Urban Mobility in the Postpandemic Stage: A Comprehensive Investigation of a Variety of Cities in China

The global outbreak of coronavirus disease 2019 (COVID-19) has affected the urban mobility of nations around the world. The pandemic may even have a potentially lasting impact on travel behaviors during the post-pandemic stage. China has basically stopped the spread of COVID-19 and reopened the economy, providing an unprecedented environment for investigating post-pandemic travel behaviors. This study conducts multiple investigations to show the changes in travel behaviors in the post-pandemic stage, on the basis of empirical travel data in a variety of cities in China. Specifically, this study demonstrates the changes in road network travel speed in 57 case cities and the changes in subway ridership in 26 case cities. Comprehensive comparisons can indicate the potential modal share in the post-pandemic stage. Further, this study conducts a case analysis of Beijing, where the city has experienced two waves of COVID-19. The variations in travel speed in the road network of Beijing at different stages of the pandemic help reveal the public's responses towards the varying severity of the pandemic. Finally, a case study of the Yuhang district in Hangzhou is conducted to demonstrate the changes in traffic volume and vehicle travel distance amid the post-pandemic stage based on license plate recognition data. Results indicate a decline in subway trips in the post-pandemic stage among case cities. The vehicular traffic in cities with subways has recovered in peak hours on weekdays and has been even more congested than the pre-pandemic levels;whereas the vehicular traffic in cities without subways has not rebounded to pre-pandemic levels. This situation implies a potential modal shift from public transportation to private vehicular travel modes. Results also indicate that commuting traffic is sensitive to the severity of the pandemic. This may be because countermeasures, e.g., work-from-home and suspension of non-essential businesses, will be implemented if the pandemic restarts. The travel speed in non-peak hours and on non-workdays is higher than pre-pandemic levels, indicating that non-essential travel demand may be reduced and the public's vigilance towards the pandemic may continue to the post-pandemic stage. These findings can help improve policymaking strategies in the post-pandemic new normal. © 2023 American Society of Civil Engineers.

"Anti-vaccinationists&Anti-vax”: Linguistic Means of Actualizing Assessment in the Headlines and Leads of Ukrainian Text Media

Since the beginning of the global Covid-19 pandemic, text media materials are full of the word "vax”, and after the appearance of vaccines against the coronavirus and the start of the vaccination campaign around the world, "anti-vax” has also been added. In the article, it is singled out the linguistic means of updating the evaluation in the headlines and leads of the text media of Ukraine in the materials dedicated to opponents of vaccination against Covid-19, and the possibility of its automatic recognition with the help of machine methods is also considered. It was found that among the language means of expressing assessment, colloquial vocabulary (jargonisms and slang) and phraseology come to the fore. © 2023 Copyright for this paper by its authors.

Doc-Handler: Document Scanner, Manipulator, and Translator based on Image and Natural language processing

Physical documents may easily be converted into digital versions in the modern digital era by employing scanning software and the internet. The day when this activity needed printers and scanners is long gone. Nowadays, even our smartphones and cameras may be used to quickly convert paper documents into digital ones. This is especially useful in the wake of the COVID-19 pandemic, where the ability to share and access documents online is more important than ever. This study proposes an application for illiterate people to quickly translate scanned papers or photos into their native language and save them in a digital format. The Application makes use of image processing methods and has capabilities including PDF conversion, image colour adjustment, cropping, and Optical Character Recognition (OCR). A user-friendly application, developed using the Flutter Framework and programmed in Python and Dart, serves as the interface for the system. The proposed application is cross-platform and works with a variety of gadgets. This method intends to increase accessibility and productivity for illiterate people in the digital age by integrating image processing with language translation. © 2023 IEEE.

