Dimuons from neutrino-nucleus collisions in the semi-inclusive DIS approach by Ilkka Helenius, Hannu Paukkunen, Sami Yrjänheikki

“…Here, we instead compute the dimuon production cross section directly as a convolution of semi-inclusive deep inelastic scattering to produce charmed hadrons, and a decay function fitted to e + e − data to produce a muon from the charmed hadrons. …”
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LFEN: A language feature enhanced network for scene text recognition by Hui Chen, Runming Jiang, Fang Hu, Min Chen, Yin Zhang

“…Furthermore, by incorporating the intrinsic semantic relationships of text content, this paper employs a sequence-to-sequence (Seq2Seq) model based on convolutional neural networks for text correction. Through the integration of language information, different feature embeddings, and global residual connections, the paper provides a robust solution for text correction in scene text recognition. …”
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Precision diagnosis of tomato diseases for sustainable agriculture through deep learning approach with hybrid data augmentation by Kamaldeep Joshi, Sahil Hooda, Archana Sharma, Humira Sonah, Rupesh Deshmukh, Narendra Tuteja, Sarvajeet Singh Gill, Ritu Gill

“…The study addresses different challenges faced throughout the model development process, like data scarcity and imbalances. …”
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Multi-Modality Sheep Face Recognition Based on Deep Learning by Sheng Liao, Yan Shu, Fang Tian, Yong Zhou, Guoliang Li, Cheng Zhang, Chao Yao, Zike Wang, Longjie Che

“…To address the challenge of recognizing sheep faces of the same type, which exhibit significant similarities and varying performance of RGB images under different lighting conditions and angles, this paper proposes a dual-branch multi-modal sheep face recognition model based on the ResNet18 architecture. …”
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Accurate, automated classification of radiographic knee osteoarthritis severity using a novel method of deep learning: Plug-in modules by Do Weon Lee, Dae Seok Song, Hyuk-Soo Han, Du Hyun Ro

“…The final deep learning model was designed through an ensemble of four different PIMs. Results The accuracy was the lowest for KL grade 1 (43%) and the highest for KL grade 4 (96%). …”
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Application of CycleGAN-based low-light image enhancement algorithm in foreign object detection on belt conveyors in underground mines by Anxin Zhao, Qiuhong Zheng, Liang Li

“…Second, a lighting enhancement module is designed to achieve global brightness equalization and reduce contrast differences between different regions. Finally, a dynamic effective self-attention aggregation module is designed to suppress the generation of noise and artifacts. …”
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Keywords, morpheme parsing and syntactic trees: features for text complexity assessment by Dmitry A. Morozov, Ivan A. Smal, Timur A. Garipov, Anna V. Glazkova

“…We conducted a comparison using four different machine learning algorithms and four annotated Russian-language text corpora. …”
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Diverse behavior clustering of students on campus with macroscopic attention by Wanghu Chen, Zongjuan Wu, Siqi Zeng, Hongle Guo, Jing Li

“…Since cognitive psychology believes that all behaviors can be regarded as attention to different objects, this paper proposes an analysis framework based on Macroscopic Attention (MA) to characterize the diverse behavior of individuals. …”
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ANN-SVM-IP: An Innovative Method for Rapidly and Efficiently Detecting and Classifying of External Defects of Apple Fruits by Nashaat M. Hussain Hassan, Mohamed M. Hassan Mahmoud, Mohamed A. Ismeil, M. Mourad Mabrook, A. A. Donkol, A. M. Mabrouk

“…The first phase attempts to detect exterior defects in apples by applying two proposed convolution kernels that were capable of identifying damaged sections of apples. …”
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High-Quality Damaged Building Instance Segmentation Based on Improved Mask Transfiner Using Post-Earthquake UAS Imagery: A Case Study of the Luding Ms 6.8 Earthquake in China by Kangsan Yu, Shumin Wang, Yitong Wang, Ziying Gu

