DBO-DELM Method for Predicting Rolling Forces in Cold Rolling by LI Xiaoyang, PIAO Chunhui, WANG Xuelei, ZHANG Mingzhi

“…Aiming at the problems of many assumptions, large computational errors and poor generalisation performance of the traditional rolling force prediction model, a cold rolling force prediction model (DBO-DELM) using the dung beetle optimizer algorithm (DBO) to optimise the deep extreme learning machine (DELM) is proposed. Based on the Bland-Ford-Hill rolling force model, the characteristic parameters of the DELM rolling force prediction model are selected for each frame of cold continuous rolling. …”
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Addressing challenges in deposition efficiency and material compatibility in low-pressure cold spray systems by Rizaldy Hakim Ash Shiddieqy, Alief Wikarta, Agus Sigit Pramono, Suwarno, Yohanes, Jung-Ting Tsai

“…These models offer the potential for autonomous adjustment of process parameters, leading to consistently higher deposition quality and greater operational efficiency. …”
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Fault Diagnosis of Gearboxes Based on AO-VMD and IAO-SVM by Wang Bo, Nan Xinyuan

“…Aiming at the problems of improving the adaptability of variational mode decomposition (VMD) and in order to optimize the intrinsic mode function (IMF) and multi-fault classification, a gearbox fault diagnosis method is proposed, with which the Aquila optimizer (AO) optimizes VMD, the comprehensive evaluation model optimizes IMF, and improves the Aquila optimizer optimization support vector machine (IAO-SVM). Firstly, AO is used to optimize the parameters of VMD and decompose the original signal. …”
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Spectral purification improves monitoring accuracy of the comprehensive growth evaluation index for film-mulched winter wheat by Zhikai Cheng, Xiaobo Gu, Yadan Du, Zhihui Zhou, Wenlong Li, Xiaobo Zheng, Wenjing Cai, Tian Chang

“…Four machine learning algorithms, partial least squares, support vector machines, random forests, and artificial neural network networks (ANN), were used to build the winter wheat growth monitoring model under film mulching, and accuracy evaluation and mapping of the spatial and temporal distribution of winter wheat growth status were carried out. …”
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Prediction of Flexural Ultimate Capacity for Reinforced UHPC Beams Using Ensemble Learning and SHAP Method by Zhe Zhang, Xuemei Zhou, Ping Zhu, Zhaochao Li, Yichuan Wang

“…The CatBoost model displays a more uniform distribution of SHAP values for all parameters, suggesting a balanced decision-making process and contributing to its superior and stable model performance. …”
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Robust Received Signal Strength Indicator (RSSI)-Based Multitarget Localization via Gaussian Process Regression by Niclas Fuhrling, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, David Gonzalez G., Osvaldo Gonsa

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Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature Selection by Deepak Dudeja, Ajit Noonia, S. Lavanya, Vandana Sharma, Varun Kumar, Sumaiya Rehan, R. Ramkumar

“…Machine learning is a science of computer algorithms that enable systems to automatically learn actions and adjust them without explicit programming and improve from experience using pattern recognition. …”
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Implementation of XGBoost Models for Predicting CO₂ Emission and Specific Tractor Fuel Consumption by Nebojša Balać, Zoran Mileusnić, Aleksandra Dragičević, Mihailo Milanović, Andrija Rajković, Rajko Miodragović, Olivera Ećim-Đurić

“…This study was conducted under real field conditions to explore how soil parameters influence variations in fuel use and exhaust emissions. …”
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Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm by Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN

“…Aiming at the problems of low accuracy and low efficiency of traditional soil water content prediction methods, support vector machine (SVM) was used to establish a prediction model, and the soil water content prediction method based on SVM optimized was proposed by the improved salp swarm algorithm.Firstly, the opposition-based learning and chaotic optimization were introduced to improve the standard salp swarm algorithm to solve the problem that the algorithm was easy to fall into the local optimal solution and its convergence speed was slow.Secondly, the improved salp swarm algorithm was used to optimize the parameters that affect the performance of SVM and the corresponding prediction model was built.Finally, the proposed model was compared with the particle swarm optimization SVM and the whale algorithm optimized SVM prediction model.The experimental results show that the mean square error and decision coefficient of the proposed model are 0.42 and 0.901, which are better than the other two models which verified the effectiveness of the proposed method.…”
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Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm by Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN

