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If you’ve ever come down with the ‘common cold’ there will always be what are called, and falsely, coronaviruses within and lab detectable in every nasal swab that any MD or RN pulls out of your nostrils or sputum or other and sends to the basement lab.Called corona because, from electron microscopy imagery, the resolution offers visual similarities to the Sun’s corona ~90 million miles away from Earth. But in EM microscopic entities are demolished by destructive chemicals before incinerated by electron beam and no scientific ability to confirm what any of these damaged and artificially altered images were or did. Blasting electrons at chemical annihilated sub-lightband particles or cells in vacuum doesn’t explain what each are and did in nature before destroyed by chemicals and cooked, they only make for really cool pictures in varying shades of grey, with all colourized ones complete fiction.Most virus images presented by the MSM to viewers are digital artistic creations of total fiction. I prefer the word bullshit. While total fiction, are really cool to see.Diagnostic labs, even with the best technology and technician expertise, cannot identify infectious pathogens by PCR amplifying of DNA template from any patient swab. No lab has ever verified that what is called Influenza C is infectious and harmful to humans or so much as what it is, other than came from.Modern medical science does not have the technical ability to locate and observe pathogenesis in real time.What are called human coronaviruses, just as influenza viruses, all made by human cells using their own RNA, do not do what medical science claims or have any biological capability to. None can ever be the guilty complicit party in any disease. Any discussion of similarities between the other is overlooking the real microscopic picture.

Here are some websites to upgrade your Information:Virus is a Signal for the Host CellA virus functions as a signal informing these repressed cellular RNAs and other elements of ancient origin how to restore suppressed degrees ...Regulation of signaling mediated by nucleic acid sensors for innate interferon-mediated responses during viral infection…..An array of innate sensors recognizes virus-derived nucleic acids to activate their downstream signaling to evoke cytokine responses including interferons. In particular, a cytoplasmic RNA sensor RIG-I (retinoic acid-inducible gene I) is involved in the detection of multiple types of not only RNA viruses but also DNA viruses. Accumulating findings have revealed that activation of nucleic acid sensors and the related signaling mediators is regulated on the basis of post-translational modification such as ubiquitination, phosphorylation and ADP-ribosylation. In addition, long non-coding RNAs (lncRNAs) have been implicated as a new class of regulators in innate signaling.Type I and type III interferons are important anti-viral cytokines that are massively induced during viral infection.Transduction of DNA information through water and electromagnetic waves.The experimental conditions by which electromagnetic signals (EMS) of low frequency can be emitted by diluted aqueous solutions of some bacterial and viral DNAs are described.Bacteria Send Electrical Pulses as Recruitment AdsA Virus Can Eavesdrop On Bacterial CommunicationJustin Silpe's discovery built on previous work in the lab in which researchers discovered a receptor within each bacterial cell for a signal that bacteria use to communicate with one another, he says. Silpe further analyzed thousands of DNA sequences, eventually broadening his search beyond the bacterial domain. That was when he found one receptor that didn't match any bacteria. It was for VP882."This was unexpected because it was suggesting that there's a virus out there that has kind of joined the bacterial conversation," says Silpe. This one virus had a receptor that previously had only been found on bacteria to facilitate their communication. But unlike bacteria, the virus doesn't take part in the conversation. It just listens. "It can only pick up on a host signal. It can only eavesdrop," he says.Silpe found that the virus uses information overheard from bacterial communication to time its attack on surrounding bacterial cells. Entering other cells is the only way they can survive.https://hal.archives-ouvertes.fr/hal-01127694/documentDNA emits low-frequency electromagnetic waves which imprint the structure of the molecule onto the water. This structure, they claim, is preserved and amplified through quantum coherence effects, and because it mimics the shape of the original DNA, the enzymes in the PCR process mistake it for DNA itself, and somehow use it as a template to make DNA match that which "sent" the signal’.Bacteria Send Electrical Pulses as Recruitment AdsFor years, scientists have known that bacteria can coordinate their behavior by exchanging specific chemicals—a process known as quorum sensing. But Süel’s bacteria were doing something else. Arthur Pringle, another member of the team, realized that they were exchanging electrical messages rather than chemical ones.On their surface, bacteria have small pores called ion channels, which allow electrically charged molecules to move in and out of the cells. When the bacteria in the center of the biofilms start to starve, they open some of these pores, allowing positively charged potassium ions to stream outwards. When neighboring cells detect these ions, they also open their pores and release their own potassium. The result is a wave of charged ions—an electrical pulse—that ripples through the biofilm, right to its edges.This is very similar to what happens when neurons fire. They are lined with ion channels, too. When one opens, ions stream through and trigger nearby channels to open as well, creating a traveling electrical pulse.

