LIGHTWEIGHT DESIGN OF THE BASE FOR RECIPROCATING PISTON DIAPHRAGM PUMP BASE ON MULTI-OBJECTIVE OPTIMIZATION ALGORITHM by MA WenSheng, FENG ZhiWei, SHEN Yan, SUN ZhongZhi, NIU PeiYu, LIU ChunChuan, LI FangZhong, CHEN Tao

“…The lightweight design of diaphragm pump base structure has an important impact on the processing and production of diaphragm pump.Based on the research of the frame structure of a certain type of diaphragm pump,the equivalent model was established for the actual working environment and the finite element analysis was carried out.The design variables were defined according to analysis results to improve the calculation efficiency.The uniform test design method was adopted for the test design,and the relationship between the design variables and the stress and deformation was calculated through simulation fitting.The lightweight optimization mathematical model was established for the diaphragm pump base structure by using the multi-objective optimization algorithm.On the premise of meeting the performance requirements,some materials were reasonably configured,and the stress,deformation and natural frequency of the diaphragm pump base structure were as small as possible.The frame structure after the lightweight optimization design was simulated and analyzed,and compared with the structure before optimization.The results show that the structural properties of the engine base remain unchanged after lightweight,and the weight is reduced from 25372 kg to 24582 kg,with a weight reduction of 790 kg.The weight reduction effect is good,the maximum stress value is reduced by 45.1%,and the maximum deformation is reduced by 12.3%.The optimization effect is remarkable,which provides a basic support for the finite element analysis and lightweight optimization design of the new diaphragm pump structure.…”
Get full text

Machine learning-enhanced near-infrared spectroscopy for high-throughput phenotyping of sweetpotato sugars across raw and cooked states by Chaochen Tang, Xueying Mo, Zhimin Ma, Zhangying Wang

“…These models exhibited outstanding performance in calibration (R2C: 0.941–0.984), cross-validation (R2CV: 0.926–0.976), external validation (R2V: 0.898–0.971), and the ratio of prediction to deviation (RPD: 5.83–10.3), confirming their robust predictive capacity. …”
Get full text

Assessing sepsis-induced immunosuppression to predict positive blood cultures by Enrique Hernández-Jiménez, Enrique Hernández-Jiménez, Erika P. Plata-Menchaca, Erika P. Plata-Menchaca, Damaris Berbel, Damaris Berbel, Guillem López de Egea, Guillem López de Egea, Macarena Dastis-Arias, Laura García-Tejada, Fabrizio Sbraga, Pierre Malchair, Nadia García Muñoz, Alejandra Larrad Blasco, Eva Molina Ramírez, Xose Pérez Fernández, Joan Sabater Riera, Arnau Ulsamer

“…Although not widely accepted, several clinical and artificial intelligence-based algorithms have been recently developed to predict bacteremia. …”
Get full text

Metaheuristic Optimization of Agricultural Machinery for the Colombian Carnation Industry by Nixon Cuenca Orozco, Federico Gutiérrez Madrid, Héctor Fabio Quintero

Get full text

Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture by Safa E. El-Mahroug, Ayman A. Suleiman, Mutaz M. Zoubi, Saif Al-Omari, Qusay Y. Abu-Afifeh, Heba F. Al-Jawaldeh, Yazan A. Alta’any, Tariq M. F. Al-Nawaiseh, Nisreen Obeidat, Shahed H. Alsoud, Areen M. Alshoshan, Fayha M. Al-Shibli, Rakad Ta’any

“…Yield projections under each scenario were further analyzed using machine learning algorithms—random forest and gradient boosting regression—to quantify the influence of individual climate variables. …”
Get full text

Comparison Of Reversible Image Watermarking Methods Based On Prediction-Errors by Burhan Baraklı, Emre Altınkaya

“…This study compares two reversible imagewatermarking algorithms applied to a digital image. The first algorithm is amethod based on adaptive watermarking of prediction-errors. …”
Get full text

Aggregating Image Segmentation Predictions with Probabilistic Risk Control Guarantees by Joaquin Alvarez, Edgar Roman-Rangel

“…In this work, we introduce a framework to combine arbitrary image segmentation algorithms from different agents under data privacy constraints to produce an aggregated prediction set satisfying finite-sample risk control guarantees. …”
Get full text

ProBoost: Reducing Uncertainty Using a Boosting Method for Probabilistic Models by Fabio Mendonca, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias, Antonio G. Ravelo-Garcia, Mario A. T. Figueiredo

