Machine learning based prediction of specific heat capacity for half-Heusler compounds by Laxman Chaudhary, Keshab Chaudhary, Ambika Shahi, Kedar Nath Jaiswal, Dipendra Prasad Kalauni, Se-Hun Kim, Madhav Prasad Ghimire

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Kinematics Analysis and Trajectory Optimization of the Hybrid Welding Robot by Cai Ganwei, Ban Caixia, Tian Junwei, Zhang Kechen

“…At the same time, a trapezoidal function with parabolic transition is used to plan the end effector of the hybrid welding robot, and the multi-objective trajectory optimization scheme is proposed. The particle swarm optimization algorithm is used to optimize the time parameters, and the optimal time parameters that meet the positioning accuracy of the end of the welding machine manipulator are solved. …”
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Risk assessment of tunnel water inrush based on Delphi method and machine learning by Leizhi Dong, Qingsong Wang, Weiguo Zhang, Yongjun Zhang, Xiaoshuang Li, Fei Liu

“…To avoid the potential water inrush, this paper proposes a risk assessment model for the metro tunnel based on Delphi survey method and machine learning. …”
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Towards solving NLP tasks with optimal transport loss by Rishabh Bhardwaj, Tushar Vaidya, Soujanya Poria

“…Incorporating such information in the computations of the probability divergence can facilitate the model’s learning dynamics.In this work, we study an under-explored loss function in NLP — Wasserstein Optimal Transport (OT) — which takes label coordinates into account and thus allows the learning algorithm to incorporate inter-label relations. …”
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CatBoost Optimization Using Recursive Feature Elimination by Agus Hadianto, Wiranto Herry Utomo

“…CatBoost is a powerful machine learning algorithm capable of classification and regression application. …”
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A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins by Harald H. Rietdijk, Patricia Conde-Cespedes, Talko B. Dijkhuis, Hilbrand K. E. Oldenhuis, Maria Trocan

“…The survey reveals that, unlike general machine learning applications, there is a limited body of literature on optimization and the application of meta-learning in personalized Human Digital Twin solutions. …”
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A machine learning computational approach for the mathematical anthrax disease system in animals. by Zulqurnain Sabir, Eman Simbawa

“…<h4>Finding</h4>The designed procedure's correctness is authenticated through the results overlapping and reducible absolute error, which are calculated around 10-05 to 10-08 for each case of the model. The best training performances are performed as 10-10 to 10-12 of the model. …”
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Optimal Partitioning of Unbalanced Datasets for BGP Anomaly Detection by Rahul Deo Verma, Pankaj Kumar Keserwani, Vinesh Kumar Jain, Mahesh Chandra Govil, M. W. P. Maduranga, Valmik Tilwari

“…The existing solutions are based on the classical machine learning (ML) models, which need to be advanced. …”
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Machining of Printed Circuit Boards Using an Industrial Robot in a Simulation Environment by Komák Martin, Pivarčiová Elena, Herčút Patrik

“…This paper is devoted to the design, simulation, and optimization of a robotic cell designed to machining printed circuit boards (PCBs) using a stationary milling machine mounted on an industrial robot. …”
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Deep Learning-Based Predictive Control of Injection Velocity in Injection Molding Machines by Zhigang Ren, Yaodong Li, Zongze Wu, Shengli Xie

“…Rapid and reliable optimal control of injection molding machines (IMMs) is critical for the effective production of injection-molded goods, especially in the situation of restricted computer resources of embedded equipment in IMMs. …”
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Dynamics Analysis of Needle Rod Mechanism of Tufting Carpet Machine based on ADAMS by Li Dinglin, Xu Yang

“…Taking the needle rod mechanism of tufting carpet machine as the research object,by using ADAMS simulation software,a simple model of tufting carpet machine needle rod mechanism is built and the dynamics simulation analysis is carried out. …”
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Globalized parameter tuning of microwave passives by dimensionality-reduced surrogates and multi-fidelity simulations by Slawomir Koziel, Anna Pietrenko-Dabrowska

