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Water makes up about 70% of the earth’s floor and is one of the most vital sources essential to maintaining life. Speedy urbanization and industrialization have led to a deterioration of water best at an alarming fee, ensuing in harrowing illnesses. Water high- quality has been conventionally estimated through expensive and time-ingesting lab and statistical analyses, which render the cutting-edge belief of real-time tracking moot. The alarming results of bad water nice necessitate an alternative approach that is quicker and inexpensive. With this motivation, this study explores a sequence of supervised device mastering algorithms to estimate the water high-quality. The proposed methodology achieves affordable accuracy the use of a minimum number of parameters to validate the possibility of its use in real time water first-class detection systems. It demonstrates the overall maintenance and management of the water quality and quantity inside a plant using modern technologies. It based on the systematic working process to reduce the man power and cost maintenance for the industrialization development.

The Internet of Things (IoT) is a cutting-edge and quickly advancing innovation wherein everything (brilliant items and keen gadgets) is connected to the web for effective correspondence between them. The web of things is an impetus for medical care and assumes a basic part in an assortment of medical services following applications. By gathering internal heat level, circulatory strain, and sugar levels, organized sensors gadgets, regardless of whether worn on the body or installed in living conditions; permit the assortment of rich information to decide a patient’s physical and psychological well-being condition. The troublesome errand in the Internet of things is conveying the gathered information to the specialist, settling on the right choices dependent on the information gathered, and advising the patient. The creator of this paper centers around an investigation of IoT-based medical care frameworks, just as promising circumstances and difficulties for IoT-based patient wellbeing checking frameworks.

In this work we use drip irrigation where the water was allowed to drip slowly to the roots of plant either from above the soil surface or buried into the surface so that the water can be placed directly into the root zone and minimize evaporation. It uses temperature sensor, soil humidity sensor to collect and monitor field information and also uses float switches to monitor ground water level through web page. When the field gets dry and ground water level falls down it will be notified through SMS. This provides a solution for the problems in developing a smart farming system. It uses node MCU, relay and water pump.

Purpose. Data mining is the forthcoming research area to solve different problems and classification is one of main problem in the field of data mining. In this paper, we use two classification algorithms J48 and Sequential Minimal Optimization alias SMO of the Weka interface. Methodology. It can be used for testing several datasets. The performance of J48 and Sequential Minimal Optimization has been analyzed to choose the better algorithm based on the conditions of the datasets. The datasets have been chosen from UCI Machine Learning Repository. Findings. Algorithm J48 is based on C4.5 decision-based learning and algorithm Sequential Minimal Optimization uses the Support Vector Machine approach for classification of datasets. When comparing the performance of both algorithms we found Sequential Minimal Optimization is better algorithm in most of the cases. Originality. This is the first implemented research work up to my knowledge, data sets classification problem handled in data mining using SMO with Weka interface.