Calibrating Microscopic Traffic Simulation Model Using Connected Vehicle Data and Genetic Algorithm by Abolfazl Afshari, Joyoung Lee, Dejan Besenski, Branislav Dimitrijevic, Lazar Spasovic

“…This study introduces a data-driven approach to calibrate microscopic traffic simulation models like VISSIM using high-resolution trajectory data, aiming to improve simulation accuracy and fidelity. …”
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Big Data and AI Algorithms for Sustainable Development Goals: A Topic Modeling Analysis by Prema Nedungadi, Simi Surendran, Kai-Yu Tang, Raghu Raman

“…AI applications notably improve healthcare by advancing disease tracking, tailored treatments, and precision medicine, fostering universal healthcare and reducing noncommunicable disease mortality. …”
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A Genetic-Algorithms Matrix-Factorization-Based Recommender System Model Using Tags by Manal Almaayah, Yousef Kilani, Ayoub Alsarhan, Mohammad Aljaidi, Mohammed Alshammari, Aadil Alshammari

“…ETagMF is an Evolutionary-based Tags-based Matrix Factorization (MF) model. Recently, researchers have introduced several MF-based approaches to improve the performance of the recommendation. …”
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Transferring Face Recognition Techniques to Entomology: An ArcFace and ResNet Approach for Improving Dragonfly Classification by Zhong Li, Shaoyan Pu, Jingsheng Lu, Ruibin Song, Haomiao Zhang, Xuemei Lu, Yanan Wang

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Modeling the prediction of spontaneous rupture and bleeding in hepatocellular carcinoma via machine learning algorithms by Juchao Chen, Zicheng Lei, Zongcai Duan, Zhili Wen

“…Abstract This study aimed to identify the risk factors associated with spontaneous rupture and bleeding in hepatocellular carcinoma, establish a prediction model for spontaneous rupture bleeding via a machine learning algorithm, and validate and evaluate the predictive efficacy of the model. …”
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Improving Generalization of Genetic Programming for High-Dimensional Symbolic Regression with Shapley Value Based Feature Selection by Chunyu Wang, Qi Chen, Bing Xue, Mengjie Zhang

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Research on Application of Big Data in Internet Financial Credit Investigation Based on Improved GA-BP Neural Network by Fei-Peng Wang

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Improving the Classification Accuracy for Near-Infrared Spectroscopy of Chinese Salvia miltiorrhiza Using Local Variable Selection by Lianqing Zhu, Haitao Chang, Qun Zhou, Zhongyu Wang

“…In order to improve the classification accuracy of Chinese Salvia miltiorrhiza using near-infrared spectroscopy, a novel local variable selection strategy is thus proposed. …”
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Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process by Jianjun Zhao, Junwu Zhoub, Weixing Su, Fang Liu

“…With the purpose of reducing the influence of outlier on parameter update of ARHMM, the strategies of detection-before-update and detection-based-update are adopted, which also improve the robustness of the algorithm. Subsequent simulation by model data and practical application verify the accuracy, robustness, and property of online detection of the algorithm. …”
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Improving ICESat-2 photon classification and tree height estimation using Moran's I and machine learning by Mei-Kuei Lu, Sorin Popescu, Lonesome Malambo

“…The results demonstrated that the model incorporating Moran's I improved classification accuracy, with R2 values ranging from 0.30 to 0.76 across ecoregions. …”
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IMPROVING ACCURACY OF PREDICTION INTERVALS OF HOUSEHOLD INCOME USING QUANTILE REGRESSION FOREST AND SELECTION OF EXPLANATORY VARIABLES by Asrirawan Asrirawan, Khairil Anwar Notodiputro, Bagus Sartono

“…The selection of explanatory variables in quantile regression forest is investigated and addressed in this paper, using several selection scenarios that consist of the full model, forward selection, LASSO, ridge regression, and random forest to improve the accuracy of household income data prediction. …”
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Improvement of switched structure semi‐active battery/supercapacitor hybrid energy storage system for electric vehicles by Ihor Shchur, Ihor Bilyakovskyy, Valentyn Turkovskyi

“…Here, such a switched structure between battery/SC HESS was improved, which made it possible to obtain new operating modes. …”
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YOLOv8-OCHD: A Lightweight Wood Surface Defect Detection Method Based on Improved YOLOv8 by Zuxing Chen, Junjie Feng, Xueyan Zhu, Bin Wang

“…Next, the Haar Wavelet Downsampling module (HWD) is employed to expand the receptive field, reduce model complexity, and improve both training and inference speeds. …”
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PCB Electronic Component Soldering Defect Detection Using YOLO11 Improved by Retention Block and Neck Structure by Youzhi Xu, Hao Wu, Yulong Liu, Xing Zhang

“…The single-stage target detection algorithm has a faster running time, but the detection accuracy needs to be improved. …”
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Program Scheduling With Multi-Skill and External Resource Coordination Consideration Using Improved NSGA-II: Case Study by Heng Zhang, Jingbo Zhou, Huaying Ruan, Yixuan Qin

“…A case study was then developed based on the R program of technology-driven company S. The allocation model was solved using both the classic and improved versions of the algorithm. …”
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Territorial Space Optimization Method Based on Multi-Objective Genetic Algorithm and FLUS Model by Lin Ge

“…The innovation of the research lies in the optimization of the multi-objective genetic algorithm by improving the crossover and mutation process of genetic operators, enhancing the adaptability of the algorithm in dynamic environments, and improving the prediction accuracy of the spatial quantity structure of the national territory. …”
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Pedestrian Re-Recognition Algorithm Based on Optimization Deep Learning-Sequence Memory Model by Feng-Ping An

“…Based on the above explanation, this paper proposes a pedestrian re-recognition algorithm based on an optimized LSTM deep learning-sequence memory learning model. …”
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Research on building energy consumption prediction algorithm based on customized deep learning model by Zheng Liang, Junjie Chen

“…This paper proposes a customized convolutional neural network with Q-Learning (CCNN-QL) based reinforcement learning algorithm for predicting energy consumption in building.The suggested CCNN-QL model offers an auto-learning feature that predicts building energy consumption through an automated method, continually improving its predictive accuracy.To assess its performance, various building types were selected to study the factors influencing excessive energy consumption, and data were collected from multiple Chinese cities. …”
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Dynamic display algorithm of sonar data based on grayscale distribution model and computational intelligence by Hongquan Lei, Diquan Li, Haidong Jiang

“…The recognition speed reached 37 m, which is a certain improvement compared to the traditional model.…”
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Research on the optimization of mortar mix proportion based on neural network models and genetic algorithm by Chunyu Jiang

“…The mix ratio optimized by the genetic algorithm significantly improves the strength and durability of the mortar. …”
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