Improved Liquefaction Hazard Assessment via Deep Feature Extraction and Stacked Ensemble Learning on Microtremor Data by Oussama Arab, Soufiana Mekouar, Mohamed Mastere, Roberto Cabieces, David Rodríguez Collantes

“…The main novelty is the integration of machine learning, particularly stacked ensemble learning, for liquefaction potential classification from imbalanced seismic datasets. …”
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Advanced Classifiers and Feature Reduction for Accurate Insomnia Detection Using Multimodal Dataset by Ameya Chatur, Mostafa Haghi, Nagarajan Ganapathy, Nima TaheriNejad, Ralf Seepold, Natividad Martinez Madrid

“…Our findings emphasize the importance of tailoring feature sets and employing appropriate reduction techniques for optimal predictive modeling in sleep-related studies. …”
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Construction of a Prediction Model for Energy Consumption in Urban Rail Transit Operations Using a Bottom–Up Approach by Boyu Chen, Ye Lin

“…The factors were grouped based on the scale of the urban rail transit network, and planned indicators were screened using stepwise regression and machine learning eigenvalue methods. Predictive models were then constructed using these planned indicators through multiple linear regression and random forest regression. …”
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Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework by Abbas Ali Hussein, Morteza Valizadeh, Mehdi Chehel Amirani, Sedighe Mirbolouk

“…In a subsequent step, Machine Learning (ML) algorithms are employed to classify these tumors as malign or benign cases. …”
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A performance evaluation of silver nanorods PDMS flexible dry electrodes for electrocardiogram monitoring by C. M. Vidhya, Ghanshyam Kumar, Yogita Maithani, Bhanu Duggal, J. P. Singh

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AI-Driven Comprehensive SERS-LFIA System: Improving Virus Automated Diagnostics Through SERS Image Recognition and Deep Learning by Shuai Zhao, Meimei Xu, Chenglong Lin, Weida Zhang, Dan Li, Yusi Peng, Masaki Tanemura, Yong Yang

“…On this basis, a negative–positive discrimination method combining SERS scanning imaging with a deep learning model (ResNet-18) was developed to analyze probe distribution patterns near the T line. …”
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Content moderation assistance through image caption generation by Liam Kearns

“…In this work, a collaborative approach is taken, where a machine learning model is used to assist human moderators in the approval and rejection of media within a scavenger hunt game. …”
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FPGA-accelerated SpeckleNN with SNL for real-time X-ray single-particle imaging by Abhilasha Dave, Cong Wang, James Russell, Ryan Herbst, Jana Thayer

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A heuristic-assisted deep reinforcement learning algorithm for flexible job shop scheduling with transport constraints by Xiaoting Dong, Guangxi Wan, Peng Zeng

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Less Is More: The Influence of Pruning on the Explainability of CNNs by Florian Merkle, David Weber, Pascal Schottle, Stephan Schlogl, Martin Nocker

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Challenges of the Biopharmaceutical Industry in the Application of Prescriptive Maintenance in the Industry 4.0 Context: A Comprehensive Literature Review by Johnderson Nogueira de Carvalho, Felipe Rodrigues da Silva, Erick Giovani Sperandio Nascimento

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Hyperspectral Detection of Pesticide Residues in Black Vegetable Based on Multi-Classifier Entropy Weight Method by Rongchang Jiang, Guoqiang Zhuang, Shijie Xie, Yang Wang, Guoqi Zhang, Dandan Qu, Wanzhi Wen

“…Models were built using eXtreme gradient boosting, random forest, and support vector machine algorithms. …”
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Prosumer Cost Efficiency and Ensuring Grid Stability Through a Hierarchical PPO Framework in Decentralized Community Energy Management by Ajay Singh, Bijaya Ketan Panigrahi

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Advanced AI techniques for classifying Alzheimer’s disease and mild cognitive impairment by Sophie Tascedda, Sophie Tascedda, Sophie Tascedda, Pierfrancesco Sarti, Pierfrancesco Sarti, Veronica Rivi, Claudia Savia Guerrera, Giuseppe Alessio Platania, Mario Santagati, Filippo Caraci, Filippo Caraci, Johanna M. C. Blom, Johanna M. C. Blom

