Ultrasound-based machine learning model to predict the risk of endometrial cancer among postmenopausal women by Yi-Xin Li, Yu Lu, Zhe-Ming Song, Yu-Ting Shen, Wen Lu, Min Ren

“…Three models were developed: (1) R model: radiomics-based machine learning (ML) algorithms; (2) CNN model: image-based CNN algorithms; (3) DLR model: a hybrid model combining radiomics and deep learning features with ML algorithms. …”
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Metabolic pathway activation and immune microenvironment features in non-small cell lung cancer: insights from single-cell transcriptomics by Yanru Liu, Yanru Liu, Yanru Liu, Hanmin Liu, Hanmin Liu, Ying Xiong, Ying Xiong

“…Wet lab experiments validated the role of KRT6B in NSCLC, demonstrating that KRT6B expression is elevated and it stimulates the proliferation of cancer cells.DiscussionThese observations not only enhance our understanding of metabolic reprogramming and its biological functions in NSCLC but also provide new perspectives for early detection, prognostic evaluation, and targeted therapy. However, future research should further explore the specific mechanisms of these metabolic pathways and their application potentials in clinical practice.…”
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AN INTELLIGENT POSTOPERATIVE CHRONIC PAIN PREDICTION SYSTEM (I-POCPP) by Elif Kartal, Fatma Önay Koçoğlu, Zeki Özen, İlkim Ecem Emre, Gürcan Güngör, Pervin Sutaş Bozkurt

“…Objective: Postoperative Chronic Pain (POCP) affects the quality of patients’ lives. Machine learning and its applications provide significant contributions to pain research. …”
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Process capability index Cpk for monitoring the thermal performance in the distribution of refrigerated products by Antonio Galvão Naclério Novaes, Orlando Fontes Lima Jr, Carolina Corrêa de Carvalho, Dmontier Pinheiro Aragão Junior

“…In this application, a refrigerated food delivery route is investigated using a simulated annealing algorithm that considers alternative delivery schemes. …”
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Machine Learning‐Enhanced Optimization for High‐Throughput Precision in Cellular Droplet Bioprinting by Jaemyung Shin, Ryan Kang, Kinam Hyun, Zhangkang Li, Hitendra Kumar, Kangsoo Kim, Simon S. Park, Keekyoung Kim

“…In this study, a high‐throughput cellular droplet bioprinter is designed, capable of printing over 50 cellular droplets simultaneously, producing the large dataset required for effective machine learning training. Among the five algorithms evaluated, the multilayer perceptron model demonstrates the highest prediction accuracy, while the decision tree model offers the fastest computation time. …”
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A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization by Mostafa Akbari, Ezatollah Hassanzadeh, Yaghuob Dadgar Asl, Amirhossein Moghanian

“…This functionality allows for immediate parameter adjustments, thus significantly improving weld consistency and quality by minimizing defects. Lastly, the third section pertains to the optimization of FSW parameters, illustrating how AI-driven algorithms analyze complex interactions among multiple variables to determine the most effective process settings. …”
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Enhancing seizure detection with hybrid XGBoost and recurrent neural networks by Santushti Santosh Betgeri, Madhu Shukla, Dinesh Kumar, Surbhi B. Khan, Muhammad Attique Khan, Nora A. Alkhaldi

“…Furthermore, EEG signal preprocessing methods improved data quality, raising classification accuracy. This Model can be implemented for real-time monitoring using wearable devices, enabling continuous patient observation and remote healthcare applications.…”
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Preference learning based deep reinforcement learning for flexible job shop scheduling problem by Xinning Liu, Li Han, Ling Kang, Jiannan Liu, Huadong Miao

“…Abstract The flexible job shop scheduling problem (FJSP) holds significant importance in both theoretical research and practical applications. Given the complexity and diversity of FJSP, improving the generalization and quality of scheduling methods has become a hot topic of interest in both industry and academia. …”
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The inconvenient truth of ground truth errors in automotive datasets and DNN-based detection by Pak Hung Chan, Boda Li, Gabriele Baris, Qasim Sadiq, Valentina Donzella

“…Assisted and automated driving functions will rely on machine learning algorithms, given their ability to cope with real-world variations, e.g. vehicles of different shapes, positions, colors, and so forth. …”
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Machine Learning Approaches for Software Defect Prediction by Hijab Zehra Zaidi, Ubaid Ullah, Muddassira Arshad, Hanan Aljuaid, Muhammad Arslan Rauf, Nadeem Sarwar, Rimsha Sajid

“…This paper analyses existing research about machine learning approaches in software defect prediction as a key element for improving software reliability and quality. The paper reviews the use of machine learning algorithms in software defect prediction framework’s bug prediction while assessing their performance across multiple environments. …”
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A refined SMR-DeepLabV3+ model for cultivated land extraction from cross-view imagery using an attention mechanism by Zheng Fang, Yuhang Gong, Heli Zhu, Canyang Shi, Zhijia Gong, Lin Tian

