Improving Discharge Predictions in Ungauged Basins: Harnessing the Power of Disaggregated Data Modeling and Machine Learning by Aggrey Muhebwa, Colin J. Gleason, Dongmei Feng, Jay Taneja

“…To test this hypothesis, we designed experiments comparing the performance of identical Long Short‐Term Memory Recurrent Neural Network (LSTM‐RNN) models forced with either lumped or distributed features. …”
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Precision and efficiency in skin cancer segmentation through a dual encoder deep learning model by Asaad Ahmed, Guangmin Sun, Anas Bilal, Yu Li, Shouki A. Ebad

“…Existing methods for skin lesion segmentation often face trade-offs between efficiency and feature extraction capabilities. This paper proposes Dual Skin Segmentation (DuaSkinSeg), a deep-learning model, to address this gap by utilizing dual encoders for improved performance. …”
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BCTDNet: Building Change-Type Detection Networks with the Segment Anything Model in Remote Sensing Images by Wei Zhang, Jinsong Li, Shuaipeng Wang, Jianhua Wan

“…Mainstream change detection methods have demonstrated promising performance in identifying building changes. However, buildings have large intra-class variance and high similarity with other objects, limiting the generalization ability of models in diverse scenarios. …”
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Machine Learning and Deep Learning Approaches for Accent Recognition: A Review by Muzaffar Ahmad Dar, Jagalingam Pushparaj

“…Accent variations are an essential factor in speech, and can drastically decrease the performance of ASR systems. This study presents an extensive examination of various accent-recognition models. …”
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Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods by Wenchao Li, Houmin Li, Cai Liu, Kai Min

“…Understanding the impact of creep on the long-term mechanical features of concrete is crucial, and constructing an accurate prediction model is the key to exploring the development of concrete creep under long-term loads. …”
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Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy by Haofang Zhang, Changbao Xu, Chenge Hu, Yunlai Xue, Daoke Yao, Yifan Hu, Ankang Wu, Miao Dai, Hang Ye

“…Comparing the performance of 9 prediction models, we found that the model constructed using XGBoost algorithm had the best performance. …”
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Development of a machine learning model for predicting renal damage in children with closed spinal dysraphism by Yu He, Wan-liang Guo, Ming-chang Zhang

“…Through the SHAP analysis, abnormal radiological lower urinary tract findings, female sex, and high-grade vesicoureteral reflux were identified as the three most influential features in predicting renal damage. Conclusion Our study effectively developed a model that accurately predicted renal damage in children with CSD based on the XGBoost algorithm, demonstrating its potential to achieve good predictive performance.…”
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Pseudo-Labeling and Time-Series Data Analysis Model for Device Status Diagnostics in Smart Agriculture by Minwoo Jung, Dae-Young Kim

“…This model aims to address the inefficiency of manual labeling for large-scale data generated by agricultural systems, enhancing the performance and scalability of predictive models. …”
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A Novel ViT Model with Wavelet Convolution and SLAttention Modules for Underwater Acoustic Target Recognition by Haoran Guo, Biao Wang, Tao Fang, Biao Liu

“…To address the limitations of existing methods in computational efficiency and recognition performance, this paper proposes an improved WS-ViT model based on Vision Transformers (ViTs). …”
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Enhancing smart contract security: Leveraging pre‐trained language models for advanced vulnerability detection by Fei He, Fei Li, Peili Liang

“…This method leverages the BERT pre‐trained model to discern semantic features from contract opcodes, which are then refined using a Bidirectional Long Short‐Term Memory Network (BiLSTM) and augmented by an attention mechanism that prioritizes critical features. …”
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Toward improving indoor magnetic field–based positioning system using pedestrian motion models by Wenhua Shao, Haiyong Luo, Fang Zhao, Antonino Crivello

“…Real-time magnetic field fingerprints are matched with model fingerprints to adjust the location possibilities. …”
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Prediction of COVID-19 cases by multifactor driven long short-term memory (LSTM) model by Yanwen Shao, Tsz Kin Wan, Kei Hang Katie Chan

“…Then, we use a long-time span data to predict the future COVID-19 new cases by training a long short-term memory (LSTM) model, a support vector regressor (SVR) and a temporal convolutional network (TCN), among which LSTM possessed the best performance and offered a good generalization ability. …”
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Deep Transfer Learning for Lip Reading Based on NASNetMobile Pretrained Model in Wild Dataset by Ashwaq Waleed Abdul Ameer, Pedram Salehpour, Mohammad Asadpour

“…Lip reading is extensively used in visual speech recognition to classify and recognize words by analyzing mouth movements in silent videos. Transfer learning models are often employed to improve performance in this area. …”
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AERO: Adaptive Edge-Cloud Orchestration With a Sub-1K-Parameter Forecasting Model by Berend J. D. Gort, Godfrey M. Kibalya, Angelos Antonopoulos

“…We formalize our approach through a comprehensive system model and extend an existing simulation framework with predictor modules to evaluate AERO’s performance in realistic cloud-edge environments. …”
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Identification and Validation of an Explainable Prediction Model of Sepsis in Patients With Intracerebral Hemorrhage: Multicenter Retrospective Study by Xianglin Liu, Zhihua Huang, Yizhi Guo, Yandeng Li, Jianming Zhu, Jun Wen, Yunchun Gao, Jianyi Liu

“…After reducing features based on their importance, an explainable final CatBoost model was developed using 8 features. …”
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Predicting bone metastasis risk of colorectal tumors using radiomics and deep learning ViT model by Guanfeng Chen, Wenxi Liu, Yingmin Lin, Jie Zhang, Risheng Huang, Deqiu Ye, Jing Huang, Jieyun Chen

“…Results: The ViT model demonstrated superior predictive performance, achieving an AUC of 0.918 on the test set, significantly outperforming all traditional radiomics-based models. …”
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The GAN Spatiotemporal Fusion Model Based on Multiscale Convolution and Attention Mechanism for Remote Sensing Images by Youping Xie, Jun Hu, Kang He, Li Cao, Kaijun Yang, Luo Chen

“…The generator in MSCAM-GAN comprises three key components: feature extraction, feature fusion, and image reconstruction. …”
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Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model by Naila Aslam, Furqan Rustam, Ernesto Lee, Patrick Bernard Washington, Imran Ashraf

“…Results suggest that the performance of machine learning models is comparatively better when BoW features are used. …”
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A Lightweight Model for Weed Detection Based on the Improved YOLOv8s Network in Maize Fields by Jinyong Huang, Xu Xia, Zhihua Diao, Xingyi Li, Suna Zhao, Jingcheng Zhang, Baohua Zhang, Guoqiang Li

“…Secondly, a four-stage inverted residual moving block (iRMB) was employed to construct a lightweight iDEMA module, which was used to replace the original C2f feature extraction module in the Neck to improve model performance and accuracy. …”
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Hyperspectral Image Classification With Re-Attention Agent Transformer and Multiscale Partial Convolution by Junding Sun, Hongyuan Zhang, Jianlong Wang, Haifeng Sima, Shuanggen Jin

“…It effectively utilizes the local learning capability of CNNs and the long-range modeling ability of Transformers. Specifically, the multiscale spatial-spectral feature learning module employs a strategy of split, refactoring, fusion to extract shallow feature information. …”
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