Preliminary exploration and application research on the model of gathering distillate according to the quality based on Fourier transform near infrared spectroscopy by LIAO Li, ZHANG Guiyu, ZOU Yongfang, ZHU Xuemei, PENG Houbo, ZHANG Wei, LI Yan

“…Multiplicative scatter correction (MSC), competitive adaptive reweighting algorithms sampling (CARS) and support vector regression (SVR) were better methods to construct the regression prediction model, with coefficient of determination R² and root mean square error (RMSE) mean values of 0.8951 and 0.03, respectively. …”
Get full text

Reply to Comment on “Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling” by Jasper Vrugt by Tao Huang, Venkatesh Merwade

“…Results reinforce the findings obtained from Huang and Merwade (2023a) https://doi.org/10.1029/2023wr034947 and further indicate that the proposed M‐H algorithm is better than the DREAM algorithm in terms of sampling efficiency and prediction accuracy. …”
Get full text

AI-driven dynamic orthodontic treatment management: personalized progress tracking and adjustments—a narrative review by Xuanchi Guo, Yuhan Shao

“…This review will focus on the fusion of AI-driven multimodal data analysis (e.g., cone-beam CT, intraoral scanning, and 3D facial images) and deep learning algorithms (e.g., convolutional neural networks) to elucidate the technological breakthroughs in key aspects such as tooth movement trajectory prediction and early detection of root resorption. …”
Get full text

Breeding of Solanaceous Crops Using AI: Machine Learning and Deep Learning Approaches—A Critical Review by Maria Gerakari, Anastasios Katsileros, Konstantina Kleftogianni, Eleni Tani, Penelope J. Bebeli, Vasileios Papasotiropoulos

“…AI has emerged as a powerful solution to overcome the limitations of traditional breeding techniques, which often involve time-consuming, resource-intensive processes with limited predictive accuracy. Through advanced algorithms and predictive models, ML and DL facilitate the identification and optimization of key traits, including higher yield, improved quality, pest resistance, and tolerance to extreme climatic conditions. …”
Get full text

Application of an improved spherical omnidirectional loudspeaker array model in environmental noise pollution control by Yinsheng Li, Wenping Li, Maixia Fu, Zhaoying Wang, Yueyang Li

“…As the target moves slowly, the noise reduction area moves along with the target, thus enabling the system to have full-space coverage for noise reduction. …”
Get full text

Assessment of prognosis and responsiveness to immunotherapy in colorectal cancer patients based on the level of immune cell infiltration by Kaili Liao, Minqi Zhu, Lei Guo, Zijun Gao, Jinting Cheng, Bing Sun, Yihui Qian, Bingying Lin, Jingyan Zhang, Tingyi Qian, Yixin Jiang, Yanmei Xu, Qionghui Zhong, Xiaozhong Wang

“…Furthermore, an accurate prognostic risk prediction model based on the co-expression of relevant genes by immune cells was developed, enabling precise prediction of survival of colon cancer patients. …”
Get full text

QoS-Aware Fault Detection in Wireless Sensor Networks by Alessandra De Paola, Giuseppe Lo Re, Fabrizio Milazzo, Marco Ortolani

“…The experimental evaluation produced good results, showing that our algorithm is able to greatly reduce the response time at the cost of a small reduction in classification accuracy.…”
Get full text

Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data by S. R. Mani Sekhar, D. M. Mushtaq Ahmed, G. M. Siddesh

“…The result shows that the proposed air pollution model predicted 96.06700 PM2.5 concentration levels, subsequently the temperature model predicted 14.06800∘C. …”
Get full text

Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study by Katarzyna Mróz, Kamil Jonak

“…<b>Methods</b>: The paper presents the application of ML algorithms, with a focus on convolutional neural networks (CNN) and recurrent neural networks (RNN), in identifying biomarkers of anxiety disorders and predicting therapy responses. …”
Get full text

Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography by Ruiting Jia, Baozhi Liu, Mohsin Ali

“…The diagnostic accuracy was 90.58%, with an overall positive predictive value of 89% and an overall negative predictive value of 86%. …”
Get full text

Aircraft maintenance system simulation mathematical model construction by Yu. I. Samulenkov, Ya. A. Filatova, A. D. Gruzd

