An Improved Tree Crown Delineation Method Based on a Gradient Feature-Driven Expansion Process Using Airborne LiDAR Data by Jiaxuan Jia, Lei Zhang, Kai Yin, Uwe Sörgel

“…Currently, raster data such as the canopy height model derived from airborne light detection and ranging (LiDAR) data have been widely used in large-scale ITCD. …”
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DFAST: A Differential-Frequency Attention-Based Band Selection Transformer for Hyperspectral Image Classification by Deren Fu, Yiliang Zeng, Jiahong Zhao

“…A 3D convolution and a spectral–spatial attention mechanism are applied to perform fine-grained modeling of spectral and spatial features, further enhancing the global dependency capture of spectral–spatial features. …”
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STL-DCSInformer-ETS: A Hybrid Model for Medium- and Long-Term Sales Forecasting of Fast-Moving Consumer Goods by Yecheng Ma, Lili He, Junhong Zheng

“…To further improve predictive performance, the Random Forest-based Recursive Feature Elimination (RF-RFE) method is applied to optimize feature selection. …”
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A novel multi-source data-driven energy consumption prediction model for Venlo-type greenhouses in China by Yangda Chen, Aiqun Bao, Yapeng Li, Yingfeng Xiang, Wanlong Cai, Zhaoqiang Xia, Jialei Li, Mingyang Ning, Jing Sun, Haixi Zhang, Xianpeng Sun, Xiaoming Wei

“…The final experimental results indicated that the proposed energy consumption prediction method demonstrates excellent performance with high prediction accuracy (R² = 0.9388) and low computational resource consumption (runtime = 926.91s), outperforming other models. …”
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VT2Music: A Multimodal Framework for Text-Visual Guided Music Generation and Comprehensive Performance Analysis by Jiaxiang Zheng, Moxi Cao, Chongbin Zhang

“…This study represents an initial exploration into the possibilities of multimodal music generation, with future work aimed at enhancing the model’s visual feature comprehension and musical naturalness.…”
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An Innovative Inversion Method of Potato Canopy Chlorophyll Content Based on the AFFS Algorithm and the CDE-EHO-GBM Model by Xiaofei Yang, Qiao Li, Honghui Li, Hao Zhou, Jinyan Zhang, Xueliang Fu

“…This approach has significantly enhanced the inversion model’s prediction performance as compared to earlier research. …”
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ADMNet: adaptive deformable convolution large model combining multi-level progressive fusion for Building Change Detection by Liye Mei, Haonan Yu, Zhaoyi Ye, Chuan Xu, Cheng Lei, Wei Yang

“…Furthermore, we design a Multi-Level Progressive Feature Fusion (MLPFF) module that enhances feature characterization capabilities to ensure internal integrity and improves model detection performance by integrating a priori knowledge from large-model transfer learning. …”
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Application of Metaheuristics for Optimizing Predictive Models in iHealth: A Case Study on Hypotension Prediction in Dialysis Patients by Felipe Cisternas-Caneo, María Santamera-Lastras, José Barrera-Garcia, Broderick Crawford, Ricardo Soto, Cristóbal Brante-Aguilera, Alberto Garcés-Jiménez, Diego Rodriguez-Puyol, José Manuel Gómez-Pulido

“…Among the evaluated models, the XGBoost classifier showed superior performance, achieving a macro F-score of 0.745 with a recall of 0.756 and a precision of 0.718. …”
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A Random Forest-Based Predictive Model for Student Academic Performance: A Case Study in Indonesian Public High Schools by Rifa Andriani Saputri, Asrianda Asrianda, Lidya Rosnita

“…The research methodology involved data preprocessing, training the Random Forest model using entropy and information gain to construct decision trees, and performance evaluation using metrics such as accuracy, precision, and recall. …”
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A hybrid CFD and machine learning study of energy performance of photovoltaic systems with a porous collector: Model development and validation by Yinling Wang, Lei Yu, Mazhar Ali, Imran Ali Khan, Tahir Maqsood, Haining Gao, Qi Wang, Xiaolei Guo

“…A systematic preprocessing pipeline was developed to enhance model performance, including outlier detection and feature normalization. …”
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The Contribution to the Integration of Management Systems Oriented to the Sustainable and TQM by Věra Pelantová, Eva Šlaichová

“…It interconnects knowledge about quality, environment, work safety and information, corporate social responsibility and performance. Based on the results of the literature research, according to the analysis of the state of management systems in the selected organisations and according to the experience of authors, it sets out the basic features for an integrated management system aimed at sustainability and social responsibility. …”
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Efficient Deep Learning Model Compression for Sensor-Based Vision Systems via Outlier-Aware Quantization by Joonhyuk Yoo, Guenwoo Ban

“…With the rapid growth of sensor technology and computer vision, efficient deep learning models are essential for real-time image feature extraction in resource-constrained environments. …”
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A machine learning-driven modeling and optimization approach for enhancing cassava mash production quality in cassava graters by Nana Yaa Serwaah Sarpong, Joseph Oppong Akowuah, Joseph Ofei Darko, Eric Asante Amoah

“…Machine performance modeling and optimization have emerged as crucial steps for process enhancement and efficiency. …”
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An integrated deep learning model for early and multi-class diagnosis of Alzheimer’s disease from MRI scans by Emanuel Raju Vinukonda, B. N. Jagadesh

“…The proposed approach integrates an improved DeepLabV3+ (IDeepLabV3+) model for lesion segmentation, followed by feature extraction using the LeNet-5 model. …”
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Development of New Electricity System Marginal Price Forecasting Models Using Statistical and Artificial Intelligence Methods by Mehmet Kızıldağ, Fatih Abut, Mehmet Fatih Akay

“…This study addresses the challenges of SMP prediction in Turkey by proposing a comprehensive forecasting framework that integrates machine learning, deep learning, and statistical models. Advanced feature selection techniques, such as Minimum Redundancy Maximum Relevance (mRMR) and Maximum Likelihood Feature Selector (MLFS), are employed to refine model inputs. …”
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Integrating high-performance computing, machine learning, data management workflows, and infrastructures for multiscale simulations and nanomaterials technologies by Fabio Le Piane, Mario Vozza, Matteo Baldoni, Francesco Mercuri

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Machine-learning models for differentiating benign and malignant breast masses: Integrating automated breast volume scanning intra-tumoral, peri-tumoral features, and clinical info... by Meixue Dai, Yueqiong Yan, Zhong Li, Jidong Xiao

“…However, the impact of peritumoral region size on predictive performance has not been systematically studied. This study aims to optimize diagnostic performance by integrating radiomics features and clinical data using multiple machine-learning models. …”
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ICKAN: A deep musical instrument classification model incorporating Kolmogorov-Arnold network by Jiaxiang Zheng, Moxi Cao, Chongbin Zhang

“…However, existing research primarily focuses on short monophonic samples for classification, which fails to capture the timbral variation characteristics in real performance scenarios. Meanwhile, traditional deep learning models still have limitations in extracting complex timbral features. …”
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Hourly surface nitrogen dioxide retrieval from GEMS tropospheric vertical column densities: benefit of using time-contiguous input features for machine learning models by J. Gödeke, A. Richter, K. Lange, P. Maaß, H. Hong, H. Lee, J. Park

“…For linear regression models, average performance gains are between 7 % and 15 %. …”
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Landslide Susceptibility Prediction Based on a CNN–LSTM–SAM–Attention Hybrid Model by Honggang Wu, Jiabi Niu, Yongqiang Li, Yinsheng Wang, Daohong Qiu

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