Difference between revisions of "Encrypted code: Ephaptic transmission"

While the potential of CNNs in transforming medical diagnostics is immense, their integration into clinical practice requires overcoming significant barriers. These include technological adaptation, data privacy concerns, and the need for substantial training for medical personnel.

*'''Output:''' The outgoing data from the network, which substantially correspond to a precise cognitive trigger request, returns a large number of data classified and correlated to the requested keyword. The clinician will have to devote time and concentration to continue decrypting the machine code. In fact, we have witnessed how, following the indications dictated by research criteria such as the 'Research Diagnostic Criteria' (RDC), our patient Mary Poppins was immediately categorized as 'TMDs'. We have also suggested a way to expand diagnostic capabilities in dentistry through a 'fuzzy' model that would allow to range in contexts other than one's own. This shows the complexity in making differential diagnoses and the difficulties in following a classical semiotic roadmap because we are anchored too much to verbal language and too little to a quantum culture of biological systems. This borders on the concept of machine language and initialization decryption command that we will briefly explain in the next paragraph.

For a moment let's imagine that the brain speaks the language of a computer and not vice versa as happens in engineering, to distinguish the aforementioned difference between machine language and human verbal language. To write a sentence, a word or a formula, the computer does not use the classic verbal mode (alphabet) or the decimal mode (numbers) with which we write mathematical formulas but its own 'writing' language code called html code for the web. Let's take as an example the writing of a fairly complex formula, it is presented to our brain in the verbal language with which we have learned to read a mathematical equation, in the following form:  

Figure 1, on the other hand, corresponds to the 'CNN' model proposed and it can be seen how the first stage of acquisition is composed of a single node while the 'Machine learning' at the first node, the more incoming variables have the greater the 'Prediction' in exit. As mentioned, it should be taken into account that the first node is of fundamental importance because it already derives from a clinical cognitive process that led the ' <math>\tau</math> Coherence Demarcator' to determine a first-ever choice of field. From the 'Initialization command', therefore, the neural network evolves in a series of states composed of a large number of nodes and then terminates at a first step of one or two nodes and then reiterates in a subsequent loop of several nodes until ending in the 'last conclusive node (decryption of the code). The initialization process of the first node, the last and the reiteration of the loop is exclusive to the human cognitive process of the clinician and not to a statistical automatism of machine learning, much less 'Hidden' stages. All open and closed loops must be known to the clinician.       

But let's see in detail how a 'CNN' is built