Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery by Andrii Zaiats, T. Trevor Caughlin, Jennyffer Cruz, David S. Pilliod, Megan E. Cattau, Rongsong Liu, Richard Rachman, Maisha Maliha, Donna Delparte, John D. J. Clare

“…Our study demonstrates that integrated models accounting for imperfect detection improve estimates of plant population abundance derived from inherently imperfect UAS imagery. …”
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Combination of ultrasound-based radiomics and deep learning with clinical data to predict response in breast cancer patients treated with neoadjuvant chemotherapy by Wu Tenghui, Liu Xinyi, Si Ziyi, Zhang Yanting, Ma Ziqian, Zhu Yiwen, Gan Ling

“…Multiple machine learning algorithms were employed to model and validate the diagnostic performance of different types of features. …”
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Enhancing sex determination in forensic anthropology: A comparative analysis of cranial measurements using artificial neural network by Matheus Jhonnata Santos Mota, Alberto Calson Alves Vieira, Lucas Silva Lima, João Victor Melquiades Sátiro, Carlos Mathias de Menezes Neto, Patrízia Lisieux Prado Paixão, Gabriel Pedro Gonçalves Lopes, Lauro Roberto de Azevedo Setton, Carlos Eduardo de Andrade, Richard Halti Cabral

“…Traditional methods often suffer from subjectivity and variability, prompting a shift towards morphometric approaches for improved precision. In this context, this study aims to identify the most accurate cranial measurements for sex estimation and ascertain the minimum number of variables needed to match the performance of a 12-measure model using a neural network. …”
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A Framework for Evaluating Driving Patterns of Automated Vehicle Based on On-Road Driving Records by Younghoon Seo, Donghyun Lim, Jooyoung Lee, Jaehyun So, Hyungjoo Kim

“…In this study, we propose a methodology for driving pattern analysis based on a hybrid clustering algorithm that combines the self-organizing map (SOM) and the K-means++ algorithm. …”
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Predicting cardiotoxicity in drug development: A deep learning approach by Kaifeng Liu, Huizi Cui, Xiangyu Yu, Wannan Li, Weiwei Han

“…In this study, we employed machine learning techniques utilizing molecular fingerprints and descriptors to predict the cardiotoxicity of compounds, with the aim of improving prediction accuracy and efficiency. We used four types of molecular fingerprints and descriptors combined with machine learning and deep learning algorithms, including Gaussian naive Bayes (NB), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), eXtreme gradient boosting (XGBoost), and Transformer models, to build predictive models. …”
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Hybrid Electric Propulsion Design and Analysis Based on Regional Aircraft Mission by Wenjuan Shan, Shengze Bao, Shixuan Lin, Le Kang

“…This study develops an integrated design methodology for hybrid propulsion systems for aircraft, incorporating multidisciplinary algorithms to establish an overall performance model. …”
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Identification method for wheel/rail tread defects based on integrated partial convolutional network by CHENG Xiang, HE Jing, ZHANG Changfan, JIA Lin

“…Additionally, the design incorporated a novel loss function to improve the integration of the proposed strategies and enhance the model's focus on the samples, with its principles clearly explained. …”
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DS-HPPO Deep Reinforcement Learning for Optimal DVFS Control on an Image Signal Processor by Yang Du, Wenhong Li, Xiaoyang Zeng

“…We apply the Parameterized Action Markov Decision Process (PAMDP) to model the above optimization problem and solve the PAMDP with the proposed DS-HPPO deep reinforcement learning algorithm. …”
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Prediction of Graduate Career Relevance Based on Academic and Non-Academic Aspects using Machine Learning by Muhammad Yusuf Luthfi Ijlal, Arif Setiawan, Diana Laily Fithri

“…The findings from the variable analysis and classification model can be utilized in developing a career alignment prediction system and as a basis for decision-making in strategies to improve graduate quality in line with labor market needs.…”
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An interpretable integrated machine learning framework for genomic selection by Jinbu Wang, Jia Zhang, Wenjie Hao, Wencheng Zong, Mang Liang, Fuping Zhao, Longchao Zhang, Lixian Wang, Huijiang Gao, Ligang Wang

“…Moreover, we introduced the NuSVR model, which achieved the highest accuracy among nine compared algorithms. …”
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Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome by Yaxuan He, Zekai Chen, Zhaohui Tang, Yuexiang Qin, Fang Wang

