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.

The Secure E-Wallet Powered by Blockchain and Distributed Ledger Technology

Last decade had been worst for humanity as it faced Covid-19. The pandemic invited many things unknown to humans such as lockdown, compulsory mask and no contact with other people or things. The 'No Contact' initiated much awaited progress in payment from being entity exchange to being digital. The digitization of payment became biggest transformation;every common citizen realized the use of digital payment. The evolution of cash payment to internet banking and internet banking to e-wallet has made transaction easier for all the services without exhibiting physical form. Payment Gateways, Digital Wallet, Internet Banking use has risen as being fast and instant. Though the recent payments are secure on theft front with the use of digital money by many users at many times online payment system might face problem on digital money transfer with issues such as wrong payment, link failure or single point failure. There might be more problems such as insider problem and transaction being transparent. For such situations more secure and private path towards security is needed as insufficiency will give rise to risk mitigation. The paper recommends a payment system which will be based on privacy and permission laid by blocks to blocks for use in financial sector. The architecture proposed integrates the digital wallet with different banks to give foundation of Blockchain for secure transactions. The peer to peer network will share transactions as well share load to minimize load on central banking system keep the load distributed and secure overall data centres. © 2022 IEEE.

Pressure Prediction System in Lung Circuit Using Deep Learning

A massive number of patients infected with SARS-CoV2 and Delta variant of COVID-19 have generated acute respiratory distress syndrome (ARDS) which needs intensive care, which includes mechanical ventilation. But due to the huge no of patients, the workload and stress on healthcare infrastructure and related personnel have grown exponentially. This has resulted in huge demand for innovation in the field of automated health care which can help reduce the stress on the current healthcare infrastructure. This work gives a solution for the issue of pressure prediction in mechanical ventilation. The algorithm suggested by the researchers tries to predict the pressure in the respiratory circuit for various lung conditions. Prediction of pressure in the lungs is a type of sequence prediction problem. Long short-term memory (LSTM) is the most efficient solution to the sequence prediction problem. Due to its ability to selectively remember patterns over the long term, LSTM has an edge over normal RNN. RNN is good for short-term patterns but for sequence prediction problems, LSTM is preferred. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Pressure Prediction System in Lung Circuit Using Deep Learning

A massive number of patients infected with SARS-CoV2 and Delta variant of COVID-19 have generated acute respiratory distress syndrome (ARDS) which needs intensive care, which includes mechanical ventilation. But due to the huge no of patients, the workload and stress on healthcare infrastructure and related personnel have grown exponentially. This has resulted in huge demand for innovation in the field of automated health care which can help reduce the stress on the current healthcare infrastructure. This work gives a solution for the issue of pressure prediction in mechanical ventilation. The algorithm suggested by the researchers tries to predict the pressure in the respiratory circuit for various lung conditions. Prediction of pressure in the lungs is a type of sequence prediction problem. Long short-term memory (LSTM) is the most efficient solution to the sequence prediction problem. Due to its ability to selectively remember patterns over the long term, LSTM has an edge over normal RNN. RNN is good for short-term patterns but for sequence prediction problems, LSTM is preferred. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 Global Prediction: A Mathematical Approach based on Data Trend Lines and Probability

To better understand data and its possible consequences, mathematical models are the must. For COVID-19 outbreak, it helps predict and therefore, policies are guidelines can be designed accordingly. In this study, we define the practical prediction model for COVID 19 by considering the different essentials such as the total number of cases, recovery cases and death cases. The special grading for countries involves the government policies as well as the involvement of the society intended for controlling COVID 19. We investigate trend lines for the data with the help of correlation coefficients and coefficient of determination. The linear and the second-degree equations help to make predictions of active patients of COVID 19 in the future. The study of existing data patterns is done and is used to predict the spread of COVID in the world. This analysis assists us to decide the futuristic guidelines, requirements, and policies for governing the spread of COVID 19.

Data Analytics: COVID-19 Prediction Using Multimodal Data

Globally, there is massive uptake and explosion of data, and the challenge is to address issues like scale, pace, velocity, variety, volume, and complexity of this big data. Considering the recent epidemic in China, modeling of COVID-19 epidemic for cumulative number of infected cases using data available in early phase was big challenge. Being COVID-19 pandemic during very short time span, it is very important to analyze the trend of these spread and infected cases. This chapter presents medical perspective of COVID-19 toward epidemiological triad and the study of state of the art. The main aim of this chapter is to present different predictive analytics techniques available for trend analysis, different models and algorithms, and their comparison. Finally, this chapter concludes with the prediction of COVID-19 using Prophet algorithm indicating more faster spread in short term. These predictions will be useful to government and healthcare communities to initiate appropriate measures to control this outbreak in time. © 2020, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.