PANACEA: An Automated Misinformation Detection System on COVID-19

In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available. © 2023 Association for Computational Linguistics.

An Improved Granulated Convolutional Neural Network Data Analysis Model for COVID-19 Prediction

As COVID-19 poses a major threat to people's health and economy, there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently. In non-stationary time series forecasting jobs, there is frequently a hysteresis in the anticipated values relative to the real values. The multilayer deep-time convolutional network and a feature fusion network are combined in this paper's proposal of an enhanced Multilayer Deep Time Convolutional Neural Network (MDTCNet) for COVID-19 prediction to address this problem. In particular, it is possible to record the deep features and temporal dependencies in uncertain time series, and the features may then be combined using a feature fusion network and a multilayer perceptron. Last but not least, the experimental verification is conducted on the prediction task of COVID-19 real daily confirmed cases in the world and the United States with uncertainty, realizing the short-term and long-term prediction of COVID-19 daily confirmed cases, and verifying the effectiveness and accuracy of the suggested prediction method, as well as reducing the hysteresis of the prediction results.

Masked Face Recognition Using Multi-Task Cascaded Convolutional Networks and FaceNet

Face masks have been widely used since the start of the COVID-19 pandemic. Facial detection and recognition technologies, such as the iPhone's Face ID, heavily rely on seeing the facial features that are now obscured due to wearing a face mask. Currently, the only way to utilize Face ID with a mask on is by having an Apple Watch as well. As such, this paper intends to find initial means of a reliable personal facial recognition system while the user is wearing a face mask without having the need for an Apple Watch. This may also be applicable to other security systems or measures. Through the use of Multi-Task Cascaded Convolutional Networks or MTCNN, a type of neural network which identifies faces and facial landmarks, and FaceNet, a deep neural network utilized for deriving features from a picture of a face, the masked face of the user could be identified and more importantly be recognized. Utilizing MTCNN, detecting the masked faces and automatically cropping them from the raw images are done. The learning phase then takes place wherein the exposed facial features are given emphasis while the masks themselves are excluded as a factor in recognition. Data in the form of images are acquired from taking multiple pictures of a certain individual's face as well as from repositories online for other people's faces. Images used are taken in various settings or modes such as different lighting levels, facial angles, head angles, colors and designs of face masks, and the presence or absence of glasses. The goal is to recognize whether it is the certain individual or not in the image. The training accuracy is 99.966% while the test accuracy is 99.921%. © 2022 IEEE.

Drug Potency Prediction of SARS-CoV-2 Main Protease Inhibitors Based on a Graph Generative Model.

The prediction of a ligand potency to inhibit SARS-CoV-2 main protease (M-pro) would be a highly helpful addition to a virtual screening process. The most potent compounds might then be the focus of further efforts to experimentally validate their potency and improve them. A computational method to predict drug potency, which is based on three main steps, is defined: (1) defining the drug and protein in only one 3D structure; (2) applying graph autoencoder techniques with the aim of generating a latent vector; and (3) using a classical fitting model to the latent vector to predict the potency of the drug. Experiments in a database of 160 drug-M-pro pairs, from which the pIC50 is known, show the ability of our method to predict their drug potency with high accuracy. Moreover, the time spent to compute the pIC50 of the whole database is only some seconds, using a current personal computer. Thus, it can be concluded that a computational tool that predicts, with high reliability, the pIC50 in a cheap and fast way is achieved. This tool, which can be used to prioritize which virtual screening hits, will be further examined in vitro.

Multiscale Approach in Deep Convolutional Networks for Minutia Extraction from Contactless Fingerprint Images

Biometric identification by contactless fingerprinting has been a trend in recent years, reinforced by the pandemic of the new coronavirus (COVID-19). Contactless acquisition tends to be a more hygienic acquisition category with greater user acceptance because it is less invasive and does not require the use of a surface touched by other people as traditional acquisition does. However, this area presents some challenging tasks. Contact-based sensors still generally provide greater biometric effectiveness since the minutiae are more pronounced due to the high contrast between ridges and valleys. On the other hand, contactless images typically have low contrast, so the methods fail with spurious or undetectable details, demonstrating the need for further studies in this area. In this work, we propose and analyze a robust scaled deep learning model for extracting minutiae in contactless fingerprint images. The results, evaluated on three datasets, show that the proposed method is competitive against other minutia extraction algorithms and commercial software. © 2022 IEEE.

