning (MCIL) framework based on 1D convolutional neural network (CNN) for fault detection in induction motors. …”
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INTEGRATING CNN AND DICTIONARY MECHANISMS FOR EFFECTIVE LOOP CLOSURE DETECTION by Ayda Mohammed Sharif, Sadegh Abdollah Aminifar

“…This paper presents a robust and scalable LCD framework that integrates a lightweight Convolutional Neural Network (CNN) with a dictionary-based voting mechanism, optimized for accuracy and real-time performance in resource-constrained settings. …”
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Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species. by Mohammad Fraiwan, Rami Mukbel, Dania Kanaan

“…Using locally field-caught and prepared samples over a period of two years, and based on convolutional neural networks, transfer learning, and early fusion of genital and pharynx images, we achieved exceptional classification accuracy (greater than 95%) across multiple performance metrics and using a wide range of pre-trained convolutional neural network models. …”
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Predicting Epileptic Seizures Using EfficientNet-B0 and SVMs: A Deep Learning Methodology for EEG Analysis by Yousif A. Saadoon, Mohamad Khalil, Dalia Battikh

“…This study proposes a novel framework combining a convolutional neural network (CNN) based on EfficientNet-B0 and an ensemble of six Support Vector Machines (SVMs) with a voting mechanism for robust seizure prediction. …”
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Spatial attention-guided pre-trained networks for accurate identification of crop diseases by Satrughan Kumar, S. S. Aravinth, Sreedhar Kollem, Munish Kumar, Quadri Noorulhasan Naveed, Azath Mubarakali, Abhishek Bhattacherjee, Addisu Frinjo Emma

“…To address these challenges, we introduce an enhanced crop disease classification framework that incorporates EfficientNet-B3 with an ancillary convolutional layer and a spatial attention module (ACSA). …”
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A Massive Image Recognition Algorithm Based on Attribute Modelling and Knowledge Acquisition by Guohua Li, An Liu, Huajie Shen

“…Then, an efficient deep neural network mapping algorithm is designed and implemented for the microprocessing architecture and software programming framework of this edge processor, and a layout scheme is proposed to place the input feature maps outside the kernel DDR and the reordered convolutional kernel matrices inside the kernel storage body and to design corresponding efficient vectorization algorithms for the multidimensional matrix convolution computation, multidimensional pooling computation, local linear normalization, etc., which exist in the deep convolutional neural network model. …”
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DMA-Net: Dynamic Morphology-Aware Segmentation Network for Remote Sensing Images by Chao Deng, Haojian Liang, Xiao Qin, Shaohua Wang

“…To address these limitations, we propose DMA-Net, a Dynamic Morphology-Aware Segmentation Network built on an encoder–decoder architecture. The proposed framework incorporates three primary parts: a Multi-Axis Vision Transformer (MaxViT) encoder achieves a balance between local feature extraction and global context modeling through multi-axis self-attention mechanisms; a Hierarchy Attention Decoder (HA-Decoder) enhanced with Hierarchy Convolutional Groups (HCG) for precise recovery of fine-grained spatial details; and a Channel and Spatial Attention Bridge (CSA-Bridge) to mitigate the encoder–decoder semantic gap while amplifying discriminative feature representations. …”
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Optimal, fast, and robust inference of reionization-era cosmology with the 21cmPIE-INN by Benedikt Schosser, Caroline Heneka, Tilman Plehn

“…Modern machine learning will allow for simulation-based inference from reionization-era 21cm observations at the Square Kilometre Array. Our framework combines a convolutional summary network and a conditional invertible network through a physics-inspired latent representation. …”
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Creating interpretable deep learning models to identify species using environmental DNA sequences by Samuel Waggoner, Jon Donnelly, Rose Gurung, Laura Jackson, Chaofan Chen

