Fog computing (FC) has been presented as a modern distributed technology that will overcome the different issues that Cloud computing faces and provide many services. It brings computation and data storage closer to data resources such as sensors, cameras, and mobile devices. The fog computing paradigm is instrumental in scenarios where low latency, real-time processing, and high bandwidth are critical, such as in smart cities, industrial IoT, and autonomous vehicles. However, the distributed nature of fog computing introduces complexities in managing and predicting the execution time of tasks across heterogeneous devices with varying computational capabilities. Neural network models have demonstrated exceptional capability in prediction tasks because of their capacity to extract insightful patterns from data. Neural networks can capture non-linear interactions and provide precise predictions in various fields by using numerous layers of linked nodes. In addition, choosing the right inputs is essential to forecasting the correct value since neural network models rely on the data fed into the network to make predictions. The scheduler may choose the appropriate resource and schedule for practical resource usage and decreased make-span based on the expected value. In this paper, we suggest a model Neural Network model for fog computing task time execution prediction and an input assessment of the Interpretive Structural Modeling (ISM) technique. The proposed model showed a 23.9% reduction in MRE compared to other methods in the state-of-arts.

Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.

In this paper advanced Sentiment Analysis techniques were applied to evaluate public opinions reported by rail users with respect to four major European railway companies, i.e., Trenitalia and Italo in Italy, SNCF in France and Renfe in Spain. Two powerful language models were used, RoBERTa and BERT, to analyze big amount of text data collected from a social platform dedicated to customers reviews, i.e., TrustPilot. Data concerning the four European railway companies were first collected and classified into subcategories related to different aspects of the railway sector, such as train punctuality, quality of on-board services, safety, etc. Then, the RoBERTa and BERT models were developed to understand context and nuances of natural language. This study provides a useful support for railways companies to promote strategies for improving their service.

Many financial crises have occurred in recent decades, such as the International Debt Crisis of 1982, the East Asian Economic Crisis of 1997–2001, the Russian economic crisis of 1992–1997, the Latin American debt Crisis of 1994–2002, the Global Economic Recession of 2007–2009, which had a strong impact on international relations. The aim of this article is to create an econometric model of the indicator for identifying crisis situations arising in stock markets. The approach under consideration includes data for preprocessing and assessing the stability of the trend of time series using higher-order moments. The results obtained are compared with specific practical situations. To test the proposed indicator, real data of the stock indices of the USA, Germany and Hong Kong in the period World Financial Crisis are used. The scientific novelty of the results of the article consists in the analysis of the initial and given initial moments of high order, as well as the central and reduced central moments of high order. The econometric model of the indicator for identifying crisis situations arising considered in the work, based on high-order moments plays a pivotal role in crisis detection in stock markets, influencing financial innovations in managing the national economy. The findings contribute to the resilience and adaptability of the financial system, ultimately shaping the trajectory of the national economy. By facilitating timely crisis detection, the model supports efforts to maintain economic stability, thereby fostering sustainable growth and resilience in the face of financial disruptions. The model’s insights can shape the national innovation ecosystem by guiding the development and adoption of monetary and financial innovations that are aligned with the economy’s specific needs and challenges.

This study uses a Time-Varying Parameter Stochastic Volatility Vector Autoregression (TVP-SV-VAR) model to conduct an empirical analysis of the dynamic effects of China’s stock market volatility on the agricultural loan market and its channels. The results show that the relationship between stock market and agricultural loan market volatility is time varying and is always positive. The investor sentiment is a major conduit through which the effect takes place. This time-varying effect and transmission mechanism are most apparent between 2011 and 2017 and have since waned and stabilized. These have significant implications for the stable and orderly development of the agricultural loan market, highlighting the importance of the sound financial market system and timely policy, better market monitoring and early warning system and the formation of a mature and sound agricultural credit mechanism.

Traditional shipping plays a crucial role in the national sea transportation system, serving inland areas, remote areas, and outer islands that are widely distributed throughout the country. However, there is still limited research on the problems of traditional shipping empowerment and its implementation. This research aims not only to analyze the obstacles encountered in empowering traditional shipping but also the implementation of the traditional shipping grant program. This study employed a quantitative descriptive approach, utilizing a likert scale, to analyze the issues that arise in the empowerment of traditional shipping. Additionally, for policy implementation analysis, the Hellmut-Wollmann policy analysis was used. The findings indicate that the most significant issues arise in the area of human resource development, such as a lack of competent teaching staff, insufficient short courses, complicated testing procedures, and the lack of crew certification. In the ex-ante stage, the variable of empowering traditional shipping transportation programs experienced the highest implementation rate. During the ongoing stage, the variable empowering traditional shipping services achieved the highest implementation score. And in the ex-post stage, traditional shipping services had the highest implementation score. This paper emphasizes the significance of collaboration and coordination among all levels of government, from the central to the local, in order to effectively implement the traditional shipping empowerment program. These findings also highlight the necessity of extending the traditional shipping grant program while making improvements in areas such as ship safety management regulations, the management and supply of traditional shipping terminals, the division of transportation types, and route determination policies.