Assessing chronic obstructive pulmonary disease risk based on exhalation and cough sounds by Geyi Wen, Chenshuo Wang, Wei Zhao, Jinliang Meng, Yanyan Xu, Ruiqi Wang, Zijing Zeng

“…Ultimately, we assessed the feasibility of utilizing smartphones to capture exhalation and cough sounds for the identification of COPD risks through the application of machine learning algorithms. Results We gathered valid data from 530 adults, of whom 171 met the criteria for being at risk of COPD. …”
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Automatic identification of discontinuities and refined modeling of rock blocks from 3D point cloud data of rock surfaces by Yaopeng Ji, Shengyuan Song, Jianping Chen, Jingyu Xue, Jianhua Yan, Yansong Zhang, Di Sun, Qing Wang

“…The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment. Traditional manual measurement is often dangerous or unreachable at some high-steep rock slopes. …”
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A Workflow for Integrated Geological Modeling for Shale Gas Reservoirs: A Case Study of the Fuling Shale Gas Reservoir in the Sichuan Basin, China by Xiaofei Shang, Huawei Zhao, Shengxiang Long, Taizhong Duan

“…Moreover, the workflow involves multiple model integration processes, which is the key to model quality. The selection and optimization of modeling methods, the innovation and development of modeling algorithms, and the evaluation techniques for model uncertainty are areas where breakthroughs may be possible in the geological modeling of shale gas reservoirs. …”
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Detection of soluble solid content in table grapes during storage based on visible-near-infrared spectroscopy by Yuan Su, Ke He, Wenzheng Liu, Jin Li, Keying Hou, Shengyun Lv, Xiaowei He

“…The soluble solid content (SSC) in grapes significantly influences their flavour and plays an integral role in evaluation of the quality and consumer acceptance. This study employed visible near-infrared (Vis-NIR) spectroscopy to rapidly quantify SSC in table grapes during storage. …”
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A review of machine learning and deep learning for Parkinson’s disease detection by Hajar Rabie, Moulay A. Akhloufi

“…We discuss the preprocessing methods applied, the state-of-the-art models utilized, and their performance. Our evaluation included different algorithms such as support vector machines (SVM), random forests (RF), convolutional neural networks (CNN). …”
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Transformed L1 Regularization for Sparse Photoacoustic Tomography by Xueyan Liu, Lu Wang, Xin Wang, Ziqi Hao, Xiaolong Hu

“…In this study, a transform L1 (TL1) regularization model was proposed to improve the quality of PAT images, which was evaluated by numerical simulation and tissue phantom experiments. …”
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An efficient cost calculation method for disparity estimation of stereo matching considering shadow occlusion by Hongjun Sha, Wei Yuan, Xunping Wang, Xiuliu Yuan, Shunichi Koshimura

“…The qualitative and quantitative evaluation results show that MCSP is superior to two classical cost calculation methods and shows good transferability and applicability in the popular disparity estimation algorithms, which can significantly enhance the definition of the edges of the ground objects and improve the quality of the disparity estimation in the shadow occlusion areas.…”
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A BERT-Based Classification Model: The Case of Russian Fairy Tales by Валерий Дмитриевич Соловьев, Марина Ивановна Солнышкина, Andrey Ten, Николай Аркадиевич Прокопьев

“…Method: We pre-train BERT using a collection of three classes of documents and fine-tune it for implementation of a specific application task. Focused on the mechanism of tokenization and embeddings design as the key components in BERT’s text processing, the research also evaluates the standard benchmarks used to train classification models and analyze complex cases, possible errors and improvement algorithms thus raising the classification models accuracy. …”
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Combining Kronecker-Basis-Representation Tensor Decomposition and Total Variational Constraint for Spectral Computed Tomography Reconstruction by Xuru Li, Kun Wang, Yan Chang, Yaqin Wu, Jing Liu

“…The proposed objective minimization model has been tackled using the split-Bregman algorithm. To evaluate the algorithm’s performance, both numerical simulations and realistic preclinical mouse studies were conducted. …”
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A Swarm-Based Multi-Objective Framework for Lightweight and Real-Time IoT Intrusion Detection by Hessah A. Alsalamah, Walaa N. Ismail

“…Internet of Things (IoT) applications and services have transformed the way people interact with their environment, enhancing comfort and quality of life. …”
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Neuropathic pain: proposal of a mechanism-based treatment by Laura Demartini, Cesare Bonezzi

