Research on a New Method of Macro–Micro Platform Linkage Processing for Large-Format Laser Precision Machining by Longjie Xiong, Haifeng Ma, Zheng Sun, Xintian Wang, Yukui Cai, Qinghua Song, Zhanqiang Liu

“…In summary, the proposed method adeptly balances efficiency and quality, rendering it particularly suitable for laser precision machining applications.…”
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Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data by Zhiqiang Wu, Yunquan Chen, Bingjian Zhang, Jingzheng Ren, Qinglin Chen, Huan Wang, Chang He

“…This study presents a systematic physics-informed machine learning method that integrates transfer learning and labeled data to construct a spatiotemporal model of the PSA process. …”
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Advanced generalized machine learning models for predicting hydrogen–brine interfacial tension in underground hydrogen storage systems by Ahmed Farid Ibrahim

“…A novel salt equivalency metric was introduced, transforming multiple salt variables into a single parameter and improving model generalization while maintaining high prediction accuracy (R2 = 0.98). …”
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Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches by Nafiseh Hosseini, Hamid Tanzadehpanah, Amin Mansoori, Mostafa Sabzekar, Gordon A. Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…The model was evaluated using accuracy, sensitivity, specificity, precision and f1-measure parameters. The receiver operating characteristic (ROC) curve and factor importance analysis were also determined. …”
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Design a Robust DDoS Attack Detection and Mitigation Scheme in SDN-Edge-IoT by Leveraging Machine Learning by Habtamu Molla Belachew, Mulatu Yirga Beyene, Abinet Bizuayehu Desta, Behaylu Tadele Alemu, Salahadin Seid Musa, Alemu Jorgi Muhammed

“…This study aims to improve DDoS detection accuracy by training a robust Machine Learning (ML) model using effective hyper-parameter tuning and Cross-Validation (CV) techniques. …”
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Predictive study of machine learning combined with serum Neuregulin 4 levels for hyperthyroidism in type II diabetes mellitus by Huilan Gu, Ye Lu

“…Pearson correlation was used to identify features correlated with NRG4. A parameter-optimized SVM model (C=1, linear kernel) was constructed for structured data modeling. …”
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Dempster Shafer-Empowered Machine Learning-Based Scheme for Reducing Fire Risks in IoT-Enabled Industrial Environments by Jayameena Desikan, Sushil Kumar Singh, A. Jayanthiladevi, Saurabh Singh, Byungun Yoon

“…By combining data from multiple different types of sensors (RGB, thermal, gas, smoke, and flame), the proposed architecture enhances the reliability of fire prediction and detection as each sensor detects different parameters of a fire and this ensures every parameter is considered for fire detection ensuring early detection and it reduces false positives. …”
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Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study by Qing Hua, Fengchun Yang, Yadan Zhou, Fenglian Shi, Xiaoyan You, Jing Guo, Li Li

“…ML models were constructed to evaluate the predictive value of maternal parameter changes on preeclampsia combined with FGR. …”
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The effect of temperature on low-cycle fatigue of shape memory alloy by Volodymyr Iasnii, Petro Yasniy, Denys Baran, Anna Rudawska

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Evolution and model prediction of mechanical ventilation temperature field in high-geotemperature tunnels: Experimental analysis and machine learning by Feng Huang, Song Wang, Shuping Jiang, Dong Yang, Zheng Hu, Aichen Zheng

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Trade-off analysis of machinability of steel alloy AISI 304L using Taguchi-grey integrated approach by Faisal Abbas, Muhammad Ali Khan, Muhammad Iftikhar Faraz, Syed Husain Imran Jaffery, Sohail Akram, Jana Petru, Refka Ghodhbani, Walid M. Shewakh

“…To address this shortcoming, multi-objective optimization of specific cutting energy, surface roughness, and material removal rate during turning of AISI 304L stainless steel was conducted at diverse machining parameters. Influential variables to include depth of cut, feed rate and cutting speed were taken as the input parameters. …”
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Non-Destructive Methods Based on Machine Learning for the Prediction of Sweet Potato Leaf Area: A Comparative Approach by Joao Everthon Da Silva Ribeiro, Ester Dos Santos Coelho, Antonio Gideilson Correia Da Silva, Pablo Henrique De Almeida Oliveira, Elania Freire Da Silva, Gisele Lopes Dos Santos, Anna Kezia Soares De Oliveira, John Victor Lucas Lima, Walter Esfrain Pereira, Lindomar Maria Da Silveira, Aurelio Paes Barros