“…The proposed simulation model can be used for the aircraft technical condition predictive estimate, aircraft gas turbine engines and functional systems.…”
Get full text

Scientific Machine Learning for Elastic and Acoustic Wave Propagation: Neural Operator and Physics-Guided Neural Network by Nafisa Mehtaj, Sourav Banerjee

Get full text

A Machine Learning Approach to the Interpretation of Cardiopulmonary Exercise Tests: Development and Validation by Or Inbar, Omri Inbar, Ronen Reuveny, Michael J. Segel, Hayit Greenspan, Mickey Scheinowitz

“…The disease classification results show that the overall predictive power of the proposed interpretive model ranged from 96% to 100%, indicating very high predictive power. …”
Get full text

Differential Diagnosis Model of Hypocellular Myelodysplastic Syndrome and Aplastic Anemia Based on the Medical Big Data Platform by Jianhui Wu, Lu Zhang, Sufeng Yin, Haidong Wang, Guoli Wang, Juxiang Yuan

“…The sensitivity, specificity, Youden index, positive likelihood ratio (+LR), negative likelihood ratio (−LR), area under curve (AUC), accuracy, Kappa value, positive predictive value (+PV), negative predictive value (−PV) of the four model training set and test set were compared, respectively. …”
Get full text

Image-Based Laser-Beam Diagnostics Using Statistical Analysis and Machine Learning Regression by Tayyab Imran, Muddasir Naeem

“…This study is a comprehensive experimental and computational investigation into high-resolution laser beam diagnostics, combining classical statistical techniques, numerical image processing, and machine learning-based predictive modeling. A dataset of 50 sequential beam profile images was collected from a femtosecond fiber laser operating at a central wavelength of 780 nm with a pulse duration of approximately 125 fs. …”
Get full text

Performance of Natural Language Processing versus International Classification of Diseases Codes in Building Registries for Patients With Fall Injury: Retrospective Analysis by Atta Taseh, Souri Sasanfar, Michelle Chan, Evan Sirls, Ara Nazarian, Kayhan Batmanghelich, Jonathan F Bean, Soheil Ashkani-Esfahani

“…The performances of the models were compared using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 ResultsA total of 1769 clinical notes were included in the final analysis for the fall occurrence task, and 783 clinical notes were analyzed for the fall mechanism classification task. …”
Get full text

Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review by Paolo Giaccone, Federico D’Antoni, Fabrizio Russo, Luca Ambrosio, Giuseppe Francesco Papalia, Onorato d’Angelis, Gianluca Vadalà, Albert Comelli, Luca Vollero, Mario Merone, Rocco Papalia, Vincenzo Denaro

“…Several different machine learning and deep learning algorithms were employed, and their predictive ability on clinical, demographic, psychosocial, and imaging data was assessed. …”
Get full text

Development of a PPP1R14B-associated immune prognostic model for hepatocellular carcinoma by Ligang Zhao, Xiaoling Jin, Qiushi Yu, Yigang He, Facai Yang, Weihu Ma, Zhengqing Lei, Jiahua Zhou

“…The study constructed a PPP1R14B-linked immune prediction model, demonstrating acceptable prognostic capability for HCC patients. …”
Get full text

Critical review of patient outcome study in head and neck cancer radiotherapy by Jingyuan Chen, Yunze Yang, Chenbin Liu, Hongying Feng, Jason M. Holmes, Lian Zhang, Steven J. Frank, Charles B. Simone, II, Daniel J. Ma, Samir H. Patel, Wei Liu

“…This review critically evaluates the evolution of data-driven approaches in predicting patient outcomes in head and neck cancer patients treated with radiation therapy. …”
Get full text

Intelligent recognition of tobacco leaves states during curing with deep neural network by Qiang Xu, Yanling Zhang, Aiguo Wang, Guangqing Chen, Xianjie Cai, Shuoye Zhou, Junying Li, Baofeng Jin, Ding Yan, Jiajie Huang, Zuxiao Chen, Heng Zhang, Jianwei Wang, Weimin Guo, Jianjun Liu

“…Then, an efficient deep learning method was proposed based on this dataset to enhance the predictive performance.ResultsThe prediction accuracy achieved for the yellowing degree, browning degree, and drying degree were 83.0%, 90.5%, and 75.6% respectively. …”
Get full text