Retrieval of nicotine content in cigar leaves by remote analysis of aerial hyperspectral combining machine learning methods by Chenyu Tian, Yifei Lu, Hengduo Xie, Yufan Yu, Liming Lu

“…The output of these operations was then further processed by CARS, SPA, and UVE algorithms to determine the nicotine sensitive bands. Three machine learning algorithms were then used to analyze the data: PLS, BP, RF, and the SVM. …”
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Identifying the Roadway Infrastructure Factors Affecting Road Accidents Using Interpretable Machine Learning and Data Augmentation by Jonghak Lee, Sangyoup Kim, Tae-Young Heo, Dongwoo Lee

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Global Navigation Satellite System (GNSS) radio occultation climatologies mapped by machine learning and Bayesian interpolation by E. Shehaj, E. Shehaj, S. Leroy, K. Cahoy, A. Geiger, L. Crocetti, G. Moeller, G. Moeller, B. Soja, M. Rothacher

“…</p> <p>In this work, we investigate the potential of machine learning (ML) to construct RO climatologies and compare the results of an ML construction with Bayesian interpolation (BI), a state-of-the-art method to generate maps of RO products. …”
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Machine learning approach for predicting tramp elements in the basic oxygen furnace based on the compiled steel scrap mix by Michael Schäfer, Ulrike Faltings, Björn Glaser

“…In this paper, we present a machine learning approach based on XGBoost to predict the chemical element content in the converter. …”
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Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials by Simin Nazari, Amira Abdelrasoul

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Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach by Iqramul Haq, Md. Ismail Hossain, Ahmed Abdus Saleh Saleheen, Md. Iqbal Hossain Nayan, Mafruha Sultana Mila

“…Here we compare the results of the optimized SIR model and a well-known machine learning algorithm (PROPHET algorithm) for the forecasting trend of the COVID-19 pandemic. …”
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REVOLUTIONIZING LUXURY: THE ROLE OF AI AND MACHINE LEARNING IN ENHANCING MARKETING STRATEGIES WITHIN THE TOURISM AND HOSPITALITY LUXURY SECTORS by Maria Nascimento CUNHA, Manuel PEREIRA, António CARDOSO, Jorge FIGUEIREDO, Isabel OLIVEIRA

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[18F]FDG PET-Based Radiomics and Machine Learning for the Assessment of Gliomas and Glioblastomas: A Systematic Review by Francesco Dondi, Roberto Gatta, Maria Gazzilli, Pietro Bellini, Gian Luca Viganò, Cristina Ferrari, Antonio Rosario Pisani, Giuseppe Rubini, Francesco Bertagna

“…The aim of this systematic review was to assess the role of [18F]FDG PET-based radiomics and machine learning (ML) in the evaluation of these neoplasms. …”
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A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults by Daisu Abe, Motoki Inaji, Takeshi Hase, Eiichi Suehiro, Naoto Shiomi, Hiroshi Yatsushige, Shin Hirota, Shu Hasegawa, Hiroshi Karibe, Akihiro Miyata, Kenya Kawakita, Kohei Haji, Hideo Aihara, Shoji Yokobori, Takeshi Maeda, Takahiro Onuki, Kotaro Oshio, Nobukazu Komoribayashi, Michiyasu Suzuki, Taketoshi Maehara

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Towards Fault Detection in Industrial Equipment through Energy Consumption Analysis: Integrating Machine Learning and Statistical Methods by Baddou Nada, Dadda Afaf, Rzine Bouchra, Hmamed Hala

Subjects: “…machine learning…”
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Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey by Sohaib K. M. Abujayyab, Moustafa Moufid Kassem, Ashfak Ahmad Khan, Raniyah Wazirali, Mücahit Coşkun, Enes Taşoğlu, Ahmet Öztürk, Ferhat Toprak

“…Forest fires caused by different environmental and human factors are responsible for the extensive destruction of natural and economic resources. Modern machine learning techniques have become popular in developing very accurate and precise susceptibility maps of various natural disasters to help reduce the occurrence of such calamities. …”
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Correlation coefficients of vibration signals and machine learning algorithm for structural damage assessment in beams under moving load by Toan Pham-Bao, Vien Le-Ngoc

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A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learning by Gbenga Lawrence Alawode, Pere Joan Gelabert, Marcos Rodrigues

“…We employed a detailed geospatial approach to assess the spatial-temporal variations in containment probability for escaped wildfires in Catalonia. Using machine learning algorithms, geospatial data, and 124 historical wildfire perimeters from 2000 to 2015, we developed a predictive model with high accuracy (Area Under the Receiver Operating Characteristics Curve = 0.81 ± 0.03) over 32,108 km2 at a 30-meter resolution. …”
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Predicting rapid decline in kidney function among type 2 diabetes patients: A machine learning approach by Eri Nakahara, Kayo Waki, Hisashi Kurasawa, Imari Mimura, Tomohisa Seki, Akinori Fujino, Nagisa Shiomi, Masaomi Nangaku, Kazuhiko Ohe

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Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models by Youzhi Lian, Yinyu Shi, Haibin Shang, Hongsheng Zhan

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Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting by Hai Tao, Ali Omran Al-Sulttani, Ameen Mohammed Salih Ameen, Zainab Hasan Ali, Nadhir Al-Ansari, Sinan Q. Salih, Reham R. Mostafa

“…The application of machine learning (ML) models in forecasting river flow has grown rapidly. …”
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Mechanistic controls on permeability evolution in thermally-upgraded low-maturity oil shales: Application of machine learning outputs by Bo He, Lingzhi Xie, Xin Liu, Jun Liu, Derek Elsworth

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Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US by Sayantan Sarkar, Javier M. Osorio Leyton, Efrain Noa-Yarasca, Kabindra Adhikari, Chad B. Hajda, Douglas R. Smith

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ExAIRFC-GSDC: An Advanced Machine Learning-Based Interpretable Framework for Accurate Gas Leakage Detection and Classification by B. Lalithadevi, S. Krishnaveni

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Relative Fat Mass and Physical Indices as Predictors of Gallstone Formation: Insights From Machine Learning and Logistic Regression by Deng L, Wang S, Wan D, Zhang Q, Shen W, Liu X, Zhang Y

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