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.

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.

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

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 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.

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.

A Fully Automated Intelligent Medicine Dispensary System Based on AIoT

The COVID-19 pandemic has caused a high rate of infection, and thus effective epidemic prevention measures of avoiding the second spread of COVID-19 in hospitals are major challenges for healthcare workers. Hospitals, where medicines are collected, are vulnerable to the rapid spread of COVID-19. Using the remote health monitoring technology of the Internet of Things (IoT) to automatically monitor and record the basic medical information of patients, reduce the workload of healthcare workers, and avoid direct contact with healthcare workers to cause secondary infections is an important research topic. This research proposes a new artificial intelligence solution based on the IoT, replacing existing medicine stations and recognizing medicine bags through the state-of-the-art optical character recognition (OCR) model and PP-OCR v2. The use of optical character recognition in identification of medicine bags can replace healthcare workers in data recording. In addition, this research proposes an administrator management and monitoring system to monitor the equipment and provide a mobile application for patients to check the latest status of medicine bags in real time, and record their medication times. The results of the experiments indicate that the recognition model works very well in different conditions (up to 80.76% in PP-OCR v2 and 94.22% in PGNet), which supports both Chinese and English languages. IEEE

Hand hygiene monitoring and compliance system using convolution neural networks.

Hand hygiene monitoring and compliance systems play a significant role in curbing the spread of healthcare associated infections and the COVID-19 virus. In this paper, a model has been developed using convolution neural networks (CNN) and computer vision to detect an individual's germ level, monitor their hand wash technique and create a database containing all records. The proposed model ensures all individuals entering a public place prevent the spread of healthcare associated infections (HCAI). In our model, the individual's identity is verified using two-factor authentication, followed by checking the hand germ level. Furthermore, if required the model will request sanitizing/ hand wash for completion of the process. During this time, the hand movements are checked to ensure each hand wash step is completed according to World Health Organization (WHO) guidelines. Upon completion of the process, a database with details of the individual's germ level is created. The advantage of our model is that it can be implemented in every public place and it is easily integrable. The performance of each segment of the model has been tested on real-time images an validated. The accuracy of the model is 100% for personal identification, 96.87% for hand detection, 93.33% for germ detection and 85.5% for the compliance system respectively.

Meter Reading using OCR for Comfort and Cost Optimization

Meter reading and billing are time-consuming activities for power, water and gas providing boards. The existing billing system relies on a manual method of taking meter readings, updating the reading in the server and finally generating the bill amount. In this project, the user simply needs to use an Android application to capture and upload the picture of the meter after performing OCR operation. The processing on the image is performed on the server side using Google Colab and Python. The meter reading obtained from OCR processing is sent to the firebase, which is further pushed to the Android application. And finally the Android application displays the meter reading and the bill amount generated. Our project ensures the safety (from communicable diseases like COVID-19) of both the board staff and the customer as they don't come in contact with each other. This project also helps in cutting down on their expenditure by reducing manpower and travel costs. © 2021 IEEE.

Video Indexing System Based on Multimodal Information Extraction Using Combination of ASR and OCR

With the ever-increasing internet penetration across the world, there has been a huge surge in the content on the worldwide web. Video has proven to be one of the most popular media. The COVID-19 pandemic has further pushed the envelope, forcing learners to turn to E-Learning platforms. In the absence of relevant descriptions of these videos, it becomes imperative to generate metadata based on the content of the video. In the current paper, an attempt has been made to index videos based on the visual and audio content of the video. The visual content is extracted using an Optical Character Recognition (OCR) on the stack of frames obtained from a video while the audio content is generated using an Automatic Speech Recognition (ASR). The OCR and ASR generated texts are combined to obtain the final description of the respective video. The dataset contains 400 videos spread across 4 genres. To quantify the accuracy of our descriptions, clustering is performed using the video description to discern between the genres of video. © 2022, Springer Nature Switzerland AG.

Contactless Tool for COVID-19 Surveillance System

Due to the COVID-19 pandemic, surveillance systems have been implemented to monitor public health and trace the infected individuals. The Malaysian government has imposed the standard operating procedure (SOP) which includes checking of temperature for fever, use of hand sanitizers, and record their name, contact number, and date of attendance at points of entry. This paper proposes a contactless tool for COVID-19 surveillance that integrates all the 3 processes into one. The system carries out each stage in sequential order for every person, starting with checking temperature, dispensing hand sanitizer, and lastly data profiling record. The temperature is done using infrared thermometry that automatically adjusts to forehead height. Hand sanitizer is automatically dispensed when hands are detected under the pump. Image processing and optical character recognition are used to capture the name and contact number that will be shown on a tag carried by the individual and saved to the database. The process is contactless and requires no human operator, and yields accurate temperature data, works as intended while demonstrating high accuracy and speed in extracting information with optical character recognition. © 2021 IEEE.

Digital Customer Acquisition and Onboarding during the Pandemic

A must for telecom industry in times of social distancing: Digital customer acquisition and onboarding. Digital channels gained more importance as classical sales channels could not work with the expected performance during the pandemic. In this paper, the digital sales paperless project carried out in the telecom industry is handled. The identification scanning with OCR (Optical Character Recognition), the verification with deep learning artificial intelligence algorithms, the management of remote vendors and other stakeholders in extensive software projects is told. © 2021 IEEE.