Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine model

As stated by World Health Organization (WHO) report, 246 million individuals have suffered with diabetes disease over worldwide and it is anticipated that by 2025 this estimation can cross 380 million. So, the proper and quick diagnosis of this disease is turned into a significant challenge for the machine learning researchers. This paper aims to design a robust model for diagnosis of diabetes using a hybrid approach of Chaotic-Jaya (CJaya) algorithm with Extreme Learning Machine (ELM), which is named as CJaya-ELM. In this paper, Jaya algorithm with Chaotic learning approach is used to optimize the random parameters of ELM classifier. Here, to assess the efficacy of the designed model, Pima Indian diabetes dataset is considered. Here, the designed model CJaya-ELM, has been compared with basic ELM, Teaching Learning Based Optimization algorithm (TLBO) optimized ELM (TLBO-ELM), Multi-Layer Perceptron (MLP), Jaya algorithm optimized MLP (Jaya-MLP), TLBO algorithm optimized MLP (TLBO-MLP) and CJaya algorithm optimized MLP models. CJaya-ELM model resulted in the highest testing accuracy of 0.9687, sensitivity of 1, specificity of 0.9688 with 0.9782 area under curve (AUC) value. Results reveal that CJaya-ELM model effectively classifies both the positive and negative samples of Pima and outperforms the competitors

A Coding Theoretic Model for Error-detecting in DNA Sequences

AbstractA major problem in communication engineering system is the transmitting of information from source to receiver over a noisy channel. To check the error in information digits many error detecting and correcting codes have been developed. The main aim of these error correcting codes is to encode the information digits and decode these digits to detect and correct the common errors in transmission. This information theory concept helps to study the information transmission in biological systems and extend the field of coding theory into the biological domain. In the cellular level, the information in DNA is transformed into proteins. The sequence of bases like Adenine (A), Thymine (T), Guanine (G) and Cytosine (C) in DNA may be considered as digital codes which transmit genetic information. This paper shows the existence of any form error detecting code in the DNA structure, by encoding the DNA sequences using Hamming code