A precise and timely graph‐based approach to identify SARS Covid19 infection from medical imaging data using IsoCovNet

More than 100 million individuals have been infected by the COVID19 virus since 2019. Even if the vaccination procedure has already begun, it will take time to attain an adequate supply. There have been several efforts by computer scientists to filter COVID19 from CXR or CT scans due to the disease's extensive prevalence. These patients' CT and CXR scans are utilized to identify COVID19 using IsoCovNet, a Graph‐Isomorphic‐Network, that is, GIN‐based model for detecting COVID19. A GIN‐based design dictates that our suggested model only takes data in the form of graphs. At the outset, the input image undergoes a conversion into an unordered network, that is, a graph that considers only the links between elements rather than the entire image. This approach significantly reduces the model's processing time. We verified the effectiveness of our proposed IsoCovNet network by using four datasets, which consist of both x‐ray and CT‐scan images, from five standard sources that are publicly available on platforms like Kaggle and GitHub. The network achieved an accuracy of 99.51% on binary datasets and a higher accuracy of 99.84% on the multi‐classification task of detecting Covid19. [ FROM AUTHOR] Copyright of International Journal of Imaging Systems & Technology is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Ensemble Learning using Transformers and Convolutional Networks for Masked Face Recognition

Wearing a face mask is one of the adjustments we had to follow to reduce the spread of the coronavirus. Having our faces covered by masks constantly has driven the need to understand and investigate how this behavior affects the recognition capability of face recognition systems. Current face recognition systems have extremely high accuracy when dealing with unconstrained general face recognition cases but do not generalize well with occluded masked faces. In this work, we propose a system for masked face recognition. The proposed system comprises two Convolutional Neural Network (CNN) models and two Transformer models. The CNN models have been fine-tuned on FaceNet pre-trained model. We ensemble the predictions of the four models using the majority voting technique to identify the person with the mask. The proposed system has been evaluated on a synthetically masked LFW dataset created in this work. The best accuracy is obtained using the ensembled models with an accuracy of 92%. This recognition rate outperformed the accuracy of other models and it shows the correctness and robustness of the proposed model for recognizing masked faces. The code and data are available at https://github.com/Hamzah-Luqman/MFR. © 2022 IEEE.

PRISTINE: Semi-supervised Deep Learning Opioid Crisis Detection on Reddit

The drug abuse epidemic has been on the rise in the past few years, particularly after the start of COVID-19 pandemic. Our preliminary observations on Reddit alone show that discussions on drugs from 2018 to 2020 increased between a range of 45% to 200%, and so has the number of unique users participating in those discussions. Existing efforts focused on utilizing social media to distinguish potential drug abuse chats from unharmful chats regardless of what drug is being abused. Others focused on understanding the trends and causes of drug abuse from social media. To this end, we introduce PRISTINE (opioid crisis detection on reddit), our work dynamically detects-and extracts evolving misleading drug names from Reddit comments using reinforced Dynamic Query Expansion (DQE) and constructs a textual Graph Convolutional Network with the aid of powerful pre-trained embeddings to detect which type of drug class a Reddit comment corresponds to. Further, we perform extensive experiments to investigate the effectiveness of our model. © 2022 IEEE.

