Methodology for Design and Analysis of Liquid-Cooled Heat Sinks in High-Power Density Inverters by Paulo H. A. S. Silva, Lucas R. Rocha, Paulo R. Eckert, Rodrigo P. Vieira

“…This paper presents a comprehensive methodology for the design, analysis, and evaluation of liquid-cooled heat sinks in high-power density inverters, integrating computational fluid dynamics (CFD), lumped parameter modeling, and experimental validation. The CFD method is used to determine lumped parameters, which are then incorporated into a PLECs model to simulate the thermal dynamics of semiconductor devices. …”
Get full text

Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia by Riantini Virtriana, Kalingga Titon Nur Ihsan, Tania Septi Anggraini, Albertus Deliar, Agung Budi Harto, Akhmad Riqqi, Anjar Dimara Sakti

“…Geospatial and remote sensing data are utilized in the study. Dynamic parameter extrapolation uses data from 2000 to 2018. …”
Get full text

Comparative Analysis of Dry, Minimum Quantity Lubrication, and Nano-Reinforced Minimum Quantity Lubrication Environments on the Machining Performance of AZ91D Magnesium Alloy by Berat Baris Buldum, Kamil Leksycki, Suleyman Cinar Cagan

“…This study provides valuable insights for optimizing the machining parameters of AZ91D magnesium alloy in industrial applications, particularly where high surface quality and tool longevity are required.…”
Get full text

Machine learning algorithms for predictive modeling of dyslipidemia-associated cardiovascular disease risk in pregnancy: a comparison of boosting, random forest, and decision tree... by Idris Zubairu Sadiq, Fatima Sadiq Abubakar, Muhammad Auwal Saliu, Babangida Sanusi katsayal, Aliyu Salihu, Aliyu Muhammad

“…Methods In this study, we utilized three different machine learning algorithms (boosting, random forest, and decision tree regression) to predict dyslipidemia-associated cardiovascular disease using atherogenic index and lipid profile parameters based on a cross-sectional study datasets of 112 pregnant women aged between 15 and 49 conducted at Aminu Kano Teaching Hospital. …”
Get full text

Rheological behavior of MWCNT-SnO2/SAE50 hybrid nanolubricant: Experimental evaluation and viscosity prediction using optimized machine learning model by Mojtaba Sepehrnia, Hamid Maleki, Alireza Hamidi, Ali Davoodi, Hamidreza Golmohammadi

Get full text

Buoyant Flow and Thermal Analysis in a Nanofluid-Filled Cylindrical Porous Annulus with a Circular Baffle: A Computational and Machine Learning-Based Approach by Pushpa Gowda, Sankar Mani, Ahmad Salah, Sebastian A. Altmeyer

“…It was found that the baffle dimensions are very crucial parameters to effectively control the flow and associated thermal dissipation rates in the domain. …”
Get full text

An Intelligent LoRaWAN-Based IoT Device for Monitoring and Control Solutions in Smart Farming Through Anomaly Detection Integrated With Unsupervised Machine Learning by Maram Fahaad Almufareh, Mamoona Humayun, Zulfiqar Ahmad, Asfandyar Khan

Get full text

Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches by Kamal Hossain Nahin, Jamal Hossain Nirob, Akil Ahmad Taki, Md Ashraful Haque, Narinderjit Singh Sawaran Singh, Liton Chandra Paul, Reem Ibrahim Alkanhel, Hanaa A. Abdallah, Abdelhamied A. Ateya, Ahmed A. Abd El-Latif

“…Utilizing regression-based machine learning, antenna parameters are optimized to attain dual-band resonance with bandwidths of 3.34 THz and 1 THz across two bands, ensuring robust data throughput and communication stability. …”
Get full text

Comparative Evaluation of Decision Tree (M5) and Least Square Support Vector Machine (LS-SVM) Models for Groundwater Level Prediction in the Mashhad Plain by Vajihe Ramezani saani, Mahdi Zarei, Seyed Morteza Seyedian

“…A comparison of the results of the models indicated that the LS-SVM model is more sensitive to changes in input parameters than the M5 model, such that the decision tree model, unlike the least squares support vector machine model, provided acceptable results in all scenarios.Conclusions: In summary, the comparison of the models used suggests that the appropriate selection of climatic parameters and the examination and analysis of data have a significant impact on the accuracy of predictions.…”
Get full text

A hybrid statistical-machine learning approach for experimental analysis of biogas production in a waste to energy plant using a vacuum evaporator systems by Vakkar Ali, Praveen Pachauri, Azhar Equbal, Osama Khan, Mohd Parvez, Haidar Howari, Taufique Ahamad, Ashok Kumar Yadav, Brahmdeo Yadav

“…Optimizing these parameters while mitigating cavitation effects improves energy efficiency, prolonged equipment lifespan, and reliable operation of vacuum evaporators in large-scale biomass digestate treatment systems. k-means machine learning clustering validated by statistical modelling combined methodology is used for this analysis. …”
Get full text

