ABSTRACT
The design of a Covid-19 testing kit is proposed in this research using a photonic crystal structure (PhC) and a violet laser beam. The basic principle of this structure relies on the phenomenon of absorbance reflectance and transmission at the signal of a 412 nm laser beam. Finally, the transmitted light energy through the PhC structure is the conclusive factor to detect the types of virus which is the function of the reflectance and absorbance. The reflected light energy is computed by plane wave expansion (PWE) whereas the absorbance of light energy is obtained through numerical computation. The notable advantages of this technique are that the virus related to Covid-19 can be recognized by observing the colour of transmitted energy through a photo energy meter. Finally, the outcomes of the research affirm that the sample could be Covid-19 if the output energy would be infrared (IR). Similarly, the sample could be a normal coronavirus, if the output energy would lie within the visible regime.
ABSTRACT
Prioritizing candidate genes is essential for genome-based diagnostics of various hereditary disorders. Furthermore, it is a difficult task with particular and noisy information about genes, illnesses, and relationships. Although several computer methods for disease gene prioritization have been developed, their efficiency is limited by manually created traits, network architecture, or pre-established data fusion criteria. Hence, this research proposes a unique gene prioritization and disease prediction model. Initially, the gathered information is pre-processed by a data cleaning model. In the proposed gene prioritization phase, the pre-processed data are tokenized. Then a new knowledge-based ontology structure is constructed with the improved skewness-based semantic similarity function. The ensemble classifier is constructed along Recurrent Neural Network (RNN), optimized fuzzy logic, and also Deep Belief Network (DBN) to forecast the gene disorders in the prediction phase. The retrieved features from the feature extraction phase are used to train RNN;while the extracted knowledge bases are used to train the DBN, then the results are fed into the optimized fuzzy logic. The fuzzy logic is the primary indication;its fuzzification function is fine-tuned employing a methodology to improve illness prediction accuracy. A recommended new hybrid system, named as Cauchy's Mutated Corona Virus Optimization Algorithm (CMCOA), is the upgraded version of the CVOA, a typical coronavirus optimization technique. Finally, to evaluate the efficiency of the projected model, a comparison of the suggested and existent models is performed with respect to various measures. In particular, the proposed model has recorded the highest accuracy as 93 % at 60 % of training, which is 42.5 %, 36.1 %, 33.3 %, 41.1 %, 48.5 %, 48.5 %, 9 %, 8 %, 8 %, 8 %, 8 %, and 14.5 % improved over existing models like GCN, GCN [6], SVM, CNN, Bi-LSTM, LSTM, GRU, fuzzy, EC + GOA, EC + SSO, EC + CMBO, EC + SMA and EC + CCVOA, respectively. The precision of the suggested work with improved features &CMCOA is 15.5 %, and 14.42 % superior to the proposed work without existing features & CMCOA and proposed work with existing features & CMCOA approaches. © 2022 Elsevier Ltd
ABSTRACT
Prioritizing candidate genes is essential for genome-based diagnostics of various hereditary disorders. Furthermore, it is a difficult task with particular and noisy information about genes, illnesses, and relationships. Although several computer methods for disease gene prioritization have been developed, their efficiency is limited by manually created traits, network architecture, or pre-established data fusion criteria. Hence, this research proposes a unique gene prioritization and disease prediction model. Initially, the gathered information is pre-processed by a data cleaning model. In the proposed gene prioritization phase, the pre-processed data are tokenized. Then a new knowledge-based ontology structure is constructed with the improved skewness-based semantic similarity function. The ensemble classifier is constructed along Recurrent Neural Network (RNN), optimized fuzzy logic, and also Deep Belief Network (DBN) to forecast the gene disorders in the prediction phase. The retrieved features from the feature extraction phase are used to train RNN;while the extracted knowledge bases are used to train the DBN, then the results are fed into the optimized fuzzy logic. The fuzzy logic is the primary indication;its fuzzification function is fine-tuned employing a methodology to improve illness prediction accuracy. A recommended new hybrid system, named as Cauchy's Mutated Corona Virus Optimization Algorithm (CMCOA), is the upgraded version of the CVOA, a typical coronavirus optimization technique. Finally, to evaluate the efficiency of the projected model, a comparison of the suggested and existent models is performed with respect to various measures. In particular, the proposed model has recorded the highest accuracy as 93 % at 60 % of training, which is 42.5 %, 36.1 %, 33.3 %, 41.1 %, 48.5 %, 48.5 %, 9 %, 8 %, 8 %, 8 %, 8 %, and 14.5 % improved over existing models like GCN, GCN [6], SVM, CNN, Bi-LSTM, LSTM, GRU, fuzzy, EC + GOA, EC + SSO, EC + CMBO, EC + SMA and EC + CCVOA, respectively. The precision of the suggested work with improved features &CMCOA is 15.5 %, and 14.42 % superior to the proposed work without existing features & CMCOA and proposed work with existing features & CMCOA approaches.
ABSTRACT
Introduction: Human blood and saliva are increasingly under investigation for the detection of biomarkers for early diagnosis of non-communicable (e.g.cancers) and communicable diseases like COVID-19. Exploring the potential application of human tears, an easily accessible body fluid, for the diagnosis of various diseases is the need of the hour.Areas covered: This review deals with a comprehensive account of applications of tear analysis using different techniques, their comparison and overall progress achieved till now. The techniques used for tear fluid analysis are HPLC/UPLC/SDS-PAGE, CE, etc., together with ELISA, Mass Spectrometry, etc. But, with advances in instrumentation and data processing methods, it has become easy to couple the various separation methods with highly sensitive optical techniques for the analysis of body fluids.Expert opinion: Tear analysis can provide valuable information about the health condition of the eyes since it contains several molecular constituents, and their relative concentrations may alter under abnormal conditions. Tear analysis has the advantage that it is totally non-invasive. This study recommends tear fluid as a reliable clinical sample to be probed by highly sensitive optical techniques to diagnose different health conditions, with special emphasis on eye diseases.