All living things, including plants, animals, and humans, need water in order to live. Even though the world has a lot of water, only about 1% of it is fresh and usable. As the population has grown and water has been used more, fresh water has become a more valuable and important resource. Agriculture uses more than 70% of the world’s fresh water. People who work in agriculture are not only the world’s biggest water users by volume, but also the least valuable, least efficient, and most subsidized water users. Technology like smart irrigation systems must be used to make agricultural irrigation more efficient so that more water is used. A system like this can be very precise, but it needs information about the soil and the weather in the area where it is going to be used. This paper analyzes a smart irrigation system that is based on the Internet of Things and a cloud-based architecture. This system is designed to measure soil moisture and humidity and then process this data in the cloud using a variety of machine learning techniques. Farmers are given the correct information about water content rules. Farming can use less water if they use smart irrigation.
Corona Virus is spreading at an alarming rate in community causing respiratory diseases like SARS and MERS, which has laid down Government agencies and healthcare organizations to adopt and recommend various strategies in order to cease the spread of corona virus. Till the dawn of Vaccine, only available cost-effective preventive aid is the use of face mask. Since the outbreak of covid-19, demand for the face mask has been increased tremendously which has led to the shortage of face mask. Various masks are available in the market, but reuse and decontamination of reusable face mask has become the topic of concern. Commonly available masks in market are N-95, Medical/Surgical Mask and cloth masks. N-95 and Respirators should be reserved for frontline primary Healthcare professionals which are involved in High-risk aerosol generating procedures, while Surgical and medical mask should be used by secondary healthcare professionals and cloth masks by General public.
More often than not values are absent in database, which ought to be managed. Missing qualities are occurred in light of the way that, the data segment individual did not know the right regard or frustration of sensors or leave the space cleanse. The course of action of missing regarded lacking case is a trying errand in machine learning approach. Divided data is not proper for classification handle. Exactly when insufficient cases are masterminded using prototype values, the last class for comparable illustrations may have distinctive results that are variable yields. We can't describe specific class for specific cases. The structure makes a wrong result which also realizes contrasting effects. So, to oversee such kind of lacking data, system executes prototype-based credal classification (PCC) strategy. The PCC procedure is intertwined with Hierarchical clustering and evidential reasoning methodology to give correct, time and memory profitable outcomes. This procedure readies the examples and perceives the class prototype. This will be useful for recognizing the missing qualities. By then in the wake of getting each and every missing worth, credal procedure is use for classification. The trial occurs exhibit that the enhanced type of PCC performs better similar to time and memory viability.
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