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\begin{document}

             \author[1]{Satish  Gajawada}

             \author[2]{Durga  Toshniwal}

             \author[3]{Hassan M. H.  Mustafa}

             \author[4]{Hassan M. H.  Mustafa}

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\date{\small \em Received: 12 December 2019 Accepted: 3 January 2020 Published: 15 January 2020}

\maketitle


\begin{abstract}
        


Artificial Intelligence and Deep Learning are good fields of research. Recently, the brother of Artificial Intelligence titled "Artificial Satisfaction" was introduced in literature [10]. In this article, we coin the term ?Deep Loving?. After the publication of this article, "Deep Loving" will be considered as the friend of Deep Learning. Proposing a new field is different from proposing a new algorithm. In this paper, we strongly focus on defining and introducing "Deep Loving Field" to Research Scientists across the globe. The future of the "Deep Loving" field is predicted by showing few future opportunities in this new field. The definition of Deep Learning is shown followed by a literature review of the "Deep Loving" field. The World's First Deep Loving Algorithm (WFDLA) is designed and implemented in this work by adding Deep Loving concepts to Particle Swarm Optimization Algorithm. Results obtained by WFDLA are compared with the PSO algorithm.

\end{abstract}


\keywords{deep learning, deep loving, artificial intelligence, artificial satisfaction, artificial mothers, swarm intelligence, artificial mother optimization.}