Intelligent ResNet-18 based Approach for Recognizing and Assessing Arabic Children's Handwriting

Studies tackling handwriting recognition and its applications using deep learning have been promoted by developing advanced machine learning techniques. Yet, a shortage in research that serves the Arabic language and helps develop teaching and learning processes still exists. Moreover, COVID-19 pandemic affected the education system considerably in many countries and yielded an immediate shift to distance learning and extensive use of e-learning tools. An intelligent system was proposed and used in this paper to recognize isolated Arabic handwritten characters. Particularly, pre-trained CNN models were exploited and fine-tuned to meet the requirements of the considered application. Specifically, the designed system automatically supports teaching Arabic letters and evaluating children's writing skills. The Arabic Handwritten Character Dataset (AHCD) was used to train the models built upon ResNet-18 and assess the overall system performance. Furthermore, several models were investigated using various hyper-parameter settings in order to determine the most accurate one. The best model with the highest accuracy rate of 99% was used and integrated into the proposed system to recognize the Arabic alphabets. © 2023 IEEE.

Enhancing Marathi Handwritten Character Recognition Using Ensemble Learning

In the current era, the Optical Character Recognition (OCR) model plays a vital role in converting images of handwritten characters or words into text editable script. During the COVID-19 pandemic, students' performance is assessed based on multiple-choice questions and handwritten answers so, in this situation, the need for handwritten recognition has become acute. Handwritten answers in any regional language need the OCR model to transform the readable machine-encoded text for automatic assessment which will reduce the burden of manual assessment. The single Convolutional Neural Network (CNN) algorithm recognizes the handwritten characters but its accuracy is suppressed when dataset volume is increased. In proposed work stacking and soft voting ensemble mechanisms that address multiple CNN models to recognize the handwritten characters. The performance of the ensemble mechanism is significantly better than the single CNN model. This proposed work ensemble VGG16, Alexnet and LeNet-5 as base classifiers using stacking and soft voting ensemble approaches. The overall accuracy of the proposed work is 98.66% when the soft voting ensemble has three CNN classifiers. © 2023 Lavoisier. All rights reserved.

PACMAN: A Framework for Pulse Oximeter Digit Detection and Reading in A Low-Resource Setting

In light of the COVID-19 pandemic, patients were required to manually input their daily oxygen saturation (SpO2) and pulse rate (PR) values into a health monitoring system—unfortunately, such a process trend to be an error in typing. Several studies attempted to detect the physiological value from the captured image using optical character recognition (OCR). However, the technology has limited availability with high cost. Thus, this study aimed to propose a novel framework called PACMAN (Pandemic Accelerated Human-Machine Collaboration) with a low-resource deep learning-based computer vision. We compared state-of-the-art object detection algorithms (scaled YOLOv4, YOLOv5, and YOLOR), including the commercial OCR tools for digit recognition on the captured images from the pulse oximeter display. All images were derived from crowdsourced data collection with varying quality and alignment. YOLOv5 was the best-performing model against the given model comparison across all datasets, notably the correctly orientated image dataset. We further improved the model performance with the digits auto-orientation algorithm and applied a clustering algorithm to extract SpO2 and PR values. The accuracy performance of YOLOv5 with the implementations was approximately 81.0-89.5%, which was enhanced compared to without any additional implementation. Accordingly, this study highlighted the completion of the PACMAN framework to detect and read digits in real-world datasets. The proposed framework has been currently integrated into the patient monitoring system utilized by hospitals nationwide. 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

Digital transformation for shipping container terminals using automated container code recognition

Keywords: Character isolation Character recognition Container codes recognition Histogram of oriented gradients Support vector machine Due to the sweeping waves of global industry development, the number of containers passing through terminal ports increases every day. [...]it is essential to automate the identification process for the container codes to replace the manual identification for more efficient logistics and safer workplace. [...]this requires high labor costs and leads to human errors due to fatigue from repetitive work. [...]the repeated manual identification process at terminal ports has hindered us from moving forward to a more efficient logistics system. [...]SVMs are deployed to determine isolated characters.

