Enhanced Position-Aided Beam Prediction Using Real-World Data and Enhanced-Convolutional Neural Networks by Ahmed Abd El Moaty Mohamed Gouda, Ehab K. I. Hamad, Aziza I. Hussein, M. Mourad Mabrook, A. A. Donkol

“…The model realized as high as a 50% power loss reduction in arguably the most challenging graphs, which is an exercise in reliability. …”
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SNet: A novel convolutional neural network architecture for advanced endoscopic image classification of gastrointestinal disorders by Samra Siddiqui, Junaid A. Khan, Tallha Akram, Meshal Alharbi, Jaehyuk Cha, Dina A. AlHammadi

“…To avoid the problem of “curse of dimensionality”, the most discriminant feature information is selected based on proposed minimum redundancy maximum relevance (MRMR) algorithm. …”
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Peatland pixel-level classification via multispectral, multiresolution and multisensor data using convolutional neural network by Luca Zelioli, Fahimeh Farahnakian, Maarit Middleton, Timo P. Pitkänen, Sakari Tuominen, Paavo Nevalainen, Jonne Pohjankukka, Jukka Heikkonen

“…Canopy height models, Sentinel-2 bands, and Sentinel-1 bands emerged as the most influential data sources for accurate classification. …”
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An adaptive search mechanism with convolutional learning networks for online social media text summarization and classification model by Reema G. AL-anazi, Muhammad Swaileh A. Alzaidi, Majdy M. Eltahir, Hassan Alkhiri, Samah Hazzaa Alajmani, Abdulbasit A. Darem, Mohammed Alshahrani, Nawaf Alhebaishi

“…A text summarization system takes more than one document as input and tries to give a fluent and concise summary of the most significant information in the input. Recent solutions for condensing and reading text are ineffective and time-consuming, provided plenty of information is available online. …”
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Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models by Imran Said, Vasit Sagan, Kyle T. Peterson, Haireti Alifu, Abuduwanli Maiwulanjiang, Abby Stylianou, Omar Al Akkad, Supria Sarkar, Noor Al Shakarji

“…Sensitivity analyses were also conducted to identify the most sensitive bands for seed protein estimation. …”
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Application of Convolutional Neural Networks in an Automatic Judgment System for Tooth Impaction Based on Dental Panoramic Radiography by Ya-Yun Huang, Yi-Cheng Mao, Tsung-Yi Chen, Chiung-An Chen, Shih-Lun Chen, Yu-Jui Huang, Chun-Han Chen, Jun-Kai Chen, Wei-Chen Tu, Patricia Angela R. Abu

“…<b>Background/Objectives:</b> Panoramic radiography (PANO) is widely utilized for routine dental examinations, as a single PANO image captures most anatomical structures and clinical findings, enabling an initial assessment of overall dental health. …”
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Covid-19 Detection from Chest X-Ray Images and Hybrid Model Recommendation with Convolutional Neural Networks by Furkan Eryılmaz, Hacer Karacan

“…In most countries around the world, difficulties in diagnosing COVID-19 remain. …”
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MTAGCN: Multi-Task Graph-Guided Convolutional Network with Attention Mechanism for Intelligent Fault Diagnosis of Rotating Machinery by Bo Wang, Shuai Zhao

“…However, their superior performance is mostly based on single-task learning, which makes them unsuitable for increasingly sophisticated engineering environments. …”
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A Real-Time Polygonal Wheel-Rail Force Identification Method Based on Convolutional Neural Networks (CNN) by Zeteng Zhang, Jinhai Wang, Jianwei Yang, Dechen Yao

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Diagnosis of Tomato Plant Diseases Using Pre-trained Architectures and A Proposed Convolutional Neural Network Model by Dilara Gerdan, Caner Koç, Mustafa Vatandaş

“…Tomatoes are of the most important vegetables in the world. Presence of diseases and pests in the growing area significantly affect the choice of variety in tomato. …”
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Using Convolutional Block Attention Module for Interpreting the Results of Artificial Neural Networks Operation in Handwritten Signature Identification by V. A. Mishchuk

“…This study explores potential application of a Siamese Convolutional Neural Network (SNN) integrated with a Convolutional Block Attention Module (CBAM) in the field of handwritten signature identification. …”
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Diagnosis of Sarcopenia Using Convolutional Neural Network Models Based on Muscle Ultrasound Images: Prospective Multicenter Study by Zi-Tong Chen, Xiao-Long Li, Feng-Shan Jin, Yi-Lei Shi, Lei Zhang, Hao-Hao Yin, Yu-Li Zhu, Xin-Yi Tang, Xi-Yuan Lin, Bei-Lei Lu, Qun Wang, Li-Ping Sun, Xiao-Xiang Zhu, Li Qiu, Hui-Xiong Xu, Le-Hang Guo

“…The SARCO model based on ultrasound images is potentially the most convenient solution for diagnosing sarcopenia. …”
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Tooth segmentation in panoramic dental radiographs using deep convolution neural network -Insights from subjective analysis by Suvarna Bhat, Gajanan K. Birajdar, Mukesh D. Patil

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Advanced Anomaly Detection in Smart Grids Using Graph Convolutional Networks With Integrated Node and Line Sensor Data by Mahdi Zarif, Ramin Moghaddass

“…Also, an optimization model was developed to find the most likely sources of anomalies across all nodes and edges of the network. …”
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A Hybrid MIL Approach Leveraging Convolution and State-Space Model for Whole-Slide Image Cancer Subtyping by Dehui Bi, Yuqi Zhang

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Performance Analysis of MobileNetV3-based Convolutional Neural Network for Facial Skin Disorder Classification by Herimanto, Arie Satia Dharma, Junita Amalia, David Largo, Christin Adelia Pratiwi Sihite

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Breast tumor segmentation in ultrasound using distance-adapted fuzzy connectedness, convolutional neural network, and active contour by Marta Biesok, Jan Juszczyk, Pawel Badura

“…We refer our results to the most recent state-of-the-art from 2022–2023 using the same datasets, finding our model comparable in segmentation performance.…”
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Diabetic Retinopathy Classification Using Hybrid Color-Based CLAHE and Blood Vessel in Deep Convolution Neural Network by Ammar Jawad Kadhim, Hadi Seyedarabi, Reza Afrouzian, Fadhil Sahib Hasan

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A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition by Farhad Mortezapour Shiri, Shingo Yamaguchi, Mohd Anuaruddin Bin Ahmadon

“…We also applied the SHAP method to identify the key features that most significantly influence employee attrition. …”
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Hydraulic System Fault Diagnosis Based on Dual-Channel Convolutional Neural Network and Improved D-S Evidence Theory by Jiyang Qi, Yue Qi, Shenghua Luo, Jiahao Zhang

“…To address the problem that the traditional convolutional neural network (CNN) cannot fully extract the fault feature information in a single channel and the accuracy of hydraulic system fault diagnosis based on a single signal is not high, a fault diagnosis method combining dual-channel CNN and improved D-S evidence theory is proposed. …”
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