The world population is expected to grow by another two billion in 2050, according to the survey taken by the Food and Agriculture Organization, while the arable area is likely to grow only by 5%. Therefore, smart and efficient farming techniques are necessary to improve agriculture productivity. Agriculture land suitability assessment is one of the essential tools for agriculture development. Several new technologies and innovations are being implemented in agriculture as an alternative to collect and process farm information. The rapid development of wireless sensor networks has triggered the design of low-cost and small sensor devices with the Internet of Things (IoT) empowered as a feasible tool for automating and decision-making in the domain of agriculture. This research proposes an expert system by integrating sensor networks with Artificial Intelligence systems such as neural networks and Multi-Layer Perceptron (MLP) for the assessment of agriculture land suitability. This proposed system will help the farmers to assess the agriculture land for cultivation in terms of four decision classes, namely more suitable, suitable, moderately suitable, and unsuitable. This assessment is determined based on the input collected from the various sensor devices, which are used for training the system. The results obtained using MLP with four hidden layers is found to be effective for the multiclass classification system when compared to the other existing model. This trained model will be used for evaluating future assessments and classifying the land after every cultivation.
Recent studies have shown the rapid adoption of digital health software applications worldwide. However, researchers are yet to fully understand users' rationale of eHealth systems. Therefore, the objective of this paper is to analyze user attitudes to eHealth applications in China and eHealth system in Ukraine, and then provide insights and suggestions to the development of an eHealth application (eZdorovya) for health information services in general. This study includes a survey conducted by Chinese and Ukrainian users, after which thorough data analyses were conducted. Based on the technology acceptance model (TAM), this research framework explores the influence of socio-technical factors affecting user's adoption of eHealth functionalities. Serial Multiple Mediator Model 6 (SMMM6) and a deep neural network-based approach were used to analyze the eHealth software users' rationale with the sample size of survey 236 end-users from China and 124 end-users from Ukraine. The key findings from the data analysis are: 1) if the software application is covering an important service function and is interesting to use, Chinese users will continue using it, 2) given an eHealth software with important or interesting function, it is inconclusive whether Ukrainian users will switch to use the application, and 3) deep neural network shows highly accurate prediction results and was given applied suggestions for Chinese and Ukrainian providers in the case of improving eHealth systems based on a raw prediction.INDEX TERMS Chinese and Ukrainian eHealth systems, mediators, deep neural networks.
RFID (Radio frequency identification) and wireless sensor networks are backbone technologies for pervasive environments. In integration of RFID and WSN, RFID data uses WSN protocols for multi-hop communications. Energy is a critical issue in WSNs; however, RFID data contains a lot of duplication. These duplications can be eliminated at the base station, but unnecessary transmissions of duplicate data within the network still occurs, which consumes nodes’ energy and affects network lifetime. In this paper, we propose an in-network RFID data filtering scheme that efficiently eliminates the duplicate data. For this we use a clustering mechanism where cluster heads eliminate duplicate data and forward filtered data towards the base station. Simulation results prove that our approach saves considerable amounts of energy in terms of communication and computational cost, compared to existing filtering schemes.
In recent years, with the continuous evolvement in Artificial Intelligence (AI) and Information and Communication Technologies (ICTs), including Internet-of-Things (IoT) and cloud computing (CC), computers are anticipated to replace human beings in almost all fields of life. Smartphones and other handheld devices have evolved from simple communication devices to personal computers. They have gained popularity due to their convenient use in everyday life for accessing various online services, social networks, and e-banking, etc. People use smartphones for not only personal use but also take advantage of these devices in their business-related tasks. Consequently, increasing amounts of private and sensitive information are being generated and stored in our
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