A hybrid model for improving customer lifetime value prediction using stacking ensemble learning algorithm by Amir Mohammad Haddadi, Hodjat Hamidi

“…The results demonstrate that integrating those features and using the Stacking Ensemble model substantially increases the prediction accuracy and decreases the errors. …”
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A novel surrogate model with deep learning for predicting spacial-temporal pressure in coalbed methane reservoirs by Yukun Dong, Xiaodong Zhang, Jiyuan Zhang, Kuankuan Wu, Shuaiwei Liu

“…The model also demonstrates strong robustness and accuracy in capturing spatial–temporal pressure features.…”
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Deep-learning model for embryo selection using time-lapse imaging of matched high-quality embryos by Lisa Boucret, Floris Chabrun, Magalie Boguenet, Pascal Reynier, Pierre-Emmanuel Bouet, Pascale May-Panloup

“…Without any knowledge of transfer history, the model achieved a satisfactory performance in predicting implantation (AUC = 0.64). …”
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Lower Limb Movement Recognition Based on a Hybrid Deep Learning Model Using Surface Electromyography by Yonglin Han, Qing Tao

“…Then, six time-domain features are extracted from the preprocessed sEMG signals, including integrated electromyography (iEMG), variance of electromyography (VAR), waveform length (WL), zero crossing (ZC), slope sign change (SSC), and Willison amplitude (WAMP), which serve as inputs for the TTLNet model. …”
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Learning the Value of Place: Machine Learning Models for Real Estate Appraisal in Istanbul’s Diverse Urban Landscape by Ahmet Hilmi Erciyes, Toygun Atasoy, Abdurrahman Tursun, Sibel Canaz Sevgen

“…In total, 168,099 residential properties were utilized along with 30 of their features from both sides of the Bosphorus. The results show that RF yielded the best performance in Beşiktaş, while XGBoost performed best in Üsküdar. …”
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A Novel Dynamic Lane-Changing Trajectory Planning Model for Automated Vehicles Based on Reinforcement Learning by Cenxin Yu, Anning Ni, Jing Luo, Jinghui Wang, Chunqin Zhang, Qinqin Chen, Yifeng Tu

“…To optimize its performance, a reward function is properly designed by combining safety, efficiency, gap, headway, and comfort features. …”
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Fusion of multi-scale attention for aerial images small-target detection model based on PARE-YOLO by Huiying Zhang, Pan Xiao, Feifan Yao, Qinghua Zhang, Yifei Gong

“…Abstract In view of the complex environments and varying object scales in drone-captured imagery, a novel PARE-YOLO algorithm based on YOLOv8 for small object detection is proposed. This model enhances feature extraction and fusion across multiple scales through a restructured neck network. …”
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Application of a Transfer Learning Model Combining CNN and Self-Attention Mechanism in Wireless Signal Recognition by Wu Wei, Chenqi Zhu, Lifan Hu, Pengfei Liu

“…To enhance performance with limited data, we incorporate transfer learning by leveraging pre-trained models, ensuring faster convergence and improved generalization. …”
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Lights-Transformer: An Efficient Transformer-Based Landslide Detection Model for High-Resolution Remote Sensing Images by Xu Wu, Xuqing Ren, Donghao Zhai, Xiangpeng Wang, Mehreen Tarif

“…However, existing models often face challenges, such as incomplete feature extraction, loss of contextual information, and high computational complexity. …”
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Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm by Amit Sagu, Nasib Singh Gill, Preeti Gulia, Noha Alduaiji, Piyush Kumar Shukla, Mohd Asif Shah

“…Further, to optimize the performance of the proposed hybrid model, self-upgraded Cat and Mouse Optimization (SUCMO) algorithm is employed, a state of art optimization technique. …”
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annATAC: automatic cell type annotation for scATAC-seq data based on language model by Lingyu Cui, Fang Wang, Hongfei Li, Qiaoming Liu, Murong Zhou, Guohua Wang

“…During the pre-training stage, by training on a vast amount of unlabeled data, the model can learn the interaction relationships between peaks, thus building a preliminary understanding of the data features. …”
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A Novel Model for Predicting PM2.5 Concentrations Utilizing Graph Convolutional Networks and Transformer by Yuan Huang, Feilong Han, Qimeng Feng

“…In response, this paper proposes a new approach based on Graph Convolutional Networks (GCN) and Transformer. To enhance the model’s predictive performance, we designed a new Transformer architecture named FFPformer, which incorporates the Fast Fourier Transform into the Transformer framework. …”
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Social Media Based Topic Modeling for Smart Campus: A Deep Topical Correlation Analysis Method by Jun Peng, Yiyi Zhou, Xiaoshuai Sun, Jinsong Su, Rongrong Ji

“…Mining social media data, especially topic modeling, from students, provides a non-intrusive method to know the instantaneous thoughts and willings of them. …”
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ATM: Adaptive transformer model for reconstruction of remote sensing multi-temporal non-equispaced sequence imagery by Zhi-peng Qi, Zi-yi Zhao, Xing Jin

“…To address this issue, we perform an adaptive transformer model (ATM) for reconstruction of remote sensing multi-temporal non-equispaced sequence imagery. …”
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Detection of Diabetic Retinopathy Using a Multi-Decision Inception-ResNet-Blended Hybrid Model by Santosh Kumar Henge, Nikhil Reddy Viraati, Musaed Alhussein, Ajay Shriram Kushwaha, Khursheed Aurangzeb, Ravleen Singh

“…The chi-square testing mechanism refines the evaluation of test cases, contributing to the model’s overall performance. Using multi-decision hybrid techniques, the model achieves a detection accuracy of 98.1%, outperforming other existing models.…”
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Development of a Cost-Efficient and Glaucoma-Specialized OD/OC Segmentation Model for Varying Clinical Scenarios by Kai Liu, Jicong Zhang

“…Most existing optic disc (OD) and cup (OC) segmentation models are biased to the dominant size and easy class (normal class), resulting in suboptimal performances on glaucoma-confirmed samples. …”
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Deterministic and Stochastic Machine Learning Classification Models: A Comparative Study Applied to Companies’ Capital Structures by Joseph F. Hair, Luiz Paulo Fávero, Wilson Tarantin Junior, Alexandre Duarte

“…Notably, all models delivered statistically similar results in the test sample, emphasizing the need to balance performance, simplicity, and interpretability. …”
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Glaucoma detection from retinal fundus images using graph convolution based multi-task model by Satyabrata Lenka, Zefree Lazarus Mayaluri, Ganapati Panda

“…The performance of the proposed multi-task model is compared with some existing methods. …”
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Forecasting Day-Ahead Electricity Demand in Australia Using a CNN-LSTM Model with an Attention Mechanism by Laial Alsmadi, Gang Lei, Li Li

“…This study compares the CNN-LSTM-Attention model with traditional methods, including Deep Neural Networks, and demonstrates superior performance. …”
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Research on Multi-Factor Coastal Waterway Depth Prediction and Application Based on Attention-Enhanced LSTM Model by LING Ganzhan, HAN Yu, WANG Jiawei, JIE Weiwei, TANG Ruikai, HU Jiakai, LIU Xiang, LIANG Guangyue, CAO Lu, LIANG Ming

“…To optimize the model's performance, an attention mechanism is introduced. …”
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