I am not sure what exactly you mean by “various techniques”, but i’ll try to explain in most detailed way possible.Start reading from here :Mayank Srivastava's answer to What is the best way to learn machine learning from scratch to master?Mayank Srivastava's answer to Is it easy to learn machine learning basics?Algorithms Grouped By SimilarityAlgorithms are often grouped by similarity in terms of their function (how they work). For example, tree-based methods, and neural network inspired methods.I think this is the most useful way to group algorithms and it is the approach we will use here.This is a useful grouping method, but it is not perfect. There are still algorithms that could just as easily fit into multiple categories like Learning Vector Quantization that is both a neural network inspired method and an instance-based method. There are also categories that have the same name that describe the problem and the class of algorithm such as Regression and Clustering.We could handle these cases by listing algorithms twice or by selecting the group that subjectively is the “best” fit. I like this latter approach of not duplicating algorithms to keep things simple.In this section, I list many of the popular machine learning algorithms grouped the way I think is the most intuitive. The list is not exhaustive in either the groups or the algorithms, but I think it is representative and will be useful to you to get an idea of the lay of the land.Please Note: There is a strong bias towards algorithms used for classification and regression, the two most prevalent supervised machine learning problems you will encounter.If you know of an algorithm or a group of algorithms not listed, put it in the comments and share it with us. Let’s dive in.Regression AlgorithmsRegression is concerned with modeling the relationship between variables that is iteratively refined using a measure of error in the predictions made by the model.Regression methods are a workhorse of statistics and have been co-opted into statistical machine learning. This may be confusing because we can use regression to refer to the class of problem and the class of algorithm. Really, regression is a process.The most popular regression algorithms are:Ordinary Least Squares Regression (OLSR)Linear RegressionLogistic RegressionStepwise RegressionMultivariate Adaptive Regression Splines (MARS)Locally Estimated Scatterplot Smoothing (LOESS)Instance-based AlgorithmsInstance-based learning model is a decision problem with instances or examples of training data that are deemed important or required to the model.Such methods typically build up a database of example data and compare new data to the database using a similarity measure in order to find the best match and make a prediction. For this reason, instance-based methods are also called winner-take-all methods and memory-based learning. Focus is put on the representation of the stored instances and similarity measures used between instances.The most popular instance-based algorithms are:k-Nearest Neighbor (kNN)Learning Vector Quantization (LVQ)Self-Organizing Map (SOM)Locally Weighted Learning (LWL)Regularization AlgorithmsAn extension made to another method (typically regression methods) that penalizes models based on their complexity, favoring simpler models that are also better at generalizing.I have listed regularization algorithms separately here because they are popular, powerful and generally simple modifications made to other methods.The most popular regularization algorithms are:Ridge RegressionLeast Absolute Shrinkage and Selection Operator (LASSO)Elastic NetLeast-Angle Regression (LARS)Decision Tree AlgorithmsDecision tree methods construct a model of decisions made based on actual values of attributes in the data.Decisions fork in tree structures until a prediction decision is made for a given record. Decision trees are trained on data for classification and regression problems. Decision trees are often fast and accurate and a big favorite in machine learning.The most popular decision tree algorithms are:Classification and Regression Tree (CART)Iterative Dichotomiser 3 (ID3)C4.5 and C5.0 (different versions of a powerful approach)Chi-squared Automatic Interaction Detection (CHAID)Decision StumpM5Conditional Decision TreesBayesian AlgorithmsBayesian methods are those that explicitly apply Bayes’ Theorem for problems such as classification and regression.The most popular Bayesian algorithms are:Naive BayesGaussian Naive BayesMultinomial Naive BayesAveraged One-Dependence Estimators (AODE)Bayesian Belief Network (BBN)Bayesian Network (BN)Clustering AlgorithmsClustering, like regression, describes the class of problem and the class of methods.Clustering methods are typically organized by the modeling approaches such as centroid-based and hierarchal. All methods are concerned with using the inherent structures in the data to best organize the data into groups of maximum commonality.The most popular clustering algorithms are:k-Meansk-MediansExpectation Maximisation (EM)Hierarchical ClusteringAssociation Rule Learning AlgorithmsAssociation rule learning methods extract rules that best explain observed