Identification of M2 macrophage-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches by Jiayi Zhang, Zhixiang Jia, Jiahui Zhang, Xiaohui Mu, Limei Ai

“…Subsequently, the constructed logistic regression model and nomogram demonstrated robust predictive performance. We further investigated the expression levels, prognostic values, and biological functions of these biomarkers. …”
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Application of selective ensemble learning soft sensor modeling based on GPR in the solar thermal power collection system by Yalu Sun, Kun Ding, Changhai Yang, Zhuxiu Wang

“…Finally, the set pruning strategy based on a genetic algorithm (GA) was used to select the MGPR model with high estimation performance, and the base models were fused by the Stacking algorithm to obtain the SESMGPR model. …”
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Identification of Candidate Tolerogenic CD8+ T Cell Epitopes for Therapy of Type 1 Diabetes in the NOD Mouse Model by Cailin Yu, Jeremy C. Burns, William H. Robinson, Paul J. Utz, Peggy P. Ho, Lawrence Steinman, Alan B. Frey

“…Proteins that elicit autoantibodies in human type 1 diabetes were analyzed by predictive algorithms for candidate epitopes. Using several different tolerizing regimes using synthetic peptides, two new predicted tolerogenic CD8+ T cell epitopes were identified in the murine homolog of the major human islet autoantigen zinc transporter ZnT8 (aa 158–166 and 282–290) and one in a non-β cell protein, dopamine β-hydroxylase (aa 233–241). …”
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Intelligent data-driven system for mold manufacturing using reinforcement learning and knowledge graph personalized optimization for customized production by Chengcai He, Jiaxing Deng, Jingchun Wu, Beicheng Qin, Jinxiang Chen, Yan Li, Qiangsheng Huang

“…When actual qualification rates exceed 88.1%, the model’s regression fit also surpasses 88.1%, indicating strong alignment between predicted and actual performance. (2) Compared with other algorithmic models, the proposed approach achieves a predictive accuracy of over 94.7%. …”
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Artificial Intelligence in Advancing Algal Bioactive Ingredients: Production, Characterization, and Application by Bingbing Guo, Xingyu Lu, Xiaoyu Jiang, Xiao-Li Shen, Zihao Wei, Yifeng Zhang

“…This review examines the multidimensional mechanisms by which AI enables and optimizes these processes: (1) AI-powered predictive models, integrated with machine learning algorithms (MLAs), Industry 4.0, and other advanced digital systems, support real-time monitoring and control of intelligent bioreactors, allowing for accurate forecasting of cultivation yields and market demand. (2) AI facilitates in-depth analysis of gene regulatory networks and key metabolic pathways, enabling precise control over the biosynthesis of targeted compounds. (3) AI-based spectral imaging and image recognition techniques enable rapid and reliable identification, classification, and quality assessment of active components. (4) AI accelerates the transition from mass production to the development of personalized medical and functional nutritional products. …”
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Adopting TOGAF Framework for Sustainable and Scalable Robusta Coffee Leaf Rust Management by Thein Oak Kyaw Zaw, Kalaiarasi Sonai Muthu Anbananthen, Saravanan Muthaiyah, Baarathi Balasubramaniam, Suraya Mohammad, Yunus Yusoff, Khairul Shafee Kalid

“…The framework leverages enterprise architecture principles to integrate learning algorithms, image detection, and systematic plantation mapping within a structured approach that enhances data organization, rust severity visualization, and predictive analysis. …”
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Using machine learning to identify key predictors of maternal success in sheep for improved lamb survival by Ebru Emsen, Bahadir Baran Odevci, Muzeyyen Kutluca Korkmaz

“…Several machine learning algorithms, including Random Forest, Decision Trees, Logistic Regression, and Support Vector Machines (SVM), were evaluated for predictive accuracy. …”
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Artificial Intelligence and Smart Technologies in Safety Management: A Comprehensive Analysis Across Multiple Industries by Jiyoung Park, Dongheon Kang

“…AI-driven solutions, such as predictive analytics, machine learning algorithms, IoT sensor integration, and digital twin models, are shown to proactively identify and mitigate potential hazards, optimize energy consumption, and enhance operational efficiency. …”
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The Hydrodynamic Performance of a Vertical-Axis Hydro Turbine with an Airfoil Designed Based on the Outline of a Sailfish by Aiping Wu, Shiming Wang, Chenglin Ding

“…Through Latin hypercube experimental design combined with optimization algorithms, four key geometric variables governing the airfoil’s hydrodynamic characteristics were systematically analyzed. …”
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Development of Electronic Nose as a Complementary Screening Tool for Breath Testing in Colorectal Cancer by Chih-Dao Chen, Yong-Xiang Zheng, Heng-Fu Lin, Hsiao-Yu Yang

