A Hybrid LSTM-GRU Model for Stock Price Prediction by Amirfarhad Farhadi, Azadeh Zamanifar, Amir Alipour, Alireza Taheri, Mohammad Asadolahi

“…Modeling the chaotic fluctuations in stock prices aids investors and traders in uncertain situations by evaluating market trends for investment decisions. Previous methods utilized Recurrent Neural Network (RNN) models for prediction. …”
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Feature fusion with attributed deepwalk for protein–protein interaction prediction by Mei-Yuan Cao, Suhaila Zainudin, Kauthar Mohd Daud

“…The fused representations are then used to train classifiers for PPI prediction. Evaluation across three datasets using multiple classifiers demonstrated that FFADW significantly improves sample clustering and performs better than existing approaches, with the XGBoost classifier showing the best results. …”
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Machine Learning Techniques for Uncertainty Estimation in Dynamic Aperture Prediction by Carlo Emilio Montanari, Robert B. Appleby, Davide Di Croce, Massimo Giovannozzi, Tatiana Pieloni, Stefano Redaelli, Frederik F. Van der Veken

“…We evaluated these approaches to determine the most effective method for reliable uncertainty estimation in dynamic aperture predictions using machine learning techniques.…”
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Impact of computing platforms on classifier performance in heart disease prediction by Beenish Ayesha Akram, Muhammad Irfan, Amna Zafar, Sidra Khan, Rubina Shaheen

“…Our evaluation revealed that SVM achieves 77% accuracy when implemented using Scikit-Learn, demonstrating superiority over other methods. …”
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A nomogram to predict cancer-specific survival of ocular melanoma by Ruisheng Huang, Jian Chen, Limin Lin, Jun Lyu, Qing Zhou

“…Subsequently, an independent variable-based nomogram was developed. Finally, multiple methods were used to verify and evaluate the performance of the nomogram. …”
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Spatiotemporal Multivariate Weather Prediction Network Based on CNN-Transformer by Ruowu Wu, Yandan Liang, Lianlei Lin, Zongwei Zhang

“…However, the existing data-based weather prediction methods cannot adequately capture the spatial and temporal evolution characteristics of the target region, which makes it difficult for the existing methods to meet practical application requirements in terms of efficiency and accuracy. …”
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Core-Level Modeling and Frequency Prediction for DSP Applications on FPGAs by Gongyu Wang, Greg Stitt, Herman Lam, Alan George

“…We evaluate CMD’s prediction methods using several high-performance DSP applications on various families of FPGAs and show an average clock-frequency prediction error of 3.6%, with a worst-case error of 20.4%, compared to the best of existing high-level prediction methods, 13.9% average error with 48.2% worst-case error. …”
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An adaptive spatiotemporal dynamic graph convolutional network for traffic prediction by Zhiguo Xiao, Qi Shen, Changgen Li, Dongni Li, Qian Liu

“…Experimental results on four public traffic datasets demonstrate that the AST-DGCN model achieves significant performance advantages over baseline methods across three critical evaluation metrics: root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), thereby fully validating its superior predictive capabilities and competitive advantages.…”
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Predictive factors of hypoglycemia in type 2 diabetes: a prospective study using machine learning by Motahare Shabestari, Akram Mehrabbeik, Sebastiano Barbieri, Pedro Marques-Vidal, Poria Heshmati-nasab, Reyhaneh Azizi

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Numerical Modeling and Analysis of Shadow Flicker Using Solar Path Functions for Enhanced Predictive Accuracy by Nicolai Radke, Patrick E. M. De Smet, Erika Ábrahám

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A novel model for malaria prediction based on ensemble algorithms. by Mengyang Wang, Hui Wang, Jiao Wang, Hongwei Liu, Rui Lu, Tongqing Duan, Xiaowen Gong, Siyuan Feng, Yuanyuan Liu, Zhuang Cui, Changping Li, Jun Ma

“…We compared the predictive performance of each model through evaluation measures: RMSE, MASE, MAD. …”
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Gas well productivity prediction based on fractional Fourier transform by Chenchang Zheng, Jun Tang, Mengfan Li, Gewei Qi

“…In summary, the study reduces the cost of reservoir evaluation and can meet the demand of gas well productivity prediction.…”
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Role of telomere maintenance genes as a predictive biomarker for colorectal cancer immunotherapy response and prognosis by Zhikai Wang, Chunyan Zhao, Yifen Huang, Chong Li

“…Immune cell infiltration was evaluated through ssGSEA, TIMER, and MCP-Counter tools, and chemotherapy responses were predicted using the pRRophetic package. …”
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Rainfall-induced Landslide Susceptibility Prediction Considering Spatial Heterogeneity by ZHANG Xingfu, JIANG Yuanjun, ABI Erdi

“…Inputs such as terrain features, soil characteristics, vegetation indices, and rainfall patterns were utilized to train and evaluate the models. The DEC, DRT, and MLANT models were compared to traditional susceptibility models to assess improvements in prediction accuracy and adaptability.Results and DiscussionsThe results demonstrated significant improvements in prediction accuracy when using the proposed models. …”
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Proactive Data Placement in Heterogeneous Storage Systems via Predictive Multi-Objective Reinforcement Learning by Suchuan Xing, Yihan Wang

“…Our method employs Long Short-Term Memory networks and Transformer architectures to model complex temporal dependencies in I/O traces, generating predictive access probability distributions for data blocks. …”
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Model Predictive Control-Based Energy Management System for Cooperative Optimization of Grid-Connected Microgrids by Sungmin Lim, Jaekyu Lee, Sangyub Lee

“…First, the performance of the algorithm was evaluated under various weather conditions. Second, its robustness against prediction errors was tested by comparing scenarios with and without disturbance prediction. …”
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Predictive fatigue life modelling for aluminum alloys winder high temperature and shot peening interact by Allawi H. Alwin, Hatem Ksibi

“…This model was compared with Miner’s rule to assess its predictive accuracy. The results show that the new model provides more accurate predictions of fatigue life than Miner’s rule, thereby improving the reliability and safety of components in high-temperature applications. …”
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Application of Thermodynamic Models to Predict the Solubility of Biologically Active Substances by K. S. Stepanov, G. N. Turmanidze, V. V. Sorokin, A. D. Sakharov

“…Theoretical determination of the dissolving power of various solvents with respect to hesperidin and glycyrrhizic acid.Materials and methods. The PMUNIFAC and NRTL-SAC thermodynamic models were used to predict solubility. …”
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Predictive modeling of rapid glaucoma progression based on systemic data from electronic medical records by Richul Oh, Hyunjoong Kim, Tae-Woo Kim, Eun Ji Lee

“…Systemic data obtained within 6 months from the time of glaucoma diagnosis were extracted from the EMRs and incorporated into a model to predict the rate of progressive RNFL thinning. The predictive model was trained and tested using a random forest (RF) method and interpreted using Shapley additive explanation plots (SHAP). …”
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Prediction of the Immune Phenotypes of Bladder Cancer Patients for Precision Oncology by Hyuna Cho, Feng Tong, Sungyong You, Sungyoung Jung, Won Hwa Kim, Jayoung Kim

“…Upon further validation in independent cohorts, this gene signature could be developed into a predictive test that can support clinical evaluation and patient care.…”
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