Chapters authored
An Algorithmic Approach to the Node Selection Problem in Industrial Wireless Sensor Networks
Industrial Wireless Sensor Networks (IWSN) are the special class of WSN where it faces many challenges like improving process efficiency and meet the financial requirement of the industry. Most of the IWSNs contains a large number of sensor nodes over the deployment field. Due to lack of predetermined network infrastructure demands, IWSNs to deploy a minimum number of sink nodes and maintain network connectivity with other sensor nodes. Capacitated Sink Node Placement Problem (CSNPP) finds its application in the Industrial wireless sensor network (IWSN), for the appropriate placement of sink nodes. The problem of placing a minimum number of sink nodes in a weighted topology such that each sink node should have a maximum number of sensor nodes within the given capacity is known as Capacitated Sink Node Placement Problem. This chapter proposes a heuristic based approach to solve Capacitated Sink Node Placement Problem.
Part of the book: Wireless Sensor Networks
Image-Based Crop Leaf Disease Identification Using Convolution Encoder Networks
Nowadays, agriculture plays a major role in the progress of our nation’s economy. However, the advent of various crop-related infections has a negative impact on agriculture productivity. Crop leaf disease identification plays a critical role in addressing this issue and educating farmers on how to prevent the spread of diseases in crops. Researchers have already used methodologies such as decision trees, random forests, deep neural networks, and support vector machines. In this chapter, we proposed a hybrid method using a combination of convolutional neural networks and an autoencoder for detecting crop leaf diseases. With the help of convolutional encoder networks, this chapter presents a unique methodology for detecting crop leaf infections. Using PlantVillage dataset, the model is trained to recognize crop infections based on leaf images and achieves an accuracy of 99.82%. When compared with existing work, this chapter achieves better results with a suitable selection of hyper tuning parameters of convolution neural networks.
Part of the book: Artificial Intelligence Annual Volume 2022