Method for identification of 10 SSR markers from monkey genomes and its statistical inference with One & Two-way ANOVA.

DNA tracts that include simple sequence repeats (SSRs), sometimes known as genetic "stutters), are composed of a few to many tandem repetitions of a short base-pair motif. These sequences frequently mutate, changing the amount of repetitions. SSRs are frequently found in promoters, untranslated regions, and even coding sequences, therefore these alterations can significantly affect practically every aspect of gene activity. SSR alleles can also contribute to normal diversity in brain and behavioural features. Mutational expansion of certain triplet repeats is the cause of a number of inherited neurodegenerative diseases. Due to its importance in genetic research, in this paper we explored Ten SSR markers TAGA, TCAT, GAAT, AGAT, AGAA, GATA, TATC, CTTT, TCTG and TCTA that are identified from the genomes of Eleven distinct monkeys: A.Nancymaae, C.C.Imitator, C.Atys, M.Leucophaeus, P.Paniscus, R.Bieti, R.Roxellana, S.Boliviensis, T.Syrichta, C.A.Palliatus and M.Nemestrina using pattern matching mechanism. We identified 4bp SSR from eleven monkey dataset's Unchr chromosome mainly in this paper. The proposed approach finds the exact place/location of the SSR's and number of times that it appears in the given genome sequence. The identified patterns are analyzed with One-way and Two-way ANOVA that gives better analysis which is useful for genomic studies. Also, this 4bp Ten SSR markers data is a valuable to illustrate genetic variation of genomic study.•The great specificity of data sets produced from monkey genomes with pattern matching has been demonstrated.•These findings show that SSR identification could be a useful tool for determining genome similarity and comparability.•Researchers can use the raw sequencing data to conduct additional bioinformatics analysis.

Smart Heart Disease Prediction System with IoT and Fog Computing Sectors Enabled by Cascaded Deep Learning Model.

Chronic illnesses like chronic respiratory disease, cancer, heart disease, and diabetes are threats to humans around the world. Among them, heart disease with disparate features or symptoms complicates diagnosis. Because of the emergence of smart wearable gadgets, fog computing and "Internet of Things" (IoT) solutions have become necessary for diagnosis. The proposed model integrates Edge-Fog-Cloud computing for the accurate and fast delivery of outcomes. The hardware components collect data from different patients. The heart feature extraction from signals is done to get significant features. Furthermore, the feature extraction of other attributes is also gathered. All these features are gathered and subjected to the diagnostic system using an Optimized Cascaded Convolution Neural Network (CCNN). Here, the hyperparameters of CCNN are optimized by the Galactic Swarm Optimization (GSO). Through the performance analysis, the precision of the suggested GSO-CCNN is 3.7%, 3.7%, 3.6%, 7.6%, 67.9%, 48.4%, 33%, 10.9%, and 7.6% more advanced than PSO-CCNN, GWO-CCNN, WOA-CCNN, DHOA-CCNN, DNN, RNN, LSTM, CNN, and CCNN, respectively. Thus, the comparative analysis of the suggested system ensures its efficiency over the conventional models.