Experimental Investigation and Optimization of Tool Life in High-Pressure Jet-Assisted Turning of Inconel 718 by Davorin Kramar, Djordje Cica

“…The application of high-pressure jet-assisted (HPJA) machining can increase tool life during machining, as the cutting fluid penetrates better into the interfaces between the tool and the workpiece. …”
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Design and Simulation Analysis of a Flexible Clamping and Conveying Device of a Green Leafy Vegetable Cutting and Bundling Integrated Machine by Zhiquan Wang, Tianxing Yang, Wei Wei

“…In order to improve the harvesting production efficiency of green leafy vegetables, this paper designs and simulates the flexible clamping and conveying device of the green leafy vegetable cutting and bundling integrated machine. Through theoretical calculation and 3D modeling, the design optimization of key components is carried out in this paper. …”
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Research on the evolution and prediction of the heights of water-conducting fracture zones in overlying rocks during layered mining of extremely thick coal seams by MENG Hailun, CHENG Xianggang, QIAO Wei

“…Based on the geological conditions of the extremely thick coal seams in the Jurassic coalfields of Xinjiang, this research selected the parameters of the typical working face 9-15 (08) in the Liuhuanggou Coal Mine of the Zhunnan Coalfield in Xinjiang, quantitatively evaluated the development characteristics and evolution patterns of the overlying rock fracture fields under layered full-mechanized mining of extremely thick coal seams through numerical simulations and fractal geometry theory analysis. A prediction model was developed for the heights of water-conducting fracture zones in layered mining of extremely thick coal seams based on particle swarm optimization support vector machine regression (PSO-SVR). …”
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Optimization of Processing Parameters in ECM of Die Tool Steel Using Nanofluid by Multiobjective Genetic Algorithm by V. Sathiyamoorthy, T. Sekar, N. Elango

“…Thirty-six experiments were designed using Design Expert 7.0 software and optimization was done using multiobjective genetic algorithm (MOGA). …”
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Applying machine learning techniques to predict the risk of distant metastasis from gastric cancer: a real world retrospective study by Xinxin Qin, Binxu Qiu, Litao Ge, Song Wu, Yuye Ma, Wei Li

“…We constructed the machine learning model using 10-fold cross-validation. …”
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Data-driven integration of synthetic representative volume elements and machine learning for improved microstructure-property linkage and material performance in ceramics by Mohammad Rezasefat, James Hogan

“…In this study, the linkage between microstructure and properties in ceramic materials is explored through a methodological approach that combines experimental observations with physics-based and machine learning models. A data-driven approach has been employed, utilizing synthetic Representative Volume Elements (RVEs) derived from X-ray computed tomography scans of ceramics. …”
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A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections by Yemeng Yang, Kun Han, Jiatao Li, Tao Zhang, Zhijing Zhu, Ling Su, Zhaoyong Han, Chunyan Xu, Yi Lu, Likun Pan, Tao Yang

“…The optimal model was then deployed as a web application for clinical outcome prediction. …”
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CFD and Artificial Intelligence-Based Machine Learning Synergy for the Assessment of Syngas-Utilizing Pre-Reformer in r-SOC Technology Advancement by Murphy M. Peksen

“…Evaluating the intricate thermochemistry of syngas-containing reforming processes involves employing an experimentally validated CFD model. The model serves as the foundation for gathering essential data, crucial for the development and training of AI-based machine learning models. …”
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Machine learning-based prediction of the thermal conductivity of filling material incorporating steelmaking slag in a ground heat exchanger system by Gyeong-o Kang, Young-sang Kim, Jung-goo Kang, Seongkyu Chang

“…A dataset containing various physical parameters of the heat-transfer materials was obtained from previous research results and Pearson correlation analysis was used to select the optimal input variable. Three machine learning models—support vector regression (SVR), random forest (RF), and multilayer perceptron (MLP)—were assessed to determine the most accurate model for predicting the thermal conductivity of the heat-transfer materials. …”
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Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer by Pu Zhou, Pu Zhou, Hongyan Qian, Pengfei Zhu, Jiangyuan Ben, Jiangyuan Ben, Guifang Chen, Qiuyi Chen, Lingli Chen, Jia Chen, Ying He, Ying He

“…Subsequently, construction of clinical predictive models and Rad score joint clinical predictive models using ML algorithms for optimal diagnostic performance. …”
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Development and Internal Validation of Machine Learning Algorithms for Predicting Subsequent Contralateral Slipped Capital Femoral Epiphysis in Patients With Unilateral Slips by David P. VanEenenaam, Jr., BS, Carter Hall, BS, Daniel A. Maranho, MD, PhD, Christopher J. DeFrancesco, MD, Eduardo N. Novais, MD, Wudbhav N. Sankar, MD

