Use Cases of Machine Learning in Queueing Theory Based on a <i>GI</i>/<i>G</i>/<i>K</i> System by Dmitry Efrosinin, Vladimir Vishnevsky, Natalia Stepanova, Janos Sztrik

“…Machine learning (ML) in queueing theory combines the predictive and optimization capabilities of ML with the analytical frameworks of queueing models to improve performance in systems such as telecommunications, manufacturing, and service industries. …”
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Reliability evaluation and multi-objective optimization of combustion chamber’s key components of marine engine by Lei Hu, Wentong Wang, Xu Wang, Jianguo Yang, Yonghua Yu, Chunyang Mei

“…Response surface methodology is used to establish reliability prediction models under sensitive factors of fuel injection, valve, and cooling systems, and a reliability comprehensive evaluation method based on entropy weight and grey correlation methods has been proposed. …”
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Dimensionless analysis and novel configuration for enhanced natural convection cooling in lithium-ion batteries by Congyu Mao, Min Liang Wang, Kewei Gao, Hongdi Jin, Woo Young Cho, Hernando Leon-Rodriguez, Kootaek Hong, Seunghwoi Han, Jihoon Jeong, Hyun Wook Kang

“…A dimensionless analysis framework was developed and coupled with response surface models and a multi-objective genetic algorithm to optimize battery layouts for heat dissipation and temperature uniformity. …”
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The Use of Machine Learning in the Diagnosis of Kidney Allograft Rejection: Current Knowledge and Applications by Tanja Belčič Mikič, Miha Arnol

“…In this review, we addressed studies that investigated the detection of kidney allograft rejection over the last decade using various ML algorithms. Various ML techniques were used in three main categories: (a) histopathologic assessment of kidney tissue with the aim to improve the diagnostic accuracy of a kidney biopsy, (b) assessment of gene expression in rejected kidney tissue or peripheral blood and the development of diagnostic classifiers based on these data, (c) radiologic assessment of kidney tissue using diffusion-weighted magnetic resonance imaging and the construction of a computer-aided diagnostic system. …”
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Influence of stiffness of the mechanical part of the drive and cutting parameters on the shaping elastic deformation control by V. L. Zakovorotny, V. Е. Gvindjiliya, А. А. Zakalyuzhny

“…Introduction. One of the ways to improve the accuracy of manufacturing parts by cutting is related to the control of elastic deformations of the tool and the workpiece. …”
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Single Vector Hydrophone DOA Estimation: Leveraging Deep Learning with CNN-CBAM by Fanyu ZENG, Yaning HAN, Hongyuan YANG, Dapeng YANG, Fan ZHENG

“…Specifically, CBAM improves the model’s focus on subtle directional cues in noisy environments, suppressing irrelevant interference while amplifying essential signal components, which is crucial for an accurate DOA estimation. …”
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Outdoor Environment Design Optimization of an Office Building Based on Indoor Thermal Conditions and Building Energy Performance by Yaolin Lin, Tao Huang, Wei Yang, Melissa Chan, Chun-Qing Li, Mingqi Dai, Pengju Chen

“…Thirdly, two machine learning prediction algorithms were used to develop discomfort degree hours and energy consumption models, and the artificial neural network models were found to have better prediction performance with R-squares greater than 0.99. …”
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A Crowd Counting and Localization Network Based on Adaptive Feature Fusion and Multi-Scale Global Attention Up Sampling by Min Wang, Li Huang, Jingke Yan, Jin Huang, Tao Yang

“…The F1-m scores obtained on the SHHA and SHHB datasets reach 72.9&#x0025; and 79.4&#x0025; respectively, significantly improving the model&#x2019;s performance.Finally, this paper extends the application of crowd counting and localization algorithms to different domains such as citrus orchards, vehicles, and campus crowds. …”
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Monitoring Pine Wilt Disease Using High-Resolution Satellite Remote Sensing at the Single-Tree Scale with Integrated Self-Attention by Wenhao Lv, Junhao Zhao, Jixia Huang

“…The results demonstrate that introducing appropriate self-attention algorithms significantly improves detection accuracy for pine wilt disease. …”
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A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs by Ahmed Alabd-Aljabar, Zain Raisan, Mohammed Adnan, Salam Dhou