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High-Precision Phenotyping in Soybeans: Applying Multispectral Variables Acquired at Different Phenological Stages by Celí Santana Silva, Dthenifer Cordeiro Santana, Fábio Henrique Rojo Baio, Ana Carina da Silva Cândido Seron, Rita de Cássia Félix Alvarez, Larissa Pereira Ribeiro Teodoro, Carlos Antônio da Silva Junior, Paulo Eduardo Teodoro

“…Remote sensing techniques and precision agriculture are being analyzed through research in different agricultural regions as a technological system aiming at productivity and possible low-cost reduction. Machine learning (ML) methods, together with the advent of demand for remotely piloted aircraft available on the market in the recent decade, have been conducive to remote sensing data processes. …”
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Arsenic toxicity exacerbates China’s groundwater and health crisis by Xin Liu, Fu-Jun Yue, Wei Wen Wong, Shao-Chong Lin, Tian-Li Guo, Si-Liang Li

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Determinants of plasma poly- and perfluoroalkyl substances during pregnancy: The Japan Environment and Children’s Study by Yonghang Lai, Shoji F. Nakayama, Yukiko Nishihama, Tomohiko Isobe

“…This study investigated the determinants of PFAS in plasma collected from pregnant women enrolled in the Japan Environment and Children’s Study from 2011 to 2014. Several machine learning approaches were used, and the XGBoost model had the best predictive performance for seven PFAS quantified in more than 50 % of the population (from R2 = 0.34 and RMSE = 0.39 ng/mL for perfluorononanoic acid to R2 = 0.85 and RMSE = 0.19 ng/mL for perfluoroundecanoic acid). …”
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Framingham Risk Score Prediction at 12 Months in the STANDFIRM Randomized Control Trial by Thanh G. Phan, Velandai K. Srikanth, Dominique A. Cadilhac, Mark Nelson, Joosup Kim, Muideen T. Olaiya, Sharyn M. Fitzgerald, Christopher Bladin, Richard Gerraty, Henry Ma, Amanda G. Thrift

“…Methods and Results We used machine learning regression methods to evaluate 35 variables encompassing demographics, risk factors, psychological, social and education status, and laboratory tests. …”
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Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis by Zohreh Javanmard, Saba Zarean Shahraki, Kosar Safari, Abbas Omidi, Sadaf Raoufi, Mahsa Rajabi, Mohammad Esmaeil Akbari, Mehrad Aria

“…Artificial Intelligence (AI) and Machine Learning (ML) algorithms offer promising solutions for automated survival prediction, driving this study’s systematic review and meta-analysis.MethodsThree online databases (Web of Science, PubMed, and Scopus) were comprehensively searched (January 2016-August 2023) using key terms (“Breast Cancer”, “Survival Prediction”, and “Machine Learning”) and their synonyms. …”
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Research on the impact of green finance on regional carbon emission reduction and its role mechanisms by Huiyun Li, Zongbao Yu, Gang Chen, Yingjun Nie

“…Therefore, taking the green financial reform and innovation pilot zone as a quasi-natural experiment, we select 270 cities from 2010 to 2021 as research samples and empirically assess the effects of the green finance policy on reducing regional carbon emissions through the double debiased machine learning (DDML) model. This study demonstrates that (1) green finance policy plays a significant role in promoting regional carbon emission reduction, and this conclusion remains valid after a variety of robustness tests; (2) the mechanism of action indicates that green finance policy contributes to regional carbon emission reduction by supporting green technological innovation and promoting the optimization of the industrial structure; (3) the analysis of heterogeneity reveals that green finance policy has a more pronounced effect on carbon emission reduction in the eastern region and in non-resource-based cities than in the central and western regions and in resource-dependent cities; and (4) the pilot policy of “Broadband China”, the pilot policy of information consumption, and the comprehensive experimental zone of big data has a synergistic effect on carbon reduction and emission reduction with green finance policy. …”
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