“…Aiming at the problems of low accuracy and low efficiency of traditional soil water content prediction methods, support vector machine (SVM) was used to establish a prediction model, and the soil water content prediction method based on SVM optimized was proposed by the improved salp swarm algorithm.Firstly, the opposition-based learning and chaotic optimization were introduced to improve the standard salp swarm algorithm to solve the problem that the algorithm was easy to fall into the local optimal solution and its convergence speed was slow.Secondly, the improved salp swarm algorithm was used to optimize the parameters that affect the performance of SVM and the corresponding prediction model was built.Finally, the proposed model was compared with the particle swarm optimization SVM and the whale algorithm optimized SVM prediction model.The experimental results show that the mean square error and decision coefficient of the proposed model are 0.42 and 0.901, which are better than the other two models which verified the effectiveness of the proposed method.…”
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Ｒecognition of Instruments’Sounds Based on VMD and PSO by HUANG Ying-lai, ＲEN Tian-li, ZHAO Peng

“…Proposing the method that based on the variational mode decomposition ( VMD) and particle swarm optimization ( PSO) optimized support vector machine ( SVM) are used to recognize the audio signals of the musical instruments aiming at the problem of the low recognition rate of musical instruments audio signals． …”
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A data-driven control method for ground locomotion on sloped terrain of a hybrid aerial-ground robot by Xinhang Xu, Yizhuo Yang, Muqing Cao, Thien-Minh Nguyen, Kun Cao, Lihua Xie

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Multi-information fusion welding defect identification combining neighborhood rough set and optimized SVM by Zhiqiang FENG, Xianping ZENG, Naiwen FANG, Daidi ZHAO, Quan LI, Jiutian LUO, Xin LI

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A survey on localization and energy efficiency in UWSN: bio-inspired approach by J. Murali, T. Shankar

“…In this comprehensive survey, the basics of UWSNs are covered in the introduction, followed by a thorough literature review of the existing works mainly focusing on localization, energy efficiency, Bio-inspired algorithms (BIA), and the impact of implementing Machine Learning (ML) are discussed. In concurrent sections, we have discussed attributes, parameters useful for analysis, issues and challenges in UWSN, soft computing techniques, software and hardware tools available for extended research, and opportunities in UWSN. …”
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A Novel Approach for Tomato Leaf Disease Classification with Deep Convolutional Neural Networks by Gizem Irmak, Ahmet Saygılı

“…Specifically, classical learning methods employed the local binary pattern (LBP) technique for feature extraction, while classification tasks were carried out using extreme learning machines, k-nearest neighborhood (kNN), and support vector machines (SVM). …”
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Civil aircraft longitudinal center-of-gravity position estimation combining domain knowledge and simulation data by Shaobo Zhai, Guangwen Li, Xinyang Du, Mingshan Hou

“…A novel aircraft LCG position estimation algorithm combining extreme learning machine (ELM) and particle swarm optimization (PSO) is developed. …”
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A novel approach for music genre identification using ZFNet, ELM, and modified electric eel foraging optimizer by Shuang Zhang, Zhiyong Sun, Hasan Jafari

“…The proposed model uses a pre-trained Zeiler and Fergus Network (ZFNet) to extract high-level features from audio signals, while an Extreme Learning Machines (ELM) is utilized for efficient classification. …”
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Interactions Between Leaf Area Dynamics and Vineyard Performance, Environment, and Viticultural Practices by Yishai Netzer, Noa Ohana-Levi

“…Early-season LAI correlates more strongly with yield, while late-season LAI predicts pruning weight and cane growth. Machine learning models reveal that excessive pre-veraison LAI in one season reduces cluster numbers in the next. …”
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Comparative Performance Evaluation of Real-Time Webcam and HOG-Based Facial Recognition Systems for Children's Screen Time Management by Fatimah Alghamdi, Abdulrahman Alghamdi

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Automated radiographic assessment of lower limb alignment using deep learning in a data-constrained clinical setting by Luca Häfliger, Andrea Cina, Louis Leuthard, Hannes A. Rüdiger, Vincent A. Stadelmann

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