Classification of Real-World Objects Using Supervised ML-Assisted Polarimetry: Cost/Benefit Analysis by Rui M. S. Pereira, Filipe Oliveira, Nazar Romanyshyn, Irene Estevez, Joel Borges, Stephane Clain, Mikhail I. Vasilevskiy

“…To this end, we look for an algorithm using less input parameters without great loss of the quality of classification. …”
Get full text

Validation of Quantitative Ultrasound and Texture Derivative Analyses-Based Model for Upfront Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer by Adrian Wai Chan, Lakshmanan Sannachi, Daniel Moore-Palhares, Archya Dasgupta, Sonal Gandhi, Rossanna Pezo, Andrea Eisen, Ellen Warner, Frances C. Wright, Nicole Look Hong, Ali Sadeghi-Naini, Mia Skarpathiotakis, Belinda Curpen, Carrie Betel, Michael C. Kolios, Maureen Trudeau, Gregory J. Czarnota

Get full text

Design and Development of High-Precision Hybrid Controller for Ultra-Precision Non-Conventional Single-Point Diamond Turning Processes by Shahrokh Hatefi, Khaled Abou-El-Hossein

“…This controller could be used in a hybrid SPDT platform for controlling implemented machining techniques and synchronizing them. In addition, this hybrid controller could connect to on-machine metrology devices for in-process data acquisition, analyzing process parameters, and determining machining conditions. …”
Get full text

Modeling of concrete-filled PVC tube columns confined with CFRP strips under uniaxial eccentric compression: machine learning and finite element approaches by Yan Wang, Mohamed A. Elmeligy, Haytham F. Isleem, Asmaa Y. Hamed, Diyar N. Qader, Mohamed Sharaf, Pradeep Jangir, Arpita, Ghanshyam G. Tejani

“…The effects of these parameters on the ultimate load and strain capacity were analyzed. …”
Get full text

Machine Description File-based Monte Carlo Simulation Modeling and its Validation Using Pencil Beam Scanning Proton Therapy Commissioning Data by Umesh Bharat Gayake, Bhushankumar J. Patil, Kantaram Darekar, Gaurav T. Bholane, Sanjay D. Dhole, Lalit Chaudhary, Siddhartha Laskar

“…Purpose: This study aims to detail the machine description file (MDF)-based modeling and validation of the independent Monte Carlo (MC) dose engine using commissioning beam data of a multi-gantry Proteus Plus proton pencil beam scanning (PBS) machine, also, enhancing the understanding of its operational parameters and clinical applications. …”
Get full text

Optimization of Interval Type-2 Fuzzy Logic System By using A New Hybrid Method of Whale Optimization algorithm and Extreme Learning Machine by Mohammed Qasim Ibrahim, Nazar Khalaf Hussein Al-Dikhil

“…We are used the (WOA) algorithm together with the Extreme Learning Machine (ELM) algorithm as a hybrid algorithm to find the best parameters ​​for the IT2FLS. …”
Get full text

Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning... by Zhe Zhang, Yang Dai, Peng Xue, Xue Bao, Xinbo Bai, Shiyang Qiao, Yuan Gao, Xuemei Guo, Yanan Xue, Qing Dai, Biao Xu, Lina Kang

“…Abstract Angio-based microvascular resistance (AMR) as a potential alternative to the index of microcirculatory resistance (IMR) and its relationship with microvascular obstruction (MVO) and other cardiac magnetic resonance (CMR) parameters still lacks comprehensive validation. This study aimed to validate the correlation between AMR and CMR-derived parameters and to construct an interpretable machine learning (ML) model, incorporating AMR and clinical data, to forecast MVO in ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (PPCI). …”
Get full text

A New Reliability Rock Mass Classification Method Based on Least Squares Support Vector Machine Optimized by Bacterial Foraging Optimization Algorithm by S. Zheng, A. N. Jiang, X. R. Yang, G. C. Luo

“…This paper presents a new reliability rock mass classification method based on a least squares support vector machine (LSSVM) optimized by a bacterial foraging optimization algorithm (BFOA). …”
Get full text

An efficient approach on risk factor prediction related to cardiovascular disease around Kumbakonam, Tamil Nadu, India, using unsupervised machine learning techniques by K Kannan, A Menaga

Get full text

Strategic forecasting of renewable energy production for sustainable electricity supply: A machine learning approach considering environmental, economic, and oil factors in Türkiye... by Yasemin Ayaz Atalan, Hasan Şahin, Abdulkadir Keskin, Abdulkadir Atalan

