1
\documentclass{article}
\hyphenation{op-tical net-works semi-conduc-tor}
\usepackage{csquotes}
\usepackage{graphicx}
\usepackage{mathtools}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{mathtools}
\usepackage{lipsum}
\DeclareMathOperator{\RelU}{RelU}
%\usepackage{url}
%\usepackage{algorithm}
%\usepackage{array}
\usepackage{subfloat}
%\usepackage{subfig}
\usepackage{xcolor}
\usepackage{longtable}
%\usepackage{subcaption}
\usepackage{epstopdf}
\usepackage[utf8]{inputenc}
\usepackage[justification=centering]{caption}
%\usepackage{booktabs}
\newcommand{\dd}[1]{\mathrm{d}#1}
%\usepackage{amsmath}
\usepackage{graphicx}
\usepackage[utf8]{inputenc}
\usepackage[english]{babel}
\usepackage{url}
%\usepackage{multicol}
\usepackage{multirow}
\usepackage{array}
\usepackage{cleveref}
\usepackage{calc}
\usepackage[english]{babel}
%\usepackage[document]{ragged2e}
\usepackage{algorithm}
\usepackage{algpseudocode}
\usepackage{subcaption}
\usepackage{booktabs}
\usepackage{wrapfig}
\begin{document}
\title{ A deep learning approach}
\author{Ghul$^{1,2}$, Javaid$^{1*}$, \\
$^{1}$COMSATS University Islamabad 44000, Pakistan\\
$^{2}$University of Engineering and Technology, Mardan, 23200, Pakistan\\
%$^{2}$CAMS, Dept of Biomedical Technology, KSU, Riyadh 11633, Saudi Arabia\\
%$^{3}$FE, Dalhousie University, Halifax, NS B3J 4R2, Canada\\
$^{*}$Corresponding author: www.njavaid.com, nadeemjavaidqau@gmail.com
}
\maketitle

\begin{abstract}

\end{abstract}

\begin{IEEEkeywords}
Short term load forecasting; deep learning; smart grid; factored conditional restricted boltzmann machine; conditional restricted boltzmann machine; rectified linear unit.
\end{IEEEkeywords}