relationships between variables in data.These rules can discover important and commercially useful associations in large multidimensional datasets that can be exploited by an organization.The most popular association rule learning algorithms are:Apriori algorithmEclat algorithmArtificial Neural Network AlgorithmsArtificial Neural Networks are models that are inspired by the structure and/or function of biological neural networks.They are a class of pattern matching that are commonly used for regression and classification problems but are really an enormous subfield comprised of hundreds of algorithms and variations for all manner of problem types.Note that I have separated out Deep Learning from neural networks because of the massive growth and popularity in the field. Here we are concerned with the more classical methods.The most popular artificial neural network algorithms are:PerceptronBack-PropagationHopfield NetworkRadial Basis Function Network (RBFN)Deep Learning AlgorithmsDeep Learning methods are a modern update to Artificial Neural Networks that exploit abundant cheap computation.They are concerned with building much larger and more complex neural networks and, as commented on above, many methods are concerned with semi-supervised learning problems where large datasets contain very little labeled data.The most popular deep learning algorithms are:Deep Boltzmann Machine (DBM)Deep Belief Networks (DBN)Convolutional Neural Network (CNN)Stacked Auto-EncodersDimensionality Reduction AlgorithmsLike clustering methods, dimensionality reduction seek and exploit the inherent structure in the data, but in this case in an unsupervised manner or order to summarize or describe data using less information.This can be useful to visualize dimensional data or to simplify data which can then be used in a supervised learning method. Many of these methods can be adapted for use in classification and regression.Principal Component Analysis (PCA)Principal Component Regression (PCR)Partial Least Squares Regression (PLSR)Sammon MappingMultidimensional Scaling (MDS)Projection PursuitLinear Discriminant Analysis (LDA)Mixture Discriminant Analysis (MDA)Quadratic Discriminant Analysis (QDA)Flexible Discriminant Analysis (FDA)Ensemble AlgorithmsEnsemble methods are models composed of multiple weaker models that are independently trained and whose predictions are combined in some way to make the overall prediction.Much effort is put into what types of weak learners to combine and the ways in which to combine them. This is a very powerful class of techniques and as such is very popular.BoostingBootstrapped Aggregation (Bagging)AdaBoostStacked Generalization (blending)Gradient Boosting Machines (GBM)Gradient Boosted Regression Trees (GBRT)Random ForestOther AlgorithmsMany algorithms were not covered.For example, what group would Support Vector Machines go into? Its own?I did not cover algorithms from specialty tasks in the process of machine learning, such as:Feature selection algorithmsAlgorithm accuracy evaluationPerformance measuresI also did not cover algorithms from specialty subfields of machine learning, such as:Computational intelligence (evolutionary algorithms, etc.)Computer Vision (CV)Natural Language Processing (NLP)Recommender SystemsReinforcement LearningGraphical ModelsAnd more…How to Study Machine Learning AlgorithmsAlgorithms are a big part of machine learning. It’s a topic I am passionate about and write about a lot on this blog. Below are few hand selected posts that might interest you for further reading.How to Learn Any Machine Learning Algorithm: A systematic approach that you can use to study and understand any machine learning algorithm using “algorithm description templates” (I used this approach to write my first book).How to Create Targeted Lists of Machine Learning Algorithms: How you can create your own systematic lists of machine learning algorithms to jump start work on your next machine learning problem.How to Research a Machine Learning Algorithm: A systematic approach that you can use to research machine learning algorithms (works great in collaboration with the template approach listed above).How to Investigate Machine Learning Algorithm Behavior: A methodology you can use to understand how machine learning algorithms work by creating and executing very small studies into their behavior. Research is not just for academics!How to Implement a Machine Learning Algorithm: A process and tips and tricks for implementing machine learning algorithms from scratch.How to Run Machine Learning AlgorithmsSometimes you just want to dive into code. Below are some links you can use to run machine learning algorithms, code them up using standard libraries or implement them from scratch.How To Get Started With Machine Learning Algorithms in R: Links to a large number of code examples on this site demonstrating machine learning algorithms in R.Machine Learning Algorithm Recipes in scikit-learn: A collection of Python code examples demonstrating how to create predictive models using scikit-learn.How to Run Your First Classifier in Weka: A tutorial for running your very first classifier in Weka (no code required!).