Machine learning for asphaltene polarizability: Evaluating molecular descriptors by Arun K. Sharma, Owen McMillan, Selsela Arsala, Supreet Gandhok, Rylend Young

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Comparative Study of Machine Learning Techniques for Predicting UCS Values Using Basic Soil Index Parameters in Pavement Construction by Mudhaffer Alqudah, Haitham Saleh, Hakan Yasarer, Ahmed Al-Ostaz, Yacoub Najjar

“…This dataset served to train various models to estimate the UCS from basic soil parameters. The methods employed included multi-linear regression (MLR), multi-nonlinear regression (MNLR), and several machine learning techniques: backpropagation artificial neural networks (ANNs), gradient boosting (GB), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN). …”
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A Family of Transformerless Boost Converters for Pulsed Electrolysis by Hossein Gholizadeh, Mojtaba Hajihosseini, Saman A. Gorji

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Investigating MIMO technology in free space optical communication systems for evaluating performance across various environment parameters by Ramanababu Challapalli, P. Chitra

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Evaluation of Ethanolic Extract of Red Seaweed (Gracilariopsis lemaneiformis) on Growth and Haematological Parameters of Nile Tilapia (Oreochromis niloticus) by A. M. Shahabuddin, Md. Abdul Hannan, Md. Foysul Hossain, Shahrear Hemal, Runi Khanam, Tahmina Afroz, Ahmed Mustafa

“…The study was designed to assess the ethanolic extract (EtOH) extracted from red seaweed (Gracilariopsis lemaneiformis) on Nile tilapia (Oreochromis niloticus) to evaluate growth, immunity and haematological parameters. …”
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Construction of a model for predicting sensory attributes of cosmetic creams using instrumental parameters based on machine learning by He Jingru, Qian Xuedan, Huang Hu, Lin Bao, Zhang Jun, Zhang Chunxiao, Chen Yuyan

“…This study aims to enhance the sensory evaluation of skin creams by using machine learning to predict sensory attributes based on instrumental parameters, addressing the limitations of conventional methods. …”
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Using Segmentation to Boost Classification Performance and Explainability in CapsNets by Dominik Vranay, Maroš Hliboký, László Kovács, Peter Sinčák

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Extreme Gradient Boosting Algorithm for Predicting Shear Strengths of Rockfill Materials by Mahmood Ahmad, Ramez A. Al-Mansob, Kazem Reza Kashyzadeh, Suraparb Keawsawasvong, Mohanad Muayad Sabri Sabri, Irfan Jamil, Arnold C. Alguno

“…XGBoost beats SVM, RF, AdaBoost, and KNN models in terms of performance evaluation metrics such as coefficient of determination (R2), Nash–Sutcliffe coefficient (NSE), and error in the root mean square ratio (RMSE) to the standard deviation of the measured data (RSR). …”
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Event-triggered boost converter model predictive control with Kalman filter by Ranya Badawi, Jun Chen

“…Extensive simulation evaluations are conducted to compare the performance of conventional time-triggered MPC and the proposed event-triggered MPC, where the event-trigger threshold is used as a tuning parameter to balance computation and control performance.…”
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Evaluation of safety and efficacy of multiple intravenous and intraosseous doses of foetal liver-derived mesenchymal stem cells in children with severe osteogenesis imperfecta: the... by Vrisha Madhuri, Sowmya Ramesh, Annika Goos, Thomas V. Paul, Shyamkumar Nidugala Kesava, Vikram Mathews, Lilian Walther-Jallow, Cecilia Götherström, on behalf of the BOOST2B Study Group

“…Conclusion: The BOOST2B trial has been approved by the regulatory agencies in India and is ongoing. …”
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The Iron Boost: A Nutritional Snack with Lepidium Sativum and Oryza Sativa by Sharma Priya, Mishra Priya

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Development of hybrid stacking machine learning for evaluating parameters affecting refrigerated shrimp coated with chitosan-loaded Salvia officinalis nanoemulsions by Mehran Sayadi, Elahe Abedi, Najmeh Oliyaei, Maryam Mousavifard

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Optimizing Olive Production: The Role of Foliar Fertilizers in Boosting Yield and Quality by Bihter Çolak Esetlili, Tülin Pekcan, Onur Bayız, Hanife Telli Karaman, Murat Ayata, Gökhan Sarı

