Earticle

Late blight, caused by Phytophthora infestans, threatens tomato and potato crops in Nepal. This study explores developing and deploying a mobile application powered by a graph neural network (GNN) to predict late blight risk for Nepali farmers. The GNN trained on 43 years of NASA satellite weather data can generate 5-days forecast for 320 weather stations in Sudurpashim and Karnali Province, Nepal. The mobile application offers user-friendly forecasts and visualizes late blight risk through clear graphical interfaces. In the visited sites, 30% of tomato and potato crops were found infected with late blight, which the app had identified as high-risk. Samples infected with late blight were collected and analyzed in a wet lab setting. All infected samples tested positive for P. infestans, confirming the app's ability to predict real-world late blight outbreaks. This study showcases the successful development and deployment of a GNN-powered mobile application for assessing late blight risk in Nepal. The application disseminates critical weather information and localized risk assessments, potentially enhancing late blight management in tomato and potato crops. Further research, including extensive field trials comparing with farmers' practices, could increase the application's usability in Nepali fields.

The global business landscape is undergoing a significant transformation driven by the rapid growth of electronic business (e-business). This study investigates the e-business evolution stages in the Philippines and South Korea through a quantitative research design, employing a survey questionnaire based on the e-business evolution model by Bradley et al. (2015). The findings indicate that the Philippines is predominantly in the Growth stage, whereas South Korea is in the Transformation stage, reflecting varying levels of e-business maturity. Both countries demonstrate positive perceptions of social, economic, and technical factors, although concerns remain regarding perceived risks and transaction costs. This comparative analysis provides valuable strategic insights for policymakers, industry leaders, and stakeholders aiming to navigate and leverage emerging e-business opportunities across Asia.

There has been a snappy forward advancement in cloud. With the developing measures of affiliations, turning number of affiliations are being utilized as assets in the cloud. Issues arise regarding the certainties of various clients utilizing these assets like round measure of room associations which helps in keeping from the cost. Putting away these associations evades the high cost while posting information during the building of machine orders. This collection of information gives better execution whilst less “far point cost” and ready to make prepared modifications. Cloud can yield better possibilities through net where security weaknesses are restricted. Despite of “wellbeing” being one of the brimming with risk weakness, it adjusts unmistakable connection to go into assumed control handling general condition. This paper uses “MapReduce” library that parallelizes the figuring, and handles perplexed issues like data spread, stack modifying and adjustment to non-basic disappointment. Enormous information, spread across finished many machines, need to parallelize. Moves the data, and gives booking, adjustment to non-basic disappointment. In this paper, a graph of MapReduce programming model along with the applications are explored. The maker has delineated here the work procedure of MapReduce process. Some fundamental issues, like adjustment to non-basic disappointment, are considered in more detail. Without a doubt, even the outline of working of Map Reduce is given.

The Nepalese Currency Counterfeit Detection System aims to address the growing threat of counterfeit currency in Nepal. Counterfeit banknotes pose a significant risk to the nation's financial stability and erode public trust in the monetary system. This system seeks to differentiate genuine banknotes from forgeries, thereby ensuring the integrity of financial transactions. Counterfeiting activities have a detrimental impact on the livelihoods of individuals and the overall economy. To combat this issue, this project will thoroughly investigate the various security features embedded within Nepali currency. Subsequently, it will leverage advanced image processing and computer vision techniques to develop a software-based solution capable of detecting and validating the authenticity of Nepali banknotes. While specialized machines are available in banks and commercial establishments for currency authentication, these systems are often inaccessible to the general public. This project aims to bridge this gap by developing a user-friendly software application that empowers individuals to independently verify the authenticity of Nepali currency.

Internet of Things (IoT) is supposed to revolutionize the future extended smart applications and also helped to create the smart environments such as smart cities, smart buildings, smart kitchen, smart vehicles and also smart hospitals for every moment monitoring of the MAPE 15.15, VAF 96.16% and R2 0.92) is better than normal IoT based kitchen. Therefore, our proposed model is Patients. Artificial Intelligence (AI) plays a spreading role in IoT development and applications. Till date, most of the major companies integrated their IoT platforms with AI capabilities such as analytics based on Deep and Machine learning based. Synergy of IoT and AI is the key to unlock the potential of IoT. This paper proposed a smart kitchen system in this regard. An approach of prediction and tracing has been adopted with various prediction-based algorithms while using the devices (components of kitchen). Predictions are calculated to achieve subsequent next acknowledgment focusing on specific occasions for mechanization. Experimental results show that our proposed model of Smart kitchen (RMSE 0.0561, better and promising way for research and development engineers in the task of implementing smart kitchen.