Comparison of Random Survival Forest Based‐Overall Survival With Deep Learning and Cox Proportional Hazard Models in HER‐2‐Positive HR‐Negative Breast Cancer by Wenqi Cai, Yan Qi, Linhui Zheng, Huachao Wu, Chunqian Yang, Runze Zhang, Chaoyan Wu, Haijun Yu

“…The optimal model will provide more accurate survival predictions for clinical decision‐making of HER2 positive and HR negative cancer patients. …”
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An AI recognition method for children's clinical operative pain by skin potential (SP) signal by Mingxuan Huang, Cangcang Fu, Linbo Chui, Jiadong He, Xiaozhi Wang, Jikui Luo, Bin Wu, Yonggang Chen, Shaohua Hu, Jihua Zhu, Yubo Li

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Great Gerbils (Rhombomys opimus) in Central Asia Are Spreading to Higher Latitudes and Altitudes by Xuan Liu, Li Xu, Jianghua Zheng, Jun Lin, Xuan Li, Liang Liu, Ruikang Tian, Chen Mu

“…The results indicate that the ensemble model integrating Random Forest (RF), Gradient Boosting Machine (GBM), and Maximum Entropy Model (MaxEnt) performed best within the present climate context. …”
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Data-Driven Mobility System for Vehicular Communication: A Step Towards Transport Resilience in Intelligent Transport Systems by Maryam Gillani, Hafiz Adnan Niaz, Beatriz Martinez-Pastor

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XAIEnsembleTL-IoV: A new eXplainable Artificial Intelligence ensemble transfer learning for zero-day botnet attack detection in the Internet of Vehicles by Yakub Kayode Saheed, Joshua Ebere Chukwuere

“…It also uses Barnacle Mating Optimizer (BMO) to optimize the hyper-parameters of deep learning models such as ResNet, Inception, Inception ResNet, and MobileNet Convolution neural network-transfer learning architecture (CNN-TL), enhancing detection capabilities without needing vast amounts of labeled data. …”
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Wearable PZT Piezoelectric Sensor Device for Accurate Arterial Pressure Pulse Waveform Measurement by Minyu Li, Jun Aoyama, Koya Inayoshi, Hedong Zhang

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Web Application Firewall Based on Anomaly Detection using Deep Learning by Sezer Toprak, Ali Gökhan Yavuz

“…The success rate of the technique - whether the user input is identified as malicious or normal - is measured using F1 scores. The proposed model demonstrated high F1 scores and success in terms of detection and classification of the attacks.…”
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Screening benzimidazole derivatives for atypical antipsychotic activity by K. Yu. Kalitin, O. Yu. Mukha, V. B. Voynov

“…The antipsychotic activity of the most promising compound was assessed in vivo using tests with apomorphine in rats and mice.Results. Machine learning models were developed and tested to predict the antipsychotic activity of benzimidazole derivatives. …”
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A novel multi-modal rehabilitation monitoring over human motion intention recognition by Saleha Kamal, Saleha Kamal, Mohammed Alshehri, Yahya AlQahtani, Abdulmonem Alshahrani, Nouf Abdullah Almujally, Ahmad Jalal, Ahmad Jalal, Hui Liu, Hui Liu, Hui Liu

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The Classification of Vestibular Schwannoma (VS) and Cerebellopontine Angle Meningioma (CPAM) Based on Multimodal Magnetic Resonance Imaging Analysis by Lihua Yuan, Jaming Lu, Xin Shu, Kun Liang, Cheng Wang, Jiu Chen, Zhishun Wang

“…Imaging features from ADC, T1W + C, and T2W sequences were extracted using medical imaging software. A support vector machine (SVM) model was trained to classify tumors based on these features, focusing on first-, second-, and third-order radiomic characteristics. …”
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Analysis of Meshing Contact Characteristics of the Gear Transmission System Based on Data Mining Technology by Li Shengjia, Ma Yali, Zhao Yongsheng, Pu Dajun, Yan Shidang

“…Under the optimal feature subset, the prediction error index of contact characteristics of the prediction model based on support vector machine decreases significantly, and its average absolute percentage error reaches 3.03%, which is 21.71% lower than the prediction error of contact characteristics before optimization. …”
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Robust Framework for PMU Placement and Voltage Estimation of Power Distribution Network by Nida Khanam, Mohd. Rihan, Salman Hameed

“…Future research will look into real-time adaptive state estimation utilizing machine learning-based predictive modelling, as well as expanding the framework to account for communication constraints and dynamic grid topology changes to improve its practical usefulness.…”
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Unveiling the Effects of Crop Rotation on Cropland Soil pH Mapping: A Remote Sensing-Based Soil Sample Grouping Strategy by Yuan Liu, Songchao Chen, Ge Shen, Cheng Chen, Zejiang Cai, Ji Zhu, Xia Zhang, Guofei Shang, Qingbo Zhou, Sonoko Dorothea Bellingrath-Kimura, Qiangyi Yu, Wenbin Wu

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A novel scheme for seamless global mapping of daily mean air temperature (SGM_DMAT) at 1-km spatial resolution using satellite and auxiliary data by Ran Huang, Shengcheng Li, Xin Zhu, Jianing Li, Yuanjun Xiao, Wei Weng, Qi Shao, Dengfeng Chai, Jingcheng Zhang, Yao Zhang, Lingbo Yang, Kaihua Wu, Zhihao Hu, Li Liu, Weiwei Sun, Weiwei Liu, Jingfeng Huang

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Fog node intrusion detection and response based on SVMIF and INSGA-II algorithm by Zhuojun Luo

“…Additionally, modified particle swarm optimization was employed to optimize the model's parameters. …”
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Projecting Forest Fire Probability in South Korea Under Climate Change, Population, and Forest Management Scenarios Using AI & Process-Based Hybrid Model (FLAM-Net) by Hyun-Woo Jo, Myoungsoo Won, Florian Kraxner, Seong Woo Jeon, Yowhan Son, Andrey Krasovskiy, Woo-Kyun Lee

“…Process-based models offer high interpretability through human domain knowledge but require extensive optimization, while machine learning models automatically identify important features but have limited interpretability. …”
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Explainable artificial intelligence for targeted protein degradation predictions by Francis J. Prael III, Jutta Blank, William C. Forrester, Lingling Shen, Raquel Rodríguez-Pérez

“…Such ML-based SAR modeling has the potential of greatly assisting in compound optimization. …”
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Ai-powered digital twin in the industrial IoT by Željko Bolbotinović, Saša D. Milić, Žarko Janda, Dragan Vukmirović

“…The paper presents the digital twin (DT) concept in a vertical Industrial Internet of Things (IIoT) framework powered by machine learning (ML) models for time series forecasting. …”
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Predicting postprandial glucose excursions to personalize dietary interventions for type-2 diabetes management by Victoria Brügger, Tobias Kowatsch, Mia Jovanova

“…We developed machine learning models trained on past individual observations to predict the next-in-time PPG excursion, using continuous glucose monitoring (CGM) data or CGM data combined with manually-logged meals and glucose-lowering agent intake. …”
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Individualized post‐operative prediction of cochlear implantation outcomes in children with prelingual deafness using functional near‐infrared spectroscopy by Zhe Chen, Xue Zhao, Haotian Liu, Yuyang Wang, Zhikai Zhang, Yuxuan Zhang, Yuhe Liu

“…Both classification and individualized regression models were constructed to predict post‐CI behavioral improvement from fNIRS data using support vector machine (SVM) learning algorithms. …”
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