Krish R. Mehta, K. Jayant Naidu, Madhav Baheti, Dev Parmar, A. Sharmila^*

Journal on Internet of Things, Vol.5, pp. 45-55, 2023, DOI:10.32604/jiot.2023.043102

Abstract Farming has been the most prominent and fundamental activity for generations. As the population has been multiplying exponentially, the demand for agricultural yield is growing relentlessly. Such high demand in production through traditional farming methodologies often falls short in terms of efficiency due to the limitations of manual labour. In the era of digitization, smart agricultural solutions have been emerging through the windows of Internet of Things and Artificial Intelligence to improve resource management, optimize the process of farming and enhance the yield of crops, hence, ensuring sustainable growth of the increasing production. By implementing modern technologies in the field… More >

Tahir Abbas Khan¹, Sagheer Abbas¹, Allah Ditta², Muhammad Adnan Khan^{3, *}, Hani Alquhayz⁴, Areej Fatima³, Muhammad Farhan Khan⁵

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2591-2605, 2020, DOI:10.32604/cmc.2020.011892

Abstract The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables… More >

David Corral-Plaza¹, Juan Boubeta-Puig¹, Guadalupe Ortiz¹, Alfonso Garcia-de-Prado^2,*

Computer Systems Science and Engineering, Vol.35, No.1, pp. 5-12, 2020, DOI:10.32604/csse.2020.35.005

Abstract Air pollution is currently receiving more attention by international governments and organizations. Nevertheless, current systems for air quality monitoring lack essential requirements which are key in order to be effective concerning users’ access to the information and efficient regarding real-time monitoring and notification. This paper presents an Internet of Things platform for air station remote sensing and smart monitoring that combines Big Data and Cloud Computing paradigms to process and correlate air pollutant concentrations coming from multiple remote stations, as well as to trigger automatic and personalized alerts when a health risk for their particular context is detected. This platform… More >