Urban Land-use Features Mapping from LiDAR and Remote Sensing Images using Visual Transformer Network Model by Q. Yuan

“…LiDAR has relatively accurate three-dimensional spatial information, while remote sensing image has rich spectral information. …”
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Sentiment Analysis of Public Satisfaction with the 'INFO BMKG' Application using Naive Bayes, SVM, and KNN by Natasya Aditiya, Pratomo Setiaji, Supriyono Supriyono

“…Thus, Naive Bayes is shown to be the most optimal for sentiment analysis in this study, whereas KNN and SVM showed certain limitations, particularly in efficiency and classification accuracy.…”
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Renal failure in patients with hematological malignancies (literature review) by E. G. Gromova

“…The main reasons for the renal failure development in hematological cancer patients and syndromes that prevent adequate antitumor therapy are considered. Diagnostic algorithm optimization and supportive intensive care of acute renal failure is the key to the successful application of highly effective modern protocols of drug anticancer treatment.A special group is represented by patients suffering from monoclonal gammopathies with acute renal injury and hyperproduction of immunoglobulins free light chains. …”
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Memory consolidation from a reinforcement learning perspective by Jong Won Lee, Min Whan Jung, Min Whan Jung

“…We suggest that memory consolidation might be viewed as a process of deriving optimal strategies based on simulations derived from limited experiences, rather than merely strengthening incidental memories. …”
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An Improved Low-Bit-Rate Image Compression Framework Based on Semantic-Aware Model and Neighborhood Attention by Chengbin Zeng, Liang Zhang

“…In addition, our proposed algorithm effectively mitigates distortions in facial and textual regions while preserving the structural integrity and visual fidelity. …”
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A Data Resource Trading Price Prediction Method Based on Improved LightGBM Ensemble Model by Wan Nie, Bingliang Shen, Desheng Li

“…To address the key challenges of limited practical application, high implementation difficulty, and poor generalization capability in existing theoretical models for data resource pricing, this study employs generative adversarial network (GAN) to augment the dataset and constructs a DRV-LightGBM model based on a Bayesian parameter optimization algorithm that maximizes the coefficient of determination (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) to predict data resource transaction prices and provide post-hoc explanations for the prediction model. …”
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Research on the policy inconsistency, network motifs and low carbon effects for municipal solid waste management by Bo Lv, Tianxu Cui, Daiheng Li, Weiyue Yao

“…It offers strategies for enhancing carbon reduction through network optimization and phased policy design, aiding low - carbon MSWM policy improvement.…”
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Parametric Sensitivity Analysis of Safe Train Interval Model Based on Relative Braking Distance by QIAN Hua, LYU Haojiong

“…This study sought to analyze the sensitivity of key parameters affecting these safe intervals, to harness the potential of virtual coupling technology in improving efficiency while ensuring operational safety. The qualitative analysis based on a safe time interval model and the quantitative analysis based on simulations using the model plus an algorithm, were conducted, shedding light on the extent of influence from key parameters in the safe interval model on stopping time differences. …”
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Challenges and Opportunities in Remote Sensing-Based Fuel Load Estimation for Wildfire Behavior and Management: A Comprehensive Review by Arnick Abdollahi, Marta Yebra

“…We reviewed the literature of the applications of remote sensing in fuel load estimation over a 12-year period, highlighting the capabilities and limitations of different remote-sensing sensors and technologies. While inherent technological constraints currently hinder optimal fuel load mapping using remote sensing, recent and anticipated developments in remote-sensing technology promise to enhance these capabilities significantly. …”
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Real-time and Cost-efficient Indoor Localization and Mapping Solution for Emergency Response Applications by H. Elsayed, A. Shaker

“…It utilizes the Google Cartographer SLAM engine to generate real-time 2D raster maps and stream position and orientation data at 200 poses per second, ensuring continuous poses feed while minimizing latency. The resulting maps are continuously being optimized using a loop-closing algorithm to reduce drift and maintain the trajectory integrity. …”
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Variable Selection for Multivariate Failure Time Data via Regularized Sparse-Input Neural Network by Bin Luo, Susan Halabi

