Transparent and reliable construction cost prediction using advanced machine learning and explainable AI by Lifei Chen, Changyong Xu, Wei Hong Lim, Abhishek Sharma, Sew Sun Tiang, Kim Soon Chong, El-Sayed M. El-kenawy, Amel Ali Alhussan, Marwa M. Eid, Doaa Sami Khafaga

“…Ten machine learning models, including Ridge Regression, Lasso Regression, Elastic Net, K-Nearest Neighbor Regression, and advanced ensemble methods such as XGBoost, CatBoost, and HistGradient Boosting, were evaluated on the RSMeans dataset. …”
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Comparative Analysis of Machine Learning Models for Predicting Innovation Outcomes: An Applied AI Approach by Marko Martinović, Kristian Dokic, Dalibor Pudić

“…Overall, the results indicate that ensemble methods generally provide robust classification performance for innovation prediction tasks. …”
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An Intraoperative Ultrasound Evaluation of Axillary Lymph Nodes: Cassandra Predictive Models in Patients with Breast Cancer—A Multicentric Study by Simona Parisi, Francesco Saverio Lucido, Federico Maria Mongardini, Roberto Ruggiero, Francesca Fisone, Salvatore Tolone, Antonio Santoriello, Francesco Iovino, Domenico Parmeggiani, David Vagni, Loredana Cerbara, Ludovico Docimo, Claudio Gambardella

“…Although various radiological investigations are available, ultrasound (US) is the preferred tool for evaluating ALNs. Despite its immediacy, widespread use, and good predictive value, US is limited by intra- and inter-operator variability. …”
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Accuracy of Predictive Equations for Metabolizable Energy Compared to Energy Content of Foods for Dogs and Cats Estimated by In Vivo Methods in Brazil by Pedro Henrique Marchi, Andressa Rodrigues Amaral, Leonardo de Andrade Príncipe, Larissa Wünsche Risolia, Mariana Fragoso Rentas, Ana Beatriz Fasolai, Rafael Vessecchi Amorim Zafalon, Gabriela Luiza Fagundes Finardi, Juliana Toloi Jeremias, Raquel Silveira Pedreira, Júlio Cesar de Carvalho Balieiro, Thiago Henrique Annibale Vendramini

“…The data were analyzed using SAS, and Student’s <i>t</i> test was used with a significance of 5%. For all predictive equations, there was a non-conformity between their results and those obtained by in vivo methods. …”
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Predictive modeling of the mechanical behavior of 3D-printed polylactic acid/wood composite: Comparison of GEP and ANN methods by Abhijit Bhowmik, Raman Kumar, Ranganathaswamy M. K., Y. Karun Kumar, Priyaranjan Samal, Abinash Mahapatro, Abdulaziz N. Alhazaa, Valentin Romanovski, A. Johnson Santhosh

“…This study introduces a novel approach for predicting the mechanical properties of 3D-printed polylactic acid wood composites using gene expression programming (GEP) and artificial neural networks (ANN) modeling methods. …”
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Predictive Modeling of Water Level in the San Juan River Using Hybrid Neural Networks Integrated with Kalman Smoothing Methods by Jackson B. Renteria-Mena, Eduardo Giraldo

“…The results show that the combination of neural networks with smoothing filters, especially the RTS filter and smoothed Kalman filter, provided the most accurate predictions, outperforming traditional methods. This research has important implications for water resource management and flood prevention in vulnerable areas such as Chocó. …”
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Optimal Control Strategy and Evaluation Framework for Frequency Response of Combined Wind–Storage Systems by Jie Hao, Huiping Zheng, Xueting Cheng, Yuxiang Li, Liming Bo, Juan Wei

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EVALUATION OF MONOCYTE TO HIGH-DENSITY LIPOPROTEIN CHOLESTEROL RATIO AS A PREDICTIVE MARKER OF DIABETES MELLITUS SEVERITY IN OLDER PATIENTS by Gülru Ulugerger Avcı, Derya Argun, Günay Can, Mehmet Fatih Kılıçlı

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Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method by Chengcheng Wang, Zhi Yan, Qifeng Li, Zhaopeng Zhu, Chengkai Zhang