“…Uncertainty analysis of classification or regression models is a key feature of probabilistic approaches to supervised learning, allowing the assessment of how trustworthy predictions are. Just as boosting algorithms aim at obtaining accurate ensembles of simple classifiers, using a process guided by the accuracy of each of these classifiers, the method proposed in this paper builds an ensemble guided by the uncertainty of each of its individual models. …”
Get full text

Portable X-ray fluorescence sensor for ecofriendly, low-cost, and fast assessment of eucalypt charcoal attributes by Renata Andrade, Lucas Benedet, Marcelo Mancini, Sérgio Henrique Godinho Silva, Camila da Silva Freitas, Marco Aurélio Carbone Carneiro, Nilton Curi

“…The objective of this study was to evaluate the use of pXRF data in machine-learning models trained to predict attributes of eucalypt charcoal. pXRF data (elemental contents) from 276 charcoal samples were used to train predictive models using six machine-learning algorithms. …”
Get full text

Early gestational diabetes mellitus risk predictor using neural network with NearMiss by Min Zhao, Xiaojie Su, Lihong Huang

Get full text

QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm–multiple linear regressions by Abolghasem Beheshti, Eslam Pourbasheer, Mehdi Nekoei, Saadat Vahdani

“…Results showed that the predictive ability of the model was satisfactory, and it can be used for designing similar group of antimalarial compounds.…”
Get full text

Stacked ensemble model for NBA game outcome prediction analysis by Guangsen He, Hyun Soo Choi

“…Abstract This research presents a stacked ensemble approach that employs artificial intelligence (AI) techniques to predict the outcomes of NBA games. Several machine learning algorithms were utilized, including Naïve Bayes, AdaBoost, Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), XGBoost, Decision Tree, and Logistic Regression. …”
Get full text

Assessment of methods for predicting physical and chemical properties of organic compounds by Tunga Salthammer

“…The algorithms underlying the respective tools are highly specialized and mathematically sophisticated. …”
Get full text

Utilization of Machine Learning for Predicting Corrosion Inhibition by Quinoxaline Compounds by Muhamad Fadil, Muhamad Akrom, Wise Herowati

“…By conducting a comparative analysis among three algorithms: AdaBoost Regressor (ADB), Gradient Boosting Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR), and optimizing parameters through hyperparameter tuning using Grid Search and Random Search, this research demonstrates that the XGBR model yields the most superior prediction results. …”
Get full text

A Machine Learning Approach for the Prediction of Thermostable β-Glucosidases by Diego Mariano

Get full text

TinyML with Meta-Learning on Microcontrollers for Air Pollution Prediction by I Nyoman Kusuma Wardana, Suhaib A. Fahmy, Julian W. Gardner

“…Tiny machine learning (tinyML) involves the application of ML algorithms on resource-constrained devices such as microcontrollers. …”
Get full text

Improving earthquake prediction accuracy in Los Angeles with machine learning by Cemil Emre Yavas, Lei Chen, Christopher Kadlec, Yiming Ji

“…Abstract This research breaks new ground in earthquake prediction for Los Angeles, California, by leveraging advanced machine learning and neural network models. …”
Get full text

The RMaP challenge of predicting RNA modifications by nanopore sequencing by Jannes Spangenberg, Stefan Mündnich, Anne Busch, Stefan Pastore, Anna Wierczeiko, Winfried Goettsch, Vincent Dietrich, Leszek P. Pryszcz, Sonia Cruciani, Eva Maria Novoa, Kandarp Joshi, Ranjan Perera, Salvatore Di Giorgio, Paola Arrubarrena, Irem Tellioglu, Chi-Lam Poon, Yuk Kei Wan, Jonathan Göke, Andreas Hildebrandt, Christoph Dieterich, Mark Helm, Manja Marz, Susanne Gerber, Nicolo Alagna

“…Results demonstrate that a low prediction error and a high prediction accuracy can be achieved on these modifications across different approaches and algorithms. …”
Get full text

Application of Random Forest Algorithm to Analyze the Confidence Level of Forest Fire Hotspots in Riau Peatland by Mitra Unik, Imas Sukaesih Sitanggang, Lailan Syaufina, I Nengah Surati Jaya

“…This study employs the Random Forest (RF) algorithm to analyze the confidence levels of hotspots, aiming to predict potential fire occurrences and improve fire management strategies. …”
Get full text

IMPLEMENTATION OF THE PARALLEL ALGORITHM OF NONISOTHERMAL HEAT AND MOISTURE MIGRATION TASK SIMULATION IN NATURAL DISPERSE ENVIRONMENTS by P. K. Shalkevich, S. P. Kundas, I. A. Gishkeluk

“…Comparative studies of the computing model performance of contaminants transport by the proposed algorithm and use of Matlab at the standard instruction level showed a significant reduction in compu-tation time.…”
Get full text