Evaluating the efficacy of using large language models in preoperative prediction of microvascular invasion in HCC: a multicenter study by Zongren Ding, Jianxing Zeng, Guoxu Fang, Pengfei Guo, Weiping Zhou, Yongyi Zeng

“…However, accurately predicting MVI preoperatively remains a challenge. This study aims to evaluate the application of large language models (LLMs), specifically ChatGPT 4o, in predicting MVI in HCC and to compare its performance with traditional clinical models. …”
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BUILDING PREDICTIVE MODELS TO ASSESS DEGRADATION OF SOIL ORGANIC MATTER OVER TIME USING REMOTE SENSING DATA by Abdulsalam Aljumaily, Ammar Kashmolaa

Subjects: “…predictive models,,…”
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Trajectory of breastfeeding among Chinese women and risk prediction models based on machine learning: a cohort study by Yi Liu, Jie Xiang, Ping Yan, Yuanqiong Liu, Peng Chen, Yujia Song, Jianhua Ren

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From maps to models: Key concepts in Geographic Information Systems by Yohannes Shifera Daka, Kassaye Hussein, Ashenafi Yimam

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A machine learning-based method for predicting the shear behaviors of rock joints by Liu He, Yu Tan, Timothy Copeland, Jiannan Chen, Qiang Tang

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Application of Mechanistic Models and the Gaussian Process Model to Predict Bacterial Growth on Baby Spinach During Refrigerated Storage by Sriya Sunil, Sarah I. Murphy, Ruixi Chen, Wei Chen, Joseph Guinness, Li-Qun Zhang, Renata Ivanek, Martin Wiedmann

“…Models that predict bacterial growth in food products can help industry with decision-making with regard to microbial food spoilage. …”
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A dynamic prediction method for surface mining subsidence based on dynamic probability integral model and Logistic model by Lei HAN, Xinxin QI, Tianjun WANG, Chunmin PEI, Xudong WANG

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Performance Comparison between the Specific and Baseline Prediction Models of Ecotoxicity for Pharmaceuticals: Is a Specific QSAR Model Inevitable? by Qingwei Bu, Qingshan Li, Yun Liu, Chun Cai

“…Nonetheless, it should be noted that 95% prediction intervals spread over approximately four orders of magnitude for both tested QSAR models specifically for pharmaceuticals.…”
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VVC coded distortion prediction model based on frame-level transform coefficient modeling of generalized Gaussian distribution by Yiyin GU, Hongkui WANG, Haibin YIN

“…In versatile video coding (VVC), a variety of advanced coding tools work together to achieve excellent coding performance.Compared with high efficient video coding (HEVC), the transform coefficient distribution (TCD) of VVC has sharper peaks.In order to solve this phenomenon, the probability density function (PDF) of frame-level TCD was modeled, and a frame-level coding distortion prediction model based on statistical modeling was proposed, which modeled frame-level distortion as a function of TCD distribution parameters and quantization parameters.The experimental results show that compared with the Laplace distribution and Cauchy distribution, the generalized Gaussian distribution has the best performance in TCD probability density fitting.The prediction results based on the generalized Gaussian distribution distortion prediction model are closest to the actual coding distortion.…”
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Real-time riding simulation model for motorcycle including path-following controller with nonlinear model predictive control by Takamitsu HATAKEYAMA, Yuichi CHIDA, Masaya TANEMURA

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Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study by Wei Wu, Peng Guan, Mirxat Alim, Guo-Hua Ye, De-Sheng Huang, Bao-Sen Zhou

“…The XGBoost model is more suitable for prediction cases of human brucellosis in mainland China.…”
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D3: A Small Language Model for Drug-Drug Interaction prediction and comparison with Large Language Models by Ahmed Ibrahim, Abdullah Hosseini, Salma Ibrahim, Aamenah Sattar, Ahmed Serag

“…Expert evaluations further confirmed that D3’s predictions were clinically relevant and closely aligned with those of larger models. …”
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Research on time series prediction model for multi-factor environmental parameters in facilities based on LSTM-AT-DP model by Longwei Liang, Longwei Liang, Hui Shi, Hui Shi, Zhaoyuan Wang, Shengjie Wang, Changhong Li, Ming Diao

“…IntroductionExisting facility environment prediction models often suffer from low accuracy, poor timeliness, and error accumulation in long-term predictions under multifactor nonlinear coupling conditions. …”
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Evaluating crop yield prediction models in illinois using aquacrop, semi-physical model and artificial neural networks by Vishal Gautam, Abdul Gani, Shray Pathak, Anoop Kumar Shukla

“…For both corn and soybean yields, the ANN model showed the highest prediction accuracy among the other models. …”
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A new preprocedural predictive risk model for post-endoscopic retrograde cholangiopancreatography pancreatitis: The SuPER model by Mitsuru Sugimoto, Tadayuki Takagi, Tomohiro Suzuki, Hiroshi Shimizu, Goro Shibukawa, Yuki Nakajima, Yutaro Takeda, Yuki Noguchi, Reiko Kobayashi, Hidemichi Imamura, Hiroyuki Asama, Naoki Konno, Yuichi Waragai, Hidenobu Akatsuka, Rei Suzuki, Takuto Hikichi, Hiromasa Ohira

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Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach by Rui Xue, Daniel (Jian) Sun, Shukai Chen

“…This paper proposes an interactive multiple model (IMM) filter algorithm-based model to predict short-term passenger demand. …”
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Developing a predictive model for anticipating technology convergence: A transformer-based model and supervised learning approach by Mokh Afifuddin, Wonchul Seo

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Developing a predictive model for anticipating technology convergence: A transformer-based model and supervised learning approach. by Mokh Afifuddin, Wonchul Seo

“…The proposed method seeks to predict technology convergence effectively by employing various machine learning and deep learning techniques to train prediction models by integrating technological similarity, link prediction measures, and causal relationships between technology topics as input features, offering a more accurate and comprehensive understanding of the intricate relationships within the technological landscape. …”
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Modeling the hillside movement in the area of Sattarkhan dam reservoir using by predictive models Logistic Regression and Neural Network by soghra andaryani, Nasrin Samandar, Ohammadreza Nikjoo

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A framework for predictive modeling of microbiome multi-omics data: latent interacting variable-effects (LIVE) modeling by Javier Munoz Briones, Douglas K. Brubaker

“…Here, we present a framework for integration of microbiome multi-omics data: Latent Interacting Variable Effects (LIVE) modeling. LIVE integrates multi-omics data using single-omic latent variables (LV) organized in a structured meta-model to determine the combinations of features most predictive of a phenotype or condition. …”
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