Preparation of land subsidence susceptibility map using machine learning methods based on decision tree (case study: Isfahan–Borkhar) by Negar Ghasemi, Iman Khosravi, Ali Bahrami

“…DT produced moderately reliable results, with an accuracy of 90.58%, while XGBoost was the least effective, achieving just 75.42% accuracy and failing to predict high-risk subsidence zones accurately.Susceptibility mapping: The RF algorithm highlighted central and eastern parts of Isfahan–Borkhar as the most vulnerable to subsidence, driven primarily by excessive groundwater withdrawal and geological factors. …”
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A Framework for High-Spatiotemporal-Resolution Soil Moisture Retrieval in China Using Multi-Source Remote Sensing Data by Zhuangzhuang Feng, Xingming Zheng, Xiaofeng Li, Chunmei Wang, Jinfeng Song, Lei Li, Tianhao Guo, Jia Zheng

“…This indicates that multi-source data can provide complementary information for SM monitoring. (2) Compared to XGBoost and LSTM, RFR and EL demonstrate superior overall performance and are the preferred models for SM prediction. Their R² for the training and test sets exceed 0.969 and 0.743, respectively, and their ubRMSE are below 0.022 and 0.063 m³/m³, respectively. (3) The SM prediction accuracy is highest for the scenario of optical + SAR + auxiliary data, followed by SAR + auxiliary data, and finally optical + auxiliary data. (4) With an increasing Normalized Difference Vegetation Index (NDVI) and SM values, the trained models exhibit a general decrease in prediction performance and accuracy. (5) In 2021 and 2022, without considering cloud cover, the IF theoretically achieved an SM revisit time of 1–3 days across 95.01% and 96.53% of China’s area, respectively. …”
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Shared gene signatures and molecular mechanisms link ankylosing spondylitis and rheumatoid arthritis by Boli Qin, Xiaopeng Qin, Jie Ma, Chenxing Zhou, Tianyou Chen, Jichong Zhu, Chengqian Huang, Shaofeng Wu, Rongqing He, Songze Wu, Sitan Feng, Jiarui Chen, Jiang Xue, Wendi Wei, Tengxiang Long, Quan Pan, Kechang He, Zhendong Qin, Tiejun Zhou, Jiayan Jiang, Xinli Zhan, Chong Liu

“…The CBC data of 23,289 patients were collected, and six machine learning algorithms were applied to develop disease prediction models for AS and RA. …”
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Digital twin-driven method for determining wind force coefficients of a bridge deck section by Hao-Yang Li, You-Lin Xu, Hai-Li Liao, Lin Huang, Qi Wang

“…Given that limitations and uncertainties exist in both methods, this study proposes a digital twin-driven method for providing more accurate predictions of wind force coefficients of a streamlined bridge deck. …”
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Analysis of reservoir rock permeability changes due to solid precipitation during waterflooding using artificial neural network by Azizollah Khormali, Soroush Ahmadi, Aleksandr Nikolaevich Aleksandrov

“…A comparison of the predicted values of rock permeability under scaling conditions with experimental data showed that the proposed ANN model makes it possible to predict formation damage due to scaling with high accuracy. …”
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Dealing with the Outlier Problem in Multivariate Linear Regression Analysis Using the Hampel Filter by Amira Wali Omer, Taha Hussein Ali

“…In this study, proposes a Hampel filter-modified algorithm to dynamically detect and mitigate extreme values, enhancing parameter estimation and predictive performance. …”
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Non-Destructive Detection of Fruit Quality: Technologies, Applications and Prospects by Jingyi Liu, Jun Sun, Yasong Wang, Xin Liu, Yingjie Zhang, Haijun Fu

“…In the future, efforts should be made to enhance the implementation of non-destructive testing technology in the fruit industry through technology integration, optimization algorithm, cost reduction, and expansion of industrial chain application, so as to help the premium growth of the fruit industry.…”
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Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease by Daryll Philip, Rebecca Hodgkiss, Swarnima Kollampallath Radhakrishnan, Akshat Sinha, Animesh Acharjee

“…These models were then employed for cross-disease analysis, revealing that models trained on GC data successfully predicted IBD biomarkers, while CRC models predicted GC biomarkers with optimal performance scores. …”
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A Multi-Modal Attentive Framework That Can Interpret Text (MMAT) by Vijay Kumari, Sarthak Gupta, Yashvardhan Sharma, Lavika Goel

“…Deep learning algorithms have demonstrated exceptional performance on various computer vision and natural language processing tasks. …”
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Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions by Giovanna Nicora, Samuele Pe, Gabriele Santangelo, Lucia Billeci, Irene Giovanna Aprile, Marco Germanotta, Riccardo Bellazzi, Enea Parimbelli, Silvana Quaglini

