S&P-500 vs. Nasdaq-100 price movement prediction with LSTM for different daily periods by Xiang Zhang, Eugene Pinsky

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Elastic modulus prediction of high volume fraction metal particle reinforced resin matrix composite by ZHAO Jinfeng, ZHU Fuxian, ZHUANG Bailiang, YANG Haoqing, ZHOU Yongquan

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Radiomic Analysis of Contrast‐Enhanced CT Predicts Recompensation in Hepatitis B‐Related Decompensated Cirrhosis by Qiaofeng Chen, Yuanping Fan, Kaini Wu, Tianpan Cai, Chunyu Lan, Mingju Yu, Yunfeng Fu, Qi Zhu, Jianhao Qiu, Xiaodong Zhou

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Near-field extremely large-scale MIMO data rate prediction based on deep learning by Guozhi Rong, Rugui Yao, Yifeng He

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Interpretable AI-driven multi-objective risk prediction in heart failure patients with thyroid dysfunction by Massimo Iacoviello, Vito Santamato, Alessandro Pagano, Agostino Marengo

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An Adaptive Spatio-Temporal Traffic Flow Prediction Using Self-Attention and Multi-Graph Networks by Basma Alsehaimi, Ohoud Alzamzami, Nahed Alowidi, Manar Ali

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Short-Term Building Electrical Load Prediction by Peak Data Clustering and Transfer Learning Strategy by Kangji Li, Shiyi Zhou, Mengtao Zhao, Borui Wei

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An Efficient Deep Learning Method for Typhoon Track Prediction Based on Spatiotemporal Similarity Feature Mining by Kaiwen Lixia, Mingyue Lu, Yifei Lu, Hui Liu, Ping Li

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Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm Optimization by Emad A. Abood, Mustafa Kamal Al-Kamal, Sabih Hashim Muhodir, Nadia Moneem Al-Abdaly, Luís Filipe Almeida Bernardo, Dario De Domenico, Hamza Imran

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Prediction of Rail Wear Under Different Railway Track Geometries Using Artificial Neural Networks by Hong Zhang, Weichen Shuai, Linya Liu, Pengfei Zhang, Kejun Zhang, Hongsong Lin, Yuke Zhang, Wei Li

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Electricity usage prediction using developed human evolutionary optimization algorithm and Xception neural network by Dongxian Yu, Di Wu, Chongyang Liao, Zaihui Cao, Somayeh Pouramini

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A Surrogate-Assisted Gray Prediction Evolution Algorithm for High-Dimensional Expensive Optimization Problems by Xiaoliang Huang, Hongbing Liu, Quan Zhou, Qinghua Su

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Enhanced prediction of hemolytic activity in antimicrobial peptides using deep learning-based sequence analysis by Ibrahim Abdelbaky, Mohamed Elhakeem, Hilal Tayara, Elsayed Badr, Mustafa Abdul Salam

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HiCDiffusion - diffusion-enhanced, transformer-based prediction of chromatin interactions from DNA sequences by Mateusz Chiliński, Dariusz Plewczynski

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Analyzing risk factors and handling imbalanced data for predicting stroke risk using machine learning by Adiwijaya Adiwijaya, Nur Ghaniaviyanto Ramadhan

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Optimal Sensor Placement in Water Distribution Networks Using Dynamic Prediction Graph Neural Networks by Aly K. Salem, Ahmed A. Abokifa

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SGTCDA: Prediction of circRNA-drug sensitivity associations with interpretable graph transformers and effective assessment by Hongwei Xia, Caiyue Dong, Xinxing Chen, Zhuoyu Wei, Lichuan Gu, Xiaolei Zhu

“…To address these issues, we propose a strategy to evaluate models based on independent test sets for association prediction-related studies. …”
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Machine learning for optimal parameter prediction in free space continuous-variable quantum key distribution by Dong Chen, Cheng Jin, Liu Ao, Chen Lanjian, Chen YuJie, Yin Peng, Wu TianYi

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Barlow Twins deep neural network for advanced 1D drug–target interaction prediction by Maximilian G. Schuh, Davide Boldini, Annkathrin I. Bohne, Stephan A. Sieber

“…Furthermore, we advance the field by proposing an influence method that elucidates model decision-making, thereby providing deeper insights into molecular interactions and improving the interpretability of drug-target interactions predictions. …”
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MAPM:PolSAR Image Classification with Masked Autoencoder Based on Position Prediction and Memory Tokens by Jianlong Wang, Yingying Li, Dou Quan, Beibei Hou, Zhensong Wang, Haifeng Sima, Junding Sun

“…In addition, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi mathvariant="italic">L</mi><mn>1</mn></msub></semantics></math></inline-formula> loss is used for MAE optimization to enhance the robustness of the model to outliers in PolSAR data. …”
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