Research on the Condition Assessment Method of Mechanical Seal Based on Fusing Multi-Graph Neural Networks From Decision Layer by Xiaoran Zhu, Binhui Wang, Junchao Chen, Zipeng Li

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Driver Attribute Filling for Genes in Interaction Network via Modularity Subspace-Based Concept Learning from Small Samples by Fei Xie, Jianing Xi, Qun Duan

“…The evaluation analysis also demonstrates the superiority of our model in the task of driver attribute filling on two gene interaction networks. …”
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Practical and Accurate Evaluation of Numerical Aperture and Beam Quality Factor in Photonic Crystal Fibers by Mechanical Learning by Mengda Wei, Meisong Liao, Liang Chen, Yinpeng Liu, Wen Hu, Lidong Wang, Dongyu He, Tianxing Wang, Shizi Yu, Weiqing Gao

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Investigating the landslide susceptibility assessment methods for multi-scale slope units based on SDGSAT-1 and Graph Neural Networks by Xiangqi Lei, Hanhu Liu, Zhe Chen, Shaoda Li, Hang Chen, Shuai Zeng, Xiao Wang, Wenqian Bai, Wei Li, Lorenzo Picco

“…Significantly, in this work, the SDGSAT-1 data were innovatively applied to the field of landslide research and the landslide susceptibility in Jiulong County, Ganzi, was evaluated based on optimal-scale slope units and Graph Neural Networks (GNN). …”
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Automatic Fracture Detection Convolutional Neural Network with Multiple Attention Blocks Using Multi-Region X-Ray Data by Rashadul Islam Sumon, Mejbah Ahammad, Md Ariful Islam Mozumder, Md Hasibuzzaman, Salam Akter, Hee-Cheol Kim, Mohammad Hassan Ali Al-Onaizan, Mohammed Saleh Ali Muthanna, Dina S. M. Hassan

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Modulation recognition of underwater acoustic communication signals based on deep learning by Biao Wang, Heng Yang, Tao Fang

“…The hybrid model is used to fuse time domain and frequency domain features and integrate multi-scale features into low-dimensional features. …”
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Should Echocardiographic Evaluation Be Performed Routinely in the First 72 Hours in Extremely Low Birth Weight Babies? by Ruya Colak, Senem Alkan Ozdemir, Ezgi Yangin Ergon, Ferit Kulali, Oguz Han Kalkanli, Tulin Gokmen Yildirim, Murat Muhtar Yilmazer, Cuneyt Zihni, Sebnem Calkavur

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A Fast Prediction Model of Supercritical Airfoils Based on Deep Operator Network and Variational Autoencoder Considering Physical Constraints by Mengxin Liu, Yunjia Yang, Chenyu Wu, Yufei Zhang

“…Flow field prediction is crucial for evaluating the performance of airfoils and aerodynamic optimization. …”
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LiSENCE: A Hybrid Ligand and Sequence Encoder Network for Predicting CYP450 Inhibitors in Safe Multidrug Administration by Abena Achiaa Atwereboannah, Wei-Ping Wu, Sophyani B. Yussif, Muhammed Amin Abdullah, Edwin K. Tenagyei, Chiagoziem C. Ukuoma, Yeong Hyeon Gu, Mugahed A. Al-antari

“…It aimed to enhance prediction accuracy and provide biological insights, improving drug development and patient safety regarding drug–drug interactions: The innovative LiSENCE AI framework comprised four modules: the Ligand Encoder Network (LEN), Sequence Encoder Network (SEN), classification module, and explainability (XAI) module. …”
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ST-YOLO: a deep learning based intelligent identification model for salt tolerance of wild rice seedlings by Qiong Yao, Qiong Yao, Pan Pan, Pan Pan, Xiaoming Zheng, Xiaoming Zheng, Guomin Zhou, Guomin Zhou, Guomin Zhou, Jianhua Zhang, Jianhua Zhang

