• International Journal of Computer Science & Network Security
• /
• v.22 no.1
• /
• pp.225-233
• /
• 2022

The recent outbreak of corona virus (COVID-19) infectious disease had made its forecasting critical cornerstones in most scientific studies. This study adopts a machine learning based time series model - Auto Regressive Integrated Moving Average (ARIMA) model to forecast COVID-19 confirmed cases in Botswana over 60 days period. Findings of the study show that COVID-19 confirmed cases in Botswana are steadily rising in a steep upward trend with random fluctuations. This trend can also be described effectively using an additive model when scrutinized in Seasonal Trend Decomposition method by Loess. In selecting the best fit ARIMA model, a Grid Search Algorithm was developed with python language and was used to optimize an Akaike Information Criterion (AIC) metric. The best fit ARIMA model was determined at ARIMA (5, 1, 1), which depicted the least AIC score of 3885.091. Results of the study proved that ARIMA model can be useful in generating reliable and volatile forecasts that can used to guide on understanding of the future spread of infectious diseases or pandemics. Most significantly, findings of the study are expected to raise social awareness to disease monitoring institutions and government regulatory bodies where it can be used to support strategic health decisions and initiate policy improvement for better management of the COVID-19 pandemic.

• Journal of Distribution Science
• /
• v.19 no.8
• /
• pp.47-56
• /
• 2021

Purpose: Maritime sector is fundamental to international trade; there is no doubt that seafarers have played an essential role in maritime shipping and distribution science industry. Thus, this study uses Grey models to predict the number of seafarers in Vietnam expecting to provide a range of future seafarers. Research design, data and methodology: Statistics data are adopted for numbers of seafarers by Vietnam Maritime Administration categorizing into three types: Officers at Management level, Officers at Operational level and Navigation - Engine officer cadet. Results: The results have showed that a lack of qualified seafarers in the distribution industry, which has become a global issue and Vietnam is facing challenges of providing enough supply of seafarers in the next few years. Since there has been a concern of the unbalance between demand and supply of seafarers, researches in maritime sector needs a high accuracy in forecasting the number of available qualified seafarers in Vietnam. Conclusion: This method can be applied to predict numbers of other human resources in transportation, distribution and/or logistics industries when the information is poor and insufficient. The next few years are predicted to witness a downtrend in sailors - oilers which leads to the fact that the total number of available seafarers is decreased.

• Asia-Pacific Journal of Business
• /
• v.13 no.2
• /
• pp.23-37
• /
• 2022

Purpose - The purpose of this paper is to analyze the dynamic factors of the daily FX implied volatility based on the fractional integration methods focusing on long memory feature and structural changes. Design/methodology/approach - This paper uses the daily FX implied volatility data of the EUR-USD and the JPY-USD exchange rates. For the fractional integration analysis, this paper first applies the basic ARFIMA-FIGARCH model and the Local Whittle method to explore the long memory feature in the implied volatility series. Then, this paper employs the Adaptive-ARFIMA-Adaptive-FIGARCH model with a flexible Fourier form to allow for the structural changes with the long memory feature in the implied volatility series. Findings - This paper finds statistical evidence of the long memory feature in the first two moments of the implied volatility series. And, this paper shows that the structural changes appear to be an important factor and that neglecting the structural changes may lead to an upward bias in the long memory feature of the implied volatility series. Research implications or Originality - The implied volatility has widely been believed to be the market's best forecast regarding the future volatility in FX markets, and modeling the evolution of the implied volatility is quite important as it has clear implications for the behavior of the exchange rates in FX markets. The Adaptive-ARFIMA-Adaptive-FIGARCH model could be an excellent description for the FX implied volatility series

• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.137-145
• /
• 2023

E-Commerce is a buzzword well known for electronic commerce activities including but not limited to the online shopping, digital payment transactions, and B2B online trading. In today's digital age, e-commerce has been playing a very important and vital role in areas such as retail shopping, sales automation, supply chain management, marketing and advertisement, and payment services. With a huge amount of data been collected from various e-commerce services available, there are multiple opportunities to use that data to analyze graphs and trends. Strategize profitable activities, and forecast future trade. This paper explains a contemporary approach for collecting key data metrics and implementing cost-effective automation that will support in improving conversion rates and sales performance of the e-commerce websites resulting in increased profitability.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.65 no.2
• /
• pp.1-11
• /
• 2023

This study analyzed the impact of greenhouse cultivation area and groundwater level changes due to the water curtain cultivation in the greenhouse complexes. The groundwater observation data in the Miryang study area were used and classified into greenhouse and field cultivation areas to compare the groundwater impact of water curtain cultivation in the greenhouse complex. We identified the characteristics of the groundwater time series data by the terrain of the study area and selected the optimal model through time series analysis. We analyzed the time series data for each terrain's two representative groundwater observation wells. The Seasonal ARIMA model was chosen as the optimal model for riverside well, and for plain and mountain well, the ARIMA model and Seasonal ARIMA model were selected as the optimal model. A suitable prediction model is not limited to one model due to a change in a groundwater level fluctuation pattern caused by a surrounding environment change but may change over time. Therefore, it is necessary to periodically check and revise the optimal model rather than continuously applying one selected ARIMA model. Groundwater forecasting results through time series analysis can be used for sustainable groundwater resource management.

