• International Journal of Computer Science & Network Security
• /
• 제22권6호
• /
• pp.131-138
• /
• 2022

Novel Coronavirus (COVID-19) is viewed as one of the main general wellbeing theaters on the worldwide level all over the planet. Because of the abrupt idea of the flare-up and the irresistible force of the infection, it causes individuals tension, melancholy, and other pressure responses. The avoidance and control of the novel Covid pneumonia have moved into an imperative stage. It is fundamental to early foresee and figure of infection episode during this troublesome opportunity to control of its grimness and mortality. The entire world is investing unimaginable amounts of energy to fight against the spread of this lethal infection. In this paper, we utilized machine learning and deep learning techniques for analyzing what is going on utilizing countries shared information and for detecting the climate factors that effect on spreading Covid-19, such as humidity, sunny hours, temperature and wind speed for understanding its regular dramatic way of behaving alongside the forecast of future reachability of the COVID-2019 around the world. We utilized data collected and produced by Kaggle and the Johns Hopkins Center for Systems Science. The dataset has 25 attributes and 9566 objects. Our Experiment consists of two phases. In phase one, we preprocessed dataset for DL model and features were decreased to four features humidity, sunny hours, temperature and wind speed by utilized the Pearson Correlation Coefficient technique (correlation attributes feature selection). In phase two, we utilized the traditional famous six machine learning techniques for numerical datasets, and Dense Net deep learning model to predict and detect the climatic factor that aide to disease outbreak. We validated the model by using confusion matrix (CM) and measured the performance by four different metrics: accuracy, f-measure, recall, and precision.

• The Plant Pathology Journal
• /
• 제32권1호
• /
• pp.1-7
• /
• 2016

Responses of Botryosphaeria dothidea to interrupted wetness periods were investigated under in vivo and in vitro conditions. Conidia of B. dothidea were allowed to germinate on apple fruits under wetting condition at $25^{\circ}C$ for 5 hr. They were air-dried for 0, 1, 2 or 4 hr, and then rewetted at $25^{\circ}C$ for 5 hr. Following an initial wetness period of 5 hr, 83% of the conidia germinated. The percent conidial germination increased to 96% when wetting was extended continuously another 5 hr. However, no further conidial germination was observed when wetting was interrupted by dry periods of 1, 2 and 4 hr, resulting in 83, 81 and 82%, respectively. The mean length of the germ tubes was $37{\mu}m$ after 5 hr of wetting and elongated to $157{\mu}m$ after 10 hr of continuous wetting. On the other hand, interruption of wetting by a dry period of 1 hr or longer after the 5 hr of initial wetting arrested the germ tube elongation at approximately $42{\mu}m$ long. Prolonged rewetting up to 40 hr did not restore germ tube elongation on slide glasses under substrate treatments. Model simulation using weather data sets revealed that ending infection periods by a dry period of at least 1 hr decreased the daily infection periods, avoiding the overestimation of infection warning. This information can be incorporated into infection models for scheduling fungicide sprays to control apple white rot with fewer fungicide applications.

• 식물병연구
• /
• 제25권4호
• /
• pp.196-204
• /
• 2019

지난 2014년에 전남의 나주, 해남, 영암, 고흥, 장흥 등과, 경남의 밀양, 진주, 고성 등에서 이삭도열병이 심하게 발생하여 벼의 재배면적 대비 발생면적 비율이 전남은 11.0%, 경남은 14.6%에 달하였다. 본 연구에서는 남부지방의 주된 발생지역들에 대하여 2014년 8월의 기상 환경, 품종별 재배 면적 차이, 식물체 질소함량에 따른 이삭도열병 발생 정도에 대하여 분석함으로써, 2014년 남부지방 이삭도열병 대발생의 원인을 구명하였다. 2014년에 각 시도 안에서 10,000 ha 이상 재배된 품종으로는 새누리, 일미벼, 황금누리, 운광, 동진1호, 남평벼 등이 있었다. 2014년의 8월 한 달 동안의 평균기온은 해남과 밀양에서 2018년보다 각각 3.2℃, 3.1℃ 낮고 평년보다 1.5℃, 1.3℃ 낮았다. 강수량은 해남과 밀양에서 2018년보다 각각 70.0%, 42.0% 많고 평년보다 40.1%, 125.7% 많았다. 도열병 감염 위험 예측모형에 의한 8월의 감염 경보 발생일수는 2014년이 2018년보다 많았고, 2014년은 전국적으로 발생일수가 많았다. 식물체의 질소함량은 병 발생이 심한 포장(병든이삭률 60% 이상)이 적은 포장(10% 이하)보다 유의하게 높았다. 결론적으로, 2014년에 특정 지역에서 이삭도열병이 대발생한 것은 감수성 품종이 재배되고 저온과 잦은 강우도 있었지만, 특히 질소질 비료가 다량으로 시용된 점이 특정 필지를 중심으로 다 발생하게 한 주요 원인이었다.

• Genomics & Informatics
• /
• 제21권4호
• /
• pp.50.1-50.9
• /
• 2023

Vaccine development is one of the key efforts to control the spread of coronavirus disease 2019 (COVID-19). However, it has become apparent that the immunity acquired through vaccination is not permanent, known as the waning effect. Therefore, monitoring the proportion of the population with immunity is essential to improve the forecasting of future waves of the pandemic. Despite this, the impact of the waning effect on forecasting accuracies has not been extensively studied. We proposed a method for the estimation of the effective immunity (EI) rate which represents the waning effect by integrating the second and booster doses of COVID-19 vaccines. The EI rate, with different periods to the onset of the waning effect, was incorporated into three statistical models and two machine learning models. Stringency Index, omicron variant BA.5 rate (BA.5 rate), booster shot rate (BSR), and the EI rate were used as covariates and the best covariate combination was selected using prediction error. Among the prediction results, Generalized Additive Model showed the best improvement (decreasing 86% test error) with the EI rate. Furthermore, we confirmed that South Korea's decision to recommend booster shots after 90 days is reasonable since the waning effect onsets 90 days after the last dose of vaccine which improves the prediction of confirmed cases and deaths. Substituting BSR with EI rate in statistical models not only results in better predictions but also makes it possible to forecast a potential wave and help the local community react proactively to a rapid increase in confirmed cases.