A Combined Deep CNN-LSTM Network for the Detection of Novel Coronavirus (COVID-19) Using X-ray Images

Healthcare monitoring system in hospitals and many other health centers has experienced significant growth, and portable healthcare monitoring systems with emerging technologies are becoming of great concern to many countries worldwide nowadays. The advent of Internet of Things (IoT) technologies facilitates the progress of healthcare from face-to-face consulting to telemedicine. This paper proposes a smart healthcare system in IoT environment that can monitor a patient’s basic health signs as well as the room condition where the patients are now in real-time. In this system, five sensors are used to capture the data from hospital environment named heart beat sensor, body temperature sensor, room temperature sensor, CO sensor, and CO2 sensor. The error percentage of the developed scheme is within a certain limit (< 5%) for each case. The condition of the patients is conveyed via a portal to medical staff, where they can process and analyze the current situation of the patients. The developed prototype is well suited for healthcare monitoring that is proved by the effectiveness of the system.

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Development of Smart Healthcare Monitoring System in IoT Environment.

Nowadays automatic disease detection has become a crucial issue in medical science with the rapid growth of population. Coronavirus (COVID-19) has become one of the most severe and acute diseases in very recent times that has been spread globally. Automatic disease detection framework assists the doctors in the diagnosis of disease and provides exact, consistent, and fast reply as well as reduces the death rate. Therefore, an automated detection system should be implemented as the fastest way of diagnostic option to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 421 X-ray images including 141 images of COVID-19 is used as a dataset in this system. The experimental results show that our proposed system has achieved 97% accuracy, 91% specificity, and 100% sensitivity. The system achieved desired results on a small dataset which can be further improved when more COVID-19 images become available. The proposed system can assist doctors to diagnose and treatment the COVID-19 patients easily.

https://medrxiv.org/content/10.1101/2020.06.18.20134718v1.full.pdf

Determinants of carbon emissions in Argentina: The roles of renewable energy consumption and globalization

Assessing the environmental sustainability corridor: Linking natural resources, renewable energy, human capital, and ecological footprint in BRICS.

Diseases is an unusual circumstance that affects single or more parts of a human’s body. Because of lifestyle and patrimonial, different kinds of disease are increasing day by day. Among all those ...

Coronary Artery Heart Disease Prediction: A Comparative Study of Computational Intelligence Techniques

Urbanization-globalization-CO2 emissions nexus revisited: empirical evidence from South Africa

At present there is a universal anxiety about the economic decline and a greener globe which is correlated to an improved and efficient system to generate and transmit electric power. With the initiation of the plug-in electric vehicles and renewable energy generators, a smarter, more efficient and customer-friendly power grid is indispensable that is “Smart Grid”. This paper depicts the importance, characteristics and evolution of smart grid. The allied new concerns, environmental impacts and benefits of smart grid are illustrated here. In brief platform for smart grid R&D is also portrayed It is highlighted that though the smart grid implementation is promising, it faces mammoth challenges. The voyage to smart grid is an extensive when diverse technologies will coexist. Finally the future key challenges are enlisted as a guideline for the successful implementation of Smart Grid for the sustainable future.

Evolution of smart grid and some pertinent issues

Generally, crimes influence organisations as it starts occurring frequently in society. Because of having many dimensions of crime data, it is difficult to mine the available information using off the shelf or statistical data analysis tools. Improving this process will aid the police as well as crime protection agencies to solve the crime rate in a faster period. Also, criminals can often be identified based on crime data. Data mining includes strategies at the convergence of machine learning and database frameworks. Using this concept, we can extract previously unknown useful information and their patterns of occurrence from unstructured data. The sole purpose of this paper is to give an idea of how data mining can be utilised by crime investigation agencies to discover relevant precautionary measures from prediction rates. Data sets are analysed by some supervised classification algorithms, namely decision tree, K-nearest neighbours (KNN) and random forest algorithms. Crime forecasting is done for frequently occurring crimes like robbery, assault, theft, etc. Specifically, the results indicate the superiority of the random forest algorithm in test accuracy.

/pdf/data-mining-technique-to-analyse-and-predict-crime-using-52qc3ppjtz.pdf

Md. Rahat Hossain

Papers

Coronary Artery Heart Disease Prediction: A Comparative Study of Computational Intelligence Techniques

Urbanization-globalization-CO2 emissions nexus revisited: empirical evidence from South Africa

Evolution of smart grid and some pertinent issues

Data mining technique to analyse and predict crime using crime categories and arrest records

Multi-channel CNN based anticancer peptides identification.