“…Objective To develop a non-invasive prediction model for MetS by integrating body roundness index (BRI), gender, age, height, and waist circumference with machine learning algorithms, and to validate its generalizability across different ethnic groups. …”
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MED-AGNeT: An attention-guided network of customized augmentation of samples based on conditional diffusion for textile defect detection by Jun Liu, Haolin Li, Hao Liu, Jiuzhen Liang

“…Ultimately, AGNet’s true positive rate (TPR), positive predictive value (PPV), and f-measure exceed those of the state-of-the-art (SOTA) algorithms by 1.88%, 0.05%, and 0.77%, respectively, and with a consistent model architecture, its parameter quantity is reduced by 56%.…”
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Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning by Zhibin Jiang, Zhibin Jiang, Keli Hu, Keli Hu, Jia Qu, Zekang Bian, Zekang Bian, Donghua Yu, Donghua Yu, Jie Zhou, Jie Zhou

“…Although transfer learning has been implemented in EEG signal recognition, many existing methods are designed specifically for certain intelligent models, limiting their application and generalization.MethodsTo broaden application and generalization, an extended-LSR-based inductive transfer learning method is proposed to facilitate transfer learning across various classical intelligent models, including neural networks, Takagi-SugenoKang (TSK) fuzzy systems, and kernel methods.Results and discussionThe proposed method not only promotes the transfer of valuable knowledge from the source domain to improve learning performance in the target domain when target domain training data are insufficient but also enhances application and generalization by incorporating multiple classic base models. …”
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To explore the molecular mechanisms and shared genetic characteristics of rheumatoid arthritis and cervical cancer based on multiple omics and clinical samples by Xifeng Xu, Yujie Huang, Wu Wei, Zhong Lin, Yongjin Luo, Kaiyi Meng

“…Single-cell enrichment scores were calculated using RA-specific transcriptome differentially expressed genes, and prognostic models were developed using 10 machine learning algorithms to assess cervical cancer differential gene expression associated with RA scores. …”
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Architectural Exploration and Performance Enhancement of the CVA6 RISC-V Core Using the Gem5 Simulation Framework by Umer Shahid, Muhammad Tahir, Bilal Zafar, Ayesha Ahmad, Shanzay Wasim, Bisal Saeed

“…This approach resulted in two distinct optimized models: a resource-efficient variant that reduced resource consumption by 12% with only a 0.2% decrease in RMS micro-benchmark IPC, and a performance-optimized model that improved performance by 29.5% while increasing resource usage by just 4.20%. …”
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Enhancing Airport Traffic Flow: Intelligent System Based on VLC, Rerouting Techniques, and Adaptive Reward Learning by Manuela Vieira, Manuel Augusto Vieira, Gonçalo Galvão, Paula Louro, Alessandro Fantoni, Pedro Vieira, Mário Véstias

“…Simulation results confirm improved route planning, reduced halting times, and enhanced coordination between AGVs and pedestrian flows. …”
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Development of intelligent tools to predict neuroblastoma risk stratification and overall prognosis based on multiphase enhanced CT and clinical features by Wei Zhao, Yahui Han, Xiaokun Yu, Jianing Liu, Jiao Zhang, Juan Li

“…The aim of this study was to develop a deep learning model based on multiphase enhanced CT images and clinical features to improve the accuracy of risk stratification and prognostic assessment of NB.MethodsMulti-phase enhanced CT images and clinical features from 202 NB patients were collected. …”
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Revealing the Driving Factors of Household Energy Consumption in High-Density Residential Areas of Beijing Based on Explainable Machine Learning by Zizhuo Qi, Lu Zhang, Xin Yang, Yanxia Zhao

“…A hyperparameter-optimized ensemble model (XGBoost, RF, GBDT) was employed, with XGBoost combined with genetic algorithm tuning performing best. …”
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Application of a GA-BP Neural Network in Accurately Characterizing the Diffusion Range of Groundwater Pollutants by Jiayun JI, Xiao XIAO, Pinlu YANG, Yang LIU, Yahong ZHOU

“…The results demonstrate that the GA-BP model significantly improves the accuracy of pollutant diffusion mapping and offers a more reliable method for environmental pollution assessment, especially in areas with limited sampling data. …”
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Speed and Path Planning Optimization of Autonomous Vehicle to Minimize Lap Time in Racing Track by Young-Jin Roh, Ji-Ung Im, Jong-Hoon Won

“…Experimental validation was conducted using a real vehicle on the AMG Speedway short track in Yongin, South Korea, where the proposed method demonstrated significant improvements in lap times across different tracks compared to the minimum curvature method. …”
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