Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models by Junchen Pan, Zhen Yue, Jing Ji, Yongping You, Liqing Bi, Yun Liu, Xinglin Xiong, Genying Gu, Ming Chen, Shen Zhang

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Harnessing artificial intelligence of things for cardiac sensing: current advances and network-based perspectives by Hao Ren, Hao Ren, Hao Ren, Fengshi Jing, Yongcong Ma, Ruining Wang, Chaocheng He, Yufan Wang, Jiandong Zhou, Yu Sun

“…A total of 2,128 papers were included in the analysis.ConclusionFrom our perspective, current advancements in AI-powered IoT cardiac sensors primarily focus on optimizing AI algorithms, such as deep learning techniques, and enhancing the functionality of smart wearable devices for precision medicine. …”
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AI-Driven Mapping System for Smart Parking Management Applications Using an INS-GNSS-Solid-State LiDAR-Monocular Camera Fusion Engine Empowered by HD Maps by Kai-Wei Chiang, Syun Tsai, Jou-An Chen, Surachet Srinara, Meng-Lun Tsai, Chih-Yun Hsieh, Jyun-Yang Hung, Chalermchon Satirapod, Naser El-Sheimy

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Low-Scalability Distributed Systems for Artificial Intelligence: A Comparative Study of Distributed Deep Learning Frameworks for Image Classification by Manuel Rivera-Escobedo, Manuel de Jesús López-Martínez, Luis Octavio Solis-Sánchez, Héctor Alonso Guerrero-Osuna, Sodel Vázquez-Reyes, Daniel Acosta-Escareño, Carlos A. Olvera-Olvera

“…Distributed computing has become necessary for storing, processing, and generating large amounts of information essential for training artificial intelligence models and algorithms that allow knowledge to be created from large amounts of data. …”
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FTIR-Based Microplastic Classification: A Comprehensive Study on Normalization and ML Techniques by Octavio Villegas-Camacho, Iván Francisco-Valencia, Roberto Alejo-Eleuterio, Everardo Efrén Granda-Gutiérrez, Sonia Martínez-Gallegos, Daniel Villanueva-Vásquez

“…A significant contribution of this research is the use of broader and more varied spectral ranges than those typically reported in the state of the art. …”
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Visualization of Moisture Distribution in Stacked Tea Leaves on Process Flow Line Using Hyperspectral Imaging by Yuying Zhang, Binhui Liao, Mostafa Gouda, Xuelun Luo, Xinbei Song, Yihang Guo, Yingjie Qi, Hui Zeng, Chuangchuang Zhou, Yujie Wang, Jingfei Zhang, Xiaoli Li

“…In this study, we utilized hyperspectral imaging (HSI) technology combined with machine learning algorithms to evaluate the moisture content and its distribution in the stacked tea leaves in West Lake Longjing and Tencha green tea products during the processing flow line. …”
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Application of precision agriculture technologies for crop protection and soil health by Emogine Mamabolo, Makgabo Johanna Mashala, Ephias Mugari, Tlou Elizabeth Mogale, Norman Mathebula, Kabisheng Mabitsela, Kwabena Kingsley Ayisi

“…This review highlights the need for continued innovation and coordinated efforts across research, policy, and education to overcome these barriers and support the global transition to more sustainable agricultural practices.…”
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Enhancing fibroblast–epithelial cell communications: Serpine2 as a key molecule in Fusobacterium nucleatum–promoted colon cancer by Xueke Li, Xueke Li, Simin Luo, Yifang Jiang, Qiong Ma, Fengming You, Fengming You, Qixuan Kuang, Xi Fu, Chuan Zheng

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The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma by Binyu Wang, Di Liu, Danfei Shi, Xinmin Li, Yong Li

“…These findings provide promising avenues for future research into targeted therapies and diagnostic tools, thereby enhancing LUAD management.…”
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Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN by Vladislav Kaverinskiy, Illya Chaikovsky, Anton Mnevets, Tatiana Ryzhenko, Mykhailo Bocharov, Kyrylo Malakhov

