EFCNet enhances the efficiency of segmenting clinically significant small medical objects by Lingjie Kong, Qiaoling Wei, Chengming Xu, Xiaofeng Ye, Wei Liu, Min Wang, Yanwei Fu, Han Chen

“…Abstract Efficient segmentation of small hyperreflective dots, key biomarkers for diseases like macular edema, is critical for diagnosis and treatment monitoring.However, existing models, including Convolutional Neural Networks (CNNs) and Transformers, struggle with these minute structures due to information loss.To address this, we introduce EFCNet, which integrates the Cross-Stage Axial Attention (CSAA) module for enhanced feature fusion and the Multi-Precision Supervision (MPS) module for improved hierarchical guidance. …”
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Lightweight Fire Detection in Tunnel Environments by Shakhnoza Muksimova, Sabina Umirzakova, Dilnoza Abduxalikovna Babaraximova, Young Im Cho

“…This study proposes a lightweight hybrid deep learning (DL) model that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal analysis, offering an efficient and robust solution for real-time tunnel fire detection. …”
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Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals by Narmatha Chellamani, Saleh Ali Albelwi, Manimurugan Shanmuganathan, Palanisamy Amirthalingam, Anand Paul

“…Photoplethysmography (PPG), a photosensitive method that detects blood volume variations, has shown promise for non-invasive glucose monitoring. Deep neural networks (DNNs) applied to PPG signals can predict BGLs with high accuracy. …”
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Automated detection of craters on the lunar surface using deep learning: A review with insights from Chandrayaan-2 TMC-2 data by Mimansa Sinha, Sanchita Paul

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Interpretable Deep Learning for Efficient Code Smell Prioritization in Software Development by Maaeda M. Rashid, Mohd Hafeez Osman, Khaironi Yatim Sharif, Hazura Zulzalil

“…The approach introduces specialized preprocessing for large-scale projects and implements a selectivity metric for validating explanation quality. Our comprehensive evaluation demonstrates that the Bi-LSTM approach consistently outperforms traditional architectures across various code smell types, achieving 0.90 across precision, recall, and F1-score metrics for complex class priortization, and precision of 0.88 with recall of 0.87 for feature envy priortization. …”
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Coverage Estimation of Droplets Sprayed on Water-Sensitive Papers Based on Domain-Adaptive Segmentation by Dae-Hyun Lee, Baek-Gyeom Seong, Seung-Yun Baek, Chun-Gu Lee, Yeong-Ho Kang, Xiongzhe Han, Seung-Hwa Yu

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Smart material selection strategies for sustainable and cost-effective high-performance concrete production using deep learning by T. Seethalakshmi, M. Murugan, P. Maria Antony Sebastin Vimalan

“…First, we collect a concrete material dataset for evaluating the suggested method. MOAC-ADenseNet utilized Dense convolutional neural networks and ant colony optimization for complex material data analysis, which makes it easier to choose expensive and sustainable materials for high-performance concrete manufacturing operations. …”
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3D densely connected CNN with multi-scale receptive fields and hybrid loss for brain tumor segmentation by Fatemehzahra Adib, Maryam Amirmazlaghani, Mohammad Rahmati

“…This paper presents a 3D convolutional neural network (CNN) model to automatically segment brain tumors from MRI scans. …”
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BFER-Net: Babies Facial Expression Recognition Model Using ResNet12 Enabled Few-Shot Embedding Adaptation and Convolutional Block Attention Modules by Sumiya Arafin, Adnan Ferdous Ashrafi, Md. Golam Rabiul Alam, Ashis Talukder

“…It allows the model to focus on the most relevant facial features. The implementation of few-shot learning techniques, like FEAT (Few-shot Embedding Adaptation with Transformer), Modified ResNet12, and Prototypical Networks, are especially appropriate for the small dataset size. …”
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FED-GEM-CN: A federated dual-CNN architecture with contrastive cross-attention for maritime radar intrusion detection by Md. Alamgir Hossain

