Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring by Fang Li, Shengguo Wang, Zhi Gao, Maofeng Qing, Shan Pan, Yingying Liu, Chengchen Hu

“…AI, particularly through machine learning (ML) techniques such as random forest models and deep learning algorithms, has shown promise in analyzing electronic health record (EHR) data to identify patterns that enable early sepsis detection. …”
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Multi-omics analysis reveals the sensitivity of immunotherapy for unresectable non-small cell lung cancer by Rui Wu, Kunchen Wei, Xingshuai Huang, Yinge Zhou, Xiao Feng, Xin Dong, Hao Tang

“…Finally, potential biomarkers were picked out by applying machine learning methods including random forest and stepwise regression and prediction models were constructed by logistic regression.ResultsThe presence of metabolites and proteins in peripheral blood plasma was causally associated with both non-small cell lung cancer and PD-L1/PD-1 expression levels. …”
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Social smart city research: interconnections between participatory governance, data privacy, artificial intelligence and ethical sustainable development by Samad Rasoulzadeh Aghdam, Samad Rasoulzadeh Aghdam, Behnaz Bababei Morad, Behnam Ghasemzadeh, Behnam Ghasemzadeh, Behnam Ghasemzadeh, Mazdak Irani, Aapo Huovila

“…A deeper analysis of key terms used in recent research revealed the following hot topics: (1) governance and citizen participation, (2) artificial intelligence technologies such as machine learning, (3) blockchain, and (4) Internet of Things. …”
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Automatic Morpheme Segmentation for Russian: Can an Algorithm Re-place Experts? by Дмитрий Алексеевич Морозов, Тимур Александрович Гарипов, Ольга Николаевна Ляшевская, Светлана Олеговна Савчук, Борис Леонидович Иомдин, Анна Валерьевна Глазкова

“…Results: In this study, we compared several state-of-the-art machine learning algorithms using three datasets structured around different segmentation paradigms. …”
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Predicting Solar Energetic Particle Events with Time Series Shapelets by Omar Bahri, Peiyu Li, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi

“…Our objective is to mitigate the interpretability challenges inherent to most machine learning models and to show that other methods exist that can not only yield accurate forecasts but also facilitate exploration and insight generation within the data domain. …”
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Large-Scale Mapping of Maize Plant Density Using Multi-Temporal Optical and Radar Data: Models, Potential and Application Strategy by Jing Xiao, Yuan Zhang, Xin Du, Qiangzi Li, Hongyan Wang, Yueting Wang, Jingyuan Xu, Yong Dong, Yunqi Shen, Sifeng Yan, Shuguang Gong, Haoxuan Hu

“…By identifying critical features for maize density and incorporating machine learning to explore optimal feature combinations, we developed a multi-temporal model that enhances estimation accuracy, particularly during leaf development, stem elongation, and tasseling stages (R² = 0.602, RMSE = 0.094). …”
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Clasificación de uso y cobertura del suelo a través de algoritmos de aprendizaje automático: revisión bibliográfica by René Tobar-Díaz, Yan Gao, Jean François Mas, Víctor Hugo Cambrón-Sandoval

“…Los métodos para la clasificación de uso y cobertura del suelo (UCS) han mostrado avances importantes en los últimos años, como la incorporación de las técnicas de aprendizaje automático (machine learning-ML) que han ganado popularidad y aceptación por sus resultados. …”
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Efficient diagnosis of diabetes mellitus using an improved ensemble method by Blessing Oluwatobi Olorunfemi, Adewale Opeoluwa Ogunde, Ahmad Almogren, Abidemi Emmanuel Adeniyi, Sunday Adeola Ajagbe, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Asif Mehmood, Habib Hamam

“…Abstract Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification accuracies due to overfitting, underfitting, and data noise. …”
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Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes by Liu Yang, Li Du, Yuanyuan Ge, Muhui Ou, Wanyan Huang, Xianmei Wang

“…Abstract Objective This study aimed to evaluate the predictive performance of inflammatory and nutritional indices for adverse cardiovascular events (ACE) in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI) using a machine learning (ML) algorithm. Methods AMI patients who underwent PCI were recruited and randomly divided into non/ACE groups. …”
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Development and Implementation of an IoT-Based Early Flood Detection and Monitoring System Utilizing Time Series Forecasting for Real-Time Alerts in Resource-Constrained Environmen... by Nik Nor Muhammad Saifudin Nik Mohd Kamal, Ahmad Anwar Zainuddin, Abu Ubaidah Shamsudin, Muhamad Syariff Sapuan, Muhammad Hazim Amin Samsudin, Mohammad Adam Haikal Zulkfli

