Image 1_Integrative prognostic modeling for stage III lung adenosquamous carcinoma post-tumor resection: machine learning insights and web-based implementation.png حسب Min Liang (363007)

Data_Sheet_1_Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.pdf حسب Mikhail Ya Yadgarov (18543769)

Image 2_Enhancing COVID-19 classification of X-ray images with hybrid deep transfer learning models.jpg حسب Maliki Moustapha (22419664)

"…Our methodology involves three different experiments: manual hyperparameter selection, k-fold retraining based on performance metrics, and the implementation of a genetic algorithm for hyperparameter optimization. …"

Image 1_Enhancing COVID-19 classification of X-ray images with hybrid deep transfer learning models.jpg حسب Maliki Moustapha (22419664)

"…Our methodology involves three different experiments: manual hyperparameter selection, k-fold retraining based on performance metrics, and the implementation of a genetic algorithm for hyperparameter optimization. …"

On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization حسب Jiancong Xiao (22265935)

"…However, we argue that the predominant approach for aligning LLMs with human preferences through a reward model—reinforcement learning from human feedback (RLHF)—suffers from an inherent algorithmic bias due to its Kullback–Leibler-based regularization in optimization. …"

Code حسب Baoqiang Chen (21099509)

"…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …"

Sensor value scenario for fuzzy logic algorithm. حسب Gourab Saha (8987405)

"…The experimental results demonstrated that fuzzy logic has faster calibration rate of 66.23% and helps to save around 61% water in comparison to average logic algorithm. The implementation of a fuzzy logic algorithm significantly optimized water usage compared to traditional manual irrigation methods. …"

The details of the publicly available dataset. حسب Ailian Gao (20629841)

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The relationship between different early stop settings and the corresponding accuracy of the LSTKT model on the EdNet dataset. حسب Ailian Gao (20629841)

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The suggested approach includes two alternate architectures for the Bi-LSTM models: the Bi-LSTM model (Top) and the Bi-LSTM with a single informer network (Bottom). حسب Ailian Gao (20629841)

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The suggested pipeline utilizes a probability attention method. حسب Ailian Gao (20629841)

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The overall framework of the proposed approach. حسب Ailian Gao (20629841)

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The comparison between the state-of-the-arts and the proposed models on the datasets. حسب Ailian Gao (20629841)

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Dropout rate ablation study results on the EdNet dataset. حسب Ailian Gao (20629841)

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The overall structure of the proposed Informer model. حسب Ailian Gao (20629841)

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The comparison between the state-of-the-arts and the proposed models on the datasets. حسب Ailian Gao (20629841)

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The workflow of the proposed approach. حسب Ailian Gao (20629841)

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The hyper-parameter settings used in this study. حسب Ailian Gao (20629841)

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The accuracy and loss curves of the proposed approach in the training process. حسب Ailian Gao (20629841)

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The comparison results between the LSTKT models with two different architectures. حسب Ailian Gao (20629841)

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