Robust coal granularity estimation via deep neural network with an image enhancement layer by Xi Chen, Hua-Yi Feng, Jia-Le Wang

“…Accurate granularity estimation of ore images is vital in automatic geometric parameter detecting and composition analysis of ore dressing progress. …”
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Prediction of Pile Bearing Capacity Using Opposition-Based Differential Flower Pollination-Optimized Least Squares Support Vector Regression (ODFP-LSSVR) by Nhat-Duc Hoang, Xuan-Linh Tran, Thanh-Canh Huynh

“…The bearing capacity of pile is a crucial parameter required during the design and construction phase of pile foundation engineering projects. …”
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Strain rate effect and micro-buckling behavior of anisotropy macromolecular separator for lithium-ion battery by W. Q. Hao, J. M. Xie, X. Zhang, P. Wang, F. H. Wang

“…The results conclude that the anisotropy effect and strain rate effect are displayed obviously along the machine direction (MD), diagonal direction (DD) and transverse direction (TD). …”
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NiaAutoARM: Automated Framework for Constructing and Evaluating Association Rule Mining Pipelines by Uroš Mlakar, Iztok Fister, Iztok Fister

“…In this paper, we introduce NiaAutoARM, a novel Automated Machine Learning (AutoML) framework that leverages stochastic population-based metaheuristics to automatically construct full association rule mining pipelines. …”
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Review of State Estimation Methods for Autonomous Ground Vehicles: Perspectives on Estimation Objects, Vehicle Characteristics, and Key Algorithms by Xiaoyu Wang, Te Chen, Renzhong Wang, Jiankang Lu, Guowei Dou

“…Based on the discussion of the importance, development history, and application fields of this topic of research, it focuses on analyzing vehicle state estimation techniques from different perspectives, namely (1) from the perspective of the estimation objects, including vehicle attitude and driving state estimations, chassis component key dynamic parameter estimations, and vehicle driving environment state estimations; (2) from the perspective of vehicle characteristics, including vehicle dynamics coupling characteristics, vehicle multi-source information redundancy characteristics, and vehicle state transition characteristics; (3) from the perspective of key estimation algorithms, including model-based Kalman filtering algorithms, data-driven machine learning algorithms, and optimization estimation algorithms combining mechanism-based and data-driven approaches. …”
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Prediction model for nonlinear variation of bolt preload with tightening torque based on mechanism and data fusion by Yueqi Qiao, Bing Zhao, Dingshan Deng, Weijin Ouyang

“…Secondly, sensitivity evaluation indicator is established to conduct parameter sensitivity analysis, and the fusion method of “mechanism model guiding data model to perform feature selection” is determined. …”
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Research status of soft anthropomorphic dexterous hands by LIU Yibo, XIAO Huaping, LIU Chuanwang, SUN Zhenhao, HAO Tianze, LIU Shuhai

“…ObjectiveIn order to achieve the capability of whole process unmanned operation of the Robo-Sweep intelligent cleaning robot, it is necessary to develop a set of unloading mechanisms that meet various needs.MethodsFirstly, according to the overall design scheme of the whole machine, the preliminary design of the unloading mechanism was carried out through the graphical method. …”
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Integrating AI predictive analytics with naturopathic and yoga-based interventions in a data-driven preventive model to improve maternal mental health and pregnancy outcomes by Neha Irfan, Sherin Zafar, Kashish Ara Shakil, Mudasir Ahmad Wani, S. N. Kumar, A. Jaiganesh, K. M. Abubeker

“…Participants were selected across all pregnancy trimesters to ensure a representative demographic, capturing variations in age, educational background, occupational status, and parity. A diverse set of machine learning models, including Random Forest, Decision Tree, Support Vector Machine (SVM), Logistic Regression, Gaussian Naive Bayes, and Multilayer Perceptron (MLP), were evaluated alongside ensemble methods to achieve robust and reliable predictions. …”
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Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images by Khaled Tarmissi, Jamal Alsamri, Mashael Maashi, Mashael M. Asiri, Abdulsamad Ebrahim Yahya, Abdulwhab Alkharashi, Monir Abdullah, Marwa Obayya