“…UAS imagery has a high spatial resolution, but the resolution is inconsistent between different flight missions. These factors make it challenging for existing methods to accurately identify individual damaged buildings in UAS images from different scenes, resulting in coarse segmentation masks that are insufficient for practical application needs. …”
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The Effect of Input Length on Prediction Accuracy in Short-Term Multi-Step Electricity Load Forecasting: A CNN-LSTM Approach by Seyda Ozdemir, Yakup Demir, Ozal Yildirim

“…The model is tested on 12 different configurations with symmetrically increasing input lengths, including weather data. …”
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Deep learning HRNet FCN for blood vessel identification in laparoscopic pancreatic surgery by Jile Shi, Ruohan Cui, Zhihong Wang, Qi Yan, Lu Ping, Hu Zhou, Junyi Gao, Chihua Fang, Xianlin Han, Surong Hua, Wenming Wu

“…Here, we evaluated the High Resolution Network (HRNet)-Full Convolutional Network (FCN) model for its ability to accurately identify vascular contours and improve surgical safety. …”
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A Novel Dual-Stream Attention-Based Hybrid Network for Solar Power Forecasting by Rafiq Asghar, Michele Quercio, Lorenzo Sabino, Assia Mahrouch, Francesco Riganti Fulginei

“…This research introduces a novel dual-steam hybrid model that uses Bidirectional Long-Short Term Memory (BiLSTM) and Convolutional Neural Networks (CNN) to predict PV power production. …”
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Development of a Mobile-Based Application for Classifying Caladium Plants Using the CNN Algorithm by Rudy Chandra, Tegar Arifin Prasetyo, Heni Ernita Lumbangaol, Veny Siahaan, Johan Immanuel Sianipar

“…However, difficulties in recognizing the type of Caladium often occur because of the similarities in shape, pattern, and color of the leaves between the different kinds of Caladium. To overcome this problem, research will use machine learning with the Convolutional Neural Network (CNN) algorithm to build a mobile application that can accurately classify four types of Caladiums. …”
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A Review on Image Enhancement and Restoration Techniques for Underwater Optical Imaging Applications by N. Deluxni, Pradeep Sudhakaran, Kitmo, Mouhamadou Falilou Ndiaye

“…Various UIE techniques are studied for different data sets, and applications. However, the selection of suitable method for particular applications among available techniques is still a challenging task. …”
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Short Term Photovoltaic Power Combination Prediction Method Based on Similar Day Selection and Data Reconstruction by Qingbin CHEN, Genghuang YANG, Liqing GENG, Juan SU, Jingsheng SUN

“…Through practical examples, it has been verified that under different weather conditions, the overall prediction error of the model is the smallest, which can effectively improve the prediction accuracy.…”
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Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm by Amit Sagu, Nasib Singh Gill, Preeti Gulia, Noha Alduaiji, Piyush Kumar Shukla, Mohd Asif Shah

“…This paper proposes a hybrid deep learning model that combines Convolutional Neural Network (CNN) and Gated Recurrent Units (GRUs) to classify the IoT security threats. …”
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Single Pixel Imaging Based on Multiple Prior Deep Unfolding Network by Quan Zou, Qiurong Yan, Qianling Dai, Ao Wang, Bo Yang, Yi Li, Jinwei Yan

“…To effectively fuse multiple prior information, we propose three different fusion strategies in the deep reconstruction sub-network. …”
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Facial expression recognition using visible and IR by early fusion of deep learning with attention mechanism by Muhammad Tahir Naseem, Chan-Su Lee, Tariq Shahzad, Muhammad Adnan Khan, Adnan M. Abu-Mahfouz, Khmaies Ouahada

“…We then extended this to a multi-modal early fusion approach using the same modified ResNet-18 with attention, achieving superior accuracy through the combination of convolutional neural network (CNN) and transfer learning (TL). …”
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MultiSenseNet: Multi-Modal Deep Learning for Machine Failure Risk Prediction by Mostafijur Rahman, Md Sabbir Hossain, Uland Rozario, Satyabrata Roy, M. F. Mridha, Nilanjan Dey

“…The model performed consistently across different hyperparameter settings, reaching peak accuracy of 94.7% on the classification dataset and 95.6% on the prediction dataset after 40 training epochs. …”
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