“…Some authors have suggested that the poor results in the treatment of neuropathic pain may be related to the different mechanisms present in each patient and have tried to correlate them with clinical characteristics in order to evaluate possible targeted treatments. This approach has been used in some studies evaluating the response to specific pharmacotherapies in clusters of patients, with encouraging results but still limited applicability to clinical practice. …”
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Universal Modeling for Non-Destructive Testing of Soluble Solids Content in Multi-Variety Blueberries Based on Hyperspectral Imaging Technology by Lingqi Meng, Guoliang Chen, Dayang Liu, Ning Tian

“…The soluble solids content (SSC) of blueberry is a key index for evaluating its quality. In view of the demand for rapid non-destructive testing of blueberry SSC and the shortcomings of the existing single-variety testing models in cross-variety applications, a universal prediction model construction method based on hyperspectral imaging (HSI) technology is proposed in this study. …”
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Advanced control parameter optimization in DC motors and liquid level systems by Serdar Ekinci, Davut Izci, Mohammad H. Almomani, Kashif Saleem, Raed Abu Zitar, Aseel Smerat, Vaclav Snasel, Absalom E. Ezugwu, Laith Abualigah

“…Furthermore, a new performance indicator, ZLG, is introduced to comprehensively evaluate control quality. The MGO-based approach consistently achieves lower ZLG values, showcasing its adaptability and robustness in dynamic system control and parameter optimization. …”
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Advancing soil mapping and management using geostatistics and integrated machine learning and remote sensing techniques: a synoptic review by Sunshine A. De Caires, Chaney St Martin, Melissa A. Atwell, Fuat Kaya, Glorious A. Wuddivira, Mark N. Wuddivira

“…Hybrid approaches combining geostatistics with ML algorithms (e.g., RF, Boost, SVM, ANN) demonstrate promise in addressing spatial uncertainty, while RS data enhances covariate enrichment and near-real-time applications. …”
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A Machine Learning Dataset of Artificial Inner Ring Damage on Cylindrical Roller Bearings Measured Under Varying Cross-Influences by Christopher Schnur, Payman Goodarzi, Yannick Robin, Julian Schauer, Andreas Schütze

“…In practical machine learning (ML) applications, covariate shifts and dependencies can significantly impact model robustness and prediction quality, leading to performance degradation under distribution shifts. …”
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A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization by Mostafa Akbari, Ezatollah Hassanzadeh, Yaghuob Dadgar Asl, Amirhossein Moghanian

“…This functionality allows for immediate parameter adjustments, thus significantly improving weld consistency and quality by minimizing defects. Lastly, the third section pertains to the optimization of FSW parameters, illustrating how AI-driven algorithms analyze complex interactions among multiple variables to determine the most effective process settings. …”
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Preference learning based deep reinforcement learning for flexible job shop scheduling problem by Xinning Liu, Li Han, Ling Kang, Jiannan Liu, Huadong Miao

“…Abstract The flexible job shop scheduling problem (FJSP) holds significant importance in both theoretical research and practical applications. Given the complexity and diversity of FJSP, improving the generalization and quality of scheduling methods has become a hot topic of interest in both industry and academia. …”
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Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review by Jie Li, Manli Zhu, Li Yan

“…Then, we comprehensively extracted relevant data related to machine learning algorithms, predictors, and predicted objectives. We subsequently performed a critical evaluation of research quality, data aggregation, and analyses.Results We screened 25 studies on predictive models for sepsis-associated acute kidney injury from a total of originally identified 2898 studies. …”
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A Large-Scale Building Unsupervised Extraction Method Leveraging Airborne LiDAR Point Clouds and Remote Sensing Images Based on a Dual P-Snake Model by Zeyu Tian, Yong Fang, Xiaohui Fang, Yan Ma, Han Li

“…The experimental results show that our PBEA achieves an area-based quality evaluation metric of 90.0% on the Vaihingen dataset and achieves the area-based quality evaluation metric of 92.4% on the Toronto dataset. …”
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Adaptive neuro-fuzzy inference systems for improved mastitis classification and diagnosis by Javad Shirani Shamsabadi, Saeid Ansari Mahyari, Mostafa Ghaderi-Zefrehei

“…However, due to the interplay of multiple evaluation criteria and the closely clustered fitted values, determining a definitive best model almost remained challenging In addition to improving udder health, milk quality, and economic viability, this research can contribute to ongoing soft computing efforts to improve mastitis detection and management in dairy cattle. …”
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