LMD_YOLO: A Lightweight and Efficient Model for Pavement Defects Detection by Shuai He, Ye Yuan, Bingyang Yin

“…To address these challenges, a novel model, LMD_YOLO, is proposed. The model incorporates several innovations: the Diverse Branch Block enhances the detection head, improving accuracy; the mg_conv module replaces conventional convolution layers in the neck network, optimizing feature fusion without increasing computational cost; and improvements to the backbone network further enhance efficiency. …”
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Fuzzy model-based analysis of user feedback for product development insights by Anna Sudár, Borbála Berki, Ildikó Horváth

“…The topic of this paper belongs to the modeling of human-software experience to support product development. …”
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RMP-UNet: An Efficient and Lightweight Model for Apple Leaf Disease Segmentation by Wenbo Zhao, Lijun Hu, Qi Wang, Hongxin Wu, Jiangbo Wang, Xu Li, Cuiyun Wu

“…To address these issues, this study proposes RMP-UNet, an efficient and lightweight model for apple leaf disease segmentation. Based on the traditional UNet architecture, RMP-UNet incorporates an efficient multi-scale attention mechanism (EMA) along with innovative lightweight reparameterization modules (RepECA) and multi-scale feature fusion dynamic upsampling modules (PagDy), optimizing feature extraction and fusion processes to improve segmentation accuracy while reducing model complexity. …”
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Enhancing geometric modeling in convolutional neural networks: limit deformable convolution by Wei Wang, Yuanze Meng, Han Li, Guiyong Chang, Shun Li, Chenghong Zhang

“…To overcome this problem, researchers introduce deformable convolution, which allows the convolution kernel to be deformable on the feature map. However, deformable convolution may introduce irrelevant contextual information during the learning process and thus affect the model performance. …”
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Predicting Employee Attrition: XAI-Powered Models for Managerial Decision-Making by İrem Tanyıldızı Baydili, Burak Tasci

“…Model performance was evaluated using accuracy, precision, recall, F1 score, and ROC AUC metrics. …”
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Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints by Daxing Lei, Yaoping Zhang, Zhigang Lu, Hang Lin, Yifan Chen

“…A dataset with five input variables was constructed to evaluate the performance of the SMA-MLP model comprehensively. The proposed model was compared with other ML models. …”
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Leveraging stacking machine learning models and optimization for improved cyberattack detection by Neha Pramanick, Jimson Mathew, Shitharth Selvarajan, Mayank Agarwal

“…The proposed method integrates two ML models: J48 and ExtraTreeClassifier for classification. …”
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Enhanced Lightweight YOLO Model for Efficient Vehicle Detection in Satellite Imagery by Mohamad Haniff Junos, Anis Salwa Mohd Khairuddin, Elmi Abu Bakar, Ahmad Faizul Hawary

“…Moreover, the proposed model achieved real-time performance on the NVIDIA Jetson Nano. …”
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A Remaining Useful Life Prediction Method for Rolling Bearings Based on Hierarchical Clustering and Transformer–GRU by Wenping Lei, Xing Dong, Fuyuan Cui, Guangzhong Huang

“…In the prediction of the remaining useful life (RUL) of rolling bearings, feature extraction and selection are critical prerequisites for accurate prediction, while the construction of the prediction model is the core. …”
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TBM shield mud cake prediction model based on machine learning by Qi Zhang, Peng Xu, Jing Zhang, Zhao Yang, Yu Li, Xintong Kong, Xiao Yuan

“…Further analysis of feature dependencies and shapley additive explanations (SHAP) is conducted to pinpoint the critical risk factors associated with mud cake formation.ResultsThe results indicate that among the four supervised machine learning models, the random forest model exhibited the best performance in predicting mud cake formation during shield tunneling, with an F1 score as high as 0.9934. …”
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A multi-scale small object detection algorithm SMA-YOLO for UAV remote sensing images by Shilong Zhou, Haijin Zhou, Lei Qian

