Affiliations: [a]
Department of Computer Science and Engineering, East West University Bangladesh, Bangladesh
| [b]
Department of Information Technology and Engineering, REC, Azamgarh, UP, India
| [c]
Department of Information Technology, Techno India College of Technology, West Bengal, India
| [d]
Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Egypt
| [e]
College of Information and Engineering, Wenzhou Medical University, Wenzhou, PR China | [f] Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Portugal
Correspondence:
[*]
Corresponding author. Md. S. Kamal, Department of Computer Science and Engineering, East West University Bangladesh, Bangladesh. E-mail: [email protected].
Abstract: Consensus is a significant part that supports the identification of unknown information about animals, plants and insects around the globe. It represents a small part of Deoxyribonucleic acid (DNA) known as the DNA segment that carries all the information for investigation and verification. However, excessive datasets are the major challenges to mine the accurate meaning of the experiments. The datasets are increasing exponentially in ever seconds. In the present article, a memory saving consensus finding approach is organized. The principal component analysis (PCA) and independent component (ICA) are used to pre-process the training datasets. A comparison is carried out between these approaches with the Apriori algorithm. Furthermore, the push down automat (PDA) is applied for superior memory utilization. It iteratively frees the memory for storing targeted consensus by removing all the datasets that are not matched with the consensus. Afterward, the Apriori algorithm selects the desired consensus from limited values that are stored by the PDA. Finally, the Gauss-Seidel method is used to verify the consensus mathematically.
Keywords: Push down automata, principal component analysis, independent component, big data, DNA