“…To capture potential nonlinear effects, we further extend the approach to a sparse-input neural network model with structured group penalties on input-layer weights. Both methods are optimized using a composite gradient descent algorithm combining standard gradient steps with proximal updates. …”
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Initialization Methods for FPGA-Based EMT Simulations by Xin Ma, Xiao-Ping Zhang

“…To improve hardware adaptability, optimized strategies are developed for address sequence, interface, update modes and dataflow. …”
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Runoff Prediction and Uncertainty Analysis for Xijiang River Basin Based on CMIP6 Climate Scenarios by WU Huiming, YAN Meng, ZHOU Shuai

“…Based on this, the Xin'anjiang hydrological model (XAJ) is built, and the particle swarm optimization (PSO) algorithm is employed to calibrate and validate the model parameters. …”
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Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector by Ruixing Ming, Osama Mohamad, Nisreen Innab, Mohamed Hanafy

“…Addressing the pressing challenge of insurance fraud, which significantly impacts financial losses and trust within the insurance industry, this study introduces an innovative automated detection system utilizing ensemble machine learning (EML) algorithms. The approach encompasses four strategic phases: 1) Tackling data imbalance through diverse re-sampling methods (Over-sampling, Under-sampling, and Hybrid); 2) Optimizing feature selection (Filtering, Wrapping, and Embedding) to enhance model accuracy; 3) employing binary classification techniques (Bagging and Boosting) for effective fraud identification; and 4) applying explanatory model analysis (Shapley Additive Explanations, Break-down plot, and variable-importance Measure) to evaluate the influence of individual features on model performance. …”
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Contagious disease outbreaks: Distinguishing between constant and variable order using the SEIAR model by S.Sepehr Tabatabaei, Mohammadreza Dehghan

“…A heuristic approach is proposed for parameter estimation using a Genetic Optimization Algorithm, which evaluates all possible scenarios for the order and suggests parameter sets that perform well across these scenarios. …”
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G-RCenterNet: Reinforced CenterNet for Robotic Arm Grasp Detection by Jimeng Bai, Guohua Cao

“…The proposed G-RCenterNet algorithm is embedded into a robotic grasping system, where a structured light depth camera captures target images, and the grasp detection network predicts the optimal grasp box. …”
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Performance of Hybrid Transmission of a D2D Communication With Energy Harvesting and Ambient Backscattering by Saranya Karattupalayam Chidambaram, Yogesh Kumar Choukiker, Abhijit Bhowmick

“…An algorithm is proposed to maximize the D2D network throughput. …”
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Generation of Spatial Structure of Urban Parks Based on Spatial Analysis of Agent-Based Models by Huizi KONG, Tianming LIU, Jiajie LIAO, Liu CUI

“…Additionally, the simulation results show that the slime mold agent model can rapidly generate foraging networks in complex environments due to its efficient organization and structural resilience, as well as optimize path selection according to behavioral objectives.ConclusionABM analysis, supported by advanced algorithms, considers the basic spatial information of the site and simulates individual behavior patterns using distributed computation rules, thereby providing a new paradigm for landscape design thinking. …”
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Laser-induced Breakdown Spectroscopy Based on Pre-classification Strategy for Quantitative Analysis of Rock Samples by Weiheng KONG, Lingwei ZENG, Yu RAO, Sha CHEN, Xu WANG, Yanting YANG, Yixiang DUAN, Qingwen FAN

“…Different element quantitative models were constructed for each rock type. The kNN algorithm was selected using cross-validation to determine the optimal k value, and the key punishment parameter C and RBF width parameter γ of the SVM algorithm were determined using a grid search method. …”
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Deep Reinforcement Learning for Efficient Scheduling of Ground-based Astronomical Observations by Hai Cao, Shaoming Hu, Junju Du, Xu Chen, Shuqi Liu, Shuai Feng, Bo Zhang, Yuchen Jiang

“…To support the training of the deep neural network, we propose a scoring mechanism to evaluate the effectiveness of the observations, which is optimized through a refined REINFORCE algorithm with a baseline. …”
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