Steganography is one of the secure techniques of protecting data inside a cover object. Images are the most popular cover objects for Steganography. It provides secret message between users. The current paper presents an enhanced Most Significant Bit (MSB) technique. In this paper, a Private Domains Approach (PDA) is proposed; each domain consists of RGB of a pixel of cover image. Bit No.5 is applied to store the secret information in light of the bit that achieved highest steganography rate and the less probability of error rate.Consequently, this technique is allowing an improved version of MSB technique based on Mean-Squared Error (MSE), Peak Signalto-Noise Ratio (PSNR). The experimental results show that our schemes perform well in terms of image quality. Generally; MSB technique produced the best stego-image quality in this paper.
Tracking student attendance and location information are at the heart of a pervasive internet thing. Several approaches are used to identify student locations in the classroom, Infrastructure and increased services are considered expensive requirements and hardly meets the need for the required room-level location accuracy. An accuracy of specifying the student’s location by Smart Entity (inertial sensors on hand) of improving the quality of service. Location information forms a core context to determine student location. The location of classroom access control using different parameters provides useful information and improves teaching and learning in the educational organization. Based on the recent IoT integration of smart entities and sensors, this paper uses new value-added proposals to monitor the actions of students inside the classroom location.
lockchain stores a series of transactions in form of a sequence of linked blocks. Hence, the concept of ledger is easily maintained. Transactions and interactions that take place among participants accessing the distributed and decentralized blockchain network are holding through ledger. In a student management system (SMS), vital information can be highly shared and well protected at the same time. This paper proposes a model for using blockchains to implement fully functional SMS that maintains students’ records, course registrations record and student marks. The proposed model adds more security via the use of hashing and data readily available with decentralized data storage. In addition, the use of ledger-based system to maintain SMS data introduces reliable and highly trusted model.
Disease detection is one of the applications where data mining techniques achieved more accurate and useful results. The healthcare sector collects massive volumes of healthcare data that are not mine to discover hidden data for better decision-making, a field of data mining introduces more efficiently and effectively to predict different kinds of diseases. Clustering medical data into small, meaningful chunks will help in pattern discovery by allowing for the retrieval of a large number of specific data points. The difference in using clustering the medical data from traditional data mining techniques is in extracting many features of the dataset that have been split into small segments to enable us to discover patterns by adding the data structure. By using clustering techniques, discovered overall correlations between data attributes. Selected data processing makes the mining process more efficient. The processed disease data are clustered using the K-means algorithm with the K values. Its ease of use and speed, which enable it to perform on a massive dataset. This paper highlights the theoretical side in using the K-Means Clustering algorithm in the context of data mining of disease detection and allowing for reliable and effective diagnosis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.