The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis by Hatice Kabaoğlu, Emine Uçar, Fecir Duran

“…Subsequently, the RF algorithm classifies these predictions into specific error conditions. …”
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SOFTWARE IMPLEMENTATION OF THE ARTIFICIAL NEURAL NETWORK FOR VIRTUAL OBJECTS СONTROL by Yu. B. Popova, S. V. Yatsynovich

“…The use of an artificial neural network has reduced the use of CPU time, which is extremely important in problems where rapid decision making is required, because complex calculations and prediction algorithms can not always be invested in 20 ms, which is fraught with skipping moves and losses. …”
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Artificial intelligence in colorectal cancer: a review by G. Singh

“…Challenges in AI development are addressed, such as data standardization and the interpretability of machine learning algorithms. The potential of AI in treatment decision support, precision medicine, and prognosis prediction is discussed, emphasizing the role of AI in selecting optimal treatments and improving surgical precision. …”
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Multimodal data integration in early-stage breast cancer by Arnau Llinas-Bertran, Maria Butjosa-Espín, Vittoria Barberi, Jose A. Seoane

“…However, existing knowledge does not fully encompass the diverse nature of breast cancer, particularly in triple-negative tumors.The integration of multi-omics and multimodal data has the potential to provide new insights into biological processes, to improve breast cancer patient stratification, enhance prognosis and response prediction, and identify new biomarkers.This review presents a comprehensive overview of the state-of-the-art multimodal (including molecular and image) data integration algorithms developed and with applicability to breast cancer stratification, prognosis, or biomarker identification. …”
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Breast Cancer Identification from Patients’ Tweet Streaming Using Machine Learning Solution on Spark by Nahla F. Omran, Sara F. Abd-el Ghany, Hager Saleh, Ayman Nabil

“…This paper presented a real-time system to predict breast cancer based on streaming patient’s health data from Twitter. …”
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Incorporating Wave-ViT for Breast Cancer Diagnosis Using MRI Imaging by Sahil Mahey, Hamid Usefi

“…Machine learning (ML) algorithms offer a transformative solution by automating this process, improving efficiency, and enhancing diagnostic accuracy. …”
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New Approach for Unambiguous High-Resolution Wide-Swath SAR Imaging by Yueguan Lin, Yida Fan, Hai Jiang, Wei Wang

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Addressing imbalanced data classification with Cluster-Based Reduced Noise SMOTE. by Javad Hemmatian, Rassoul Hajizadeh, Fakhroddin Nazari

“…CRN-SMOTE combines SMOTE for oversampling minority classes with a novel cluster-based noise reduction technique. In this cluster-based noise reduction approach, it is crucial that samples from each category form one or two clusters, a feature that conventional noise reduction methods do not achieve. …”
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Free field Active Noise Control system development using a 3D Finite Element based approach by Budnik Maximilian, Mees Valentin

“…It further allows for the optimization of control algorithms, evaluation of the attenuation performance, and methodical iteration of the overall concept design. …”
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Multi-function supported privacy protection data aggregation scheme for V2G network by Baiji HU, Xiaojuan ZHANG, Yuancheng LI, Rongxin LAI

“…In view of the problem that the functions of the current privacy protection data aggregation scheme were insufficient to meet the increasingly rich application requirements, a multi-function supported privacy protection data aggregation (MFPDA) scheme for V2G network was proposed.By using cryptographic algorithms such as BGN, BLS, and Shamir’s secret sharing, as well as fog computing and consortium blockchain technology, multiple security functions like fault tolerance, resistance to internal attacks, batch signature verification, no need for trusted third parties, and multiple aggregation functions were integrated into one privacy protection data aggregation scheme.Security analysis shows that the proposed scheme can protect data aggregation’s security, privacy and reliability.The performance evaluation shows that the introduction of fog computing can significantly reduce the computing overhead of the control center, and the reduction rate can be as high as 66.6%; the improvement of the consortium blockchain can effectively reduce the communication and storage overhead of the system, and the reduction rate can reach 16.7% and 24.9% respectively.…”
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Cardiac computed tomography: Current practice, guidelines, applications, and prospects by Yu-Tai Shih, Jia-Hao Zhou, Jong-Kai Hsiao