“…This study introduces an alternative approach for rapid global optimization of microwave passive components using artificial intelligence (AI) techniques, specifically, machine learning (ML). …”
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Optimization of non-smooth functions via differentiable surrogates. by Shikun Chen, Zebin Huang, Wenlong Zheng

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”
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Comparisons of Machine Learning Methods in Ship Speed Prediction Based on Shipboard Observation by Weidong Gan, Dianguang Ma, Yu Duan

“…The results highlight the potential of the LightGBM model in optimizing ship navigation and improving maritime operational efficiency. …”
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Outdoor location scheme with fingerprinting based on machine learning of mobile cellular network by Zhichao ZHOU, Yi FENG, Xiaohan XIA, Yuyao FENG, Chao CAI, Jiahui QIU, Lihui YANG, Yunxiao WU

“…The positioning scheme based on mobile cellular network technology is one of the important technical approaches to provide network optimization, emergency rescue, police patrol and location services.The traditional positioning scheme based on cell base station location information has low positioning accuracy and large positioning error, so it cannot meet the requirements of some positioning applications.The scheme based on fingerprint location can greatly improve the location accuracy, save computational cost and enhance the usability based on the coarse location scheme of the cell and become the hotspot of the research.Rasterization and non-rasterization of outdoor fingerprint location scheme based on machine learning were studied and analyzed to meet the business requirements of outdoor fingerprint location.By means of parameter weighting, data fitting and other methods, large-scale fingerprint data were cleaned to improve the effectiveness of data sources.Through the realization of sub-modules such as demarcating research area, rasterizing, constructing fingerprint database, training model, correcting model, non-rasterizing, rough positioning coupling, matching parameter and training parameter, the operation efficiency and positioning accuracy of the algorithm were analyzed and optimized, and the key indexes affecting the algorithm performance were determined.Then, the performance of two fingerprint-based localization schemewas analyzed based on the simulation results.Finally, the typical scenarios of the fingerprint location scheme based on machine learning in practical application were presented.…”
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Predictive Analytics in Maternal Health: A Machine Learning Approach for Classification of Preeclampsia by Pakiza Amin, Saima Gulzar Ahmad, Hikmat Ullah Khan, Ehsan Ullah Munir, Naeem Ramzan

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Assessing cyber risks in construction projects: A machine learning-centric approach by Dongchi Yao, Borja García de Soto

“…This approach comprises three components: (1) For risk prediction, a simulated dataset is generated using Monte Carlo simulations, which is utilized for model training. A two-phase model development strategy is proposed to select the optimal model for each risk. (2) For risk factor analysis, ML feature analysis methods are adapted to identify risk factors that contribute significantly to risks of specific projects. (3) For the risk reduction strategy, a greedy optimization algorithm is proposed to efficiently address high-contributing risk factors. …”
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Efficient cutting stock optimization strategies for the steel industry. by Chattriya Jariyavajee, Suthida Fairee, Charoenchai Khompatraporn, Jumpol Polvichai, Booncharoen Sirinaovakul

“…This study addresses a cutting stock problem in steel cutting industry by developing a mathematical model in which machine specifications and cutting conditions are constraints. …”
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Predicting the Activity Level of the Great Gerbil (Rhombomys opimus) via Machine Learning by Fan Jiang, Peng Peng, Zhenting Xu, Yu Xu, Ding Yang, Shouquan Chai, Shuai Yuan, Limin Hua, Dawei Wang, Xuanye Wen

“…Because traditional assessment methods are difficult to monitor and cannot effectively predict the population growth trend of R. opimus, an R. opimus activity prediction model was constructed using the particle swarm optimization algorithm‐extreme learning machine (PSO‐ELM). …”
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Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings by Jianbin Xiong, Qinghua Zhang, Qiong Liang, Hongbin Zhu, Haiying Li

“…The algorithm optimizes the correlation kernel parameters of the SVM using evolutionary search principles of multiple swarm genetic algorithms to obtain a superior SVM prediction model. …”
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