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Short-term vital parameter forecasting in the intensive care unit: A benchmark study leveraging data from patients after cardiothoracic surgery. by Nils Hinrichs, Tobias Roeschl, Pia Lanmueller, Felix Balzer, Carsten Eickhoff, Benjamin O'Brien, Volkmar Falk, Alexander Meyer

“…Patients in an Intensive Care Unit (ICU) are closely and continuously monitored, and many machine learning (ML) solutions have been proposed to predict specific outcomes like death, bleeding, or organ failure. …”
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Enhanced brain tumor diagnosis using combined deep learning models and weight selection technique by Karim Gasmi, Najib Ben Aoun, Najib Ben Aoun, Khalaf Alsalem, Ibtihel Ben Ltaifa, Ibrahim Alrashdi, Lassaad Ben Ammar, Manel Mrabet, Abdulaziz Shehab

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Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy by Tobias Schraut, Anne Schützenberger, Tomás Arias-Vergara, Melda Kunduk, Matthias Echternach, Stephan Dürr, Julia Werz, Michael Döllinger

“…This study investigates a machine learning-based approach for hoarseness severity assessment using synchronous HSV and acoustic recordings, alongside conventional voice examinations.MethodsThree databases comprising 457 HSV recordings of the sustained vowel /i/, 634 HSV-synchronized acoustic recordings, and clinical parameters from 923 visits were analyzed. …”
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Identification of Parameters of the Hydrostatic Model of the Working Equipment Drive System of a Wheel Loader to Study the Effectiveness of the Energy Recovery System by Cezary RUDZKI (cezary.rudzki@wat.edu.pl), Arkadiusz RUBIEC (arkadiusz.rubiec@wat.edu.pl), Adam BARTNICKI (adam.bartnicki@wat.edu.pl)

“…Research conducted on hybrid drive systems of working machines is quite often limited to simulation studies, omitting the experimental determination of the parameters of a real machine. …”
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Machine learning framework for oxytetracycline removal using nanostructured cupric oxide supported on magnetic chitosan alginate biocomposite by Hassan Rasoulzadeh, Hossein Azarpira, Mojtaba Pourakbar, Amir Sheikhmohammadi, Alieh Rezagholizade-shirvan

“…This research introduces an efficient method for removing oxytetracycline (OTC) from liquids using CuO-M-CAB nanoparticles. By optimizing key parameters such as pH and reaction time through machine learning models (Tikhonov Regularization and PSO), removal efficiency is significantly enhanced. …”
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Prediction and Mapping of Soil Total Nitrogen Using GF-5 Image Based on Machine Learning Optimization Modeling by LIU Liqi, WEI Guangyuan, ZHOU Ping

“…Among these samples, 140 were randomly selected as the modeling sample set for calibration, and the remaining 31 samples were used as the test sample set. Three machine learning algorithms were introduced: Partial least squares regression (PLSR), backpropagation neural network (BPNN), and support vector machine (SVM) driven by a polynomial kernel function (Poly). …”
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Enhanced Reinforcement Learning Algorithm Based-Transmission Parameter Selection for Optimization of Energy Consumption and Packet Delivery Ratio in LoRa Wireless Networks by Batyrbek Zholamanov, Askhat Bolatbek, Ahmet Saymbetov, Madiyar Nurgaliyev, Evan Yershov, Kymbat Kopbay, Sayat Orynbassar, Gulbakhar Dosymbetova, Ainur Kapparova, Nurzhigit Kuttybay, Nursultan Koshkarbay

“…The proposed DDQN-PER algorithm showed PDR improvement in the range of 6.2–8.11% compared to other existing RL and machine-learning-based works.…”
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Establishment and validation of a convenient and efficient screening tool for active pulmonary tuberculosis in lung cancer patients based on common parameters by Fan Zhang, Fei Qi, Mengyan Sun, Peng Jiang, Minghang Zhang, Xiaomi Li, Yujie Dong, Juan Du, Liang Li, Tongmei Zhang

“…Baseline information, clinicopathological features, imaging manifestations, and blood testing results were collected and analyzed. Five machine learning methods, including logistic regression (LR), random forest (RF), support vector machine (SVM), decision tree (DT), and neural network (NN), were employed to develop a screening model for PTB-LC. …”
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