Classification of Fritillaria thunbergii appearance quality based on machine vision and machine learning technology by DONG Chengye, LI Dongfang, FENG Huaiqu, LONG Sifang, XI Te, ZHOU Qin’an, WANG Jun

“…In addition, to optimize YOLO-X, according to the unique features of F. thunbergii dataset, a dilated convolution structure was embedded into the end of the backbone feature extraction network of YOLO-X as it could improve the model sensitivity to the dimension feature. …”
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Estimation of Flood Inundation Area Using Soil Moisture Active Passive Fractional Water Data with an LSTM Model by Rekzi D. Febrian, Wanyub Kim, Yangwon Lee, Jinsoo Kim, Minha Choi

“…Accurate flood monitoring and forecasting techniques are important and continue to be developed for improved disaster preparedness and mitigation. Flood estimation using satellite observations with deep learning algorithms is effective in detecting flood patterns and environmental relationships that may be overlooked by conventional methods. …”
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Techno-economic modeling and analysis of a PV EV charged with battery energy storage system (BESS) on Kalimantan Island by Aripriharta, Satria Adiguna, Arif N. Afandi, Muhammad Cahyo Bagaskoro

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Deep convolutional neural network (DCNN)-based model for pneumonia detection using chest x-ray images by S. I. Ele, U. R. Alo, H. F. Nweke, A. H. Okemiri, E. O. Uche-Nwachi

“…This study focuses on developing and implementing a machine learning model tailored specifically for medical diagnosis, leveraging advancements in computer vision and deep learning algorithms. …”
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Method and experimental verification of spatial attitude prediction for an advanced hydraulic support system under mining influence by Zhuang Yin, Kun Zhang, ZengBao Zhang, Hongyue Chen, Lingyu Meng, Zhen Wang, Mingchao Du, Xiangpeng Hu, Defu Zhao, Dan Tian

“…This improvement enhances the accuracy and parameter optimization efficiency of the advanced support attitude prediction model, thereby providing robust theoretical and technical support for the intelligent, safe, and efficient mining operations of the advanced coupling support system.…”
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Development and validation of machine learning-based diagnostic models using blood transcriptomics for early childhood diabetes prediction by Xin Huang, Xin Huang, Di Ouyang, Weiming Xie, Huawei Zhuang, Siyu Gao, Pan Liu, Lizhong Guo

“…Five feature selection methods (Lasso, Elastic Net, Random Forest, Support Vector Machine, and Gradient Boosting Machine) were employed to optimize gene sets. Nine machine learning algorithms (Decision Tree, Gradient Boosting Machine, K-Nearest Neighbors, Linear Discriminant Analysis, Logistic Regression, Multilayer Perceptron, Naive Bayes, Random Forest, and Support Vector Machine) were combined with selected features, generating 45 unique model combinations. …”
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Real-Time Anomaly Detection in IoMT Networks Using Stacking Model and a Healthcare- Specific Dataset by Hadjer Goumidi, Samuel Pierre

“…This model integrates XGBoost as the meta-learner with Random Forest and ANN as base models, leveraging their strengths to optimize anomaly detection. …”
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Application of machine learning and temporal response function modeling of EEG data for differential diagnosis in primary progressive aphasia by Heather Dial, Lokesha S. Pugalenthi, G. Nike Gnanateja, Junyi Jessy Li, Maya L. Henry

“…Early diagnosis is essential for optimal provision of care but differential diagnosis by PPA subtype can be difficult and time consuming. …”
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Explainable predictive models of short stature and exploration of related environmental growth factors: a case-control study by Jiani Liu, Xin Zhang, Wei Li, Francis Manyori Bigambo, Dandan Wang, Xu Wang, Beibei Teng

“…Additionally, we evaluated the performance of the nine machine learning algorithms to determine the optimal model. The Shapley additive explanation (SHAP) method was subsequently employed to prioritize factor importance and refine the final model. …”
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Autonomous Dogfight Decision-Making for Air Combat Based on Reinforcement Learning with Automatic Opponent Sampling by Can Chen, Tao Song, Li Mo, Maolong Lv, Defu Lin