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Comparative performance of twelve machine learning models in predicting COVID-19 mortality risk in children: a population-based retrospective cohort study in Brazil by Adriano Lages dos Santos, Maria Christina L. Oliveira, Enrico A. Colosimo, Robert H. Mak, Clara C. Pinhati, Stella C. Gallante, Hercílio Martelli-Júnior, Ana Cristina Simões e Silva, Eduardo A. Oliveira

“…The COVID-19 pandemic has catalyzed the application of advanced digital technologies such as artificial intelligence (AI) to predict mortality in adult patients. …”
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A Machine Learning Dataset of Artificial Inner Ring Damage on Cylindrical Roller Bearings Measured Under Varying Cross-Influences by Christopher Schnur, Payman Goodarzi, Yannick Robin, Julian Schauer, Andreas Schütze

“…In practical machine learning (ML) applications, covariate shifts and dependencies can significantly impact model robustness and prediction quality, leading to performance degradation under distribution shifts. …”
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Enhanced Metabolic Control in a Pediatric Population with Type 1 Diabetes Mellitus Using Hybrid Closed-Loop and Predictive Low-Glucose Suspend Insulin Pump Treatments by Irina Bojoga, Sorin Ioacara, Elisabeta Malinici, Victor Chiper, Olivia Georgescu, Anca Elena Sirbu, Simona Fica

“…The analyzed parameters, such as HbA1c (6.9 ± 0.7% vs. 6.7 ± 0.6%, <i>p</i> = 0.023), time in range (69.3 ± 11.2% vs. 76 ± 9.9%, <i>p</i> < 0.001), time in tight range (47.4 ± 10.9% vs. 53.7 ± 10.7%, <i>p</i> < 0.001), time below range (5.6 ± 2.9% vs. 3.5 ± 1.9%, <i>p</i> < 0.001), time above range (25 ± 11.2% vs. 20.4 ± 9.4%, <i>p</i> = 0.001), and coefficient of variation (37.9 ± 4.8% vs. 35.6 ± 4.6%, <i>p</i> = 0.001), showed significant improvements. Conclusions: The application of these sensor-integrated insulin pumps can significantly enhance metabolic control in pediatric populations, minimizing glycemic variations to mitigate complications and enrich the quality of life.…”
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VQABG: Vietnamese question/answers benchmark generator for field-specific chatbot ground-truth dataset using EMINI (Exact Match wIth Numeric Information) indicator by Anh-Khoa NGO-HO, Khuong-Duy Vo, Anh-Khoi NGO-HO

“…However, there is almost no complete model available for this task in Vietnamese. Regarding quality assessment, this is usually performed manually by experts using Human Evaluation (HE) indicators, which is also costly. …”
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Fishing for biomarkers: analyzing mass spectrometry data with the new ClinProTools™ software by Ralf Ketterlinus, Sen-Yung Hsieh, Shih-Hua Teng, Helen Lee, Wolfgang Pusch

“…For this purpose, efficient visualization of large data sets derived from patient cohorts is crucial to provide clinical experts an interactive impression of the data quality. Additionally, it is necessary to apply statistical analysis and pattern matching algorithms to attain validated signal patterns that may allow for later applications in sample classification. …”
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Nonreference Medical Image Edge Map Measure by Karen Panetta, Chen Gao, Sos Agaian, Shahan Nercessian

“…It is widely used to extract features, perform segmentation, and further assist in diagnosis. A poor quality edge map can result in false alarms and misses in cancer detection algorithms. …”
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Short-term Wind Power Forecasting Based on BWO‒VMD and TCN‒BiGRU by LU Jing, ZHANG Yanru, WANG Rui

“…Its capacity to manage complex time series data is demonstrated, and the advancement and innovation of algorithms and models are supported. New directions for technological progress and practical application in the field of wind power prediction are established.…”
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VQABG: Vietnamese question/answers benchmark generator for field-specific chatbot ground-truth dataset using EMINI (Exact Match wIth Numeric Information) indicator by Anh-Khoa NGO-HO, Khuong-Duy Vo, Anh-Khoi NGO-HO

“…However, there is almost no complete model available for this task in Vietnamese. Regarding quality assessment, this is usually performed manually by experts using Human Evaluation (HE) indicators, which is also costly. …”
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Sensitivity of biological sewage disposal efficiency functional to parameters of biogen concentration dynamics model by Е. S. Zhmenya, N. S. Buzalo

“…To simplify the providing data process, the sensitivity of the water quality functional to the model parameters is evaluated.Materials and Methods. …”
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