<p>Cost optimization approach of operational research is a predictive power and economy of compactness that is applied to solve specific clinical needs relevant to healthcare cost reduction. Technology helps the healthcare management, decision making, and policy that we have implemented in the interest of improving quality of patient care and treatment outcomes, thereby reducing costs and improving efficiency. The treatment cost of brain tumor is high. Sometimes, cost becomes a problem for individuals to get their complete treatment, which makes their health at risk and may lead to higher cost in future. Here we address neuroinformatics approach to optimize diagnosis cost in neurology through an operational research tool (optimization) on how the diagnosis cost of neuro-patient can optimize. In this context, we introduce a new and unique optimization approach in healthcare, yet what we are clearly lacking for applying applications of operational tools to translate this understanding to the different level to apply the concept in healthcare. The costs of treatment achieved by three standard initial basic feasible solutions (IBFS) methods (North-west corner method, Minimum cost method, Vogel’s approximation method) are 763, 763, and 779. The optimal solution is 761, and three random tests (RT’s) are 826, 783, and 788. Optimal solution provided an overall difference in treatment cost with IBFS 2, 2, 18 and with RT’s 65, 22, and 27. These results establish the basis for a deliberate integration of operational research tools and neuroscience into diagnosis of cost optimization mechanisms for neuro- patient.</p>

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Agriculture industries are comparatively slow in adopting emerging technologies than any other industries despite lot of exciting research. The use of Wireless sensor network (WSN) is very important role in agriculture for more productive and sustainable growth. The structure of WSN is tightly application dependent. Every WSN have sensors, processing unit, low frequency radio wave transmitter and power supply using battery. With increasing number of interconnected WSN devices, there is substantial increase in data generation. It contains both control messages as well as application dependent data, collected by the sensors. The collected data are frequently sent to the nearest centralized controller for further processing and decision making. For continuous functioning of WSN, uninterrupted power supply is needed. Many researches are carried out to overcome these challenges. In this manuscript we are proposing a simple and effective machine learning techniques combined with pause and play method to increase battery life of WSN. This could be achieved in three stages play-pause-play (PPP) model. First by gathering data for some time (play mode), Second by putting WSN to sleep (pause mode), in the backend, apply machine learning algorithm that helps to build model from training data to predict the future data. At the final stage, put WSN back to play mode. Compare the result with actual data and fine tune the model to reduce the error. Using this method WSN will get enough sleep time to increase overall life by simulating the normal behaviour of sensor node. The sleep time will be calculated dynamically.