“…The training outcomes demonstrate that this improved PPO algorithm with an AOS framework outperforms existing reinforcement learning methods such as the soft actor–critic (SAC) algorithm and the PPO algorithm with prioritized fictitious self-play (PFSP). …”
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Design, modeling and manufacture error identification of a new 6-degree-of-freedom (6-DOF) compliant parallel manipulator by H. Li, W. Chen, L. Yi, C. Leng, H. Wu

“…The Levenberg–Marquardt optimization algorithm is utilized to solve the identification model, with the results verified through finite-element analysis. …”
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Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning by Jun Tan, Raoof Mohammed Radhi, Kimia Shirini, Sina Samadi Gharehveran, Zamen Parisooz, Mohsen Khosravi, Hossein Azarinfar

“…The proposed framework was evaluated through extensive simulations in a MATLAB environment, where it demonstrated remarkable improvements in system performance. The integration of Digital Twins allowed for precise real-time modeling of system behavior, while Deep Learning algorithms effectively identified and predicted faults. …”
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Energy management system design for high energy consuming enterprises integrating the Internet of Things and neural networks by Zhaolin Wang, Zhiping Zhang

“…The combination of neural network model prediction and optimization algorithms can achieve real-time monitoring, prediction, and optimization control of energy consumption. …”
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IoT-driven smart assistive communication system for the hearing impaired with hybrid deep learning models for sign language recognition by Mashael Maashi, Huda G. Iskandar, Mohammed Rizwanullah

“…Finally, the attraction-repulsion optimization algorithm (AROA) adjusts the hyperparameter values of the CNN-BiGRU-A method optimally, resulting in more excellent classification performance. …”
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Artificial Intelligence-Based Techniques for Fouling Resistance Estimation of Shell and Tube Heat Exchanger: Cascaded Forward and Recurrent Models by Ikram Kouidri, Abdennasser Dahmani, Furizal Furizal, Alfian Ma’arif, Ahmed A. Mostfa, Abdeltif Amrane, Lotfi Mouni, Abdel-Nasser Sharkawy

“…The training process is optimized using the Levenberg–Marquardt (LM) algorithm, ensuring rapid convergence and high accuracy. …”
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Response mitigations of adjacent structure with MPTMD under real and stochastic excitations by Mohammad Alibabaei Shahraki

“…The performance of the MPTMD system is optimized using the Particle Swarm Optimization (PSO) algorithm. …”
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State-of-the-Art Fault Detection and Diagnosis in Power Transformers: A Review of Machine Learning and Hybrid Methods by Lebo Dina Mashifane, Bongumsa Mendu, Bessie Baakanyang Monchusi

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Predictive modeling of hydrogen production and methane conversion from biomass-derived methane using machine learning and optimisation techniques by Adegboyega Bolu Ehinmowo, Bright Ikechukwu Nwaneri, Joseph Oluwatobi Olaide

“…The study hence established the great opportunity of integration of machine learning models with optimisation techniques in attempts to improve the prediction of hydrogen yield and methane conversion in processes for hydrogen production.…”
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Addressing the Return Visit Challenge in Autonomous Flying Ad Hoc Networks Linked to a Central Station by Ercan Erkalkan, Vedat Topuz, Ali Buldu

“…This paper presents different approaches to efficiently directing UAVs and explains how heuristic algorithms can enhance our understanding and improve current methods for task assignments.…”
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Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning by Hui Xu, Xingwang Peng, Ziyu Peng, Rui Wang, Rui Zhou, Lianguo Fu

“…First, dual feature selection was conducted to identify important feature variables for model construction. Second, ML models were constructed based on the logistic regression (LR), decision tree (DT), support vector machine (SVM) and eXtreme Gradient Boosting (XGBoost) algorithms, after which random sampling and tenfold cross-validation were separately used to evaluate and compare these models and identify the optimal model. …”
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