Name Entity Recognition on Covid-19 Dataset using Machine Learning algorithms

The COVID-19 pandemic has been accompanied by such an explosive increase in media coverage and scientific publications that researchers find it difficult to keep up. So we are working on COVID-19 dataset on Omicron variant to recognise the name entity from a given text. We collect the COVID related data from newspaper or from tweets. This article covered the name entity like COVID variant name, organization name and location name, vaccine name. It include tokenisation, POS tagging, Chunking, levelling, editing and for run the program. It will help us to recognise the name entity like where the COVID spread (location) most, which variant spread most (variant name), which vaccine has been given (vaccine name) from huge dataset. In this work, we have identified the names. If we assume unemployment, economic downfall, death, recovery, depression, as a topic we can identify the topic names also, and in which phase it occurred. © 2022 IEEE.

A user-oriented multi-input Sentiment Analysis and Feedback System for mental state awareness necessitated due to COVID-19

Development in technology has led to a spike in sharing of opinions about different subjects on social media, for instance, movie or product reviews. Unprecedented COVID-19 led to forced isolation and affected mental health negatively. This paper introduces a system to detect users' emotions and mental states based on provided input. Among the different data sources available on social media, real-time Twitter data is used in this analysis. Sentiment analysis can be used as a tool at various levels, right from individual to organizational development. Deep learning algorithms like LSTM and CNN lay the foundation of this system. Python libraries and Google APIs are used to add functionalities. Earlier studies only focused on detecting emotions, whereas the proposed system provides the user with a graphical analysis of detected emotions and apt suggestions like motivational quotes or videos. The system accepts multilingual text input, speech, or video input. The scope of this system is not restricted to COVID-19 related texts. This research will assist individuals and businesses and aid future development. © 2022 IEEE.

Novel ABC: Aspect Based Classification of Sentiments Using Text Mining for COVID-19 Comments

Intricate text mining techniques encompass various practices like classification of text, summarization and detection of topic, extraction of concept, search and retrieval of ideal content, document clustering along with many more aspects like sentiment extraction, text conversion, natural language processing etc. These practices in turn can be used to discover some non-trivial knowledge from a pool of text-based documents. Arguments, difference in opinions and confrontations in the form of words and phrases signify the knowledge regarding an ongoing situation. Extracting sentiment from text that is gathered from online networking web-based platforms entitles the task of text mining in the field of natural language processing. This paper presents a set of steps to optimize the text mining techniques in an attempt to simplify and recognize the aspect-based sentiments behind the content obtained from social media comments. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Oxygen: A Distributed Health Care Framework for Patient Health Record Management and Pharmaceutical Diagnosis

With the COVID-19 pandemic, the world is confronting various healthcare issues, and healthcare automation is more crucial than ever. The pandemic has revealed the limitations of existing digital healthcare systems to manage public health emergencies. There is no registered population for many healthcare institutions in Sri Lanka, as a result, there is a communication gap. Electronic Health Record systems (EHRs) are becoming popular to share patient details but accessing scattered data across several EHRs while safeguarding patient privacy remains a challenge. Most of these medical records are in printed format and manually entering those into EHR systems is time-consuming and error prone. Not only that pharmaceutical error is a critical healthcare problem, but it is even riskier to visit doctors for pharmaceutical diagnosis during a pandemic. This research introduces a Blockchain-based patient health record system, an Optical Character Recognition (OCR) and Natural Language Processing (NLP) based Medical Document Scanner, a Drug Identifier based on Image Processing and a Medical Chatbot powered by NLP as four novel approaches to address these issues. Altogether with the results, this research aims at introducing a solution for the limitations in healthcare while providing a distributed healthcare framework for the healthcare community worldwide. © 2022 IEEE.