Multimodal negative sentiment recognition of online public opinion on public health emergencies based on graph convolutional networks and ensemble learning

To improve the effect of multimodal negative sentiment recognition of online public opinion on public health emergencies, we constructed a novel multimodal fine-grained negative sentiment recognition model based on graph convolutional networks (GCN) and ensemble learning. This model comprises BERT and ViT-based multimodal feature representation, GCN-based feature fusion, multiple classifiers, and ensemble learning-based decision fusion. Firstly, the image-text data about COVID-19 is collected from Sina Weibo, and the text and image features are extracted through BERT and ViT, respectively. Secondly, the image-text fused features are generated through GCN in the constructed microblog graph. Finally, AdaBoost is trained to decide the final sentiments recognized by the best classifiers in image, text, and image-text fused features. The results show that the F1-score of this model is 84.13% in sentiment polarity recognition and 82.06% in fine-grained negative sentiment recognition, improved by 4.13% and 7.55% compared to the optimal recognition effect of image-text feature fusion, respectively. © 2023 Elsevier Ltd

Effective multi-step ahead container throughput forecasting under the complex context

Accurate and effective container throughput forecasting plays an essential role in economic dispatch and port operations, especially in the complex and uncertain context of the global Covid-19 pandemic. In light of this, this research proposes an effective multi-step ahead forecasting model called EWT-TCN-KMSE. Specifically, we initially use the empirical wavelet transform (EWT) to decompose the original container throughput series into multiple components with varying frequencies. Subsequently, the state-of-the-art temporal convolutional network is utilized to predict the decomposed components individually, during which an improved loss function that combines mean square error (MSE) and kernel trick is employed. Eventually, the deduced prediction results can be obtained by integrating the predicted values of each component. In particular, this research introduces the MIMO (multi-input and multi-output) strategy to conduct multi-step ahead container throughput forecasting. Based on the experiments in Shanghai port and Ningbo-Zhoushan port, it can be found that the proposed model shows its superiority over benchmark models in terms of accuracy, stability, and significance in container throughput forecasting. Therefore, our proposed model can assist port operators in their daily management and decision making. © 2023 John Wiley & Sons Ltd.

Multi-view POI-level Cellular Trajectory Reconstruction for Digital Contact Tracing of Infectious Diseases

Digital contact tracing is an effective solution to prevent such a pandemic, but the low adoption rate of a required mobile app hinders its effectiveness. A large collection of cellular trajectories from mobile subscribers can be an out-of-the-box solution that is free from the low adoption issue, but has been overlooked due to its low spatial resolution. In this paper, to increase the resolution of this cellular trajectory, we present a new problem that estimates the user's visited places at the point-of-interest(POI) level, which we call POI-level cellular trajectory reconstruction. We propose a novel algorithm, Pincette, that accomplishes more accurate POI reconstruction by leveraging various external data such as road networks and POI contexts. Specifically, Pincette comprises multi-view feature extraction and GCN-LSTM-based POI estimation. In the multi-view feature extraction, Pincette extracts three complementary features from three views: efficiency, periodicity, and popularity. In the GCN-LSTM-based POI estimation, these three views are seamlessly integrated, where spatio-temporal periodic patterns are captured by graph convolutional networks (GCNs) and an LSTM. With extensive experiments on two real data collections of two cities, we show that Pincette outperforms four POI estimation baselines by up to 21.20%. We believe that our work sheds light on the use of cellular trajectories for digital contact tracing. We release the source code at https://github.com/kaist-dmlab/Pincette. © 2022 IEEE.

VQ-ViCNet: Strengthen Unique Features Comparison Autoencoder with Embedding Space for Covid-19 Image Classification

In this paper, we propose a new novel coronavirus pneumonia image classification model based on the combination of Transformer and convolutional network(VQ-ViCNet), and present a vector quantization feature enhancement module for the inconspicuous characteristics of lung medical image data. This model extracts the local latent layer features of the image through the convolutional network, and learns the deep global features of the image data through the Transformer's multi-head self attention algorithm. After the calculation of convolution and attention, the features learned by the Transformer Encoder are enhanced by the vector quantization feature enhancement module and able to better complete the final downstream tasks. This model performs better than convolutional architectures, pure attention architectures and generative models on all 6 public datasets. © 2022 IEEE.