“…Cardiac computed tomography (CT) has evolved significantly as a critical tool in diagnosing and managing cardiac diseases, greatly facilitated by technological advancements in multidetector systems, dose-reduction techniques, and sophisticated imaging algorithms. …”
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A Feasibility Study of Domain Adaptation for Exercise Intensity Recognition Based on Wearable Sensors by Lei Pang, Yi Li, Ming-Xia Liao, Jia-Gang Qiu, Hui Li, Zhen Wang, Gang Sun

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The value of multi-phase CT based intratumor and peritumoral radiomics models for evaluating capsular characteristics of parotid pleomorphic adenoma by Qian Shen, Cong Xiang, Yongliang Han, Yongmei Li, Kui Huang

“…Quantitative radiomics features of the intratumoral and peritumoral regions of 2 mm and 5 mm on CT images were extracted, and radiomics models of Tumor, External2, External5, Tumor+ External2, and Tumor+External5 were constructed and used to train six different machine learning algorithms. Meanwhile, the prediction performances of different radiomics models (Tumor, External2, External5, Tumor+External2, Tumor+External5) based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared. …”
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Risk assessment of water inrush from coal floor based on enhanced samples with class distribution by Shiwei Liu, Jiaxin Zhao, Hao Yu, Jiaqi Chen

“…Compared to other optimization models, our model showed the best prediction performance, with an error reduction of 42.95–51.27% and results biased towards safety. …”
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Development and validation of a chronic kidney disease progression model using patient-level simulations by Mafalda Ramos, Laetitia Gerlier, Anastasia Uster, Louise Muttram, Dominik Steubl, Andrew H Frankel, Mark Lamotte

“…The CKD-PM demonstrated satisfactory modeling properties. Accurate prediction of all-cause and CV mortality was achieved without calibration, while prediction of CV events through CKD-specific equations required implementation of a calibration factor to balance time-dependent versus baseline risk. …”
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Hyperspectral imaging combined with machine learning for high‐throughput phenotyping in winter wheat by Sehijpreet Kaur, Vijaya Gopal Kakani, Brett Carver, Diego Jarquin, Aditya Singh

“…This highlights the importance of utilizing appropriate machine learning algorithms for improved prediction of plant traits.…”
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A Review of Vehicle Dynamics and Control Approaches for Mitigating Motion Sickness in Autonomous Vehicles by Ilhan Yunus, Georgios Papaioannou, Jenny Jerrelind, Lars Drugge

“…Effective mitigation requires accurate and reliable prediction. In this context, motion-based prediction approaches, recognised for their practicality, cost-effectiveness, and promising results, are examined in detail with particular focus on ISO-based methods and sensory conflict theory-based models. …”
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Optimizing Immunotherapy: The Synergy of Immune Checkpoint Inhibitors with Artificial Intelligence in Melanoma Treatment by Mohammad Saleem, Abigail E. Watson, Aisha Anwaar, Ahmad Omar Jasser, Nabiha Yusuf

“…Immune checkpoint inhibitors (ICIs) have transformed melanoma treatment; however, predicting patient responses remains a significant challenge. …”
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Explainable AI-driven intelligent system for precision forecasting in cardiovascular disease by Anas Bilal, Abdulkareem Alzahrani, Khalid Almohammadi, Muhammad Saleem, Muhammad Sajid Farooq, Raheem Sarwar

“…IntroductionCardiovascular diseases (CVDs) are complex and affect a large part of the world’s population; early accurate and timely prediction is also complicated. Typically, predicting CVDs involves using statistical models and other forms of standard machine learning. …”
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Trends in indirect liver function marker testing in Wales from 2000 to 2017 and their association with age and sex: an observational study by Joe West, Joanne R Morling, Trevor Alexander Hill, Colin John Crooks

“…AST and ALT values varied by age and sex, particularly in males with the upper normal range of ALT values decreasing rapidly from 90 U/L at age 30 to 45 U/L by age 80.Conclusion The reduction in AST testing to only 1% of the adult population limits the use of many non-invasive liver marker algorithms. …”
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