A Protected Single Sign-On Technique Using 2D Password in Distributed Computer Networks

Single Sign-On (SSO) is a new authentication mechanism that enables a legal user with a single credential to be authenticated by multiple service providers in a distributed computer network. Recently, a new SSO scheme providing well-organized security argument failed to meet credential privacy and soundness of authentication. The main goal of this project is to provide security using Single Sign-On scheme meeting at least three basic security requirements, i.e., unforgetability, credential privacy, and soundness. User identification is an important access control mechanism for client–server networking architectures. The concept of Single Sign-On can allow legal users to use the unitary token to access different service providers in distributed computer networks. To overcome few drawbacks like not preserving user anonymity when possible attacks occur and extensive overhead costs of time-synchronized mechanisms, we propose a secure Single Sign-On mechanism that is efficient, secure, and suitable for mobile devices in distributed computer networks. In a real-life application, the mobile user can use the mobile device, e.g., a cell phone, with the unitary token to access multiservice, such as downloading music; receive/reply electronic mails etc. Our scheme is based on one-way hash functions and random nonce to solve the weaknesses described above and to decrease the overhead of the system. The proposed scheme is more secure with two types of password scheme namely, Text password and Graphical Password referred as 2D password in distributed computer networks that yields a more efficient system that consumes lower energy. The proposed system has less communication overhead. It eliminates the need for time synchronization and there is no need of holding multiple passwords for different services

Optimizing Pre-Trained Models of Deep Learning for Identification of Plant Disease

The Plant diseases should be identified early to prevent the economic loss of farmers and ensure the availability of food for humans. The plant disease identification can be automated by using the Artificial Intelligence techniques. Researchers have proposed many deep learning methods for identifying plant diseases. Deep learning models use an increased number of parameters, it requires higher computational power, training a deep learning model from start requires more time. In this article we utilized transfer learning along with fine tuning for identification of plant diseases. Cassava plant disease dataset was utilised for training. and evaluate the suggested model. The performance accuracy achieved by Resnet50 is 73.12 % and fine-tuned Resnet50 is 80.84 %. The fine-tuned model achieves greater accuracy with a lesser amount of parameters Impact Statement–Artificial Intelligence is evolving all around the world. The AI techniques are used to automate the process of plant disease identification. Traditional methods are not accurate and time consuming. To help the farmers in diagnosing plant disease and stop economic loss to them, we employ deep learning models to do the work. The pretrained models predict the plant diseases, further we fine-tune them in order to get high accuracy. Early identification of the diseases accurately will avoid loss and improve productivity of the crops