• Journal of Korean Society of Rural Planning
• /
• v.28 no.4
• /
• pp.105-117
• /
• 2022

This study tested SVM(support vector machine), RF(random forest), and ANN(artificial neural network) machine-learning models that can predict net irrigation water requirements in paddy fields. For the Jeonju and Jeongeup meteorological stations, the net irrigation water requirement was calculated using K-HAS from 1981 to 2021 and set as the label. For each algorithm, twelve models were constructed based on cumulative precipitation, precipitation, crop evapotranspiration, and month. Compared to the CE model, the R² of the CEP model was higher, and MAE, RMSE, and MSE were lower. Comprehensively considering learning performance and learning time, it is judged that the RF algorithm has the best usability and predictive power of five-days is better than three-days. The results of this study are expected to provide the scientific information necessary for the decision-making of on-site water managers is expected to be possible through the connection with weather forecast data. In the future, if the actual amount of irrigation and supply are measured, it is necessary to develop a learning model that reflects this.

• Journal of Drive and Control
• /
• v.20 no.2
• /
• pp.24-30
• /
• 2023

This study aimed to analyze the fatigue of forklifts in industrial settings by assessing their stress levels during operation. Strain gauges were affixed to the dynamic components of the forklifts to gather real-time data and enhance the reliability of the analysis. Although monitoring structural loads in harsh testing environments can be challenging, the affixed strain gauges on the dynamic components can provide more precise results and improve the interpretation of data. By creating testing modes that simulate forklift usage environments and performing experiments with selected cargo and driving modes, a comparison of the damage severity of forklift parts under different driving conditions was done. These results can be utilized to forecast the lifespan of forklift parts under extreme driving conditions and assist in the design and optimization of new parts in the future.

• Journal of Environmental Science International
• /
• v.31 no.11
• /
• pp.933-940
• /
• 2022

In this study, not only the increase in rainfall for a short period of time but also the increase in rainfall for a longer duration is frequently occurring according to climate change. Changes in rainfall patterns due to climate change are increasing damage to steep slopes. The Ministry of Public Administration and Security has been operating the criteria for evacuation of residents in steep slopes since 2015. However, the damage to steep slopes due to torrential rains in 2020 has been increasing. In this study, rainfall data from areas affected by steep slopes from 1999 to 2020 were collected and compared with the existing criteria(2015) for evacuation of residents at steep slopes of the Ministry of Public Administration and Security, and the status of the issuance of resident evacuation forecast was compared. Through this study, the rainfall criteria for each region were calculated and presented by reflecting the rainfall characteristics of the steep slope destruction area due to climate change, and it is believed that it can be used as a standard rainfall to reduce human casualties in the steep slope area in the future.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.76-76
• /
• 2022

과거 2015년 파리협정 채택을 기점으로 전 세계는 산업화 이전 대비 지구 평균온도 상승폭을 1.5℃ 이하로 억제하기 위한 노력을 지속적으로 강조하였다. 기후변화 완화를 위한 가장 적극적인 해결책으로 탄소중립 사회 전환이 제시되고 있으며, 이를 실행하기 위해서는 각 부문별 구체화된 탄소중립 추진 계획 수립이 요구된다. 특히 국내에서는 기후기술 분야에 특화된 기술수준 정보가 부족하여 국가 정책 수립에 어려움이 있다. 기술개발을 위한 정책 수립 시에는 기후기술의 정량적인 수준을 고려한 정책 방향을 결정해야 하지만, 국내에는 기술에 대한 분석에 대한 사례가 미흡한 실정이다. 본 연구에서는 수자원 분야의 국가경쟁력을 분석하고 미래기술전략을 도출하기 위해 논문·특허정보를 기반한 정량평가(활동력, 기술력, 포트폴리오)와 미래기술 예측을 수행하였다. 수자원 분야 기술은 2017년 과학기술정보통신부가 승인한 45대 기후기술 분류체계를 기본으로 하며, 적응 부문에서 '물관리 기술'과 '기후변화 예측 및 모니터링 기술'을 대상으로 하였다. 분석을 위해 수자원 분야 기술을 주요 5개국(한국, 중국, 일본, 미국, EU) 대상으로 수행하였으며, 데이터 기간은 2009년부터 2020년까지 총 12년간이다. 기술의 미래예측하기 위해 Bass 모형, Logistic 모형, Gompertz 모형 등을 활용하였으며, 향후 기술을 전망하고자 한다. 본 분석에서 수행하는 수자원 분야 기술예측은 탄소중립 실현을 위한 미래사회에 대비하고, 기술개발에 대한 불확실성을 감소시킬 수 있을 것으로 기대된다.

• Asian Journal of Innovation and Policy
• /
• v.12 no.2
• /
• pp.261-267
• /
• 2023

In the paper of 'Natural Selection Favors AIs over Humans,' Dan Hendrycks applies principles of Darwinian evolution to forecast potential trajectories of AI development. He proposes that competitive pressures within corporate and military realms could lead to AI replacing human roles and exhibiting self-interested behaviors. However, such claims carry the risk of oversimplifying the complex issues of competition and natural selection without clear criteria for judging whether AI is selfish or altruistic, necessitating a more in-depth analysis and critique. Other studies, such as ''The Threat of AI and Our Response: The AI Charter of Ethics in South Korea,' offer diverse opinions on the natural selection of artificial intelligence, examining major threats that may arise from AI, including AI's value judgment and malicious use, and emphasizing the need for immediate discussions on social solutions. Such contemplation is not merely a technical issue but also significant from an ethical standpoint, requiring thoughtful consideration of how the development of AI harmonizes with human welfare and values. It is also essential to emphasize the importance of cooperation between artificial intelligence and humans. Hendrycks's work, while speculative, is supported by historical observations of inevitable evolution given the right conditions, and it prompts deep contemplation of these issues, setting the stage for future research focused on AI safety, regulation, and ethical considerations.