PREDICTIVE ASSESSMENT OF POST-COVID-19 IMPACT USING ARTIFICIAL INTELLIGENCE NEURAL NETWORK MODELS

The novel COVID-19 pandemic has spread all over the world. Due to its easy transmission, it is crucial to develop techniques to accurately and efficiently identify the presence of COVID-19 and distinguish it from other forms of flu and pneumonia. Recent research has shown that the chest X-rays of patients suffering from COVID-19 depict specific radiography abnormalities. This study aims to construct a deep convolutional neural network (CNN) capable of performing feature extraction and binary classification of CT scans of COVID-19 patients from a publicly available dataset sourced from the University of California San Diego and Berkeley (UC San Diego & UC Berkeley). This work presents a 3-step technique to fine-tune pre-trained VGG19, Xception, and Inception V3 architectures to improve model performance and reduce training time. It was achieved by progressively re-sizing input images to 224x224x3 pixels and fine-tuning the network at each stage. Among three selected pre-trained models, the VGG 19 outperformed with 0.99, 0.88, 0.85, 0.86, 0.83, 0.85, 0.85 for Train accuracy, validation accuracy, test accuracy, precision, recall, F1 Score, the area under the curve values, respectively. Keywords: SARS-CoV2, Coronavirus, Deep Learning, Transfer Learning, Convolutional Neural Network DOI: 10.7176/JNSR/14-6-06 Publication date: April 30th 202

ServeNet: A Deep Neural Network for Web Services Classification

Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this paper, we present a novel deep neural network to automatically abstract low-level representation of both service name and service description to high-level merged features without feature engineering and the length limitation, and then predict service classification on 50 service categories. To demonstrate the effectiveness of our approach, we conduct a comprehensive experimental study by comparing 10 machine learning methods on 10,000 real-world web services. The result shows that the proposed deep neural network can achieve higher accuracy in classification and more robust than other machine learning methods.Comment: Accepted by ICWS'2

Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction

Social media is an useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from twitter. Medical information extraction from social media is challenging, mainly due to short and highly information nature of text, as compared to more technical and formal medical reports. Current methods in ADR mention extraction relies on supervised learning methods, which suffers from labeled data scarcity problem. The State-of-the-art method uses deep neural networks, specifically a class of Recurrent Neural Network (RNN) which are Long-Short-Term-Memory networks (LSTMs) \cite{hochreiter1997long}. Deep neural networks, due to their large number of free parameters relies heavily on large annotated corpora for learning the end task. But in real-world, it is hard to get large labeled data, mainly due to heavy cost associated with manual annotation. Towards this end, we propose a novel semi-supervised learning based RNN model, which can leverage unlabeled data also present in abundance on social media. Through experiments we demonstrate the effectiveness of our method, achieving state-of-the-art performance in ADR mention extraction.Comment: Accepted at DTMBIO workshop, CIKM 2017. To appear in BMC Bioinformatics. Pls cite that versio