Mosquitoes carries a parasitic infection that affects millions of peoples globally. The initial and meticulous prediction of outbursts, patient verdict, and treatment enhancement can all considerably diminish the cons...
详细信息
The research of sports training scheme has always been an important link in sports training, and it is also a difficult problem. The existing training planning research focuses on information collection and processing...
详细信息
The global energy sector operates within a highly competitive market, necessitating uninterrupted power supply to industrial, commercial, and domestic sectors. Transformers serve a critical role in electricity transmi...
详细信息
data augmentation is an important technique to enhance the performance of models in different fields, such as computer vision and natural language processing. In entity resolution, few papers have applied general or t...
详细信息
ISBN:
(纸本)9798350372977;9798350372984
data augmentation is an important technique to enhance the performance of models in different fields, such as computer vision and natural language processing. In entity resolution, few papers have applied general or task-specific data augmentation techniques, and systematic knowledge is sparse. This study evaluates eight rule-based, model-based, and sample interpolation by comparing them systematically across 10 datasets and two transformer-based models or entity resolution matching systems: BERT and Ditto, leveraging DistilBERT. The results show the potential of data augmentation to increase performance, although the influence of the technique used depends heavily on the model and dataset. Here, MixDA and random word insertion performed best, so they are most suitable for recommendation to practitioners dealing with entity resolution tasks.
Cyberbullying is a severe issue that impacts teens and adults alike. Errors like hopelessness and suicide have resulted from it. The demand for material on social media platforms to be regulated is growing. In the wor...
详细信息
Thyroid disease represents a significant contributor to challenges in both medical diagnosis and the prediction of its onset, making it a complex area of study within medical research. This research thoroughly analyse...
详细信息
In the process of deepening the data processing of State Grid Power, a machinelearning algorithm is proposed to solve the problem that the data efficiency of State Grid Power cannot be effectively improved. Using mec...
详细信息
Federated learning, as a distributed machinelearning approach, enables the full exploitation of data value while protecting data privacy. However, traditional Federated learning methods are significantly impacted in ...
详细信息
machinelearning based Direction-of-arrival (DOA) estimation methods heavily depends on the labeled data. When it is difficult to obtain a large number of labeled samples, semi supervised learning can use unlabeled sa...
详细信息
ISBN:
(纸本)9798400717048
machinelearning based Direction-of-arrival (DOA) estimation methods heavily depends on the labeled data. When it is difficult to obtain a large number of labeled samples, semi supervised learning can use unlabeled samples to improve the training performance. Therefore, this paper proposes a direction finding method based on semi supervised learning, which uses a small number of labeled samples and a large number of unlabeled samples to gradually modify the DOA estimation function through manifold regularization constraints, so as to improve the DOA estimation performance when labeled data is limited. Simulation results have demonstrated the superiority of the proposed method compared with purely supervised learning when limited labeled samples are available.
In order to diagnose lumpy skin disease in cattle herds, machinelearning techniques such as Support Vector machine (SVM), Gradient Boosting, and Random Forest algorithms were used in this research work. The objective...
详细信息
暂无评论