Optimizing machine learning algorithms for diabetes data: A metaheuristic approach to balancing and tuning classifiers parameters by Hauwau Abdulrahman Aliyu, Ibrahim Olawale Muritala, Habeeb Bello-Salau, Salisu Mohammed, Adeiza James Onumanyi, Ore-Ofe Ajayi

“…Additionally, setting the best parameters for machine learning classifiers remains a challenging task. …”
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Machine learning predictions on the output parameters of common rail direct injection engines fueled with ternary blend by Subramanian Karthikeyan, Paramasivam Sathiyagnanam Amudhavalli, Dillikannan Damodharan

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Leveraging boosting machine learning for drilling rate of penetration (ROP) prediction based on drilling and petrophysical parameters by Raed H. Allawi, Watheq J. Al-Mudhafar, Mohammed A. Abbas, David A. Wood

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Evaluation of the influence of laser quenching mode parameters on the quality of the surface and surface layer of machine parts (overview) by S. V. Petrochenko, Q. Hao, X. Yu, K. Zhao

“…The conclusion is made in the form of recommendations on the selection of parameters of the laser hardening mode to obtain a given surface quality and surface layer of machine parts.…”
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Machine learning-based prediction of physical parameters in heterogeneous carbonate reservoirs using well log data by Fuyong Wang, Xianmu Hou

“…Six machine learning algorithms are utilized: support vector machine (SVM), backpropagation (BP) neural network, gaussian process regression (GPR), extreme gradient boosting (XGBoost), K-nearest neighbor (KNN), and random forest (RF). …”
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Predicting low ionospheric parameters and low frequency sky wave propagation strength using machine learning by Jian Wang, Chengsong Duan, Qiao Yu, Cheng Yang

“…Therefore, to enhance the predicting accuracy of LF sky wave propagation, we proposed an improved method based on the machine learning method. Firstly, we employed a machine learning method to create a prediction model for the critical frequency of the low ionospheric E layer (f oE), which significantly affects LF sky wave propagation. …”
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Prediction of Metabolic Parameters of Diabetic Patients Depending on Body Weight Variation Using Machine Learning Techniques by Oana Vîrgolici, Daniela Lixandru, Andrada Mihai, Diana Simona Ștefan, Cristian Guja, Horia Vîrgolici, Bogdana Virgolici

“…Several machine learning models, namely linear regression, polynomial regression, Gradient Boosting, and Extreme Gradient Boosting, were employed to predict changes in medical parameters as a function of body weight variation. …”
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Development of Decline Curve Analysis Parameters for Tight Oil Wells Using a Machine Learning Algorithm by Weirong Li, Zhenzhen Dong, John W. Lee, Xianlin Ma, Shihao Qian

“…In this study, 10,000 groups of reservoir/completion input data were generated by Latin hypercube sampling method, and then, 10,000 groups of output (oil rate and cumulative production data) were obtained by numerical simulation. Next, a machine learning technique was applied to establish a model between the input data and determining parameters of a decline curve analysis model by fitting the generated cumulative production rate. …”
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Using Physical Parameters for Phase Prediction of Multi-Component Alloys by the Help of TensorFlow Machine Learning with Limited DataUsing Physical Parameters for Phase Prediction of Multi-Component Alloys by the Help of TensorFlow Machine Learning with Limited Data by Kağan Şarlar

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Integration of IoT and Machine Learning for Real-Time Monitoring and Control of Heart Disease Patients by Neelamadhab Padhy, Rasmita Panigrahi, Sibo Prasad Patro, Vishal Kumar Swain, Kiran Kumar Sahu

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Parameter Calculation of Steam Pipeline Based on Hybrid Modeling by Hongwei CHEN, Zherui MA, Chunwang LV, Yuqiang LIU, Wei ZHANG, Ruikun WANG

“…In order to verify the validity of the hybrid modeling calculation, through the case studies, the pipe end steam parameters are calculated by using the mechanism model and the steam parameter prediction model based on vector machine algorithm, and then compared with the mixed model calculation results. …”
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Double Sided Lapping/Polishing Machine Grinding Trajectory Studies by XU Li, LIU Bing, WU Shi, DUAN Hao-yang, DONG Rui

“…The double-sided lapping /polishing machine processing，the actual operation and the selection of technological parameters has practical value，and provides a theoretical basis for the future research work.…”
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Kinematical analysis and optimal design of discal precise seeding machine by YANG Wen-zheng, LU Qiu-jun, ZHAO Yun

Subjects: “…earth bowl machine…”
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Pedotransfer functions for estimating the van Genuchten model parameters in the Cerrado biome by Mariana F. Veloso, Lineu N. Rodrigues, Elpídio I. Fernandes Filho, Carolina F. Veloso, Bruna N. Rezende

“…For the other parameters, the models did not perform satisfactorily for α and n (fit parameters).…”
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Advanced Trajectory Analysis of NASA’s Juno Mission Using Unsupervised Machine Learning: Insights into Jupiter’s Orbital Dynamics by Ashraf ALDabbas, Zaid Mustafa, Zoltan Gal

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Calculation of quality parameters of the functional surfaces of steady-rests by Erokhin V.V.

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COMPARATIVE MACHINE LEARNING ALGORITHM FOR CARDIOVASCULAR DISEASE PREDICTION by Ashish Mishra, Jyoti Mishra, Victor Hugo, Aloísio Vieira Lira Neto

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Application of machine learning to growth model in fisheries by Semra Benzer, Recep Benzer, Ali Gül

“…In this study, the growth parameter of Eastern mosquitofish, Gambusia holbrooki (135 females: 21–58.78 mm and 0.152–3.424 g; 59 males: 19.25–43.20 mm; 0.108–1.075 g), was determined with traditional LWRs, VB, and machine learning algorithms. …”
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Application of Support Vector Machines in High Power Device Technology by RAO Wei, LI Yong, YAN Ji

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Effect of Different Parameter Values for Pre-processing of Using Mammography Images by Hanife Avcı, Jale Karakaya

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