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Optimizing maize systems with raised beds: boosting productivity, profitability, and sustainability by Raj Kumar Jat, Vijay Singh Meena, Vijay Singh Meena, Illathur R. Reddy, R. K. Sohane, R. N. Singh, Shubham Durgude, Suneel Kumar, S. Pazhanismy, Sunita Kumari Meena, Kumari Sharda, Susheel Singh, Rama Kant Singh, Rama Kant Singh, Seema Kumari, Seema Kumari, K. M. Singh, Govind Kumar, Amit Kumar Lenka, Asheesh Chaurasiya, Asheesh Chaurasiya, Raghubar Sahu, Raghubar Sahu, Gopal Lal Choudhary, Paras Nath, Pankaj Kumar Yadav, Abhay Kumar, Ratnesh Kumar Jha, Ujjwal Kumar, Anup Das, Anil Kumar Jha, Dhananjay Pati Tripathi, Swati Sagar

“…In this study, the implications of the raised bed planting (RBP) system on smallholder maize farming in Bihar, India, for four consecutive Rabi seasons were evaluated from 2020–2021 to 2023–2024. The research focuses on key parameters such as productivity, profitability, water use efficiency (WUE), and nutrient use efficiency (NUE) to present a sustainable alternative to traditional flatbed planting systems. …”
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Tumor bed boost in breast cancer: Brachytherapy versus electron beam by Sanjoy Roy, Devleena, Tapas Maji, Prabir Chaudhuri, Debarshi Lahiri, Jaydip Biswas

“…Background: The prospective study aimed to evaluate the effectiveness of Electron beam or HDR 192Ir Interstitial Implant used as a boost in breast Conservation cases after completion of EBRT. …”
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Single source switched capacitor boosting nine-level inverter for PV applications by Lakshmi Prasanna, T.R. Jyothsna, Allam Venkatesh, CH. Nayak bhukya

“…In this study, a nine-level inverter is constructed to improve its boosting capabilities while minimizing its CF value and switch count. …”
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Enhanced AdaBoostM1 with Multilayer Perceptron for Stock Price Prediction by Rebwar Mala Nabi, Soran AB. Saeed, Habibollah Haron

“…Among the predictive models, various adaptations of the AdaBoostM1 algorithm have been applied to stock market prediction, either by tuning parameters or experimenting with different base learners. …”
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Integrating explainable artificial intelligence and light gradient boosting machine for glioma grading by Teuku Rizky Noviandy, Ghalieb Mutig Idroes, Irsan Hardi

“…Utilizing a dataset from The Cancer Genome Atlas, which comprises molecular and clinical characteristics of 839 glioma patients, the LightGBM model is meticulously trained, and its parameters finely tuned. Its performance is benchmarked against various other ML models through a comprehensive evaluation involving metrics such as accuracy, precision, recall, and F1-score. …”
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Machine Learning Prediction of CO₂ Diffusion in Brine: Model Development and Salinity Influence Under Reservoir Conditions by Qaiser Khan, Peyman Pourafshary, Fahimeh Hadavimoghaddam, Reza Khoramian

“…This study employs three machine learning (ML) models—Random Forest (RF), Gradient Boost Regressor (GBR), and Extreme Gradient Boosting (XGBoost)—to predict DC based on pressure, temperature, and salinity. …”
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Evaluation of Early Maturity Group of Soybean (Glycine max L. Merr.) for Agronomic Performance and Estimates of Genetic Parameters in Sudanian Zone of Burkina Faso by Gilles Ibié Thio, Nofou Ouédraogo, Inoussa Drabo, Frank Essem, Fidèle Bawomon Neya, Fabrice Wendyam Nikiema, Soumabéré Coulibaly, Pierre Alexandre Eric Djifaby Sombié, Oumar Boro, Abdoul-Kawiyou Hassane, Abdoul-Aziz Ouédraogo, Hervé Bépio Bama, Mahamadou Sawadogo, Paco Sérémé

“…The introduction of new genotypes with high agronomic potential and adapted to the climatic conditions of the Sudanian zone of Burkina Faso will boost soybean production in the region. Twenty-four (24) newly introduced soybean genotypes were evaluated for their agromorphological and adaptation characteristics in the Sudanian zone of Burkina Faso. …”
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