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\begin{document}

\title{Translations Of Neural Networks based on Fuzzy Weights for Binary Keys
within Delayed and Actual Time}



\author[1]{Amitesh Kumar Singam}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\selectlanguage{english}
\begin{abstract}
The Key facts of Translations are meant to validated the Fuzzy Weights.
Moreover, In our case Fuzzy weights are based on Logical Reasoning ~and
Logical Thinking, even though fuzzy logics are not concerned with data
rather it depends on thought process based on human brain Imitation.
Generally, at some point mankind depends on his own creations and tries
to understand its usage through Machine Language or so called Machine
Teaching and this is were humans try to understand fuzzy concepts based
on binary language, moreover this translations are ~meant to become
complicated the more we go deeper but here comes our originality ~of
introducing fuzzy weights or logic based on Switching Theory Logical
design which produces binary keys instead of values, we concentrated on
reducing complexity of Machine Teaching through fuzzy weights.%
\end{abstract}%



\sloppy


\section*{Keywords}

{\label{108982}}

Machine Learning, Fuzzy Logic, Neural Networks

\textbf{CCS Concepts~}

\textbf{CS.HCI(HUMAN COMPUTER INTERFACE), CS.MACHINE TEACHING}

\section*{Short Introduction}

{\label{108982}}

In Reality, Its unusual to find a gap between Artificial Intelligence
and Machine Learning were AI is dedicated to Algorithms ~and Machine
Learning is concerned with Principles and errors and both needed data
for Validating so called issues, But we came across something
interesting topic so called Machine Teaching, were it is a concept
related to fill the gap of artificiality or its trajectory

\section*{Methodology}

Its an Hypothetical Assumption, since this predicted possibilities
resides between Artificial Intelligence and Machine Learning concepts,
in other words ~its a barrier between reality and imaginary

mathematical expression is followed by

f(x,y)= cos(x)+ \textbar{}sin(y)\textbar{} -\textgreater{} Real +
Imaginary,~

=\textgreater{} f(z)=a+i*b where a, b are assumed as binary keys~where x
,y are real and imaginary planes in two dimensional space~

\section*{Expected Results}

Its obvious that we are considering an assumption with certain
restrictions to avoid misconceptions, so basically validation of ~Fuzzy
weights a, b resides in real and imaginary planes and moreover Predicted
thoughts(Imitation based)are defined with binary values `1','0' for
instance.

\begin{quote}
~if value is 1 then fuzzy values is ~ ~ ~ ~0 0 1

else value is 2 then fuzzy values is ~ 0 1 0

else if value is 3 then fuzzy value is ~1 0 0 ~
\end{quote}

\begin{itemize}
\tightlist
\item
  fuzzy values defines fuzzy weights based on neural schema
\item
  fuzzy values or inputs are conceptual values based on hypothetical
  assumptions used to quantify fuzzy weights.
\end{itemize}

\par\null

\section*{Conclusions}

In the time domain, Logical thinking is based on Assumption of thought
process as imaginary (captured within sinusoidal wave) and imitations
(cosine) as real towards evolution of predicted one may be logical
reasoning. To be noted that, cosine as delayed and sinusoidal as wave
within time, therefore complexity of proposed approach is based on
originality and assumed one.

\section*{Noted Facts}

\begin{itemize}
\tightlist
\item
  Predicted thoughts based on imitation may result in answer or logical
  reason but no original one
\end{itemize}

\par\null

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