“…ML is reported as highly effective in predicting movement intentions, assessing clinical outcomes, and detecting compensatory movements, providing insights into the future of personalized rehabilitation interventions. …”
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Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas by Safiah Almarri, Hur Al Safwan, Shahd Al Qisoom, Soufien Gdaim, Abdelkrim Zitouni

“…The system also integrates artificial intelligence (AI) algorithms (multiclass logistic regression) to process sensor data and predict fire risk levels with 99.97% accuracy, enabling proactive wildfire mitigation. …”
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Assessing CO2 separation performances of IL/ZIF-8 composites using molecular features of ILs by Hasan Can Gulbalkan, Alper Uzun, Seda Keskin

“…The accurate prediction power of these ML models was shown by comparing their estimates with the experimental and simulation data. …”
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An Enhanced Seamless Localization Framework Using Spatial-temporal Uncertainty Predictor Under Obscured Indoor and Outdoor Scenes by Y. Yu, S. Bai, J. Wang, K. Tang, S. Bao, L. Chen, R. Chen

“…An iPDR-based trajectory estimation structure is proposed, using the integration of INS/PDR mechanizations, magnetic observations, and deep-learning based speed estimation to enhance the performance of traditional PDR algorithm. A period of human motion features extracted from hybrid location sources are modelled instead of only one or adjacent location points to realize time-varying measured uncertainty errors prediction, and the predicted uncertainty errors of different indoor and outdoor location sources are integrated with iPDR to realize robust seamless positioning performance. …”
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Establishment and validation of an immune-related nomogram for the prognosis of pancreatic adenocarcinoma by Kan Wang, Yunkun Lu, Yanfei Cao, Ping Feng, Qiu Wu, Peng Xiao, Yimin Ding

“…This study aims to improve prognosis prediction to guide therapeutic decision-making, and to identify novel targets for immunotherapy of PDAC. …”
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Development of Recurrent Neural Networks for Thermal/Electrical Analysis of Non-Residential Buildings Based on Energy Consumptions Data by Elisa Belloni, Flavia Forconi, Gabriele Maria Lozito, Martina Palermo, Michele Quercio, Francesco Riganti Fulginei

“…Simplifying input variables can enhance the applicability of artificial intelligence techniques in predicting energy and thermal performance. This study proposes a neural network-based approach to characterize the thermal–energy relationship in commercial buildings, aiming to provide an efficient and scalable solution for performance prediction. …”
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Identification of a Hypoxia-Angiogenesis lncRNA Signature Participating in Immunosuppression in Gastric Cancer by Zicheng Wang, Xisong Liang, Hao Zhang, Zeyu Wang, Xun Zhang, Ziyu Dai, Zaoqu Liu, Jian Zhang, Peng Luo, Jiarong Li, Quan Cheng

“…As a result, we found that HARM predicted patient survival with high accuracy and was correlated with higher stages of gastric cancer. …”
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Determination of Pear Cultivars (Pyrus communis L.) Based on Colour Change Levels by Using Data Mining by Dilara Gerdan, Abdullah Beyaz, Mustafa Vatandaş

“…The relationship between fruit hardness and colour change were also demonstrated. The predictions were done supervised machine learning algorithms (Decision Tree and Neural Networks with Meta-Learning Techniques; Majority Voting and Random Forest) by using KNIME Analytics software. …”
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Hybridize Machine Learning Methods and Optimization Techniques to Analyze and Repair Welding Defects via Digital Twin of Jidoka Simulator by Ahmed M. Abed, Tamer S. Gaafar

“…Hybridising the Random-Forest algorithm with Dingo optimisation and called Regulated Random Forest (RRF) to precisely identify defect clusters and then predict the welding defect growth rate (<inline-formula> <tex-math notation="LaTeX">$\boldsymbol {{R}_{s}}$ </tex-math></inline-formula>) using the Cat-boost optimiser, which is enhanced by a beetle search mechanism called CatBAS. …”
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Deep learning radiomics based on MRI for differentiating tongue cancer T - staging by Zhaoyi Lu, Bowen Zhu, Hang Ling, Xi Chen

“…Abstract Objective To develop a deep learning-based MRI model for predicting tongue cancer T-stage. Methods This retrospective study analyzed clinical and MRI data from 579 tongue cancer patients (Xiangya Cancer Hospital and Jiangsu Province Hospital). …”
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Study on the interaction characteristics between pot seedling and planter based on hanging cup transplanter by Hongbin Bai, Fandi Zeng, Qiang Su, Ji Cui, Xuying Li

“…Comparative analysis reveals that the GA-BP algorithm demonstrates superior performance in ensuring model accuracy and stability, exhibiting better fitting performance with relative error rates between target and predicted values ranging from 2.25 to 10.54%. …”
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