“…In addition, improving model performance requires a delicate balance between improving accuracy and managing computational complexity. …”
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Development of generalized terrain-following FVCOM model for the steep terrain seas by Yixuan Bu, Yang Chen, Yang Chen, Shouxian Zhu, Wenjing Zhang, Guangsong Cao, Zhenguo Ding

“…We further investigated the feasibility of designing density-feature-informed hybrid coordinates based on the λ coordinate system, along with other vertical coordinate formulations, which can substantially improve the FVCOM model’s adaptive operational capacity in coastal and shelf marine environments.…”
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Deep ensemble learning with transformer models for enhanced Alzheimer’s disease detection by Shiza Latif, Naeem Ul Islam, Zaki Uddin, Khalid Mehmood Cheema, Syed Sohail Ahmed, Muhammad Farhan Khan

“…Our proposition involves the data augmentation of textual data; after this, we deploy our proposed BERT-based deep learning model to make use of its advanced capabilities for improved feature extraction and text comprehension. …”
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Transformation of Geospatial Modelling of Soil Erosion Susceptibility Using Machine Learning by Muhammad Ramdhan Olii, Sartan Nento, Nurhayati Doda, Rizky Selly Nazarina Olii, Haris Djafar, Ririn Pakaya

“…This study assesses the use of Machine Learning (ML) methods—Support Vector Machines (SVM) and Generalized Linear Models (GLM)—to model Soil Erosion Susceptibility (SES) in the Saddang Watershed, Indonesia. …”
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Experimental animal models for rheumatoid arthritis-associated interstitial lung disease by Qianqian Yan, Lianhua He, Lili Wang, Liting Xu, Aimin Zhou, Chunfang Liu, Na Lin

“…Transgenic animal models exhibit pathological features similar to the nonspecific interstitial pneumonia subtype of human RA-ILD and are useful for studying the genetic effects on RA-ILD. …”
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Construction and application of foundational models for intelligent processing of microseismic events in mines by Anye CAO, Maotao LI, Xu YANG, Yao YANG, Sen LI, Yaoqi LIU, Changbin WANG

“…A comprehensive dataset containing over 300 000 microseismic waveforms was established, incorporating three key innovations: multi-scale convolutional modules for multi-dimensional feature extraction, an adaptive feature fusion strategy for noise-resistant signal representation, and a feature-aggregated multi-head attention mechanism for temporal sequence modeling. …”
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IDDNet: Infrared Object Detection Network Based on Multi-Scale Fusion Dehazing by Shizun Sun, Shuo Han, Junwei Xu, Jie Zhao, Ziyu Xu, Lingjie Li, Zhaoming Han, Bo Mo

“…A two-stage training strategy optimizes the model’s performance, enhancing its accuracy and robustness in foggy environments. …”
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Models for sustainable management of livestock waste based on neural network architectures by Anatoliy Tryhuba, Krzysztof Mudryk, Inna Tryhuba, Marian Kotsylovskyi, Dmytro Sorokin, Olena Bezaltychna, Pawel Pysz, Taras Hutsol

“…The optimized MLP model demonstrated high predictive performance, achieving a mean squared error (MSE) of 0.0005 and a mean absolute percentage error (MAPE) of 6.51%, compared to 8.01% for the baseline model. …”
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Enhancing No Reference Laparoscopic Video Quality Assessment with Evolutionary ANFIS by Biswas Sria, Palanisamy Rohini

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AI-powered machine learning models for monitoring and optimization of biodrying process by Abhisit Bhatsada, Panida Payomthip, Tanik Itsarathorn, Ye Nyi Nyi Lwin, Eka Wahyanti, Sirintornthep Towprayoon, Suthum Patumsawad, Chart Chiemchaisri, Komsilp Wangyao

“…Among all models tested, deep learning methods demonstrated superior accuracy, with Long Short-Term Memory (LSTM) achieving the highest predictive performance (R² = 0.988 training, R² = 0.987 testing). …”
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