Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games by Jun Zhao, Jintian Ji, Robail Yasrab, Shuxin Wang, Liang Yu, Lingzhen Zhao

“…However, existing models often struggle with poor adaptability and limited interpretability when applied to dynamic language prediction tasks such as <b>Wordle</b>. To address these challenges, this study proposes an interpretable reinforcement learning framework based on an Enhanced Deep Deterministic Policy Gradient (Enhanced-DDPG) algorithm. …”
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Fair and Transparent Student Admission Prediction Using Machine Learning Models by George Raftopoulos, Gregory Davrazos, Sotiris Kotsiantis

“…Student admission prediction is a crucial aspect of academic planning, offering insights into enrollment trends, resource allocation, and institutional growth. …”
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DeepInvesting: Stock market predictions with a sequence-oriented BiLSTM stacked model – A dataset case study of AMZN by Ashkan Safari, Mohammad Ali Badamchizadeh

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Optimal Driving Torque Control Strategy for Front and Rear Independently Driven Electric Vehicles Based on Online Real-Time Model Predictive Control by Hang Yin, Chao Ma, Haifeng Wang, Zhihao Sun, Kun Yang

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IoT driven smart health monitoring for heart disease prediction using quantum kernel enhanced sardine diffusion and CNN by Dibas Kumar Hembram, Satya Narayan Tripathy, CH Narsimha Reddy, Debabrata Dansana, Quadri Noorulhasan Naveed, Shafat khan, Mohammed Kareemullah, Addisu Frinjo Emma

Subjects: “…Heart disease prediction…”
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Enhancing PM_2.5 Air Pollution Prediction Performance by Optimizing the Echo State Network (ESN) Deep Learning Model Using New Metaheuristic Algorithms by Iman Zandi, Ali Jafari, Aynaz Lotfata

Subjects: “…PM_2.5 air pollution concentration prediction…”
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Ensemble intelligence prediction algorithms and land use scenarios to measure carbon emissions of the Yangtze River Delta: A machine learning model based on Long Short-Term Memory. by Qi Dai, Xiao-Yan Liu, Fang-Yi Sun, Fang-Rong Ren

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A web-based tool for predicting gastric ulcers in Chinese elderly adults based on machine learning algorithms and noninvasive predictors: A national cross-sectional and cohort study by Xingjian Xiao, Xiaohan Yi, Zumin Shi, Zongyuan Ge, Hualing Song, Hailei Zhao, Tiantian Liang, Xinming Yang, Suxian Liu, Bo Sun, Xianglong Xu

“…We employed nine machine learning algorithms to construct predictive models for gastric ulcers over the next seven years (2011–2018, with 1482 samples) and the next three years (2014–2018, with 2659 samples). …”
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Evaluation of deep learning and convolutional neural network algorithms accuracy for detecting and predicting anatomical landmarks on 2D lateral cephalometric images: A systematic review and meta-analysis by Jimmy Londono, Shohreh Ghasemi, Altaf Hussain Shah, Amir Fahimipour, Niloofar Ghadimi, Sara Hashemi, Zohaib Khurshid, Mahmood Dashti

“…This meta-analysis included 21 of 577 articles initially collected on the accuracy of ML algorithms for detecting and predicting anatomical landmarks. …”
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Predicting Tropical Cyclone Extreme Rainfall in Guangxi, China: An Interpretable Machine Learning Framework Addressing Class Imbalance and Feature Optimization by Yuexing Cai, Cuiyin Huang, Fengqin Zheng, Guangtao Li, Sheng Lai, Liyun Zhu, Qiuyu Zhu

“…ABSTRACT Accurate prediction of tropical cyclone‐induced extreme rainfall (TCER) is of utmost importance for disaster mitigation in coastal regions. …”
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Interpretable machine learning approaches for predicting prostate cancer by using multiple heavy metal exposures based on the data from NHANES 2003–2018 by Zu-Ming You, Yuan-Sheng Li, Fan-Shuo Meng, Rui-Xiang Zhang, Chen-Xi Xie, Zhijiang Liang, Ji-Yuan Zhou

