Estimating canopy height in tropical forests: Integrating airborne LiDAR and multi-spectral optical data with machine learning by Brianna J. Pickstone, Hugh A. Graham, Andrew M. Cunliffe

“…This study aims to compare the performance of three machine learning algorithms (Multiple Linear Regression (MLR), Random Forest (RF), and Convolutional Neural Networks (CNN)) when using PlanetScope and Sentinel-2 imagery to improve the accuracy of height predictions. …”
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Implementation of a neural network model in the Statistica 12 for mudflow frequency forecasting by B. A. Ashabokov, A. A. Tashilova, L. A. Kesheva, N. V. Teunova

“…It follows from the linear trend equation that, on average, over the entire period, including the predicted one, the number of mudflows tends to grow slightly by 0.3/10 years. …”
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Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan. by Ana Torres, Brima Musa Younis, Samuel Tesema, Jose Carlos Solana, Javier Moreno, Antonio J Martín-Galiano, Ahmed Mudawi Musa, Fabiana Alves, Eugenia Carrillo

“…Today, basic knowledge of this neglected disease and how to predict its progression remain largely unknown.<h4>Methods and findings</h4>This study addresses the use of several biochemical, haematological and immunological variables, independently or through unsupervised machine learning (ML), to predict PKDL progression risk. …”
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Development and validation of novel machine learning-based prognostic models and propensity score matching for comparison of surgical approaches in mucinous breast cancer by Chunmei Chen, Jundong Wu, Yutong Fang, Yong Li, Qunchen Zhang

“…We have successfully developed 6 optimal prognostic models utilizing the XGBoost algorithm to accurately predict the survival of MBC patients. …”
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Machine Learning Approach to Model Soil Resistivity Using Field Instrumentation Data by Md Jobair Bin Alam, Ashish Gunda, Asif Ahmed

“…This research leverages various machine learning algorithms to develop predictive models trained on a comprehensive dataset of sensor-based soil moisture, matric suction, and soil temperature obtained from prototype ET covers, with known resistivity values. …”
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Model Optimization for High-Yield Biocrude in Co-Hydrothermal Liquefaction of Municipal Sludge by Botian HAO, Yunfei DIAO, Ya WEI, Donghai XU

“…High sludge ratios significantly suppress yield, primarily because the high ash content (59.1%) dilutes the organic biomass concentration. Finally, a genetic algorithm combined with an ANN was used to predict the optimal process conditions for the co-HTL of MS and microalgae, achieving a maximum biocrude yield of 32.2%. …”
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Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification by Sylvio Barbon, Ana Paula Ayub da Costa Barbon, Rafael Gomes Mantovani, Douglas Fernandes Barbin

“…Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. …”
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Application of Monte Carlo Simulation (MCS) and Fuzzy Finite Element (FFEM) for Investigating the Uncertainty of Seepage in Homogeneous Earth Dams by Milad Kheiry, Farhoud Kalateh

“…The purpose of this research is to investigate the impact of uncertainty in the prediction of seepage flow through earth dams using the Fuzzy Monte Carlo Simulation (FMCS) new hybrid algorithm, which is implemented with the help of the finite element method and monte carlo simulation. …”
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Backdoor Defence for Voice Print Recognition Model Based on Speech Enhancement and Weight Pruning by Jiawei Zhu, Lin Chen, Dongwei Xu, Wenhong Zhao

“…Finally, the model is pruned using an automatic progressive weight pruning algorithm, which can avoid the accuracy degradation caused by neurons pruning. …”
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Multi-exit Kolmogorov–Arnold networks: enhancing accuracy and parsimony by James Bagrow, Josh Bongard

“…Here we introduce multi-exit KANs, where each layer includes its own prediction branch, enabling the network to make accurate predictions at multiple depths simultaneously. …”
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Implementasi Algoritma Support Vector Machine (SVM) Untuk Klasifikasi Penyakit Stroke by Danis Rifa Nurqotimah, Ahsanun Naseh Khudori, Risqy Siwi Pradini

“…Classification is one of a few methods in predicting stroke symptoms with the aim of obtaining accurate prediction of disease. …”
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Optimization Method for Transit Signal Priority considering Multirequest under Connected Vehicle Environment by Song Xianmin, Yuan Mili, Liang Di, Ma Lin