“…The presented MTLSO-BMCC technique employs the hybrid kernel extreme learning machine (HKELM) method for the BM classification method. …”
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An enhanced alpha evolution moss growth optimizer for prognostic prediction in spontaneous intracerebral hemorrhage by Lingxian Hou, Yongsheng Wang, Xiuqi Lin, Chengye Li, Huangrong Guo, Congcong Jin, Yi Chen, Huiling Chen, Jing Ji, Wenzong Zhu

“…Additionally, it uses an adaptive β parameter to dynamically balance the search process. …”
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Applications of artificial intelligence and computational intelligence in hydraulic optimization of centrifugal pumps: a comprehensive review by Yuanhui Xu, Xingcheng Gan, Ji Pei, Wenjie Wang, Jia Chen, Shouqi Yuan

“…With societal progress and the rising safety standards, pump design has evolved into a highly complex optimisation problem characterised by multi-objective, multi-parameter, and multi-component integration under nonlinear and strongly constrained conditions. …”
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Applying a transformer architecture to intraoperative temporal dynamics improves the prediction of postoperative delirium by Niklas Giesa, Maria Sekutowicz, Kerstin Rubarth, Claudia Doris Spies, Felix Balzer, Stefan Haufe, Sebastian Daniel Boie

“…Abstract Background Patients who experienced postoperative delirium (POD) are at higher risk of poor outcomes like dementia or death. Previous machine learning models predicting POD mostly relied on time-aggregated features. …”
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Integrating response surface methodology and decision tree algorithms for valorization of cheese whey wastewater by Iremsu Kayan, Nilgun Ayman Oz

“…The goal of this study is to find optimum conditions for microalgae growth and the concentration of cheese whey wastewater (CWW) to get the best treatment efficiency by using response surface methodology (RSM) and the decision tree algorithm for different pollutant parameters. The study used reactors with different amounts of CWW and Nannochloropsis sp. to find the best concentrations for each parameter. …”
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(IoT) Network intrusion detection system using optimization algorithms by Luo Shan

“…Compared with traditional models like the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) and Support Vector Machine (SVM), the proposed framework significantly improves the sensitivity and generalization ability for detecting various types of attacks through dynamic feature selection and parameter optimization. …”
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Fast sparse representative tree splitting via local density for large-scale clustering by Renmin Wang, Jie Li

“…Abstract Large-scale clustering remains an active yet challenging task in data mining and machine learning, where existing algorithms often struggle to balance efficiency, accuracy, and adaptability. …”
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Method of continuous generation grinding tooth surface modification based on electronic gearbox superimposed high-order motion by HAN Jiang, RUI Xiaoyu, TIAN Xiaoqing, YOU Tongfei, LI Guanghui, XIA Lian

“…Then, a continuous generative grinding machining model was established based on the parameter equation of the worm wheel gear surface and the linkage relation with the electronic gearbox. …”
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JVM optimization: An empirical analysis of JVM configurations for enhanced web application performance by Darlan Noetzold, Anubis Graciela de Moraes Rossetto, Luis Augusto Silva, Paul Crocker, Valderi Reis Quietinho Leithardt

“…This research presents software for empirically analyzing Java Virtual Machine (JVM) parameter configurations to enhance web application performance. …”
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Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data by Abiel Aguilar-González, Alejandro Medina Santiago

“…Traditional approaches often require complex feature engineering or the use of machine learning models, which can be computationally intensive, especially when dealing with real-time data from high-frequency vibration and acceleration sensors. …”
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Self-supervision advances morphological profiling by unlocking powerful image representations by Vladislav Kim, Nikolaos Adaloglou, Marc Osterland, Flavio M. Morelli, Marah Halawa, Tim König, David Gnutt, Paula A. Marin Zapata

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Hierarchical Aggregation for Federated Learning in Heterogeneous IoT Scenarios: Enhancing Privacy and Communication Efficiency by Chen Qiu, Ziang Wu, Haoda Wang, Qinglin Yang, Yu Wang, Chunhua Su

“…Furthermore, the conventional parameter server-based FL paradigm aggregates the trained parameters of devices directly, which incurs high communication overhead. …”
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