“…The proposed architecture integrates dual parallel convolutional neural network (CNN) pipelines to independently process network and radar modality features, which are subsequently fused via a multi-head cross-attention mechanism to capture intricate inter-modal dependencies. …”
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To the determination of the capacity descheduling coefficients of railway sections by S. Yu. Kirillova, K. Yu. Nikolaev

“…The article investigates factors arising from the use topology of the railway network and affecting the calculation and of new technologies for organizing train operation, features of the use of the available capacity of railway sections. …”
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A radiomics approach for predicting gait freezing in Parkinson’s disease based on resting-state functional magnetic resonance imaging indices: A cross-sectional study by Miaoran Guo, Hu Liu, Long Gao, Hongmei Yu, Yan Ren, Yingmei Li, Huaguang Yang, Chenghao Cao, Guoguang Fan

“…Neurological and clinical characteristics were also evaluated. We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators. …”
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A Short-Term Carbon Emission Accounting Method for Power Industry Using Electricity Data Based on a Combined Model of CNN and LightGBM by ZENG Jincan, HE Gengsheng, LI Yaowang, DU Ershun, ZHANG Ning, ZHU Haojun

“…This method utilizes convolutional neural networks (CNNs) for feature extraction, and light gradient boosting machine (LightGBM) for carbon emission estimation based on extracted features. …”
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Authors’ Moral Rights in the Digital Environment by E. Sápi

“…Objective: to answer the question whether the authors’ moral rights the in the digital environment correspond to their original purpose, and to determine the impact of the development of social networking platforms, artificial intelligence technologies and non-fungible tokens (NFT) on the transformed role and features of the protection of the author’s moral rights under modern conditions.Methods: the research is based on historical-legal, comparative-legal and formal-dogmatic methods. …”
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Short-Term Energy Consumption Forecasting Analysis Using Different Optimization and Activation Functions with Deep Learning Models by Mehmet Tahir Ucar, Asim Kaygusuz

“…In this study, firstly, the data was evaluated and organized with exploratory data analysis (EDA) on a ready dataset and the features of the data were extracted. …”
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Infections among adults hospitalized in intensive care after the 2023 earthquake in the southeastern part of Türkiye: a multi-center observational study by Esat Kivanc Kaya, Burcin Halacli, Goksel Guven, Mehmet Yildirim, Asir Eren Seven, Emin Gemcioglu, Meltem Simsek, Begum Erdemir Sullu, Recep Civan Yuksel, Ahmet Safa Kaynar, Aliye Esmaoglu, Banu Kilicaslan, Seda Banu Akinci, Kursat Gundogan, Ebru Ortac Ersoy, Jordi Rello, Arzu Topeli, On behalf of the Turkish Intensive Care Studies-Network (TRICS-Net)

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MACHINE LEARNING TECHNIQUES FOR RETINOPATHY DETECTION IN DIABETIC PATIENTS by Ajay Kushwaha, Ahankari Sachin Suresh, Chennoju Phanindra, Anil Kumar Sahu, Devanand Bhonsle, Yamini Chouhan

“…The core technology involves deep learning algorithms, particularly Convolutional Neural Networks (CNNs), which are designed to identify complex patterns and features within retinal images. …”
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Exotic Megaherbivores as Ecosystem Engineers in Australian Savannas: Do They Facilitate Predator Movement? by Georgina Neave, Brett P. Murphy, Tiwi Rangers, Hugh F. Davies

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Remotely‐Sensed Game Trails Are a Behavioral Footprint That Explains Patterns of Herbivore Habitat Use by Keenan Stears, Melissa H. Schmitt, Mike J. Peel, Tsumbedzo Ramalevha, Douglas J. McCauley, Dave I. Thompson, Deron E. Burkepile

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Parasitism as a Long‐Lasting Interaction—First Evidence From Paleozoic Corals by Mikołaj K. Zapalski, Jan J. Król, Julien Denayer, Michał Zatoń

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