Recently, tooth decay detection is considered as one of the emerging topics. Many diagnostic techniques have been successfully presented to diagnose the problems. However, the complexity in the tooth decaying diagnosis ascends when the environs are moderately difficult. Thus, this study introduces a novel caries detecting model for the accurate detection of tooth cavities. The model is divided into two phases: feature extraction and classification. Here, the feature extraction is based on multi‐linear principal component analysis (MPCA), and the classification is processed using renowned neural network (NN) classifier. The NN classifier is trained using the adaptive dragonfly algorithm (ADA) algorithm. The proposed MPCA model Non‐linear Programming with ADA (MNP‐ADA) performance is compared with other existing methods and the performance of the approach is analysed in terms of measures such as accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, negative predictive value, false discovery rate, F 1 ‐score, and Mathews correlation coefficient. The performance of the proposed model is analysed in terms of feature analysis and classifier analysis by comparing other models and proves the superiority of the developed caries detection model.

A variety of photo sharing applications overmobile networks have gained popularity in recent years. The mobile network is a public network and is highly vulnerable to security attacks. This makes it essential to deploy cryptographic algorithms to address the security issues. However, the encrypted data when transmitted over the mobile network is vulnerable to the channel noise and is improved by various error correction codes. The sequential process of encryption followed by error correction code adds to the resources, computational power and time of the overall system. The proposed Modified Cryptographic Turbo Code (MCTC) integrates the error correction and encryption into a single entity to ensure reliability and security of the image over an open communication channel. Additionally it provides dual level of security to the sensitive portion of the image by hiding it in the less sensitive portion. The proposed MCTC depicts a similar bit error performance as that of Turbo Code (TC) for a 0.7 dB rise in per bit energy. The accuracy of the image recovered by MCTC is compared with Least Significant Bit and Least Significant Bit Hamming Code data hiding methods followed by TC known as LSBTC and LSBHCTC respectively. The analysis shows 99.99 % accuracy in the recovered bits as compared to 93.72 % and 98.44 % for LSBTC and LSBHCTC over an AWGN channel for 3dB SNR respectively. Moreover, the MCTC provides a higher embedding capacity of 26.5% as compared to 11% and 5% for LSBTC and LSBHCTC.

Turbo convolutional codes (TCC) are excellent error correcting codes for wireless channels. However, TCC decoders require large decoding complexity. Moreover, complexity of TCC decoder does not reduce even if puncturing is used to change the coding rate. Modified turbo codes require lower decoding complexity than TCC as they use multiple concatenations of simple block codes and convolutional codes. Recently, a class of modified turbo codes called low complexity hybrid turbo codes (LCHTC) and improved low complexity hybrid turbo codes (ILCHTC) have been proposed. It has been shown that LCHTC and ILCHTC achieve bit error rate (BER) which is comparable to TCC and have much lower decoding complexity. Simulation results show that BER performance of ILCHTC is better than that of LCHTC. Rate-1/3 ILCHTC achieve BER of 10−5 at bit energy-to-noise ratio (Eb/N0) of 1.9 dB, which is 0.4 dB higher than Eb/N0 for TCC adopted by third generation partnership project (3GPP). Moreover, ILCHTC and LCHTC decoders require half the number of computations as compared to those required for TCC decoder. In this study, union-bound analysis of ILCHTC is presented to investigate BER performance<10−6. For large interleaver lengths, analysis of theoretical union bound requires numerous computations. Therefore approximate analysis of union bound is derived from theoretical union bound. It is shown that the analysis of approximate union bound achieves reasonable accuracy. Moreover, approximate union bound can be evaluated with significantly less computational complexity than the theoretical union bound.

Low complexity modified turbo codes (MTC) offer bit error rate performance close to Shannon's limit with significantly reduced decoding complexity. However, since interleavers used in turbo codes interleave bit positions in one-dimensional (1D) array, they cannot be used for MTC which encode information bits arranged in 2D array. Moreover, MTC use several interleavers which increase the memory requirement to store permutation patterns. Objective of this study is to design two-stage interleaver for MTC with low memory requirement, large values of column spreading factor and dispersion in the interleaved pattern. Expressions have been derived for limits on maximum values of spreading factor and dispersion for 2D information array. Simulation results show superior performance of MTC with two-stage interleavers than that with random interleavers at high bit energy to noise ratio. Moreover, analysis shows that two-stage interleaver requires 50% less memory storage than random interleaver. Proposed interleaver can also be used for turbo codes in which 1D information array is being used.

This paper proposes a new class of Turbo type codes referred to as Improved Low Complexity Hybrid Turbo Codes (ILCHTC). For low Bit Energy to Noise Ratio (Eb/No)ILCHTC shows better convergence of errors than that of Low Complexity Hybrid Turbo Code (LCHTC). Simulation results show that ILCHTC achieves Bit Error Rate (BER) of 10-5at Eb/Noof 1.9 dB, which is 0.4 dB more than that for Turbo Convolutional Codes (TCC). Analysis of ILCHTC and LCHTC decoders show that the number of computations reduce by a factor of two as compared to the computations of TCC decoder.

The key implementation issue for most of the powerful error correcting codes is overall decoding complexity. Compared with 8-state turbo codes, proposed code can achieve nearly the same performance with significantly reduced decoding complexity. The proposed code is modified turbo code which uses parallel concatenation of component codes. First few component codes are series combination of zigzag codes and convolutional codes. Since multiple codes are used in component codes, proposed code is a hybrid code. Code rate of the low complexity hybrid turbo code (LCHTC) can be adjusted by changing the array size of zigzag codes and by puncturing the parity bits of convolutional code. Performance analysis of LCHTC shows that optimum number of constituent codes and of convolutional codes leads to BER of 10-5at signal to noise ratio (SNR) of 1.6dB.