Non-Sequential Indexing of Videos using Linguistic Computation

Since the outbreak of Corona Virus Disease(COVID-19), the education sector has seen a drift from traditional in-person teaching methods to virtually-assisted learning. This new trend has paved its path for students to easily gain access to a variety of educational instructors across the globe. But online education comes with its own potential and challenges. Factors like high availability, flexibility, and affordability of the online learning platforms add to the effective deliverance of the content in this progressive present-day online learning. Some key disadvantages are lack of powerful conveyance of content to listeners and sequential navigation of videos. Linearly searching for specific topics through long videos is a common problem that students face, while learning from the internet. This research study proposes a novel approach to promote the application of non-sequential navigation of videos by identifying key-topics and automatically generating timestamps. The model utilizes Natural Language Processing (NLP) and Optical Character Recognition (OCR) techniques for determining the key topics from the video. Timestamps are identified for the keywords before they are uttered, using a novel algorithm for audio indexing. Finally, timestamps are successfully generated for every keyword. Through this study, the objective of non-sequential navigation of videos using a new audio-indexing algorithm is achieved. © 2022 IEEE

A novel neural network model fusion approach for improving medical named entity recognition in online health expert question-answering services

Because of the frequent occurrence of chronic diseases, the COVID-19 pandemic, etc., online health expert question-answering (HQA) services have been unable to cope with the rapidly increasing demand for online consultations. Building a virtual health assistant based on medical named entity recognition (NER) can effectively assist with the consultation process, but the unstandardized expressions within HQA text pose a serious challenge for medical NER tasks. The main goal of this study is to propose a novel deep medical NER approach based on a collaborative decision strategy (CDS), i.e., co_decision_NER (CDN), that can identify standard and nonstandard medical entities in the HQA context. We collected 10,000 question–answer pairs from HaoDF, extracted medical entities from 15 entity categories, and used a CDS to fuse the advantages of different NER models. Ultimately, CDN achieved a performance (precision = 84.50%, recall = 84.30%, F1 = 84.40%) that was significantly better than that of the state-of-the-art (SOTA) method. Our empirical analysis suggests that the entity types Disease (DIS), Sign (SIG), Test (TES), Drug (DRU), Surgery (SUR), Precaution (PRE), and Region (REG) can be most easily expressed arbitrarily in the doctor–patient interaction scenario of HQA services. In addition, CDN can identify not only standard but also nonstandard medical entities, effectively alleviating the severe out-of-vocabulary (OOV) problem faced by HQA services when performing medical NER tasks. The core contribution of this study is the development of a novel neural network model fusion algorithm that can improve the performance of entity recognition in medical domain-specific tasks. © 2023 Elsevier Ltd

A novel stock counting system for detecting lot numbers using Tesseract OCR.

Counting stock is one of the warehouse's methods for preventing insatiable stock. Moreover, it could help the company forecast how many products they need to store and predict the replenished goods for customers. However, stock count in the medical business, which sells specialized medical equipment, needs more focus on, because it uses to treat the patient. So that lack of inventory should not happen. In a normal situation, stock count at some hospitals is quite hard for salespeople, especially hospitals in upcountry that far away. During the COVID-19 situation, many limits need to be strict. At this point, it causes a shortage of goods in many hospitals. In this paper, we represent how computer vision can help this process. When the hospital's officer sends images of stock to our system. The system will recognize the quantity and lot number of goods that remain in the hospital. Therefore, salespeople can decrease the times to visit hospitals. The result showed that for text detection and text recognition in a specific use case. Our prototype system achieves 84.17% in accuracy.

Spanish Corpora of tweets about COVID-19 vaccination for automatic stance detection

The paper presents new annotated corpora for performing stance detection on Spanish Twitter data, most notably Health-related tweets. The objectives of this research are threefold: (1) to develop a manually annotated benchmark corpus for emotion recognition taking into account different variants of Spanish in social posts;(2) to evaluate the efficiency of semi-supervised models for extending such corpus with unlabelled posts;and (3) to describe such short text corpora via specialised topic modelling. A corpus of 2,801 tweets about COVID-19 vaccination was annotated by three native speakers to be in favour (904), against (674) or neither (1,223) with a 0.725 Fleiss' kappa score. Results show that the self-training method with SVM base estimator can alleviate annotation work while ensuring high model performance. The self-training model outperformed the other approaches and produced a corpus of 11,204 tweets with a macro averaged f1 score of 0.94. The combination of sentence-level deep learning embeddings and density-based clustering was applied to explore the contents of both corpora. Topic quality was measured in terms of the trustworthiness and the validation index. © 2023 The Author(s)

