The entire world has been affected by the outbreak of COVID-19 since early 2020. Human carriers are largely the spreaders of this new disease, and it spreads much faster compared to previously identified coronaviruses and other flu viruses. Although vaccines have been invented and released, it will still be a challenge to overcome this disease. To save lives, it is important to better understand how the virus is transmitted from one host to another and how future areas of infection can be predicted. Recently, the second wave of infection has hit multiple countries, and governments have implemented necessary measures to tackle the spread of the virus. We investigated the three phases of COVID-19 research through a selected list of mathematical modeling articles. To take the necessary measures, it is important to understand the transmission dynamics of the disease, and mathematical modeling has been considered a proven technique in predicting such dynamics. To this end, this paper summarizes all the available mathematical models that have been used in predicting the transmission of COVID-19. A total of nine mathematical models have been thoroughly reviewed and characterized in this work, so as to understand the intrinsic properties of each model in predicting disease transmission dynamics. The application of these nine models in predicting COVID-19 transmission dynamics is presented with a case study, along with detailed comparisons of these models. Toward the end of the paper, key behavioral properties of each model, relevant challenges and future directions are discussed.
One of the most important tasks used by the medical profession for disease identification and recovery preparation is automatic medical image processing. Statistical approaches are the most commonly used algorithms, and they consist several important step. Brain tumors are the foremost causes of death of cancerous diseases all over the world. The hippocampus is the human body’s primary control structure. Since a tumor attacks the brain, it can kill the patient if it is not detected early. Among the various imaging modalities available, Magnetic Resonance Image (MRI) is a better implement for calculating area and classifying tumors based on their grade. MRI does not emit any toxic radiation. There is currently no automated method for detecting and identifying the grade of a tumor. This study mainly focusses on classifying and segmenting brain tumors from MRI scan data. It aids physicians in the planning of future care or surgery. This procedure consists of four steps: image de-noising, tumor extraction, attribute extraction, and hybrid classification. In the first step of image de-noising, the curvelet transformation (CT) is used. Then, in the next stage, Artificial Bee Colony (ABC) Optimization is used in conjunction with the thresholding process to remove tumors from brain MRI scans. Another optimization approach is used to recover the learning rate of the Convolutional Neural Network for the final hybrid classification. The experiment model is assessed by using the multimodal brain tumor (BRATS) 2013 and 2015 challenge datasets from medical image computing. The outcomes of the experiment presented that the method achieved the segmentation 95.23% and 94% of accuracy, where the proposed optimized CNN achieved classification accuracy of 98.5% and 99% for both datasets.
The concept of cloud computing makes it possible to have a shared pool of reconfigurable computing resources that can be deployed and released with little involvement from administration work or service providers. Cloud computing makes this possible. The communication among the nodes is possible with the help of internet. All users are able to use the services of cloud. The small-scale industries are really happy to use the cloud services. The attackers are degrading the performance of services, and also the users are not receiving the response. This paper presents the imprint of cloud computing. Flooding attacks or the DoS attack is one attack that reserves the communication resources in network, and the rest of the attacks, like Sybil attack, misguide the users, and also it is not easy to identify the exact identification of the sender. The security schemes are able to remove attacker infection, and on the basis of that, it is possible to design better schemes against attackers in the cloud.
Background Road traffic collisions (RTCs) are one of the leading causes of childhood morbidity and mortality, representing a significant public health burden. Children, being smaller and less visible to traffic, are at greater risk of severe consequences of RTCs. Data from the electronic reporting system used by the police, known as STATS19, informs national road safety policies in Wales. Objectives This project aimed to establish whether the number of children injured due to RTCs in Wales is under-represented in STATS19. We did this by comparing data from a Major Trauma Centre (MTC) in South Wales to STATS19. In addition, we characterised RTCs with child casualties and mapped the geographical distribution with the objective of identifying clusters and to ascertain if more injuries occurred in deprived areas using the Welsh Index of Multiple Deprivation. Methods We analysed data from STATS19, the Emergency Medical Retrieval and Transfer Service (EMRTS) and a MTC from 2017-2019 for child pedestrians, cyclists and car occupants aged 0-16 years injured following RTCs. We studied age, gender, the time of RTC occurrence, the road type, speed limit and presence of crossing facilities. Population-based injury rates for each year were calculated for age group, gender and deprivation fifth. The geographical distribution of RTCs was mapped using QGIS 3.16. Results We found that STATS19 under-reported paediatric trauma due to RTCs. From 2017-2019 STATS19 recorded 1,859 child casualties across all of Wales compared to 1,170 local child RTC attendances at one MTC. Given the distribution of the Welsh population and the availability of emergency departments throughout the country, it is unlikely that 62.9% of all paediatric trauma following RTCs came to one MTC. Males aged 11-16 years had the highest rates of injury at 92.2 per 100,000 population, compared with females aged 1-4 years which had the lowest rates of injury at 26.2 per 100,000 population. Injuries peaked at school journey times and were highest between 2pm-5pm (45.1% in STATS19, 63.5% in EMRTS and 35.2% at one MTC). Most RTCs were located on single carriageways (84.7%), in 30 mph zones (66.9%), between junctions (54.1%) and with no pedestrian crossing within 50 metres (85.0%). The rate ratio of injury was 2.03 (95% confidence interval 1.72-2.38) significantly higher for the most deprived areas compared to the least deprived areas. Conclusions Emergency departments play an important role in recording child casualties due to RTCs. Our findings reveal the large scale of data that the Welsh Government could be missing. Without this knowledge we are failing to see the whole picture and cannot accurately characterise risks to road users. Collaboration between services and improvements in
This paper suggest a well-organized information system for facilitate the litigation procedures Information System courts. The purpose is to decrease the duration of processing cases in courts. The aspiration is to save the time and effort of judges and lawyer. In addition, we make use of the advantages of electronic systems and reducing traffic especially in developed countries. Advanced Encryption Standard is used to encrypt all the manipulated data for each case. All read document are encrypted to attain secure information system Litigation process. This is because the big data for all cases will be stored on cloud environment.
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