CNN-Based Deep Features with Ensemble Learning for COVID-19 Classification

In this paper, we propose a hybrid system that can automatically detect coronavirus disease and speed up medical image analysis processes by using artificial intelligence technique. Our system consists of two parts: First, to perform feature extraction, we used a deep convolutional network that is based on the transfer learning technique, in this step, we include eight well-known convolutional neural networks for comparison purposes. In the second part, a voting classifier is considered, combining three classifiers, including random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN), to classify radiological images into three classes: COVID-19, normal, and pneumonia, collected from two public medical repositories. The results show that deep learning and radiological images are able to retrieve relevant COVID-19 features with an accuracy of 96.87%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

HateNet: A Graph Convolutional Network Approach to Hate Speech Detection

The COVID-19 pandemic has caused hate speech on online social networks to become a growing issue in recent years, affecting millions. Our work aims to improve automatic hate speech detection to prevent escalation to hate crimes. The first c hallenge i n h ate s peech r esearch i s t hat e xisting datasets suffer from quite severe class imbalances. The second challenge is the sparsity of information in textual data. The third challenge is the difficulty i n b alancing t he t radeoff b etween utilizing semantic similarity and noisy network language. To combat these challenges, we establish a framework for automatic short text data augmentation by using a semi-supervised hybrid of Substitution Based Augmentation and Dynamic Query Expansion (DQE), which we refer to as SubDQE, to extract more data points from a specific c lass f rom T witter. W e a lso p ropose the HateNet model, which has two main components, a Graph Convolutional Network and a Weighted Drop-Edge. First, we propose a Graph Convolutional Network (GCN) classifier, using a graph constructed from the thresholded cosine similarities between tweet embeddings to provide new insights into how ideas are connected. Second, we propose a weighted Drop-Edge based stochastic regularization technique, which removes edges randomly based on weighted probabilities assigned by the semantic similarities between Tweets. Using 3 different SubDQE-augmented datasets, we compare our HateNet model using eight different tweet embedding methods, six other baseline classification models, and seven other baseline data augmentation techniques previously used in the realm of hate speech detection. Our results show that our proposed HateNet model matches or exceeds the performance of the baseline models, as indicated by the accuracy and F1 score. © 2022 IEEE.

The Graph Convolutional Networks Framework for Predicting Pandemic Impact on Stock Prices

Covid-19 has dealt an unprecedented hit to the global economy and all industries, with varying degrees of decline from retail to real estate. This volatility is most evident in stock prices. Previous stock price forecasting methods typically used historical data for each stock as a separate input into the system. This paper proposes an attention-based parallel graph convolutional network framework, which consists of two parallel GCNs. The first GCN takes stock features as input, and the second GCN takes other industry features as input, and sets an attention model to reflect the pairwise interactions between networks. Experimental results on selected stock data show that the model outperforms both the LSTM model and the GCN model in accuracy and F1 score. © 2022 IEEE.

A novel design of a smart interactive guiding robot for busy airports

The COVID-19 pandemic has had a massive impact on the global aviation industry. As a result, the airline industry has been forced to embrace new technologies and procedures in order to provide a more secure and bio-safe travel. Currently, the role of smart technology in airport systems has expanded significantly as a result of the contemporary Industry 4.0 context. The article presents a novel construction of an intelligent mobile robot system to guide passengers to take the plane at the departure terminals at busy airports. The robot provides instructions to the customer through the interaction between the robot and the customer utilizing voice communications. The usage of the Google Cloud Speech-to-Text API combined with technical machine learning to analyze and understand the customer's requirements are deployed. In addition, we use a face detection technique based on Multi-task Cascaded Convolutional Networks (MTCNN) to predict the distance between the robot and passengers to perform the function. The robot can guide passengers to desired areas in the terminal. The results and evaluation of the implementation process are also mentioned in the article and show promise.

Detection of Covid-19 Through the Analysis of Radiographic Images of the Chest using Convolutional Neural Networks

There are different ways to detect Covid-19, which have emerged so far giving an effective response in detecting the disease, the PCR test is a reliable diagnostic method, which requires a well-equipped laboratory to obtain results, which can take hours or days. Another detection technique for this disease is by analyzing the chest image;This technique is used as a diagnostic tool in emergency areas in health centers, because it can reveal characteristics related to lung involvement. For this reason, it is important to develop an automatic detection system, as an alternative diagnosis option for Covid-19. Deep Learning techniques can help detect the SARS-CoV-2 virus by analyzing chest radiographic images. Thanks to the high availability of the datasets available, and using convolutional neural networks, the analysis is carried out by classifying images. In this research, two CNN models were created whose outputs are normal or covid19, the same ones that were trained with two datasets from public research repositories. The performance of the models trained in Pytorch were compared with the models trained in Keras under similar conditions of parameters and hyperparameters, obtaining a higher performance with Pytorch however since the two types of models have learned adequately with an accuracy that is above the 90% recommended the use of both models. © 2022 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.

