Colorectal Cancer (CRC) is the third most dangerous cancer in the world and also increasing day by day. So, timely and accurate diagnosis is required to save the life of patients. Cancer grows from polyps which can be either cancerous or noncancerous. So, if the cancerous polyps are detected accurately and removed on time, then the dangerous consequences of cancer can be reduced to a large extent. The colonoscopy is used to detect the presence of colorectal polyps. However, manual examinations performed by experts are prone to various errors. Therefore, some researchers have utilized machine and deep learning‐based models to automate the diagnosis process. However, existing models suffer from overfitting and gradient vanishing problems. To overcome these problems, a convolutional neural network‐ (CNN‐) based deep learning model is proposed. Initially, guided image filter and dynamic histogram equalization approaches are used to filter and enhance the colonoscopy images. Thereafter, Single Shot MultiBox Detector (SSD) is used to efficiently detect and classify colorectal polyps from colonoscopy images. Finally, fully connected layers with dropouts are used to classify the polyp classes. Extensive experimental results on benchmark dataset show that the proposed model achieves significantly better results than the competitive models. The proposed model can detect and classify colorectal polyps from the colonoscopy images with 92% accuracy.

The rationale of the research work is to suggest a multi-modal biometric authentication and secure transaction operation framework for E-Banking. The literature survey identifies the various types of E-Banking Channels available as on-date, the various types of biometric technologies available as on-date as well the significant metrics affecting their performance while deploying them in various different e-banking channels. The performance analysis of various types of biometric technologies based on significant metrics for Biometrics Implementation further identifies the currently implementable biometric technologies for the various different e-banking channels. Subsequently a requirement analysis of potential e-banking channels is followed by System Suitability Analysis to identify which multi-biometrics and support mechanisms are suitable for particular e-banking channels. The final conclusion suggests a viable multi-modal biometric authentication and secure transaction operation framework for various e-banking channels.

Authentication is the primary and mandatory process for any Information and Communication Technology (ICT) application to prove the legitimacy of the genuine user. It becomes more important and crucial for public platforms like e-governance platforms. The Government of India is transforming the country into Digital India through various e-governance initiatives based on ICT. For authentication, National e-Authentication Framework (NeAF) was proposed by the Indian government which is a policy framework for authentication. This framework does not provide any technical and unified solution for authentication systems while it is based on centralized verification data. In this paper, we proposed a solution for the authentication which provides the unified authentication solution for the Indian e-governance system with existing infrastructure. This solution also provides the features such as scalability, security, and transparency based on distributed computing and working on multiserver architecture. This solution also fulfills the need of the current Indian government to provide multiple e-governance services through a single smart card.