A recurrence model for non-puerperal mastitis patients based on machine learning. by Gaosha Li, Qian Yu, Feng Dong, Zhaoxia Wu, Xijing Fan, Lingling Zhang, Ying Yu

“…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”
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Comprehensive Analysis of Phenotypic Traits in Chinese Cabbage Using 3D Point Cloud Technology by Chongchong Yang, Lei Sun, Jun Zhang, Xiaofei Fan, Dongfang Zhang, Tianyi Ren, Minggeng Liu, Zhiming Zhang, Wei Ma

“…Based on the plant spread and plant height, a linear regression prediction of Chinese cabbage weights was conducted, yielding an R² value of 0.76. …”
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Multi-omics identification of a polyamine metabolism related signature for hepatocellular carcinoma and revealing tumor microenvironment characteristics by Yuexi Yu, Huiru Liu, Kaipeng Liu, Meiqi Zhao, Yiyan Zhang, Runci Jiang, Fengmei Wang

“…Utilizing 101 machine learning algorithms, we constructed a polyamine metabolism-related signature (PMRS) and validated its predictive power across training, testing, and external validation cohorts. …”
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Future Era of Accountants under the Impact of AI by Guan Ran

“…As AI reshapes the industry, accountants must develop expertise in data analytics, predictive modeling, and cybersecurity to remain competitive. …”
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Optimizing solar energy utilization in facilities using machine learning-based scheduling techniques: A case study by Hussam J. Khasawneh, Waseem M. Al-Khatib, Zaid A. Ghazal, Ahmad M. Al-Hadi, Zaid M. Arabiyat, Osama Habahbeh

“…Our approach overcomes these limitations by employing ML algorithms to accurately predict solar generation patterns, enabling more efficient scheduling of electrical appliances. …”
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Strong parameter hierarchy in the interstellar phosphorus chemical network by Marina Fernández-Ruz, Marina Fernández-Ruz, Izaskun Jiménez-Serra, Mario Castro, Mario Castro, Marta Ruiz-Bermejo, Jacobo Aguirre, Jacobo Aguirre

“…In this study, we introduce the Fisher Information Spectral Reduction (FISR) algorithm, a novel and computationally efficient method to reduce the complexity of such sloppy models. …”
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A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review by Ivan Maleš, Marko Kumrić, Andrea Huić Maleš, Ivan Cvitković, Roko Šantić, Zenon Pogorelić, Joško Božić

“…These tools also predict disease severity, aiding decisions between conservative management and surgery. …”
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A Survey of Matrix Completion Methods for Recommendation Systems by Andy Ramlatchan, Mengyun Yang, Quan Liu, Min Li, Jianxin Wang, Yaohang Li

“…The collaborative filtering problem can be viewed as predicting the favorability of a user with respect to new items of commodities. …”
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Automated interpretation of deep learning-based water quality assessment system for enhanced environmental management decisions by Javed Mallick, Saeed Alqadhi, Majed Alsubih, Mohamed Fatahalla Mohamed Ahmed, Hazem Ghassan Abdo

“…Methodologically, the study used CNN algorithms optimised by Bayesian techniques for the prediction of eight water quality indices, coupled with SHAPley Additive exPlanations (SHAP) analysis under XAI to interpret the complex decision-making processes of these models. …”
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Resilience evaluation of memristor based PUF against machine learning attacks by Hebatallah M. Ibrahim, Heorhii Skovorodnikov, Hoda Alkhzaimi

“…Our results yield low accuracy and ROC results of within $$0.49-0.52$$ 0.49 - 0.52 and $$0.49-0.52$$ 0.49 - 0.52 respectively, indicating failure in predicting random data demonstrates efficient randomness prediction resiliency of the MR-PUF. …”
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AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view by Mostafa Jalilifar, Mahdi Sadeghi, Alireza Emami-Ardekani, Ahmad Bitarafan-Rajabi, Kouhyar Geravand, Parham Geramifar

“…The MLP-predicted dose values across all organs represented superior performance with the lowest mean absolute error in the liver but higher in the spleen and salivary glands. …”
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Optimization of Flavor Quality of Lactic Acid Bacteria Fermented Pomegranate Juice Based on Machine Learning by Wenhui ZOU, Fei PAN, Junjie YI, Linyan ZHOU

