Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming
The digitalization of data has resulted in a data tsunami in practically every industry of data-
driven enterprise. Furthermore, man-to-machine (M2M) digital data handling has …
driven enterprise. Furthermore, man-to-machine (M2M) digital data handling has …
IoT-equipped and AI-enabled next generation smart agriculture: A critical review, current challenges and future trends
Smart agriculture techniques have recently seen widespread interest by farmers. This is
driven by several factors, which include the widespread availability of economically-priced …
driven by several factors, which include the widespread availability of economically-priced …
IoT in smart cities: A survey of technologies, practices and challenges
AS Syed, D Sierra-Sosa, A Kumar, A Elmaghraby - Smart Cities, 2021 - mdpi.com
Internet of Things (IoT) is a system that integrates different devices and technologies,
removing the necessity of human intervention. This enables the capacity of having smart (or …
removing the necessity of human intervention. This enables the capacity of having smart (or …
A systematic review of IoT solutions for smart farming
The world population growth is increasing the demand for food production. Furthermore, the
reduction of the workforce in rural areas and the increase in production costs are challenges …
reduction of the workforce in rural areas and the increase in production costs are challenges …
PFVAE: a planar flow-based variational auto-encoder prediction model for time series data
Prediction based on time series has a wide range of applications. Due to the complex
nonlinear and random distribution of time series data, the performance of learning prediction …
nonlinear and random distribution of time series data, the performance of learning prediction …
Deep learning in the industrial internet of things: Potentials, challenges, and emerging applications
Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of
interconnected devices, allowing the use of various smart applications. The enormous …
interconnected devices, allowing the use of various smart applications. The enormous …
A reversible automatic selection normalization (RASN) deep network for predicting in the smart agriculture system
Due to the nonlinear modeling capabilities, deep learning prediction networks have become
widely used for smart agriculture. Because the sensing data has noise and complex …
widely used for smart agriculture. Because the sensing data has noise and complex …
Deep-learning forecasting method for electric power load via attention-based encoder-decoder with bayesian optimization
Short-term electrical load forecasting plays an important role in the safety, stability, and
sustainability of the power production and scheduling process. An accurate prediction of …
sustainability of the power production and scheduling process. An accurate prediction of …
Digital transformation in smart farm and forest operations needs human-centered AI: challenges and future directions
The main impetus for the global efforts toward the current digital transformation in almost all
areas of our daily lives is due to the great successes of artificial intelligence (AI), and in …
areas of our daily lives is due to the great successes of artificial intelligence (AI), and in …
Deep‐stacking network approach by multisource data mining for hazardous risk identification in IoT‐based intelligent food management systems
J Kong, C Yang, J Wang, X Wang… - Computational …, 2021 - Wiley Online Library
Food quality and safety issues occurred frequently in recent years, which have attracted
more and more attention of social and international organizations. Considering the …
more and more attention of social and international organizations. Considering the …