Oops! i bought it! An Attempt to Control the Impulsive Buying Behavior

Impulse buying is such a craving that satisfies the happiness of an individual. The tendency of a customer to buy goods and services without prior planning is known as impulsive buying. When a customer makes such impulsive purchases, it is usually motivated by emotions and feelings. This study dives into the factors that lead to impulsive purchases. The study showcases insights from 118 individuals and their views on different situations which go alongside in the flow of an impulsive purchase. The data that was studied from secondary research and interactions with individuals helped in identifying key findings with the help of different methods used to do it. Based on these findings, we propose 'Curbit' a solution to curb the impulsiveness while buying. This solution utilizes technologies such as OCR (Optical Character Recognition) reading, image processing and data analytics. The concept is yet to be prototyped and validated which will be the next step to perform. © 2022 ACM.

OCR for TIFF Compressed Document Images Directly in Compressed Domain Using Text segmentation and Hidden Markov Model

In today's technological era, document images play an important and integral part in our day to day life, and specifically with the surge of Covid-19, digitally scanned documents have become key source of communication, thus avoiding any sort of infection through physical contact. Storage and transmission of scanned document images is a very memory intensive task, hence compression techniques are being used to reduce the image size before archival and transmission. To extract information or to operate on the compressed images, we have two ways of doing it. The first way is to decompress the image and operate on it and subsequently compress it again for the efficiency of storage and transmission. The other way is to use the characteristics of the underlying compression algorithm to directly process the images in their compressed form without involving decompression and re-compression. In this paper, we propose a novel idea of developing an OCR for CCITT (The International Telegraph and Telephone Consultative Committee) compressed machine printed TIFF document images directly in the compressed domain. After segmenting text regions into lines and words, HMM is applied for recognition using three coding modes of CCITT-horizontal, vertical and the pass mode. Experimental results show that OCR on pass modes give a promising results. © 2022 IEEE.

Can Students with Dyslexia Learn Independently? A Seven-Week Study of Chinese Character Learning in an Informal Learning Environment

Dyslexia is a specific learning difficulty that affects primary school students, which is difficult to tackle through traditional school learning due to limited resources. To this end, large-scale digital learning presents new opportunities to address the need for inclusive education. A unique challenge for students with dyslexia in Hong Kong is learning Chinese characters. In this paper, we investigate whether students with dyslexia can learn the writing of Chinese characters independently in an informal learning environment. For that purpose, we developed a mobile application for learning to write Chinese characters with three different writing conditions. First, students learn new Chinese characters in Condition 1: Grid+Contour+Instruction. Then, students strengthen their memory of the learned Chinese characters in Condition 2: Grid+Contour. Finally, students retrieve the memory of the learned Chinese characters in Condition 3: Grid Only. Students with dyslexia demonstrated a significant improvement after practicing with the three-condition design. For example, they wrote much slower than students without dyslexia before the study but caught up over time. This study contributes an approach to facilitate the self-paced learning of students with dyslexia at scale. IEEE

Named Entity Recognition on CORD-19 Bio-Medical Dataset with Tolerance Rough Sets

Biomedical named entity recognition is becoming increasingly important to biomedical research due to a proliferation of articles and also due to the current pandemic disease. This paper addresses the task of automatically finding and recognizing biomedical entity types related to COVID (e.g., virus, cell, therapeutic) with tolerance rough sets. The task includes i) extracting nouns and their co-occurring contextual patterns from a large BioNER dataset related to COVID-19 and, ii) annotating unlabelled data with a semi-supervised learning algorithm using co-occurence statistics. 465,250 noun phrases and 6,222,196 contextual patterns were extracted from 29,500 articles using natural language text processing methods. Three categories were successfully classified at this time: virus, cell and therapeutic. Early precision@N results demonstrate that our proposed tolerant pattern learner (TPL) is able to constrain concept drift in all 3 categories during the iterative learning process. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.