The multilayered artificial neural network with learning by back-propagation algorithm configuration is the most common in use, due to of its ease in training. It is estimated that over 80% of all the neural network projects in development use back-propagation. In back-propagation algorithm, there are two phases in its learning cycle, one to propagate the input patterns through the network and other to adapt the output by changing the weights in the network. The back-propagation-feed forward neural network can be used in many applications such as character recognition, weather and financial prediction, face detection etc. The paper implements one of these applications by building training and testing data sets and finding the number of hidden neurons in these layers for the best performance. In the present research, possibility of predicting average rainfall over Udupi district of Karnataka has been analyzed through artificial neural network models. In formulating artificial neural network based predictive models three layered network has been constructed. The models under study are different in the number of hidden neurons.

A cost-effective and effective agriculture management system is created by utilizing data analytics (DA), internet of things (IoT), and cloud computing (CC). Geographic information system (GIS) technology and remote sensing predictions give users and stakeholders access to a variety of sensory data, including rainfall patterns and weather-related information (such as pressure, humidity, and temperatures). They have unstructured format for sensory data. The current systems do a poor job of analysing such data since they cannot effectively balance speed and memory usage. An effective categorization model (ECM) on agriculture management system is proposed to address this research difficulty. First, a classification technique called priority-based k-nearest neighbour (KNN) is provided to categorize unstructured multi-dimensional data into a structured form. Additionally, the Hadoop MapReduce (HMR) framework is used to do classification utilizing a parallel approach. Data from real-time IoT sensors used in agriculture is the subject of experiments. The suggested approach significantly outperforms previous approaches that are computing time, memory efficiency, model accuracy, and speedup.

Deployment in wireless sensor network (WSN) is an important aspect and depends on the application, the WSN is designed for. In this research, a deterministic approach for sensor deployment in three-dimensional (3D) surface has been proposed, in order to maximise the coverage. For this purpose, sectoring of the terrain approach is utilised which is based on the Voronoi diagram and then the Delaunay triangulation is performed on the sectors to establish the connectivity of the deployed nodes. The basic idea is to give a deployment technique that works for the 3D terrain. The traditional Voronoi diagram algorithm is modified for sectoring the terrain and the simulated results validate the proposed algorithm. The performance of the proposed algorithm is compared with other deployment techniques like Territorial Predator Scent Marking Algorithm (TPSMA) and random deployment on the basis of coverage ratio and uniformity and comparison show the enhanced result when compared with other techniques.