Maximum likelihood self-calibration for direction-dependent gain-phase errors with carry-on instrumental sensors:case of deterministic signal model by WANG Ding1, PAN Miao2, WU Ying1

“…Aim at the self-calibration of direction-dependent gain-phase errors in case of deterministic signal model,the maximum likelihood method（MLM） for calibrating the direction-dependent gain-phase errors with carry-on instrumental sensors was presented.In order to maximize the high-dimensional nonlinear cost function appearing in the MLM,an improved alternative projection iteration algorithm,which could optimize the azimuths and direction-dependent gain-phase errors was proposed.The closed-form expressions of the Cramér-Rao bound（CRB） for azimuths and gain-phase errors were derived.Simulation experiments show the effectiveness and advantage of the novel method.…”
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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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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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Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients by Aref Andishgar, Sina Bazmi, Kamran B. Lankarani, Seyed Alireza Taghavi, Mohammad Hadi Imanieh, Gholamreza Sivandzadeh, Samira Saeian, Nazanin Dadashpour, Alireza Shamsaeefar, Mahdi Ravankhah, Hamed Nikoupour Deylami, Reza Tabrizi, Mohammad Hossein Imanieh

“…Seven survival machine learning algorithms were used: LASSO, Ridge, RSF, E-NET, GBS, C-GBS, and FS-SVM. …”
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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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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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Adjustment of angle error and tolerance allocation methods for RV reducers by ZHANG Bowen, ZHOU Jianxing, CUI Quanwei, LIN Kaihong, ZHOU Yadong, XU Wenqiang

“…Finally, with the minimum total processing cost as the objective function, the angular tolerance allocation of key components was completed by using the genetic algorithm.ResultsThe research results prove the effectiveness of this method in improving the transmission accuracy and stability of RV reducers, but different accuracy weight values should be selected according to actual accuracy requirements to minimize costs.…”
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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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A PSO-XGBoost Model for Predicting the Compressive Strength of Cement–Soil Mixing Pile Considering Field Environment Simulation by Jiagui Xiong, Yangqing Gong, Xianghua Liu, Yan Li, Liangjie Chen, Cheng Liao, Chaochao Zhang

“…Utilizing data mining on 84 sets of experimental data with various preparation parameter combinations, a prediction model for the as-formed strength of CSM Pile was developed based on the Particle Swarm Optimization-Extreme Gradient Boosting (PSO-XGBoost) algorithm. …”
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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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Advanced day-ahead scheduling of HVAC demand response control using novel strategy of Q-learning, model predictive control, and input convex neural networks by Rahman Heidarykiany, Cristinel Ababei

“…More specifically, new input convex long short-term memory (ICLSTM) models are employed to predict dynamic states in an MPC optimal control technique integrated within a Q-Learning reinforcement learning (RL) algorithm to further improve the learned temporal behaviors of nonlinear HVAC systems. …”
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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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Calculation method of line loss rate of substation areas considering tidal current variation with photovoltaic power generation access by Shengbo Sun, Chao Cheng, Shujun Ji, Wei Cui, Wei Ge, Shifang Hao

“…The proposed method employs an improved K-medoids clustering algorithm for substation area classification, optimized by an enhanced Cuckoo algorithm to minimize classification errors. …”
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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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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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A novel bivariate regression model derived from the clayton copula and the Odd Dagum-G family and its application by Julius Kwaku Adu-Ntim, Akoto Yaw Omari-Sasu, Maxwell Akwasi Boateng, Isaac Adjei Mensah

“…The models cumulative distribution function (CDF) and probability distribution function (PDF) are derived and the parameters were estimated using the maximum likelihood estimation (MLE) where the likelihood function was optimized using the Broyden-Fletcher-Goldfarb-Shannon (BFGS) algorithm.Simulation is conducted under various scenarios to validate the model’s robustness, exhibiting consistent estimators, reduced bias, and decreasing mean square errors (MSEs) with increasing sample size. …”
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