“…Diversified feature extraction paths are introduced to enhance the ability of feature extraction; Introducing CAFM (Context Aware Feature Modulation) convolution and attention fusion modules into the backbone network to enhance feature representation capabilities while improving the fusion of features at various scales; Design a more flexible and effective spatial pyramid pooling layer using deformable convolution and spatial information enhancement modules to improve the model’s ability to represent target features and detection accuracy.ResultsThe experimental results show that the improved algorithm improves the average precision by 2.7% compared with the original network; the accuracy rate improves by 3.5%; and the recall rate improves by 4.9%.ConclusionThe experimental results show that the improved model significantly improves in precision compared with the current mainstream model, and the model evaluates the salt tolerance level of wild rice varieties, and screens out a total of 2 varieties that are extremely salt tolerant and 7 varieties that are salt tolerant, which meets the real-time requirements, and has a certain reference value for the practical application.…”
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Recurrent academic path recommendation model for engineering students using MBTI indicators and optimization enabled recurrent neural network by Anupama V, Sudheep Elayidom M

“…A hybrid optimization-based deep recurrent neural network (DRNN) with Myers-Briggs Type Indicator (MBTI) is presented for Recurrent Academic Path Recommendation (RAPR) for engineering students. …”
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Degradation Prediction of Proton Exchange Membrane Fuel Cell Based on Multi-Head Attention Neural Network and Transformer Model by Yikai Tang, Xing Huang, Yanju Li, Haoran Ma, Kai Zhang, Ke Song

“…In view of the problem of many input parameters and complex distribution of degradation features, a neural network model based on a multi-head attention mechanism and class token is first proposed to analyze the impact of different operating parameters on the output voltage prediction. …”
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Optical Coherence Tomography in the Diagnosis of Open-angle Glaucoma by Yu. N. Yusef, E. E. Kazaryan

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A superpixel based self-attention network for uterine fibroid segmentation in high intensity focused ultrasound guidance images by Shen Wen, Dong Zhang, Yuting Lei, Yan Yang

“…The network takes superpixel feature matrices and their positional information as input, and classifies superpixels using self-attention modules and convolutional layers. …”
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YOLOv7-DWS: tea bud recognition and detection network in multi-density environment via improved YOLOv7 by Xiaoming Wang, Xiaoming Wang, Zhenlong Wu, Guannan Xiao, Guannan Xiao, Chongyang Han, Cheng Fang

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Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE by Nan Wei, René Michael Mathy, De-Hua Chang, Philipp Mayer, Jakob Liermann, Christoph Springfeld, Michael T Dill, Thomas Longerich, Georg Lurje, Hans-Ulrich Kauczor, Mark O. Wielpütz, Osman Öcal

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Raw-Data Driven Functional Data Analysis with Multi-Adaptive Functional Neural Networks for Ergonomic Risk Classification Using Facial and Bio-Signal Time-Series Data by Suyeon Kim, Afrooz Shakeri, Seyed Shayan Darabi, Eunsik Kim, Kyongwon Kim

“…The proposed model introduces a novel adaptive basis layer composed of micro-networks tailored to each individual time-series feature, enabling end-to-end learning of discriminative temporal patterns directly from raw data. …”
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Comparative evaluation of deep learning and machine learning techniques for sentiment analysis of electronic product review data by Nagelli Archana, Saleena B., Prakash B.

“…The Naïve Bayes, support vector machine, decision tree, convolution neural network, long short term memory, recursive neural networks, and recurrent neural networks were used on the dataset after applying different data preprocessing. …”
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Optimized disease prediction in healthcare systems using HDBN and CAEN framework by G. Prabaharan, S.M. Udhaya Sankar, V. Anusuya, K. Jaya Deepthi, Rayappan Lotus, R. Sugumar

“…To address these challenges, we propose a robust hybrid framework comprising three key phases: feature extraction using a Hybrid Deep Belief Network (HDBN), dynamic prediction aggregation via a Custom Adaptive Ensemble Network (CAEN), and an optimization mechanism ensuring adaptability and robustness. …”
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Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam by Tuan Vu Dinh, Nhat-Duc Hoang, Xuan-Linh Tran

“…Model evaluation employed a historical dataset consisting of ten explanatory variables and soil erosion featured four different land use managements on hillslopes in Northwest Vietnam. …”
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