Identification and Verification of Novel Biomarkers Involving Rheumatoid Arthritis with Multimachine Learning Algorithms: An In Silicon and In Vivo Study by Fucun Liu, Juelan Ye, Shouli Wang, Yang Li, Yuhang Yang, Jianru Xiao, Aimin Jiang, Xuhua Lu, Yunli Zhu

“…Six gene expression profiles and corresponding clinical information of GSE12021, GSE29746, GSE55235, GSE55457, GSE77298, and GSE89408 were adopted to perform differential expression gene analysis, enrichment, and immune component difference analyses of RA. Four machine learning algorithms, including LASSO, RF, XGBoost, and SVM, were used to identify RA-related biomarkers. …”
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A QUALITATIVE ANALYSIS OF THE INFLUENCE OF MANAGEMENT INFORMATION SYSTEMS ON ORGANIZATIONAL DECISION-MAKING PROCESSES by Mubashshir Bin Mahbub, Mushfiq Nabil, Taskin Ahmed

“…Looking toward the future, participants expressed optimism about the potential of advanced technologies, such as artificial intelligence and machine learning, to enhance the capabilities of MIS and further refine decision-making processes. …”
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A novel multi-source data-driven energy consumption prediction model for Venlo-type greenhouses in China by Yangda Chen, Aiqun Bao, Yapeng Li, Yingfeng Xiang, Wanlong Cai, Zhaoqiang Xia, Jialei Li, Mingyang Ning, Jing Sun, Haixi Zhang, Xianpeng Sun, Xiaoming Wei

“…To overcome the challenges concerning heterogeneity, redundancy, and interdependence among different data sources, this paper proposed a novel energy consumption method that integrates multi-source data through feature engineering and machine learning techniques, which significantly enhances the efficiency of data utilization and improves prediction accuracy. …”
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Deep learning empowered sensor fusion boosts infant movement classification by Tomas Kulvicius, Dajie Zhang, Luise Poustka, Sven Bölte, Lennart Jahn, Sarah Flügge, Marc Kraft, Markus Zweckstetter, Karin Nielsen-Saines, Florentin Wörgötter, Peter B. Marschik

“…GMA has been increasingly augmented through machine learning approaches intending to scale-up its application, circumvent costs in the training of human assessors and further standardize classification of spontaneous motor patterns. …”
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Evaluation of an Interdisciplinary Educational Program to Foster Learning Health Systems: Education Evaluation by Sathana Dushyanthen, Nadia Izzati Zamri, Wendy Chapman, Daniel Capurro, Kayley Lyons

“…The course covered a number of topics including background on LHS, establishing learning communities, the design thinking process, data preparation and machine learning analysis, process modeling, clinical decision support, remote patient monitoring, evaluation, implementation, and digital transformation. …”
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Enhancing Arabic text-to-speech synthesis for emotional expression in visually impaired individuals using the artificial hummingbird and hybrid deep learning model by Mahmoud M. Selim, Mohammed S. Assiri

“…Natural Language Processing (NLP) and machine learning (ML) techniques provide powerful tools for analysing social media text data, helping detect emotional distress and providing timely support. …”
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The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review by Ricardo Smits Serena, Florian Hinterwimmer, Rainer Burgkart, Rudiger von Eisenhart-Rothe, Daniel Rueckert

“…Furthermore, our analysis reveals the current dominance of machine learning models in 76% on the surveyed studies, suggesting a preference for traditional models like linear regression, support vector machine, and random forest, but also indicating significant growth potential for deep learning models in this area. …”
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Proteomic, metabolomic and lipidomic profiles in community acquired pneumonia for differentiating viral and bacterial infections by Samuel Rischke, Robert Gurke, Ann-Sophie Zielbauer, Nicole Ziegler, Lisa Hahnefeld, Michaela Köhm, Aimo Kannt, Maria JGT Vehreschild, Gerd Geisslinger, Gernot Rohde, Carla Bellinghausen, Frank Behrens, CAPNETZ Study group

“…Statistical methods, machine learning and pathway analyses were conducted investigating differences between the infection types. …”
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Development of data-driven algal bloom alert models with low temporal resolution data and application to Hong Kong rivers by Shujie Xu, Zhongnan Ye, Shu-Chien Hsu, Xiaoyi Liu, Chunmiao Zheng

“…This study presents a data-driven algal bloom alert model employing machine learning and data discretization techniques to solve the data issue, utilizing monthly water quality data of 12 rivers and daily weather data in Hong Kong. …”
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NeuTox 2.0: A hybrid deep learning architecture for screening potential neurotoxicity of chemicals based on multimodal feature fusion by Xudi Pang, Xuejun He, Ying Yang, Ling Wang, Yuzhen Sun, Huiming Cao, Yong Liang