“…Significant relationships between groups were used for feature selection in the predictive model. Receiver operator characteristic curves were generated to determine optimal thresholds for significant continuous variables. …”
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Linking Electrocardiogram and Echocardiogram: Comparing Classical Machine Learning and Deep Learning Neural Networks for the Detection of Regional Wall Motion Abnormalities by Shantanu M. Joshi, Hana R. Shaik, Shivam Rai Sharma, Philip Strong, Uma Srivatsa, Imo Ebong, Hyoyoung Jeong, Chen-Nee Chuah, Lihong Mo

“…Our study aimed to predict RWMA using both classical machine learning (ML) methods and a one-dimensional convolutional neural network (1D CNN) model on the 12-lead ECG data from 3,750 unique patients provided by the Nightingale Open Science platform. …”
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Adaptive deep SVM for detecting early heart disease among cardiac patients by S. N. Netra, N. N. Srinidhi, E. Naresh

“…To solve this issue, the research work focuses on developing a novel framework for detecting heart disease in its early stages by using machine learning techniques. In the initial phase, the significant data required for the validation is collected from benchmark resources, and it is subjected to the weighted optimal features selection phase. …”
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The state-of-the-art review on biochar as green additives in cementitious composites: performance, applications, machine learning predictions, and environmental and economic implic... by Ping Ye, Binglin Guo, Huyong Qin, Cheng Wang, Yang Liu, Yuyang Chen, Pengfei Bian, Di Lu, Lei Wang, Weiping Zhao, Yonggan Yang, Li Hong, Peng Gao, Peiyong Ma, Binggen Zhan, Qijun Yu

“…Therefore, it is recommended to explore commercialization pathways tailored to local conditions and to develop machine learning models for performance prediction and life-cycle analysis, thereby promoting the widespread application of BC in industry and construction. …”
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Machine learning framework to estimate ridership loss in public transport during external crises: case study of bus network in Stockholm by Mahsa Movaghar, Erik Jenelius, David Hunter

“…Abstract Recent technologies for recording and storing data, as well as advancements in data processing techniques, have opened up novel possibilities for urban planners to design a more optimal public transport network. This study aims to initially develop a robust framework for making an insightful understanding of already recorded and available data sets using machine learning approaches. …”
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ARKAIV: Predicting Data Exfiltration Using Supervised Machine Learning Based on Tactics Mapping From Threat Reports and Event Logs by Arif Rahman Hakim, Kalamullah Ramli, Muhammad Salman, Bernardi Pranggono, Esti Rahmawati Agustina

“…To optimize model performance, we benchmarked three resampling methods, five feature selection techniques, and five ML algorithms. …”
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Multisource data fusion for enhanced gold mineral prospectivity mapping in Yagba West, Kogi State: a machine learning approach by Momohjimoh Abdulsalami, Joseph Omonile, Fahad Abubakar, Abdullateef Aliyu, kabire Yahaya

“…These findings underscore the potential of machine learning in enhancing gold prospectivity mapping and optimizing exploration strategies in structurally controlled gold-bearing terrains. …”
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Hate Speech Detection and Online Public Opinion Regulation Using Support Vector Machine Algorithm: Application and Impact on Social Media by Siyuan Li, Zhi Li

“…Detecting hate speech in social media is challenging due to its rarity, high-dimensional complexity, and implicit expression via sarcasm or spelling variations, rendering linear models ineffective. In this study, the SVM (Support Vector Machine) algorithm is used to map text features from low-dimensional to high-dimensional space using kernel function techniques to meet complex nonlinear classification challenges. …”
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Developing supervised machine learning algorithms to classify lettuce foliar tissue samples into interpretation zones for 11 plant essential nutrients by Patrick Veazie, Hsuan Chen, Kristin Hicks, Jake Holley, Nathan Eylands, Neil Mattson, Jennifer Boldt, Devin Brewer, Roberto Lopez, Brian Whipker

“…Critical nutrient ranges vary for each species, and the potential for error in interpretation increases due to this complexity. Machine learning can be utilized to develop algorithms to accurately classify new information using models developed on known data from a training dataset. …”
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Prediction of prognosis of immune checkpoint inhibitors combined with anti-angiogenic agents for unresectable hepatocellular carcinoma by machine learning-based radiomics by Xuni Xu, Xue Jiang, Haoran Jiang, Xiaoye Yuan, Mengjing Zhao, Yuqi Wang, Gang Chen, Gang Li, Yuxia Duan

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