Sung Hwan Jeon,Sungzoon Cho
Sung Hwan Jeon
Discriminating the matched named entity pairs or identifying the entities' canonical forms are critical in text mining tasks. More precise named entity normalization in text mining will benefit other subsequent text analytic applications. W...
Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review [0.03%]
元启发式算法在神经网络和深度学习结构训练中的应用：全面回顾
Mehrdad Kaveh,Mohammad Saadi Mesgari
Mehrdad Kaveh
The learning process and hyper-parameter optimization of artificial neural networks (ANNs) and deep learning (DL) architectures is considered one of the most challenging machine learning problems. Several past studies have used gradient-bas...
COVID-19 Detection from Chest X-rays Using Trained Output Based Transfer Learning Approach [0.03%]
使用基于训练输出的迁移学习方法从胸部X光片检测COVID-19
Sanjay Kumar,Abhishek Mallik
Sanjay Kumar
The recent Coronavirus disease (COVID-19), which started in 2019, has spread across the globe and become a global pandemic. The efficient and effective COVID-19 detection using chest X-rays helps in early detection and curtailing the spread...
Attention Based Convolutional Neural Network with Multi-frequency Resolution Feature for Environment Sound Classification [0.03%]
基于注意力的多频率分辨率特征卷积神经网络在环境声音分类中的应用研究
Minze Li,Wu Huang,Tao Zhang
Minze Li
The environmental sound classification has great research significance in the fields of intelligent audio monitoring and other fields. A novel multi-frequency resolution (MFR) feature is proposed in this paper to solve the problem that the ...
A New Time Series Forecasting Model Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Temporal Convolutional Network [0.03%]
基于自适应噪声完备经验模式分解与时空卷积网络的新型时间序列预测模型
Chen Guo,Xumin Kang,Jianping Xiong et al.
Chen Guo et al.
In this paper, a new hybrid time series forecasting model based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a temporal convolutional network (TCN) (CEEMDAN-TCN) is proposed. The CEEMDAN is used to...
Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review [0.03%]
基于深度学习的新冠肺炎诊断方法研究综述式审查
Yogesh H Bhosale,K Sridhar Patnaik
Yogesh H Bhosale
Covid-19 is now one of the most incredibly intense and severe illnesses of the twentieth century. Covid-19 has already endangered the lives of millions of people worldwide due to its acute pulmonary effects. Image-based diagnostic technique...
Effectiveness of Federated Learning and CNN Ensemble Architectures for Identifying Brain Tumors Using MRI Images [0.03%]
基于MRI图像识别脑肿瘤的联邦学习和CNN集成架构的有效性研究
Moinul Islam,Md Tanzim Reza,Mohammed Kaosar et al.
Moinul Islam et al.
Medical institutions often revoke data access due to the privacy concern of patients. Federated Learning (FL) is a collaborative learning paradigm that can generate an unbiased global model based on collecting updates from local models trai...
A Novel CNN-TLSTM Approach for Dengue Disease Identification and Prevention using IoT-Fog Cloud Architecture [0.03%]
基于物联网和雾计算架构的新型CNN-TLSTM登革热疾病识别与预防方法
S N Manoharan,K M V Madan Kumar,N Vadivelan
S N Manoharan
One of the mosquito-borne pandemic viral infections is Dengue which is mostly transmitted to humans by the Aedes agypti or female Aedes albopictis mosquitoes. The dengue disease expansion is mainly due to the different factors such as clima...
Intelligent Identification of Jute Pests Based on Transfer Learning and Deep Convolutional Neural Networks [0.03%]
基于迁移学习和深度卷积神经网络的黄麻害虫智能识别
Md Sakib Ullah Sourav,Huidong Wang
Md Sakib Ullah Sourav
Pest attacks pose a substantial threat to jute production and other significant crop plants. Jute farmers in Bangladesh generally distinguish between different pests that appear to be the same using their eyes and expertise, which isn't alw...
A Novel Distant Domain Transfer Learning Framework for Thyroid Image Classification [0.03%]
一种新型的甲状腺图像分类远域迁移学习框架
Fenghe Tang,Jianrui Ding,Lingtao Wang et al.
Fenghe Tang et al.
Medical ultrasound imaging technology is currently the preferred method for early diagnosis of thyroid nodules. Radiologists' analysis of ultrasound images is highly dependent on their clinical experience and is susceptible to intra- and in...