“…We then used machine learning algorithms to develop predictive models and provided the estimated accuracy and reliability of the breath testing. (3) Results: We enrolled 77 patients, with 40 cases and 37 controls. …”
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Comparative Analysis of Machine Learning Techniques for Fault Diagnosis of Rolling Element Bearing with Wear Defects by Devendra Sahu, Ritesh Kumar Dewangan, Surendra Pal Singh Matharu

“…The findings contribute to a more accurate and reliable identification of faults, offering significant advancements in the field of machinery health monitoring and predictive maintenance.…”
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Transfer and deep learning models for daily reference evapotranspiration estimation and forecasting in Spain from local to national scale by Yu Ye, Aurora González-Vidal, Miguel A. Zamora-Izquierdo, Antonio F. Skarmeta

“…During forecasting, we used predicted weather data as input, and despite inherent biases in some variables, the TL models successfully adapted using 9-36 days of new data, significantly improving predictive performance (reducing MAE from -1.1% to 134.3%). …”
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Unravelling the importance of spatial and temporal resolutions in modeling urban air pollution using a machine learning approach by Alireza Zhalehdoost, Mohammad Taleai

“…Among the evaluated algorithms, MLP consistently achieves the highest predictive accuracy across both temporal and spatial scenarios. …”
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The clinical performance of an office-based risk scoring system for fatal cardiovascular diseases in North-East of Iran. by Sadaf G Sepanlou, Reza Malekzadeh, Hossein Poustchi, Maryam Sharafkhah, Saeed Ghodsi, Fatemeh Malekzadeh, Arash Etemadi, Akram Pourshams, Paul D Pharoah, Christian C Abnet, Paul Brennan, Paolo Boffetta, Sanford M Dawsey, Farin Kamangar

“…Most of these risk score algorithms have been based on a long array of risk factors including blood markers of lipids. …”
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Validation study of bullous pemphigoid and pemphigus vulgaris recording in routinely collected electronic primary healthcare records in England by Yana Vinogradova, Julia Hippisley-Cox, Sonia Gran, Sinéad M Langan, Monica S M Persson, Karen E Harman, Kim S Thomas

“…We assessed the positive predictive value (PPV) for bullous pemphigoid and pemphigus vulgaris primary care Read codes in the Clinical Practice Research Datalink (CPRD) using linked inpatient data (Hospital Episode Statistics (HES)) as the diagnostic benchmark.Setting Adult participants with bullous pemphigoid or pemphigus vulgaris registered with HES-linked general practices in England between January 1998 and December 2017. …”
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Intratumoral Heterogeneity Scores as Predictors of Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules: Insights from Explainable Machine Learning-Based Ter... by Wang Peng BS, Wanyin Qi BS, Yunhua Li BS, Sanhong Zhang BS, Juan Long BS

“…Therefore, this study aimed to develop ternary classification models to classify AIS, MIA, and IAC by leveraging insights from 15 machine-learning algorithms and integrating ITH scores with clinical data. …”
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Neural Network Optimization of Mechanical Properties of ABS-like Photopolymer Utilizing Stereolithography (SLA) 3D Printing by Abdulkader Ali Abdulkader Kadauw

“…This approach uses machine learning algorithms to analyze and predict the relationships between various printing parameters and the resulting mechanical properties, thereby allowing the engineering of better materials specifically designed for targeted applications. …”
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PLOD3 as a novel oncogene in prognostic and immune infiltration risk model based on multi-machine learning in cervical cancer by Lingling Qiu, Xiuchai Qiu, Xiaoyi Yang

“…In this study, offer a precision medicine methods for predicting patient outcomes as well as fresh insights into the metabolic foundations, which may contribute to the prognosis and immunotherapy of CC. …”
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A Machine Learning and Remote Sensing‐Based Model for Algae Pigment and Dissolved Oxygen Retrieval on a Small Inland Lake by Maxwell R. W. Beal, Mutlu Özdoğan, Paul J. Block

“…Machine learning methods are implemented with existing algorithms to model chlorophyll‐a, phycocyanin, and Pc:Chla. …”
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A review on artificial intelligence thermal fluids and the integration of energy conservation with blockchain technology by Abdullah Ayub Khan, Asif Ali Laghari, Syed Azeem Inam, Sajid Ullah, Laila Nadeem

“…In order to support sustainable energy goals, these highlighted machine learning algorithms offer a potent environment for optimising energy flow, temperature regulation, and application stability. …”
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