Mask Detection for Covid-19 under Extreme Conditions

Mask detection has become a hot topic since the COVID-19 pandemic began in recent years. However, most scholars only focus on the speed and accuracy of detection, and fail to pay attention to the fact that mask detection is not suitable for people living under extreme conditions due to the degraded image quality. In this work, a denoising convolutional auto-encoder, a multitask cascaded convolutional networks (MTCNN) and a MobileNet were used to solve the problem of mask detection for COVID-19 under extreme environments. First of all, a network based on AlexNet is designed for the auto-encoder. This study found that the two-layer max pooling layers in AlexNet could not accurately extract image features but damage the quality of restored image. Therefore, they were deleted, and other parameters such as channel number were also modified to fit the new net, and finally trained using cosine distance. In addition, for MTCNN, this study changed the output condition of ONet from thresholding to maximum return, and lowered the thresholds of PNet and RNet to solve the problem that faces might not be found in low-quality images with mask and other covers. Furthermore, MobileNet was trained using categorical cross entropy loss function with adam optimizer. In the end, the accuracy of system for the photos captured under extreme conditions enhance from 50 % to 85% in test images. © 2022 IEEE.

Forecasting energy consumption demand of customers in smart grid using Temporal Fusion Transformer (TFT)

Energy consumption prediction has always remained a concern for researchers because of the rapid growth of the human population and customers joining smart grids network for smart home facilities. Recently, the spread of COVID-19 has dramatically increased energy consumption in the residential sector. Hence, it is essential to produce energy per the residential customers' requirements, improve economic efficiency, and reduce production costs. The previously published papers in the literature have considered the overall energy consumption prediction, making it difficult for production companies to produce energy per customers' future demand. Using the proposed study, production companies can accurately have energy per their customers' needs by forecasting future energy consumption demands. Scientists and researchers are trying to minimize energy consumption by applying different optimization and prediction techniques;hence this study proposed a daily, weekly, and monthly energy consumption prediction model using Temporal Fusion Transformer (TFT). This study relies on a TFT model for energy forecasting, which considers both primary and valuable data sources and batch training techniques. The model's performance has been related to the Long Short-Term Memory (LSTM), LSTM interpretable, and Temporal Convolutional Network (TCN) models. The model's performance has remained better than the other algorithms, with mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) of 4.09, 2.02, and 1.50. Further, the overall symmetric mean absolute percentage error (sMAPE) of LSTM, LSTM interpretable, TCN, and proposed TFT remained at 29.78%, 31.10%, 36.42%, and 26.46%, respectively. The sMAPE of the TFT has proved that the model has performed better than the other deep learning models. © 2023 The Author(s)

MPSKT: Multi-head ProbSparse Self-Attention for Knowledge Tracing

Over the past two years, COVID-19 has led to a widespread rise in online education, and knowledge tracing has been used on various educational platforms. However, most existing knowledge tracing models still suffer from long-term dependence. To address this problem, we propose a Multi-head ProbSparse Self-Attention for Knowledge Tracing(MPSKT). Firstly, the temporal convolutional network is used to encode the position information of the input sequence. Then, the Multi-head ProbSparse Self-Attention in the encoder and decoder blocks is used to capture the relationship between the input sequences, and the convolution and pooling layers in the encoder block are used to shorten the length of the input sequence, which greatly reduces the time complexity of the model and better solves the problem of long-term dependence of the model. Finally, experimental results on three public online education datasets demonstrate the effectiveness of our proposed model. © 2022 Association for Computing Machinery.