An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique by Lubna Kiran, Asim Zeb, Qazi Nida Ur Rehman, Taj Rahman, Muhammad Shehzad Khan, Shafiq Ahmad, Muhammad Irfan, Muhammad Naeem, Shamsul Huda, Haitham Mahmoud

“…We introduce a cutting-edge deep-learning approach employing a binary convolutional neural network (BCNN) to address this. …”
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Real-time aerial fire detection on resource-constrained devices using knowledge distillation by Sabina Jangirova, Branislava Jankovic, Waseem Ullah, Latif U. Khan, Mohsen Guizani

“…Existing approaches predominantly utilize convolutional neural networks and vision transformer models. …”
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An Adaptive Methodology for Constructing Domain-Specific Sentiment Lexicons Based on Chinese Social Media Data by Xue Xu, Haidong Liu, Lei Liu

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Fault Diagnosis Method for Transformer Winding Based on the Load Normalized Lissajous Graphical Analysis of Leakage Magnetic Field by Bowen ZHANG, Jian FENG, Bowen WANG, Feiran YANG, Yitong XING

“…This study proposes a transformer winding fault diagnosis method based on Lissajous graphics and convolutional neural networks (CNN).Methods First, a simulation model consistent with an actual transformer is developed, and the simulation system is utilized to obtain magnetic flux leakage signal data from different measurement points outside the winding under both normal and fault conditions. …”
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Permanent Magnet Synchronous Motor Stator and Rotor Fault Detection Using Transfer Learning and Field-Circuit Model by Maciej Skowron, Mateusz Krzysztofiak

“…Currently, modern permanent magnet synchronous motor diagnostic systems based on artificial neural networks are designed to detect selected types of damage. …”
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Research Review of Deep Learning in Colon Polyp Image Segmentation by LI Guowei, LIU Jing, CAO Hui, JIANG Liang

“…Subsequently, the deep learning-based segmentation methods are summarized, covering fully convolutional networks, Mask R-CNN, generative adversarial networks, U-Net, Transformer, and multi-network fusion models. …”
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Finding the Optimal Convolutional Kernel Size for Semantic Segmentation of Pole-like Objects in Lidar Point Clouds by Z. Zhang, D. Shojaei, K. Khoshelham

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A practical temporal transfer learning model for multi-step water quality index forecasting using A CNN-coupled dual-path LSTM network by Kok Poh Wai, Chai Hoon Koo, Yuk Feng Huang, Woon Chan Chong, Ahmed El-Shafie, Mohsen Sherif, Ali Najah Ahmed

“…A hybrid deep learning architecture was developed by combining a 1d-Convolutional Neural Network (CNN) with a dual-path Long Short-Term Memory (LSTM) network to capture long-term hydrological memory and site-specific temporal variability. …”
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RCLT-Net: An Indoor Panoramic Room Layout Estimation Framework Based on Consecutive Dilated Convolutions and Transformer by Qiucheng Sun, Zhengkai Deng, Jinlong Zhu

“…Current mainstream models for indoor panoramic room layout depth estimation primarily utilize deep residual networks and various Transformer-based modifications to reconstruct room boundaries from 2D images. …”
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MRI-based brain tumor ensemble classification using two stage score level fusion and CNN models by Oussama Bouguerra, Bilal Attallah, Youcef Brik

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Monitoring and Analyzing Driver Physiological States Based on Automotive Electronic Identification and Multimodal Biometric Recognition Methods by Shengpei Zhou, Nanfeng Zhang, Qin Duan, Xiaosong Liu, Jinchao Xiao, Li Wang, Jingfeng Yang

“…Secondly, a deep learning model is employed to analyze physiological signals, specifically combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. …”
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Harnessing image processing for precision disease diagnosis in sugar beet agriculture by Varucha Misra, A.K. Mall

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Application of state of health estimation and remaining useful life prediction for lithium-ion batteries based on AT-CNN-BiLSTM by Feng-Ming Zhao, De-Xin Gao, Yuan-Ming Cheng, Qing Yang

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A Heavy Metal Ion Water Quality Detection Model Based on Spectral Analysis: New Methods for Enhancing Detection Speed and Visible Spectral Denoising by Bingyang Sun, Shunsheng Yang, Xu Cheng

“…By focusing on indicators of heavy metal ion water pollution, this study aims to achieve the “rapid and accurate detection of water quality using spectral analysis” and emphasizes key technologies such as “visible absorption spectroscopy in photoelectric detection technology and spectral analysis”, “spectral denoising methods”, and “Convolutional Neural Network (CNN) modeling and deployment”. …”
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A deep learning-based framework for large-scale plant disease detection using big data analytics in precision agriculture by Mahmoud Khaled Elfouly, Amr M. AbdelAziz, Wael Hassan Gomaa, Mohammed Abdalla

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Implicit Is Not Enough: Explicitly Enforcing Anatomical Priors inside Landmark Localization Models by Simon Johannes Joham, Arnela Hadzic, Martin Urschler

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OSAD: Open-Set Aircraft Detection in SAR Images by Xiayang Xiao, Zhuoxuan Li, Xiaolin Mi, Dandan Gu, Haipeng Wang

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Can machine learning distinguish between elite and non-elite rowers? by Orten Kristine Fjellkårstad, Helgesen Sander Elias Magnussen, Chen Bihui, Baselizadeh Adel, Torresen Jim, Herrebrøden Henrik

“…A potentially relevant tool is machine learning, useful because of its ability to extract patterns from data. In the current study, we employed various deep learning frameworks, including Gated Recurrent Unit networks (GRUs), Convolutional Neural Networks (CNNs), and Multi-Layer Perceptrons (MLPs), to search for differences between elite and non-elite rowers using a rowing ergometer. …”
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Machine Learning for Chronic Kidney Disease Detection from Planar and SPECT Scintigraphy: A Scoping Review by Dunja Vrbaški, Boban Vesin, Katerina Mangaroska

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DGYOLOv8: An Enhanced Model for Steel Surface Defect Detection Based on YOLOv8 by Guanlin Zhu, Honggang Qi, Ke Lv

“…DGYOLOv8 incorporates a deformable convolution C2f (DCNv4_C2f) module into the backbone network to allow adaptive adjustment of the receptive field. …”
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