“…Comparative experiments with traditional LSTM and Convolutional Neural Network (CNN) models demonstrate that the LSTM-Attention model outperforms these models across multiple evaluation metrics, significantly reducing prediction errors and enhancing robustness and adaptability. …”
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Comparison and Application of Pore Pressure Prediction Methods for Carbonate Formations: A Case Study in Luzhou Block, Sichuan Basin by Wenzhe Li, Pingya Luo, Yatian Li, Jinghong Zhou, Xihui Hu, Qiutong Wang, Yiguo He, Yi Zhang

“…To address this issue, this study systematically investigates and compares three classical pore pressure prediction approaches—namely, the equivalent depth method, the Eaton method, and the effective stress method—within the geological context of the Luzhou Block. …”
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Unified Calibration-Based Failure Prediction Quantization for Automatic Target Recognition by Sihang Dang, Yunlong Zhang, Zhaoqiang Xia, Xiaoyue Jiang, Shuliang Gui, Xiaoyi Feng

“…For inference, it employs a quantification method based on similarity and dissimilarity to evaluate unknown samples. …”
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Evaluating Online Cannabis Health Information for Thai Breast Cancer Survivors Using the Quality Evaluation Scoring Tool (QUEST): Mixed Method Study by Thanarpan Peerawong, Tharin Phenwan, Meiko Makita, Sojirat Supanichwatana, Panupong Puttarak, Naowanit Siammai, Prakaidao Sunthorn

“…MethodsA mixed methods study was conducted between January 2021 and May 2022, involving a breast cancer survivor support group. …”
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Plasma thrombomodulin is a valuable biomarker to predict the severity of hemorrhagic fever with renal syndrome caused by the Hantaan virus by Han-Dong Zhao, Han-Dong Zhao, Yan Zhang, Xiao-Hong Wang, Hong-Bo Qian, Tong-Bo Yu, Peng Li, Kang-Xiao Ma, Hong-Li Liu

“…ObjectivesThis study aimed to investigate the Thrombomodulin (TM) levels in patients who suffered hemorrhagic fever with renal syndrome (HFRS) of varying severities, and to evaluate the predictive properties of TM for the seriousness of HFRS, thereby providing a clue for the monitoring and management of this patients in the future.MethodsChemiluminescence was used to determine the concentrations of TM in 196 patients with HFRS and 49 healthy controls. …”
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Automated Global Method to Detect Rapid and Future Urban Areas by Heather S. Sussman, Sarah J. Becker

“…Furthermore, prediction methods are typically developed in a single location and are not evaluated across diverse geographies. …”
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Deciphering Customer Satisfaction: A Machine Learning-Oriented Method Using Agglomerative Clustering for Predictive Modeling and Feature Selection by Rezki Nisrine, Mansouri Mohamed, Oucheikh Rachid

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Evaluation of ultrasound accuracy in thyroid mass measurement and its impact on 131I treatment for Graves’ disease by Xiangxiang Li, Xu Han, Nan Liu, Shen Wang, Hongyuan Zheng, Ziyu Ma, Ruiguo Zhang, Qiang Jia, Wei Zheng

“…Based on these factors, a predictive model was developed and evaluated using ROC curves, DCA and CAL. …”
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A Comparative Study of Applying Active-Set and Interior Point Methods in MPC for Controlling Nonlinear pH Process by Syam Syafiie, Chia Yaw Kwan

“…A comparative study of Model Predictive Control (MPC) using active-set method and interior point methods is proposed as a control technique for highly non-linear pH process. …”
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Comparative evaluation of six phenotypic methods for detecting extended-spectrum beta-lactamase-producing Enterobacteriaceae by Rajkumar Manojkumar Singh, Huidrom Lokhendro Singh

“…Introduction: Various conventional phenotypic methods and automated systems have been evaluated for extended-spectrum beta-lactamase (ESBL) detection. …”
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Comparative Analysis of Machine Learning and Deep Learning Models for Classification and Prediction of Liver Disease in Patients with Hepatitis C by Shalem Preetham Gandu, M. Roshni Thanka, E. Bijolin Edwin, Ebenezer Veemaraj, S. Stewart Kirubakaran

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Evaluating Deep Learning Networks Versus Hybrid Network for Smart Monitoring of Hydropower Plants by Fatemeh Hajimohammadali, Emanuele Crisostomi, Mauro Tucci, Nunzia Fontana

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