“…The study aims to apply unsupervised clustering algorithms to ECG data to detect latent risk profiles related to heart failure, based on distinctive ECG features. …”
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Continuous Speech-Based Fatigue Detection and Transition State Prediction for Air Traffic Controllers by Susmitha Vekkot, Surya Teja Chavali, Charan Tej Kandavalli, Rama Sai Abhishek Podila, Deepa Gupta, Mohammed Zakariah, Yousef Ajami Alotaibi

“…The ensemble algorithms demonstrated the best performance, achieving a maximum accuracy of 100% on the XGBoost test set. …”
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Decentralized multi-agent federated and reinforcement learning for smart water management and disaster response by H. Mancy, Naglaa E. Ghannam, Amr Abozeid, Ahmed I. Taloba

“…IoT devices are adopted for sensing and data acquisition, adaptive learning for decision-making, and optimization of energy use among the agents in the system through metaheuristic algorithms. The research methodology for implementing the proposed solution involves the design of a multi-layered architecture, including data acquisition, decentralized learning, and real-time execution. …”
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Prediction of the volume of shallow landslides due to rainfall using data-driven models by J. Tuganishuri, C.-Y. Yune, G. Kim, S. W. Lee, M. D. Adhikari, S.-G. Yum

“…The objectives of this research are to construct a model using advanced data-driven algorithms (i.e., ordinary least squares or linear regression (OLS), random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), generalized linear model (GLM), decision tree (DT), deep neural network (DNN), <span class="inline-formula"><i>k</i></span>-nearest-neighbor (KNN), and ridge regression (RR) algorithms) for the prediction of the volume of landslides due to rainfall, considering geological, geomorphological, and environmental conditions. …”
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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

“…Model 3 added breast sonography response data to the clinical variables in model 1. Algorithms including logistic regression, random forest, support vector machines, and extreme gradient boosting were used. …”
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Mitochondrial insights: key biomarkers and potential treatments for diabetic nephropathy and sarcopenia by Yi Wei Chen, Shan He, Yu Wang, Lian Ying Hu, Qin Kai Chen, Si Yi Liu

“…Utilizing four machine learning algorithms (LASSO, SVM, XGBoost, RF), we pinpointed three mitochondrial hub genes. …”
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Unsupervised Clustering Successfully Predicts Prognosis in NSCLC Brain Metastasis Cohorts by Emre Uysal, Gorkem Durak, Ayse Kotek Sedef, Ulas Bagci, Tanju Berber, Necla Gurdal, Berna Akkus Yildirim

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Development and validation of machine learning-based diagnostic models using blood transcriptomics for early childhood diabetes prediction by Xin Huang, Xin Huang, Di Ouyang, Weiming Xie, Huawei Zhuang, Siyu Gao, Pan Liu, Lizhong Guo

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The Relationship Between Stiff Knee Gait Runner’s Dystonia and Musculoskeletal Knee Pathology: A Case Series by Jared A. Stowers, Derek S. Day, Steven Jow, Sarah Heins, Euan Forrest, Yonathan M. Assefa, Paige M. Lind, Afreen Mushtaheed, Frances T. Sheehan, Katharine E. Alter

“…Addressing both issues is essential for optimizing treatment outcomes, reducing pain, and improving function, especially since pain can trigger dystonia. Future research should determine the ideal sequence of interventions for RD patients with MSK issues to develop effective, personalized treatment algorithms.…”
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Sensor-Based Yield Prediction in Durum Wheat Under Semi-Arid Conditions Using Machine Learning Across Zadoks Growth Stages by Süreyya Betül Rufaioğlu, Ali Volkan Bilgili, Erdinç Savaşlı, İrfan Özberk, Salih Aydemir, Amjad Mohamed Ismael, Yunus Kaya, João P. Matos-Carvalho

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Climate change will alter Amazonian bumblebees’ distribution, but effects are species-specific by Patrícia Nunes-Silva, André Luis Acosta, Rafael Cabral Borges, Breno Magalhães Freitas, Ricardo Caliari Oliveira, Tereza Cristina Giannini, Vera Lucia Imperatriz-Fonseca

“…An ensemble modeling approach combining five different algorithms was used to predict areas of stability, habitat loss, and potential range expansion.ResultsBy 2060, B. brevivillus is projected to lose 41.6% of its current suitable habitat, with significant reductions in northern and coastal regions. …”
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