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.

: In 21st century one of the emerging issues is to secure the information stored and communicated in digital form. There is no assurance that the data we have sent may be hacked by any hacker and the data we have sent may reach correctly to the receiver or not. Thus, confidentiality, integrity, and authentication services play major role in Internet communication. Encryption is the process of encoding messages in such a way that only authorized parties can read and understand after successful decryption. Several data security techniques have been emerged in recent years, but still there is a need to develop new and different techniques to protect the digital information from attackers. This paper provides a new idea for data encryption and decryption using the notion of binary tree traversal to secure digital data. The proposed method is experimented and observed that the method is suitable for providing confidentiality service in real time.

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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.