\section*{Nomenclature}
\addcontentsline{toc}{section}{Nomenclature}
\begin{IEEEdescription}[\IEEEusemathlabelsep\IEEEsetlabelwidth{$V_1,V_2,V_3$}]
\item[$E$]  {Error function}
\item[$v$]  {Visible layer}
\item[$u$]  {History layer}
\item[$h$]  Hidden layer
\item[$a$] Visible layer bias
\item[$b$] Hidden layer bias
\item[$w^{vh}$] Bi-directional weight matrix between hidden and visible layers
\item[$w^{uh}$] Unidirectional weight matrix between hidden and visible layers
\item[$w^{uv}$] Unidirectional weight matrix between visible and history layers
\item[$Sigmoid$]  Sigmoidal activation function
%\item[$RelU$]  Rectified linear unit activation function
\item[$w^{vh}_{t+1}$] Updated bi-directional weight matrix between hidden and visible layers
\item[$w^{uh}_{t+1}$] Updated unidirectional weight matrix between hidden and visible layers
\item[$w^{uv_{t+1}}$] Updated unidirectional weight matrix between visible and history layers
\item[$a_{t+1}$] Updated bias of visible layer
\item[$b_{t+1}$] Updated bias of hidden layer
\item[$\hat a$]  Visible layer dynamic bias
\item[$\hat b$]  Hidden layer dynamic bias
\item[$w^v$] Visible layer weight
\item[$w^y$] Style layer weight
\item[$w^h$] Hidden layer weight
\item[$A^u$] History layer connection for dynamic bias $\hat a$
\item[$A^v$] Visible layer connection for dynamic bias $\hat a$
\item[$A^y$] Style layer connection for dynamic bias $\hat a$
\item[$B^u$] History layer connection for dynamic bias $\hat b$
\item[$B^y$] Style layer connection for dynamic bias $\hat b$
\item[$B^h$] Hidden layer connection for dynamic bias $\hat b$
\item[$A^u_{t+1}$] Updated history layer connection for dynamic bias $\hat a$
\item[$A^v_{t+1}$] Updated visible layer connection for dynamic bias $\hat a$
\item[$A^y_{t+1}$] Updated style layer connection for dynamic bias $\hat a$
\item[$B^u_{t+1}$] Updated history layer connection for dynamic bias $\hat b$
\item[$B^y_{t+1}$] Updated style layer connection for dynamic bias $\hat b$
\item[$B^h_{t+1}$] Updated hidden layer connection for dynamic bias $\hat b$
\item[$\hat a_{t+1}$]  Updated visible layer dynamic bias
\item[$\hat b_{t+1}$] Updated hidden layer dynamic bias
\item[$RelU$] Rectified linear unit activation function
\item[$\circ$] Hadamard product
\item[$RMSE$] Root mean square error
\item[$r$] Correlation coefficient
\item[$MAPE$] Mean absolute percentage error
\item[$R_t$] Real value
\item[$F_t$] Forecast value
\item[$\mu _R$] Mean of real values
\item[$\mu_F$] Mean of forecasted values\\
\end{IEEEdescription}
\end{document} 
  • 1
    Could you please make your example more minimal? THere are a ton of packages that I am very sure you don't need to replicate your issue. Quoting the exact error message you get is also generally a good idea. – leandriis Mar 23 at 11:07
  • The error message one recieves is the following Environment IEEEkeywords undefined. This environment is efined in the IEEEtran documentclass, so if you replace article by IEEEtran your document will be compilable. – leandriis Mar 23 at 11:08
  • Please make this executable in the article environment. Thanks – Ghulam Hafeez Mar 23 at 11:15
  • Apart from you issue: Please make sure that you don't load packages more than once (e.g. mathtools) and please also mind the load order of some packages (e.g. cleveref) – leandriis Mar 23 at 11:15
  • 1
    @GhulamHafeez What do you mean by “please use article environment”? You cannot use IEEEkeywords in the article class, because it is not defined in it. – egreg Mar 23 at 11:49
1

I suggest you load the IEEEtrantools package. It provides quite a few of the macros and environments of the IEEEtran document class, for use with other document classes -- such as the article class. For instance, the IEEEtrantools package provides an environment called IEEEdescription, which you appear to use.

The IEEEtrantools package does not, though, provide code for an environment called IEEEkeywords. You may want to try something like

\newenvironment{IEEEkeywords}{\raggedright\noindent\textsc{Keywords}:}{\par}

to be inserted in the preamble.

Some additional comments. (a) The cleveref package should be loaded last. (b) I can think of no good reason for typesetting the words Sigmoid, RMSE, and MAPE in math mode. (c) I think it's rather questionable to write boltzmann; do write Boltzmann instead.

enter image description here

\documentclass{article}
%% New instructions:
\usepackage{geometry}  % choose suitable page parameters
\usepackage{IEEEtrantools}
\newenvironment{IEEEkeywords}{\raggedright\noindent\textsc{Keywords}:}{\par}

%% Rest of preamble may remain unchanged
\hyphenation{op-tical net-works semi-conduc-tor}
\usepackage{csquotes}
\usepackage{graphicx}
\usepackage{mathtools}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{mathtools}
\usepackage{lipsum}
\DeclareMathOperator{\RelU}{RelU}
%\usepackage{url}
%\usepackage{algorithm}
%\usepackage{array}
\usepackage{subfloat}
%\usepackage{subfig}
\usepackage{xcolor}
\usepackage{longtable}
%\usepackage{subcaption}
\usepackage{epstopdf}
\usepackage[utf8]{inputenc}
\usepackage[justification=centering]{caption}
%\usepackage{booktabs}
\newcommand{\dd}[1]{\mathrm{d}#1}
%\usepackage{amsmath}
\usepackage{graphicx}
\usepackage[utf8]{inputenc}
\usepackage[english]{babel}
\usepackage{url}
%\usepackage{multicol}
\usepackage{multirow}
\usepackage{array}
\usepackage{calc}
\usepackage[english]{babel}
%\usepackage[document]{ragged2e}
\usepackage{algorithm}
\usepackage{algpseudocode}
\usepackage{subcaption}
\usepackage{booktabs}
\usepackage{wrapfig}
\usepackage{cleveref}  % this package should be loaded LAST

