Data compression is concerned with how information is organized in data. Efficient storage means removal of redundancy from the data being stored in the DNA molecule. Data compression algorithms remove redundancy and are used to understand biologically important molecules. We present a compression algorithm, “DNABIT Compress” for DNA sequences based on a novel algorithm of assigning binary bits for smaller segments of DNA bases to compress both repetitive and non repetitive DNA sequence. Our proposed algorithm achieves the best compression ratio for DNA sequences for larger genome. Significantly better compression results show that “DNABIT Compress” algorithm is the best among the remaining compression algorithms. While achieving the best compression ratios for DNA sequences (Genomes),our new DNABIT Compress algorithm significantly improves the running time of all previous DNA compression programs. Assigning binary bits (Unique BIT CODE) for (Exact Repeats, Reverse Repeats) fragments of DNA sequence is also a unique concept introduced in this algorithm for the first time in DNA compression. This proposed new algorithm could achieve the best compression ratio as much as 1.58 bits/bases where the existing best methods could not achieve a ratio less than 1.72 bits/bases.
As Similarity measure for fuzzy sets is one of the important research topics of fuzzy set theory, there are several methods to measure similarity between two fuzzy sets (FS), Intuitionistic fuzzy sets (IFS) and Intuitionistic fuzzy multi sets (IFMS). In this paper, the Normalized Hamming Similarity measure of Intuitionistic Fuzzy Multi sets (IFMS) is introduced. This new measure for IFMS is based on the geometrical interpretation of IFS which involves both similarity and dissimilarity. Using this measure, the application of medical diagnosis and pattern recognition are shown. Pattern Recognition, Medical Diagnosis. As various distance and similarity methods of IFS are extended for IFMS distance and similarity measures [16], [17], [18] and [19], this paper is an extension of the new similarity measure based on Normalized Hamming distance and Complement of IFS to IFMS. II. PRELIMINARIES Definition: 2.1Let X be a nonempty set. A fuzzy set A in X is given by A = {〈 , ( )〉/ ∈ }
In this paper, we propose a new method for solving Fully Fuzzy linear programming Problem (FFLP) using ranking method .In this proposed ranking method, the given FFLPP is converted into a crisp linear programming (CLP) Problem with bound variable constraints and solved by using Robust's ranking technique and the optimal solution to the given FFLP problem is obtained and then compared between our proposed method and the existing method. Numerical examples are used to demonstrate the effectiveness and accuracy of this method.
The evolution of Internet of Things (IoT) brought about several challenges for the existing Hardware, Network and Application development. Some of these are handling real-time streaming and batch bigdata, real- time event handling, dynamic cluster resource allocation for computation, Wired and Wireless Network of Things etc. In order to combat these technicalities, many new technologies and strategies are being developed. Tiarrah Computing comes up with integration the concept of Cloud Computing, Fog Computing and Edge Computing. The main objectives of Tiarrah Computing are to decouple application deployment and achieve High Performance, Flexible Application Development, High Availability, Ease of Development, Ease of Maintenances etc. Tiarrah Computing focus on using the existing opensource technologies to overcome the challenges that evolve along with IoT. This paper gives you overview of the technologies and design your application as well as elaborate how to overcome most of existing challenge.
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.