“…Binary classification models of HWPS and LWPS were established by random forest (RF) and adaptive boosting (AdaBoost) algorithms, and RF algorithm had higher prediction precision and accuracy. …”
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Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India by Srikanth Bhoopathi, Nitish Kumar, Somesh, Manali Pal

“…Conversely, for a 15-day lead time in Zone-1, XGBoost better predicts temperature peaks in both phases. In Zone-3 (T30AMT: 30 °C–35 °C) and Zone-4 (T30AMT < 30 °C) for both lead times, the performance of both models decline, indicating models and input variables are more effective in predicting higher temperatures typical of Zone-1 and 2 but less so in Zone-3 and 4. …”
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Ground Based Cloud Recognition with an Anchor Free Method by LI Yichao, GUO Rui, ZHANG Shaodi, SHOU Zefeng, CHEN Jing, LIU Yifei

“…In order to solve the problems of cloud type recognition of the ground-based cloud such as complex target candidate box selection and slow detection speed, a recognition method of cloud types in ground-based cloud map based on anchor free is proposed.First, the paper takes Center Net as the basic architecture of cloud type recognition.Based on thermodynamic diagrams prediction, key point prediction, center point prediction and candidate box prediction, a anchor free ground-based cloud type detection process is constructed.And then, the main network, loss function and candidate box prediction method of cloud type recognition model are designed.Finally, take CenterNet Resdcn101 as the model, compared the algorithm recognition accuracy, candidate boxes predict confidence and identify speed with mainstream target recognition methods and cloud type recognition method The results showed that the cloud type recognition method of the paper has higher recognition accuracy and faster recognition speed.…”
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Toward sustainable machining of hardened SKD11: Machine learning-based evaluation and optimization of surface roughness, tool wear, and CO2 emissions by Van-Canh Nguyen, Dung Hoang Tien, Van-Hung Pham, Thinh-Viet Nguyen, Thuy-Duong Nguyen

“…Results indicated that the second-order model demonstrated superior predictive accuracy for Ra (R² = 0.997) and CE (R² = 0.994), whereas the Vb prediction model failed to achieve sufficient reliability. …”
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Bio-Inspired Compliant Joints and Economic MPC Co-Design for Energy-Efficient, High-Speed Locomotion in Snake-like Robots by Shuai Zhou, Gengbiao Chen, Mingyu Gong, Jing Liu, Peng Xu, Binshuo Liu, Nian Yin

“…The proposed predictive control strategy optimizes locomotion speed while minimizing energy consumption. …”
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Transcriptomics-based exploration of ubiquitination-related biomarkers and potential molecular mechanisms in laryngeal squamous cell carcinoma by Qiu Chen, Zhimin Wu, Yifei Ma

“…Then, therapeutic agents for biomarkers were predict. In addition, the regulatory networks of the biomarkers were mapped. …”
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Integrated Ultrasound‐Enrichment and Machine Learning in Colorimetric Lateral Flow Assay for Accurate and Sensitive Clinical Alzheimer's Biomarker Diagnosis by Shuqing Wang, Yan Zhu, Zhongzeng Zhou, Yong Luo, Yan Huang, Yibiao Liu, Tailin Xu

“…The LFA device is integrated with a portable ultrasonic actuator to rapidly enrich microparticles using ultrasound, which is essential for sample pre‐enrichment to improve the sensitivity, followed by ML algorithms to classify and predict the enhanced colorimetric signals. …”
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Blockchain and Bigdata to Secure Data Using Hash and Salt Techniques by AlSelami Fudhah A.

“…In recent times amount of data is increasing rapidly and analysis of data is a must to come up with business decisions, predictions etc. It’s not just text are numbers which has to be stored properly. …”
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AFQSeg: An Adaptive Feature Quantization Network for Instance-Level Surface Crack Segmentation by Shaoliang Fang, Lu Lu, Zhu Lin, Zhanyu Yang, Shaosheng Wang

“…The trainable crack post-processing module incorporates edge-guided post-processing algorithms to correct false predictions and refine segmentation results. …”
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