Multimodal Learning for Traffic Risk Prediction: Combining Aerial Imagery With Contextual Data by Hanlin Tian, Yuxiang Feng, Mohammed Quddus, Yiannis Demiris, Panagiotis Angeloudis

“…Using DeepLabV3+, UNet++, and SegFormer as baseline models, we demonstrate that combining building and traffic data enhances prediction accuracy compared to models relying solely on visual data. …”
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Enhanced AdaBoostM1 with Multilayer Perceptron for Stock Price Prediction by Rebwar Mala Nabi, Soran AB. Saeed, Habibollah Haron

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Semi-Supervised Learning via Cross-Prediction-Powered Inference for Wireless Systems by Houssem Sifaou, Osvaldo Simeone

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Leveraging Cross-Project Similarity for Data Augmentation and Security Bug Report Prediction by Jinfeng Ji, Geunseok Yang

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A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction by Runpeng Liu, Seong-Yoon Shin

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Machine learning study on predicting depressive symptoms and genetic correlations in Parkinson’s disease by Haijun Zhang, Yifan Zhang, Guihua Li, Guihua Li

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Machine Learning Applications for Predicting High-Cost Claims Using Insurance Data by Esmeralda Brati, Alma Braimllari, Ardit Gjeçi

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An experimental study and prediction of dynamic deformation of wind turbine blade based on DIC by Jing Jia, Liru Zhang, Wei Gao, Tong Qiu, YuQi Hou, Jianwen Wang

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Software Defect Prediction Using Extreme Gradient Boosting(XGBoost) with Tune Hyperparameter by Tariq AL-Hadidi, Safwan Omar Hasoon

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Braking control strategy of distributed electric vehicle based on vehicle velocity prediction by Meiying Li, Chengxin Li, Xuebo Li, Shu Wang, Xuan Zhao

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Machine learning-based prediction of speed of sound in fatty acid ethyl esters by Kusum Yadav, Shahad Almansour, Lulwah M. Alkwai, Anupam Yadav, Mehrdad Mottaghi

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The role of personality traits in predicting educational use of generative AI in higher education by Ibrahim Arpaci, Ismail Kuşci, Omer Gibreel

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Differential genetic susceptibility to child risk at birth in predicting observed maternal behavior. by Keren Fortuna, Marinus H van Ijzendoorn, David Mankuta, Marsha Kaitz, Reut Avinun, Richard P Ebstein, Ariel Knafo

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Comparison of Linear Regression and Polynomial Regression for Predicting Rice Prices in Lhokseumawe City by Muhammad Iqbal, Rozzi Kesuma Dinata, Rizki Suwanda

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Resting-state EEG network variability predicts individual working memory behavior by Chunli Chen, Shiyun Xu, Jixuan Zhou, Chanlin Yi, Liang Yu, Dezhong Yao, Yangsong Zhang, Fali Li, Peng Xu

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Development and external validation of a nomogram for the early prediction of acute kidney injury in septic patients: a multicenter retrospective clinical study by Qin-Yue Su, Wen-Jie Chen, Yan-Jun Zheng, Wen Shi, Fang-Chen Gong, Shun-Wei Huang, Zhi-tao Yang, Hong-Ping Qu, En-Qiang Mao, Rui-Lan Wang, Du-Ming Zhu, Gang Zhao, Wei Chen, Sheng Wang, Qian Wang, Chang-Qing Zhu, Gao Yuan, Er-Zhen Chen, Ying Chen

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Large Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models by Saad Hameed, Basheer Qolomany, Samir Brahim Belhaouari, Mohamed Abdallah, Junaid Qadir, Ala Al-Fuqaha

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From Monitoring to Prediction: Velocity-Based Strength Training in Female Floorball Athletes by Basil B. Achermann, Naire Regazzi, Rahel Heynen, Dennis Lüdin, Julia Suter, Anna Drewek, Silvio R. Lorenzetti

“…Seventeen participants completed 12 training sessions, a strength block set (Sessions 1–6) and a power block set (Sessions 7–12). The predictive models explained 54% to 79% (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> = 0.54–0.79) of the performance improvement in the strength-related tests. …”
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Optimizing Machine Learning-Based Ovarian Cancer Prediction Through Normalization Strategies by Roopashri Shetty, Siddhant Gupta, Vansh Mediratta, Shwetha Rai, M. Geetha

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Tuberculosis disease burden in China: a spatio-temporal clustering and prediction study by Jingzhe Guo, Ce Liu, Fang Liu, Erkai Zhou, Runxue Ma, Ling Zhang, Bin Luo

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