“…In this work, we introduce an interpretable, prototype-based CNN using the ProtoPNet framework that surpasses previous accuracy on a challenging eDNA dataset. …”
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Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction by Sung Jae Kim, Yongbok Cho

“…These interpolated images are then used as input for a ConvLSTM (Convolutional Long Short-Term Memory) network, which integrates convolutional layers to capture spatial patterns and LSTM units to model temporal dependencies. …”
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YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11 by Qian Wu, Heming Huang, Dongke Song, Jie Zhou

“…Validation on the COCO benchmark confirms the framework’s generalization capabilities, maintaining computational efficiency for real-time deployment. …”
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A Hybrid Learnable Fusion of ConvNeXt and Swin Transformer for Optimized Image Classification by Jaber Qezelbash-Chamak, Karen Hicklin

“…Our findings highlight the benefits of combining convolutional inductive biases and transformer-based global context in a single learnable framework, positioning ConvTransGFusion as a robust and versatile solution for real-world clinical applications.…”
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A Deep Learning Model with Axial Attention for Radar Echo Extrapolation by Yu-Mei Xie, Ying-Liang Zhao, Shu-Yan Huang

“…Under the sequence-to-sequence framework, we have developed a novel convolutional recurrent neural network called Self-attention Trajectory Gated Recurrent Unit (SA-TrajGRU), which incorporates the self-attention mechanism. …”
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Feature-enriched hyperbolic network geometry by Roya Aliakbarisani, M. Ángeles Serrano, Marián Boguñá

“…In this paper, we present a comprehensive framework that treats features as tangible entities and establishes a bipartite graph connecting nodes and features. …”
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4D trajectory lightweight prediction algorithm based on knowledge distillation technique by Weizhen Tang, Jie Dai, Zhousheng Huang, Boyang Hao, Weizheng Xie

“…The student network adopts a Temporal Convolutional Network–LSTM (TCN–LSTM) design, integrating dilated causal convolutions and two LSTM layers for efficient temporal modeling. …”
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Optimizing Group Activity Recognition With Actor Relation Graphs and GCN-LSTM Architectures by M. R. Tejonidhi, C. V. Aravinda, S. V. Aruna Kumar, C. K. Madhu, A. M. Vinod

“…In response to these limitations, we propose a novel deep learning framework that effectively captures both temporal and spatial characteristics of group interactions. …”
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Multi-Modal Deep Learning for Lung Cancer Detection Using Attention-Based Inception-ResNet by Mohamed Hosny, Ibrahim A. Elgendy, Mousa Ahmad Albashrawi

“…Accordingly, we introduce a novel DL framework that blends diverse imaging modalities, including X-rays, computed tomography (CT) and histopathological images. …”
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Cloud-based configurable data stream processing architecture in rural economic development by Haohao Chen, Fadi Al-Turjman

“…Methodology The RL-CNN-GRU framework combines reinforcement learning (RL), convolutional neural network (CNN), and gated recurrent unit (GRU) to improve agricultural price predictions using multidimensional time series data, including historical prices, weather, soil conditions, and other influencing factors. …”
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Skin Lesion Diagnosis Through Deep Learning and Hybrid Texture Feature Augmentation by Irpan Adiputra Pardosi, Roni Yunis, Arwin Halim

“…The framework integrates handcrafted features with Convolutional Neural Networks (CNNs) to enhance classification accuracy. …”
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Enhanced Detection and Segmentation of Brain Tumors Using a Dense BW-CNN Approach by Sangeeta Kakarwal, Rahul Mapari, Anuj Kumar Jain, Vipin Sharma, Pranoti Prashant Mane, Jambi Ratna Raja Kumar, Ranjan K. Pradhan

“…Leveraging advancements in Convolutional Neural Networks (CNNs) and optimization algorithms, the proposed BW-DCNN framework utilizes a unique integration of preprocessing, segmentation enhancement, and classification techniques optimized through Black Widow Optimization to improve diagnostic accuracy and efficiency. …”
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