Endoscopic ultrasound-based artificial intelligence for gastrointestinal subepithelial lesions by Huan Jiang, Liansong Ye, Xianglei Yuan, Qi Luo, Nuoya Zhou, Bing Hu

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Robust Simplified Modeling of Microgrid in the Context of Constructing New Power Systems by Daxing WANG, Yan Ning, Jingpei WANG, Yang XU, Jun BI, Mingbiao ZHOU, Peng WANG

“…Next, key parameter selection-based parameter identification method is applied to avoid the issue of multiple solutions in parameter identification process. Then, the convolutional neural network is used to generalize the model parameters with respect to different typical system operation conditions. …”
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High-Concentration Time-Frequency Representation and Instantaneous Frequency Estimation of Frequency-Crossing Signals by Hui Li, Xiangxiang Zhu, Yingfei Wang, Xinpeng Cai, Zhuosheng Zhang

“…Through TF data generation, the construction of a U-net, and training, the high-concentration TF representation network achieves high-resolution TF characterization of different frequency-crossing signals. The IF separation and estimation network, with its discriminant model, offers flexibility in determining the number of components within multi-component signals. …”
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Multi-species Fish Identification using Hybrid DeepCNN with Refined Squeeze and Excitation Architecture by Jansi Rani Sella Veluswami, Nivetha Panneerselvam

“…In this research, we develop a new method by refining the squeeze and excitation network for the automatic fish species classification model to identify 23 different types of fish species. To achieve this, a hybrid framework using deep learning is proposed on a large-scale dataset and implemented transfer learning for a small-scale dataset. …”
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Risk-Adjusted Deep Reinforcement Learning for Portfolio Optimization: A Multi-reward Approach by Himanshu Choudhary, Arishi Orra, Kartik Sahoo, Manoj Thakur

“…Instead of relying solely on a singular reward function, our approach integrates three different functions aiming at diverse objectives. The proposed approach is tested on daily data of four real-world stock market instances: Sensex, Dow, TWSE, and IBEX. …”
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Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks by Cunhe Li, Zian Xie, Haotian Wang

“…Specifically, we introduce a novel weighting scheme, Term Frequency-Document Frequency Category-Distribution Weight (TF-IDF-CDW), where Category Distribution Weight (CDW) reflects the distribution pattern of words across different categories. By weighting the pretrained Word2Vec vectors with TF-IDF-CDW and concatenating them with part-of-speech (POS) feature vectors, semantically enriched and more discriminative word embedding vectors are generated. …”
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Copula entropy regularization transformer with C2 variational autoencoder and fine-tuned hybrid DL model for network intrusion detection by Srinivas Akkepalli, Sagar K

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Median interacted pigeon optimization-based hyperparameter tuning of CNN for paddy leaf disease prediction by Jasmy Davies, S. Sivakumari

“…The experimental results confirm that the proposed approach enhances prediction accuracy, also helps in efficient identification of co-infections of different viruses in rice plants. Graphical Abstract…”
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Creating and Validating a Ground Truth Dataset of Unified Modeling Language Diagrams Using Deep Learning Techniques by Javier Torcal, Valentín Moreno, Juan Llorens, Ana Granados

“…Large, good-quality datasets containing UML diagrams are essential for different areas in the industry, research, and teaching purposes; however, few exist in the literature and it is common to find duplicate elements in the existing datasets. …”
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Semantic-Aware Remote Sensing Change Detection with Multi-Scale Cross-Attention by Xingjian Zheng, Xin Lin, Linbo Qing, Xianfeng Ou

“…Second, old-school methods usually simply rely on differences and computation at the pixel level without giving enough attention to the information at the semantic level. …”
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Deep Learning-Based Multimode Fiber Distributed Temperature Sensing by Luxuan Yang, Xiaoyan Wang, Tong Wu, Huichuan Lin, Songjie Luo, Ziyang Chen, Yongxin Liu, Jixiong Pu

“…The precision of the predicting heating point was less than 1 cm. Different types of MMFs were used in temperature measurements, showing that the accuracy remained quite high. …”
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Predicting steady degradation in ship power system: A deep learning approach based on comprehensive monitoring parameters by Xingshan Chang, Xiaojian Xu, Bohua Qiu, Muheng Wei, Xinping Yan, Jie Liu

“…The correlation between model performance variations and degradation mechanisms is validated through statistical analysis of the YC2Model's performance in different stages of the SD process. During the SD process, YC2Model exhibits high predictive accuracy, an ability to capture changes in degradation mechanisms and robust adaptability to degradation trends. …”
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A Deep Curriculum Learning Semi-Supervised Framework for Remote Sensing Scene Classification by Qing Zhang, Jialu Chen, Baohua Yuan

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SeqConv-Net: A Deep Learning Segmentation Framework for Airborne LiDAR Point Clouds Based on Spatially Ordered Sequences by Bin Guo, Chunjing Yao, Hongchao Ma, Jie Wang, Junhao Xu

“…To address this issue, we propose a novel sequence convolution semantic segmentation architecture that integrates Convolutional Neural Networks (CNN) with a sequence-to-sequence (seq2seq) structure, termed SeqConv-Net. …”
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Robust SOH estimation for Li-ion battery packs of real-world electric buses with charging segments by Heng Li, Shilong Zhuo, Yun Zhou, Muaaz Bin Kaleem, Yu Jiang, Fu Jiang

“…The proposed method is validated using approximately four years of operational data from three different types of electric buses. Through cross-validation, the method demonstrates high accuracy, achieving absolute errors below 3% in over 80% of cycle cases. …”
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Enhancing Real Estate Listings Through Image Classification and Enhancement: A Comparative Study by Eyüp Tolunay Küp, Melih Sözdinler, Ali Hakan Işık, Yalçın Doksanbir, Gökhan Akpınar

“…A dataset of 3000 labeled images was utilized to compare different image classification models, including convolutional neural networks (CNNs), VGG16, residual networks (ResNets), and the LLaVA large language model (LLM). …”
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A non-anatomical graph structure for boundary detection in continuous sign language by Razieh Rastgoo, Kourosh Kiani, Sergio Escalera

“…During the second step, the sliding window method with the pre-defined window size is moved on the continuous sign video, including the un-processed isolated sign videos with different frame lengths. More concretely, the content of each window is processed using the pre-trained model obtained from the first step and the class probabilities of the Fully Connected (FC) layer embedded in the Transformer model are fed to the post-processing module, which aims to detect the accurate boundary of the un-processed isolated signs. …”
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Deep Learning‐Based Prediction of Global Ionospheric TEC During Storm Periods: Mixed CNN‐BiLSTM Method by Xiaochen Ren, Biqiang Zhao, Zhipeng Ren, Yan Wang, Bo Xiong

“…Additionally, by comparing different input parameters, it is found that incorporating the Kp, ap, and Dst indices as inputs to the model effectively improves its accuracy, especially in long‐term forecasting where R2 increased by 3.49% and Root Mean Square Error decreased by 13.48%. …”
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A comprehensive framework for multi-modal hate speech detection in social media using deep learning by R. Prabhu, V. Seethalakshmi

“…Hence, this research proposes a novel Multi-modal Hate Speech Detection Framework (MHSDF) that combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to analyze complex, heterogeneous data streams. …”
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Automated detection of quiet eye durations in archery using electrooculography and comparative deep learning models by Fatma Söğüt, Hüseyin Yanık, Evren Değirmenci, İnci Kesilmiş, Ülkü Çömelekoğlu

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