“…Evaluations of anti-noise ability indicated that NeuTox 2.0 has excellent noise resistance relative to traditional machine learning. We applied the NeuTox 2.0 model to predict the neurotoxicity of 315,790 compounds in the REACH database. …”
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Development and validation of a novel risk-predicted model for early sepsis-associated acute kidney injury in critically ill patients: a retrospective cohort study by Bo Li, Kun Zhang, Cong-Cong Zhao, Zi-Han Nan, Yan-Ling Yin, Li-Xia Liu, Zhen-Jie Hu

“…The least absolute shrinkage and selection operator regression method was used to screen the risk factors, and the final screened risk factors were constructed into four machine learning models to determine an optimal model. …”
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Comprehensive approach to predictive analysis and anomaly detection for road crash fatalities by Chopparapu Gowthami, S. Kavitha

“…The research offers policymakers, transportation authorities, and safety advocates practical insights by utilizing sophisticated machine-learning algorithms and integrating multiple datasets. …”
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Synthetic Data Generation and Evaluation Techniques for Classifiers in Data Starved Medical Applications by Wan D. Bae, Shayma Alkobaisi, Matthew Horak, Siddheshwari Bankar, Sartaj Bhuvaji, Sungroul Kim, Choon-Sik Park

“…With their ability to find solutions among complex relationships of variables, machine learning (ML) techniques are becoming more applicable to various fields, including health risk prediction. …”
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Learning and forecasting open quantum dynamics with correlated noise by Xinfang Zhang, Zhihao Wu, Gregory A. L. White, Zhongcheng Xiang, Shun Hu, Zhihui Peng, Yong Liu, Dongning Zheng, Xiang Fu, Anqi Huang, Dario Poletti, Kavan Modi, Junjie Wu, Mingtang Deng, Chu Guo

“…Here we propose a physics-inspired supervised machine learning approach to efficiently and accurately predict the functioning of quantum processors in the presence of correlated noise, which only requires data from randomized benchmarking experiments. …”
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A Comparison of Classification Algorithms for Predicting Dis-tinctive Characteristics in Fine Aroma Cocoa Flowers Using WE-KA Modeler by Daniel Tineo, Yuriko S. Murillo, Mercedes Marín, Darwin Gomez, Victor H. Taboada, Malluri Goñas, Lenin Quiñones Huatangari

“…This research provides a comprehensive overview of the use of machine learning to analyze functional traits of flowers that most influence cocoa genetic diversity. …”
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Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment by Fatma S. Alrayes, Mohammed Maray, Asma Alshuhail, Khaled Mohamad Almustafa, Abdulbasit A. Darem, Ali M. Al-Sharafi, Shoayee Dlaim Alotaibi

“…Privacy-preserving machine learning (ML) training in the development of aggregation permits a demander to firmly train ML techniques with the delicate data of IoT collected from IoT devices. …”
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Spatiotemporal Variation and Driving Factors of Carbon Sequestration Rate in Terrestrial Ecosystems of Ningxia, China by Yi Zhang, Chunxiao Cheng, Zhihui Wang, Hongxin Hai, Lulu Miao

“…Based on ground observation data and multimodal datasets, the optimal machine learning model (EXT) was used to invert a 30 m high-resolution vegetation and soil carbon density dataset for Ningxia from 2000 to 2023. …”
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Urine proteomics defines an immune checkpoint-associated nephritis signature by Cassian Yee, Jamie S Lin, James P Long, Shailbala Singh, Yanlan Dong

“…Using statistical and machine learning methods, we constructed a novel urine biomarker signature—IL-5+Fas—that achieved an area under the curve of 0.94 for diagnosing ICI-AIN.By leveraging high-sensitivity proteomics, we developed a non-invasive strategy for diagnosing ICI-AIN. …”
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Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity by Caden P. Chamberlain, Garrett W. Meigs, Derek J. Churchill, Jonathan T. Kane, Astrid Sanna, James S. Begley, Susan J. Prichard, Maureen C. Kennedy, Craig Bienz, Ryan D. Haugo, Annie C. Smith, Van R. Kane, C. Alina Cansler

“…Our framework used (1) machine learning to identify key bioclimatic, topographic, and fire weather drivers of burn severity in each fire, (2) standardized workflows to statistically sample untreated control units, and (3) spatial regression modeling to evaluate the effects of treatment type and time since treatment on burn severity. …”
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Development of improved deep learning models for multi-step ahead forecasting of daily river water temperature by Mehdi Gheisari, Jana Shafi, Saeed Kosari, Samaneh Amanabadi, Saeid Mehdizadeh, Christian Fernandez Campusano, Hemn Barzan Abdalla

“…These models integrate ensemble empirical mode decomposition (EEMD) with machine learning techniques for forecasting WT across multiple time horizons (one, three, and five days). …”
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