An interpretable machine learning model for predicting bone marrow invasion in patients with lymphoma via 18F-FDG PET/CT: a multicenter study by Xinyu Zhu, Denglu Lu, Yang Wu, Yanqi Lu, Liang He, Yanyun Deng, Xingyu Mu, Wei Fu

“…We aimed to develop and validate an interpretable machine learning model that integrates clinical data, 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) parameters, radiomic features, and deep learning features to predict BMI in lymphoma patients. …”
Get full text

Detecting the Distribution of Callery Pear (<i>Pyrus calleryana</i>) in an Urban U.S. Landscape Using High Spatial Resolution Satellite Imagery and Machine Learning by Justin Krohn, Hong He, Timothy C. Matisziw, Lauren S. Pile Knapp, Jacob S. Fraser, Michael Sunde

Get full text

Parametric study of the decomposition of methane for COx-free H2 and high valued carbon using Ni-based catalyst via machine-learning simulation by Dinghao Xue, Pingyang Zhang, Yuanyuan Lin, Wenshuo Wang, Jiachang Shi, Qiang Hu, Gartzen Lopez, Cristina Moliner, Jin Sun, Tao Wang, Xinyan Zhang, Yingping Pang, Xiqiang Zhao, Yanpeng Mao, Zhanlong Song, Ziliang Wang, Wenlong Wang

“…However, affected by various factors, the proper process parameters are challenge to be ascertained by the time-consuming experimental method. …”
Get full text

A novel machine learning approach for numerical simulation on the hybrid nanofluid flow past a converging/diverging channel: Properties of tantalum and alumina nanoparticles by Shilpa B, Jasgurpreet Singh Chohan, N Beemkumar, Ankur Kulshreshta, Refka Ghodhbani, Nashwan Adnan Othman, Barno Abdullaeva, M Ijaz Khan

Get full text

Comparison of two pre-fuel cell carbon capture systems employing methane decomposition and hydrogen membrane: Regression-based machine learning and optimization approach by Amirali Nouri, Araz Emami, Ata Chitsaz, Alireza Fathipour, Navid Kousheshi, Ava Karshenas

“…Both carbon and hydrogen are produced within their respective systems and serve as fuels for heat generation. Machine learning techniques are applied to both systems, enabling precise prediction of their performance and costs over the operational cycle. …”
Get full text

A wireless sensor data-based coal mine gas monitoring algorithm with least squares support vector machines optimized by swarm intelligence techniques by Peng Chen, Yonghong Xie, Pei Jin, Dezheng Zhang

“…Due to the fact that the “negative samples” of coal mine safety data are scarce, least squares support vector machine is introduced to deal with this problem. In addition, several swarm intelligence techniques such as particle swarm optimization, artificial bee colony algorithm, and genetic algorithm are applied to optimize the hyper parameters of least squares support vector machine. …”
Get full text

Building a composition-microstructure-performance model for C–V–Cr–Mo wear-resistant steel via the thermodynamic calculations and machine learning synergy by Shuaiwu Tong, Shuaijun Zhang, Chong Chen, Tao Jiang, Peng Li, Shizhong Wei

“…By using phase content and experimental parameters as input features, the Gradient Boosted Tree model and Support Vector Regression model demonstrated strong applicability in predicting frictional performance and wear, respectively. …”
Get full text

A CT-based machine learning model for using clinical-radiomics to predict malignant cerebral edema after stroke: a two-center study by Lingfeng Zhang, Gang Xie, Yue Zhang, Yue Zhang, Junlin Li, Junlin Li, Wuli Tang, Wuli Tang, Ling Yang, Ling Yang, Kang Li

“…Subsequently, machine learning models were constructed based on clinical, radiomics, and clinical-radiomics. …”
Get full text

Risk factors and predictive models for post-operative moderate-to-severe mitral regurgitation following transcatheter aortic valve replacement: a machine learning approach by Zhenzhen Li, Jianing Fan, Jiajun Fan, Jiaxin Miao, Dawei Lin, Jingyan Zhao, Xiaochun Zhang, Wenzhi Pan, Daxin Zhou, Junbo Ge

“…This study aimed to identify risk factors and develop predictive models for post-operative MR following TAVR using machine learning (ML) techniques to enhance early detection and intervention. …”
Get full text

Machine Learning-Enhanced Model-Based Optical Proximity Correction Framework With Convolutional Neural Network-Based Variable Threshold Method Near the Diffraction Limit by Jinhao Zhu, Liwan Yue, Ying Li, Xianhe Liu, Qiang Wu, Qi Wang, Yanli Li

“…This study proposes a Machine Learning (ML)-enhanced MBOPC framework that employs a convolutional neural network (CNN) to predict mask edge imaging thresholds, thereby mitigating modeling deviations caused by complex lithographic conditions in the 28 nm technology node under immersion lithography. …”
Get full text