“…The results revealed that all ten chosen deep learning models experienced a similar or improved performance when used in conjunction with a machine learning classifier. …”
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Enhanced Real-Time Simulation of ROV Attitude and Trajectory Under Ocean Current and Wake Disturbances by Yujing Zhao, Shipeng Xu, Xiaoben Zheng, Lisha Luo, Boyan Xu, Chunru Xiong

“…The proposed system significantly improves trajectory evolution stability and consistency, compared to traditional models. …”
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Research of Physical and Mechanical Properties of Self-Compacting Concrete Based on Polyfractional Binder by Meiram M. Begentayev, Daniyar A. Akhmetov, Rauan E. Lukpanov, Erzhan I. Kuldeyev, Zhanar O. Zhumadilova, Tolebi Myrzaliyev, Duman S. Dyussembinov, Aigerim K. Tolegenova

“…A software and calculation complex featuring a three-dimensional modeling algorithm, Drop and Roll, was used to select the optimal composition. …”
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A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data by M. Priyadharshini, B. Deevena Raju, A. Faritha Banu, P. Jagdish Kumar, V. Murugesh, Oleg Rybin

“…QSVM employs quantum kernels to map data into a higher-dimensional Hilbert space, so that the model can detect complex patterns in MM drug resistance. qPCA reduces dimensionality without loss of important variance, and thus improves computation efficiency. …”
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Equitable hospital length of stay prediction for patients with learning disabilities and multiple long-term conditions using machine learning by Emeka Abakasanga, Rania Kousovista, Georgina Cosma, Ashley Akbari, Francesco Zaccardi, Navjot Kaur, Navjot Kaur, Danielle Fitt, Gyuchan Thomas Jun, Reza Kiani, Satheesh Gangadharan

“…Bias mitigation algorithms reduced disparities in prediction performance across ethnic groups, with the threshold optimiser yielding the most notable improvements. …”
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Reinforcement Learning for Autonomous Process Control in Industry 4.0: Advantages and Challenges by Nuria Nievas, Adela Pagès-Bernaus, Francesc Bonada, Lluís Echeverria, Xavier Domingo

“…The analysis focuses on application types, problem modeling, training algorithms, validation methods, and deployment statuses. …”
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Effective tweets classification for disaster crisis based on ensemble of classifiers by Christopher Ifeanyi Eke, Kholoud Maswadi, Musa Phiri, Mulenga, Mohammad Imran, Dekera Kwaghtyo, Akeremale Olusola Collins

“…This represents a notable improvement over the conventional standalone classifiers and highlights the advantage of ensemble methods in enhancing model robustness and minimizing overfitting. …”
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Precision autofocus in optical microscopy with liquid lenses controlled by deep reinforcement learning by Jing Zhang, Yong-feng Fu, Hao Shen, Quan Liu, Li-ning Sun, Li-guo Chen

“…Additionally, parallel “state” dataset lists with random sampling training are proposed which enhances the model’s adaptability to unknown samples, thereby improving its generalization capability. …”
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SDGTrack: A Multi-Target Tracking Method for Pigs in Multiple Farming Scenarios by Tao Liu, Dengfei Jie, Junwei Zhuang, Dehui Zhang, Jincheng He

“…This method improves tracking performance across various farming environments by enhancing the model’s adaptability to different domains and integrating an optimized tracking strategy, significantly increasing the generalization of group pig tracking technology across different scenarios. …”
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Evaluation of the predictors of tooth loss using artificial intelligence-based machine learning approach: A retrospective study by Amit Rajabhau Pawar, Sankari Malaiappan, Pradeep Kumar Yadalam, P R Ganesh

“…Early intervention and accurate prediction of tooth loss are crucial for improving oral health outcomes. Conventional prognostic models have their constraints in sensitivity, prompting the exploration of alternative approaches. …”
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Unsupervised Class Generation to Expand Semantic Segmentation Datasets by Javier Montalvo, Álvaro García-Martín, Pablo Carballeira, Juan C. SanMiguel

“…Our approach aims to improve the performance of unsupervised domain adaptation methods by introducing novel samples into the training data without modifications to the underlying algorithms. …”
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