“…And the green time compensation algorithm is developed after considering the arrival information of the buses in the next cycle of compensational phase. …”
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Offline Single-Polarization Radar Quantitative Precipitation Estimation Based on a Spatiotemporal Deep Fusion Model by Yonghong Zhang, Shiwei Chen, Wei Tian, Guangyi Ma, Shuai Chen

“…Quantitative precipitation estimation (QPE) based on Doppler radar plays an important role in severe weather monitoring, industrial and agricultural production, and natural disaster prediction and prevention. However, the temporal and spatial variability of precipitation leads to large errors in radar estimates of mixed precipitation. …”
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Load forecasting of microgrid based on an adaptive cuckoo search optimization improved neural network by Liping Fan, Pengju Yang

“…Finally, the weights and biases of the forecasting model were optimized by the improved cuckoo search algorithm. The results showed that the BP network optimized by the improved cuckoo search optimization enhanced the global search ability, avoided the local optima, quickened the convergence speed, and presented excellent performance in load forecasting. …”
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Camouflage Target Detection Method with Mutual Compensation of Local-Global Features by HE Wenhao, GE Haibo

“…Experiments on 3 public datasets show that this algorithm is better than the other 8 latest models. …”
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Machine-Learning-Based Optimal Feed Rate Determination in Machining: Integrating GA-Calibrated Cutting Force Modeling and Vibration Analysis by Yu-Peng Yeh, Han-Hao Tsai, Jen-Yuan Chang

“…This study proposes a machine learning-based approach to optimize feed rate in machining operations by integrating a genetic algorithm (GA)-calibrated cutting force model with vibration analysis. …”
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Application of support vector machine system introducing multiple submodels in data mining by Weinan Tang

“…Finally, combined with the designed parallel support vector machine algorithm, video facial and expression recognition is carried out. …”
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“Bias Correction Method” for Regional Correction Experiment of Warm Season Rainstorm in Zhejiang by Chengyan Mao, Xin Pan, Haowen Li, Weibiao Li, Haoya Liu

“…This study employs the K-means clustering algorithm to partition warm-season precipitation in Zhejiang Province into distinct regions. …”
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Co-Optimization of Vibration Suppression and Data Efficiency in Robotic Manipulator Dynamic Modeling by Xiaowei Han, Kunru Wu, Nanmu Hui

“…Then, a linear regression formulation of the manipulator’s structural dynamics is established, and the BLS network is employed to model the unstructured residuals—primarily arising from nonlinear friction—with high precision. Finally, the PSO algorithm is applied to optimize the hyperparameters of the BLS network, achieving global model optimality. …”
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STUDY ON ENERGY ABSORPTION CHARACTERISTIC OF SINUSOIDAL BELLOWS FILLED EMULSION ABSORBER by ZHANG JianZhuo, ZHANG WanJiu, PAN YiShan, GUO Hao, WANG ShuWen

“…The pre⁃folded energy absorber is the only energy absorber structure used in rock burst mine at present.It has excellent energy absorption characteristics,but there are some problems in the buckling deformation process such as large load fluctuation and spark due to friction.In view of the above situation,a sinusoidal bellows(reduced diameter round pipe)filled with emulsion was designed to form a solid⁃liquid coupling body energy absorption component(composite component).The effects of three factors,namely wall thickness of the bellows,amplitude of the busbar and diameter of the liquid outlet,on the energy absorption characteristics of the energy absorption component were studied.The prediction model of average load,load fluctuation coefficient and specific energy absorption was established.The smoothed partiole hydrodynamics(SPH)particle algorithm of Abaqus finite element software was used for fluid⁃structure coupling analysis of the model.The structural parameters of energy absorption components were optimized by response surface method and the optimization results were obtained.The results show that when the wall thickness of corrugated pipe is 3.959 mm,the amplitude of busbar is 3.721 mm,and the diameter of outlet is 22.161 mm,the energy absorption characteristics of the member are the best.The average load is 438.684 kN,the load fluctuation coefficient is 1.178,and the specific energy absorption is 12.123 kJ/kg.Through comparison and verification,the results have high reliability,which provides a superior energy absorption component for the energy absorption link of anti⁃impact support equipment.…”
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