“…These sensors continually send data to a central processing unit for analysis, and a machine learning model based on Time Series forecasting is used for predictive analysis in the ThingSpeak platform, which is available via an internet dashboard for real-time monitoring. …”
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Prenatal depression level prediction using ensemble based deep learning model by Abinaya Gopalakrishnan, Xujuan Zhou, Revathi Venkataraman, Raj Gururajan, Ka Ching Chan, Guohun Zhu, Niall Higgins

“…The accuracy of this approach applied to three benchmark datasets produced better results compared to all commonly applied machine learning models, including an Ensemble based Deep Learning model. …”
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Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare by Marina Ramzy Mourid, Hamza Irfan, Malik Olatunde Oduoye

“…Search terms encompassed “pediatric epilepsy,” “artificial intelligence,” “machine learning,” “ethical considerations,” and “data security.” …”
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DualTransAttNet: A Hybrid Model with a Dual Attention Mechanism for Corn Seed Classification by Fei Pan, Dawei He, Pengjun Xiang, Mengdie Hu, Daizhuang Yang, Fang Huang, Changmeng Peng

“…Compared to typical machine learning and deep learning models, the proposed model exhibits superior performance with an overall accuracy, F1-score, and Kappa coefficient of 90.01%, 88.9%, and 88.4%, respectively. …”
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Advanced Efficient Feature Selection Integrating Augmented Extreme Learning Machine and Particle Swarm Optimization for Predicting Nitrogen Use Efficiency and Yield in Corn by Josselin Bontemps, Isa Ebtehaj, Gabriel Deslauriers, Alain N. Rousseau, Hossein Bonakdari, Jacynthe Dessureault-Rompré

“…In addition, various soil health indicators, including physical, chemical, and biochemical properties, were monitored to understand their interaction with nitrogen use efficiency. Machine learning techniques, such as augmented extreme learning machine (AELM) and particle swarm optimization (PSO), were employed to optimize nitrogen recommendations by identifying the most relevant features for predicting yield and nitrogen use efficiency (NUE). …”
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Identification of hub biomarkers and immune cell infiltrations participating in the pathogenesis of endometriosis by Kang Li, Jiaxu Wang, Xuyue Liu, Yifei Dang, Kaiting Wang, Manyu Li, Xiaoli Zhang, Yuan Liu

“…The hub genes were screened using machine learning. The qRT-PCR results showed that only CHMP4C and KAT2B differentially expressed in ectopic tissues compared to the normal. …”
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Enhance health evidence quality in classification tasks: A triangulation approach utilizing case-based reasoning and process features by Ruihua Guo, Ross Smith, Qifan Chen, Angus Ritchie, Simon Poon

“…Objective Machine learning (ML) has enabled healthcare discoveries by facilitating efficient modeling, such as for cancer screening. …”
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Enhancing heat exchanger design using autoencoder model for predicting efficiency and cost in chemical processing by Manimegalai T, Anitha Gopalan, Vanmathi Murugesan, Jayant Giri, Praveen Barmavatu, Praveenkumar T R, Dinesh Mavaluru, Rafath Samrin

“…Furthermore, the model enables rapid exploration of design alternatives and sensitivity analysis, facilitating informed decision-making in the design phase. By leveraging machine learning techniques, this approach offers a promising avenue for advancing heat exchanger design towards higher efficiency and lower cost in chemical processing applications. …”
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Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 Patients by Dominique J. Monlezun, Alfred T. Samura, Ritesh S. Patel, Tariq E. Thannoun, Prakash Balan

“…We conducted a retrospective cohort analysis of a prospectively collected multicenter dataset of adult patients who sequentially presented to Houston metro area hospitals from 01/01/07-01/01/16. Then AI-based machine learning (backward propagation neural network) augmented multivariable regression and GIS heat mapping were performed. …”
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Prediction of Dst During Solar Minimum Using In Situ Measurements at L5 by R. L. Bailey, C. Möstl, M. A. Reiss, A. J. Weiss, U. V. Amerstorfer, T. Amerstorfer, J. Hinterreiter, W. Magnes, R. Leonhardt

“…Using the STEREO‐B satellite as a proxy, we map data measured near L5 to the near‐Earth environment and make a prediction of the Dst from this point using the Temerin‐Li Dst model enhanced from the original using a machine learning approach. We evaluate the method accuracy with both traditional point‐to‐point error measures and an event‐based validation approach. …”
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AI-powered estimation of tree covered area and number of trees over the Mediterranean island of Cyprus by Anna Zenonos, Sizhuo Li, Martin Brandt, Jean Sciare, Philippe Ciais

“…Artificial Intelligence is a powerful tool that can enable the development of tree monitoring systems by applying machine learning models to high-resolution image data. …”
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