“…In obtaining forecast data, 15 variables were considered under the oil resources, environmental parameters, and economic factors which are the main parameters affecting renewable energy usage rates. …”
Get full text

Efficient field correction of low-cost particulate matter sensors using machine learning, mixed multiplicative/additive scaling and extended calibration inputs by Slawomir Koziel, Anna Pietrenko-Dabrowska, Marek Wojcikowski, Bogdan Pankiewicz

Get full text

Predicting the high-strain-rate deformation behavior and constructing processing maps of 304L stainless steel through machine learning and deep learning by M. Ghaffari Farid, H.R. Abedi, R. Ghasempour, A. Taylor, S. Khoddam, P.D. Hodgson

“…The Random Forest model was optimized with various parameters, and the best performance came from a tree depth of 15, 150 estimators, and 150 leaf nodes. …”
Get full text

Prediction of cutting depth in abrasive water jet machining of Ti-6AL-4V alloy using back propagation neural networks by Yakub Iqbal Mogul, Ibtisam Mogul, Jaimon Dennis Quadros, Ma Mohin, Abdul Aabid, Muneer Baig, Mohammad Abdul Malik

“…The current study focusses on developing a back propagation neural network model for depth of cut during the abrasive water jet machining of a Ti-6AL-4V aluminum alloy. The study analyzed depth of cut for five different water jet abrasive parameters namely, water pressure, transverse speed, abrasive mass flow rate, abrasive orifice size, and nozzle to orifice diameter. …”
Get full text

A unified approach to systematization of multidiscipline multidimensional nonlinear initial boundary value problems by A. V. Korovaytsev, E. A. Korovaytseva, V. A. Lomovskoy

Get full text

Applied AMT machine learning and multi-objective optimization for enhanced performance and reduced environmental impact of sunflower oil biodiesel in compression ignition engine by Ali A. Al-jabiri, Hyder H. Balla, Mudhaffar S. Al-zuhairy, Hussein Alahmer, Ahmed Al-Manea, Raed Al-Rbaihat, Ali Alahmer

“…This comprehensive study investigates the influence of biodiesel, specifically derived from sunflower oil, through the esterification method, on crucial engine performance parameters and environmental effects. The study examines the impact of varying engine torque on the performance of a single-cylinder, four-stroke compression ignition engine, encompassing parameters such as brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC), as well as exhaust emissions, including unburned hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx). …”
Get full text

A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques by Arunik Baruah, Dimitrios Bousiotis, Seny Damayanti, Alessandro Bigi, Grazia Ghermandi, O. Ghaffarpasand, Roy M. Harrison, Francis D. Pope

“…We introduce a novel machine learning methodology to predict PM2.5 levels at 30 m long segments along the roads and at a temporal scale of 10 seconds. …”
Get full text

Machine learning-driven optimization of culture conditions and media components to mitigate charge heterogeneity in monoclonal antibody production: current advances and future pers... by Hossein Kavoni, Iman Shahidi Pour Savizi, Saratram Gopalakrishnan, Nathan E. Lewis, Seyed Abbas Shojaosadati

“…This review highlights machine learning (ML) as a powerful approach for modeling these relationships and forecasting charge variant profiles in CHO cell-based mAb process development. …”
Get full text

Analyzing the efficacy of trimethylolpropane trioleate oil for predicting cutting power and surface roughness in high-speed drilling of Al-6061 through machine learning. by Pramod S Kathmore, Bhanudas D Bachchhav, Duran Kaya, Sachin Salunkhe, Lenka Cepova, Ondřej Mizera, Emad Abouel Nasr

“…Varying additive concentration affects more on surface quality rather than power consumption. Furthermore, a machine learning algorithm was developed to forecast and compare various key aspects of high-speed drilling machinability, including power and surface roughness. …”
Get full text

Digital Twin-Enabled Building Information Modeling–Internet of Things (BIM-IoT) Framework for Optimizing Indoor Thermal Comfort Using Machine Learning by Fahad Iqbal, Shayan Mirzabeigi

“…Furthermore, it also uses a hybrid machine learning model that combines Facebook Prophet and Long Short-Term Memory (LSTM) to predict the future indoor environmental parameters. …”
Get full text

Comparative effects of metformin and varying intensities of exercise on miR-133a expression in diabetic rats: Insights from machine learning analysis by Elahe Alivaisi, Sabrieh Amini, Karimeh Haghani, Hori Ghaneialvar, Fatemeh Keshavarzi

Get full text