“…The synergistic effect analysis further identified blood Pb, urinary Sb, and urinary Cs as the major contributing factors. The predictive model established in this study can provide valuable strategies for the prevention and the control of PCA.…”
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Compressive strength prediction of fly ash/slag-based geopolymer concrete using EBA-optimised chemistry-informed interpretable deep learning model by Yang Yu, Iman Munadhil Abbas Al-Damad, Stephen Foster, Ali Akbar Nezhad, Ailar Hajimohammadi

“…The CNN architecture includes two convolution layers, global max-pooling, and two fully connected layers, with 11 input variables and a single output for CS prediction. To optimise model accuracy, the enhanced bat algorithm (EBA) is designed for metaparameter tuning. …”
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A Method for Service Function Chain Migration Based on Server Failure Prediction in Mobile Edge Computing Environment by Joelle Kabdjou, Norihiko Shinomiya

Subjects: “…Failure prediction…”
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QSAR Model for Prediction of some Non-Nucleoside Inhibitors of Dengue Virus Serotype 4 NS5 using GFA-MLR Approach by Samuel Adawara, Gideon Shallangwa, Paul Mamza, Abdulkadir Ibrahim

“…Thus, the model can be used to predict the activity of new chemicals within its applicability domain. …”
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CFD investigation and ANN prediction of heat transfer coefficient for fully developed turbulent air flow around double V-baffle turbulators by Abdulaziz Alasiri, H.E. Fawaz

“…The ANN model demonstrates excellent predictive performance, yielding values close to 1 for R2 and r, along with extremely low values for MSE, MAPE, MSLE, and log-cosh loss (0.01, 0.6 %, 0.001, and 0.01, respectively), demonstrating the ANN model's high predictive accuracy.…”
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Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective S... by Chun-Chi Lai, Cheng-Yu Chen, Tzu-Hao Chang

“…ObjectiveThe objective of this study was to develop robust, machine learning–based prediction models for pCR following neoadjuvant therapy, leveraging clinical, laboratory, and imaging data. …”
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Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study by Gaoyang Li, Shrikant S. Kolan, Franco Grimolizzi, Joseph Sexton, Giulia Malachin, Guro Goll, Tore K. Kvien, Tore K. Kvien, Nina Paulshus Sundlisæter, Manuela Zucknick, Siri Lillegraven, Espen A. Haavardsholm, Espen A. Haavardsholm, Bjørn Steen Skålhegg

“…The model performance was evaluated through five independent unseen tests with nested 5-fold cross-validation. The predictive power of each feature was assessed using a composite measure derived from individual algorithm estimates.ResultsThe model demonstrated a mean AUC-ROC of 0.75-0.76, with mean sensitivity of 0.77-0.81, precision (also referred to as Positive Predictive Value) of 0.77-0.79 and specificity of 0.63-0.66 across the criteria. …”
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An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning by Harsh Nagar, Rajendra Machavaram, Ambuj, Peeyush Soni, Subhajit Saha, T. Subhash Chandra Bose

Subjects: “…Tractor drawbar pull prediction…”
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Spatiotemporal Multivariate Weather Prediction Network Based on CNN-Transformer by Ruowu Wu, Yandan Liang, Lianlei Lin, Zongwei Zhang

“…Finally, we demonstrated the excellent effect of STWPM in multivariate spatiotemporal field weather prediction by comprehensively evaluating the proposed algorithm with classical algorithms on the ERA5 dataset in a global region.…”
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A Method for Predicting Coal-Mine Methane Outburst Volumes and Detecting Anomalies Based on a Fusion Model of Second-Order Decomposition and ETO-TSMixer by Qiangyu Zheng, Cunmiao Li, Bo Yang, Zhenguo Yan, Zhixin Qin

“…The ability to predict the volume of methane outbursts in coal mines is critical for the prevention of methane outburst accidents and the assurance of coal-mine safety. …”
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