Welcome to the 3rd International Conference on Wired/Wireless Internet C- munications (WWIC). After a successful start in Las Vegas and a selective c- ference in Germany, this year’s WWIC demonstrated the event’s ma...
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ISBN:
(数字)9783540321040
ISBN:
(纸本)9783540258995
Welcome to the 3rd International Conference on Wired/Wireless Internet C- munications (WWIC). After a successful start in Las Vegas and a selective c- ference in Germany, this year’s WWIC demonstrated the event’s maturity. The conference was supported by several sponsors, both international and local, and became the o?cial venue for COST Action 290. That said, WWIC has now been established as a top-quality conference to promote research on the convergence of wired and wireless networks. This year we received 117 submissions, which allowed us to organize an - citing program with excellent research results, but required more e?ort from the 54 members of the international Program Committee and the 51 additional reviewers. For each of the 117 submitted papers we asked three independent - viewers to provide their evaluation. Based on an online ballot phase and a TPC meeting organized in Colmar (France), we selected 34 high-quality papers for presentation at the conference. Thus, the acceptance rate for this year was 29%.
Drug-drug interactions (DDIs) are an important biological phenomenon which can result in medical errors from medical practitioners. Drug interactions can change the molecular structure of interacting agents which may ...
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Drug-drug interactions (DDIs) are an important biological phenomenon which can result in medical errors from medical practitioners. Drug interactions can change the molecular structure of interacting agents which may prove to be fatal in the worst case. Finding drug interactions early in diagnosis can be pivotal in side-effect prevention. The growth of big data provides a rich source of information for clinical studies to investigate DDIs. We propose a hierarchical classification model which is double-pass in nature. The first pass predicts the occurrence of an interaction and then the second pass further predicts the type of interaction such as effect, advice, mechanism, and int. We applied different deep learning algorithms with Convolutional Bi-LSTM (ConvBLSTM) proving to be the best. The results show that pre-trained vector embeddings prove to be the most appropriate features. The F1-score of the ConvBLSTM algorithm turned out to be 96.39% and 98.37% in Russian and English language respectively which is greater than the state-of-the-art systems. According to the results, it can be concluded that adding a convolution layer before the bi-directional pass improves model performance in the automatic classification and extraction of drug interactions, using pre-trained vector embeddings such as Fasttext and Bio-Bert.
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