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{{\textit{CrossRef DOI of original article:}} \underline{10.34257/GJCSTDVOL20IS1PG51}}
\end{textblock*}\let\tabcellsep& 	 	 		 
\section[{Direct Contribution: Satish Gajawada and Hassan}]{Direct Contribution: Satish Gajawada and Hassan}\par
M. H. Mustafa contributed directly, completely, and equally to this article. 
\section[{I.}]{I.}\par
Definition of Deep Loving Field ust like Mothers in real-world solve real problems, Artificial Mothers (in Deep Loving Field) move in the search space for solving optimization problems. In Deep Loving, we imitate mothers in the real space. In the Artificial Human Optimization field \hyperref[b8]{[6]}\hyperref[b9]{[7]}, Artificial Soul Optimization field \hyperref[b10]{[8]}, and Artificial God Optimization field \hyperref[b1]{[9]}, the basic entities in search space are Artificial Humans, Artificial Souls, and Artificial Gods respectively. Similarly, the basic entities in the Deep Loving Field are Artificial Mothers. Whenever we think of the term "Mother," the Deep Love that each mother shows towards their family, children, etc. comes to mind. Hence the name "Deep Loving Field" is given to the field when Artificial Mothers in search space are imitating Mothers in real-world to solve optimization problems. Instead of naming the field as "Artificial Mother Optimization," a better name "Deep Loving" is chosen by us. 
\section[{II.}]{II.} 
\section[{Infinite Opportunities in the new}]{Infinite Opportunities in the new}\par
Deep Loving Field  
\section[{Literature Review}]{Literature Review}\par
There are many Deep Learning papers published in the literature. But there is not even a single paper which is based on Deep Loving. The World's First Deep Loving method is created in this article. For the sake of completeness, references [2] to \hyperref[b7]{[5]} show Deep VI. 
\section[{Results}]{Results}\par
The ASA algorithm in \hyperref[b2]{[10]}, and WFDLA designed in this paper are MATHEMATICALLY equal. In \hyperref[b2]{[10]} it was shown that both ASA and PSO algorithms performed well on all benchmark functions. Hence due to MATHEMATICAL EQUALITY, both WFDLA and PSO performed well on all benchmark functions.  \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.05682451253481894\textwidth}P{0.03640320334261838\textwidth}P{0.6676880222841226\textwidth}P{0.07694986072423397\textwidth}P{0.012134401114206127\textwidth}}
\tabcellsep \tabcellsep \multicolumn{2}{l}{Deep Loving -The Friend of Deep Learning}\\
\tabcellsep IV.\tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Learning articles. You can easily find references for}\tabcellsep \\
\multicolumn{3}{l}{Deep Learning on websites like deeplearning.net. We}\tabcellsep \\
\multicolumn{3}{l}{just showed four references for Deep Learning for completeness.}\tabcellsep There are INFINITE OPPORTUNITIES for Artificial Intelligence field Research Scientists in Deep\\
V.\tabcellsep \multicolumn{2}{l}{World's first Deep Loving Algorithm (wfdla)}\tabcellsep Loving Field. Some of them are shown below: 1. International Conference on Deep Loving (ICDL 2020) 2. IEEE TRANSACTIONS on Deep Loving (IEEE TDL\tabcellsep Year 2020\\
\tabcellsep 1) 2) 3) 4) 5) 6) 7) 8) 9) 10)\tabcellsep \multicolumn{2}{l}{2025) 3. International Workshop on Deep Loving, Harvard University, 2050 4. B.Tech Thesis on Deep Loving, IIT Roorkee, the Year 2075 5. IBM Deep Loving Research Labs, IBM Italy 6. Applied Deep Loving -A New Course 7. Advanced Deep Loving Course, IIT Mumbai 8. M.Tech in Deep Loving Field 9. International Institute of Deep Loving, Greece 10. Ph.D. Thesis on Deep Loving, Stanford University 11. Invited Talk on Deep Loving at Google R\&D Conference, USA Initialize all Artificial Mothers. Set Iteration Counter to 0. Find local best of all Artificial Mothers Find global best of all Artificial Mothers Find local worst of all Artificial Mothers Find global worst of all Artificial Mothers for each Artificial Mother do if (Random\textunderscore Number\textunderscore Generated < CoronavirusProbability) then if ( Random\textunderscore Number\textunderscore Generated < HelpProbability) then Update Velocity of Artificial Mother Update Position of Artificial Mother}\tabcellsep 51 Volume XX Issue I Version I )\\
J\tabcellsep 11) 12) 13) 14) 15) 16) 17) 18) 19) 20) 21) 22)\tabcellsep \multicolumn{3}{l}{12. Foundation of Deep Loving, Germany 13. International Association of Deep Loving, China 14. Deep Loving team at Microsoft Research and Development 15. YouTube videos on Deep Loving by Samsung R\&D Team 16. Springer Journals on Deep Loving Field 17. Elsevier Book on Deep Loving Field 18. A Course by Deep Loving Experts on Coursera 19. Presentation on Deep Loving Field at Technical Festivals in Singapore Colleges 20. IBMSUR Award for Deep Loving Field Professor at IIT Hyderabad 21. To become a Scientist in Deep Loving Field. III. Deep Learning According to Wikipedia, the definition of Deep Learning is shown below in double-quotes as it is: "Deep Learning is part of a broader family of machine learning methods based on Artificial Neural Networks with representation learning. Deep Learning Figure 1: World's First Deep Loving Algorithm (WFDLA) else // Mothers affected by coronavirus without help does nothing end if else Update Velocity of Artificial Mother Update Position of Artificial Mother end if end for Update Iteration Counter if (termination\textunderscore condition\textunderscore reached is not true) then go to line number 2 architectures such as deep neural networks, Global Journal of Computer Science and Technology ( D end if}\\
\multicolumn{2}{l}{© 2020 Global Journals}\tabcellsep \tabcellsep © 2020 Global Journals\end{longtable} \par
  {\small\itshape [Note: Author ?: Indian Institute of Technology Roorkee, Independent Inventor and Scientist, The Creator of Artificial Satisfaction Field, Inventor of Artificial Soul Optimization and Artificial God Optimization Fields, Founder and Father of Artificial Human Optimization Field, Inventor of Deep Loving Field. e-mail: satish.gajawada.iit@gmail.com ? Author ?: Banha University, Egypt.]} 
\caption{\label{tab_0}}\end{figure}
 			\footnote{Deep Loving -The Friend of Deep Learning © 2020 Global Journals scratch rather than modifying existing algorithms like PSO.} 		 		\backmatter   			 
\subsection[{Acknowledgments}]{Acknowledgments}\par
Thanks to Very Excellent Editorial Team of "Global Journal of Computer Science and Technology (GJCST)" and reviewers for accepting our innovative invention titled "Deep Loving." Thanks to everyone (and everything) who directly or indirectly helped us to reach the stage where we are now today. 			  			  				\begin{bibitemlist}{1}
\bibitem[Srivastava ()]{b4}\label{b4} 	 		\textit{},  		 			R Srivastava 		.  		 \url{https://en.m.wikipedia.org/wiki/Deep\textunderscore learning2.Trainingverydeepnetworks}  		2015.  	 
\bibitem[ Machine Learning and Artificial Intelligence ()]{b0}\label{b0} 	 		\textit{},  	 	 		\textit{Machine Learning and Artificial Intelligence}  		2019. 7  (5)  p. .  	 
\bibitem[Gajawada et al. ()]{b1}\label{b1} 	 		‘Artificial God Optimization -A Creation’.  		 			Satish Gajawada 		,  		 			M H Hassan 		,  		 			Mustafa 		.  	 	 		\textit{Computer and Information Science}  		2020. 13  (1) .  	 
\bibitem[Gajawada et al. (2020)]{b2}\label{b2} 	 		‘Artificial Satisfaction -The Brother of Artificial Intelligence’.  		 			Satish Gajawada 		,  		 			M H Hassan 		,  		 			Mustafa 		.  	 	 		\textit{Global Journal of Computer Science and Technology}  		June 2020.  	 	 (S.I.) 
\bibitem[Gajawada and Mustafa]{b10}\label{b10} 	 		‘Artificial Soul Optimization -An Invention’.  		 			Satish Gajawada 		,  		 			Hassan Mustafa 		.  	 	 		\textit{Transactions on}  		 	 
\bibitem[Gajawada and Mustafa]{b8}\label{b8} 	 		 			Satish Gajawada 		,  		 			Hassan Mustafa 		.  		 arXiv:1903.12011.  		\textit{Novel Artificial Human Optimization Field Algorithms -The Beginning},  				 	 	 (cs.NE) 
\bibitem[Yosinski ()]{b6}\label{b6} 	 		\textit{How transferable are features in deep neural networks?},  		 			J Yosinski 		.  		2014.  	 
\bibitem[Nguyen ()]{b5}\label{b5} 	 		 			A Nguyen 		.  		\textit{Deep neural networks are easily fooled: High confidence predictions for unrecognisable images},  				2015.  	 
\bibitem[Razavian ()]{b7}\label{b7} 	 		 			A Razavian 		.  		\textit{CNN features off-the-Shelf: An astounding baseline for recognition},  				2014.  	 
\bibitem[References Références Referencias]{b3}\label{b3} 	 		\textit{References Références Referencias},  		 	 
\bibitem[Gajawada and Mustafa ()]{b9}\label{b9} 	 		‘Ten Artificial Human Optimization Algorithms’.  		 			Satish Gajawada 		,  		 			Hassan Mustafa 		.  	 	 		\textit{Transactions on Machine Learning and Artificial Intelligence}  		2019. 7  (3)  p. .  	 
\end{bibitemlist}
 			 		 	 
\end{document}