\title{ A deep learning approach}
\author{Ghul$^{1,2}$, Javaid$^{1*}$, \\
$^{1}$COMSATS University Islamabad 44000, Pakistan\\
$^{2}$University of Engineering and Technology, Mardan, 23200, Pakistan\\
%$^{2}$CAMS, Dept of Biomedical Technology, KSU, Riyadh 11633, Saudi Arabia\\
%$^{3}$FE, Dalhousie University, Halifax, NS B3J 4R2, Canada\\
$^{*}$Corresponding author: www.njavaid.com, nadeemjavaidqau@gmail.com}

\usepackage{lipsum} % provides filler text
\begin{document}

\maketitle

\begin{abstract}
\lipsum*[2]
\end{abstract}

\begin{IEEEkeywords}
Short term load forecasting; deep learning; smart grid; factored conditional restricted Boltzmann machine; conditional restricted Boltzmann machine; rectified linear unit.
\end{IEEEkeywords}

\tableofcontents % optional

\section*{Nomenclature}
\addcontentsline{toc}{section}{Nomenclature}

\begin{IEEEdescription}[\IEEEusemathlabelsep%
      \IEEEsetlabelwidth{Sigmoid}]
\item[$E$]  {Error function}
\item[$v$]  {Visible layer}
\item[$u$]  {History layer}
\item[$h$]  Hidden layer
\item[$a$] Visible layer bias
\item[$b$] Hidden layer bias
\item[$w^{vh}$] Bi-directional weight matrix between hidden and visible layers
\item[$w^{uh}$] Unidirectional weight matrix between hidden and visible layers
\item[$w^{uv}$] Unidirectional weight matrix between visible and history layers
\item[Sigmoid]  Sigmoidal activation function
%\item[$RelU$]  Rectified linear unit activation function
\item[$w^{vh}_{t+1}$] Updated bi-directional weight matrix between hidden and visible layers
\item[$w^{uh}_{t+1}$] Updated unidirectional weight matrix between hidden and visible layers
\item[$w^{uv_{t+1}}$] Updated unidirectional weight matrix between visible and history layers
\item[$a_{t+1}$] Updated bias of visible layer
\item[$b_{t+1}$] Updated bias of hidden layer
\item[$\hat a$]  Visible layer dynamic bias
\item[$\hat b$]  Hidden layer dynamic bias
\item[$w^v$] Visible layer weight
\item[$w^y$] Style layer weight
\item[$w^h$] Hidden layer weight
\item[$A^u$] History layer connection for dynamic bias $\hat a$
\item[$A^v$] Visible layer connection for dynamic bias $\hat a$
\item[$A^y$] Style layer connection for dynamic bias $\hat a$
\item[$B^u$] History layer connection for dynamic bias $\hat b$
\item[$B^y$] Style layer connection for dynamic bias $\hat b$
\item[$B^h$] Hidden layer connection for dynamic bias $\hat b$
\item[$A^u_{t+1}$] Updated history layer connection for dynamic bias $\hat a$
\item[$A^v_{t+1}$] Updated visible layer connection for dynamic bias $\hat a$
\item[$A^y_{t+1}$] Updated style layer connection for dynamic bias $\hat a$
\item[$B^u_{t+1}$] Updated history layer connection for dynamic bias $\hat b$
\item[$B^y_{t+1}$] Updated style layer connection for dynamic bias $\hat b$
\item[$B^h_{t+1}$] Updated hidden layer connection for dynamic bias $\hat b$
\item[$\hat a_{t+1}$]  Updated visible layer dynamic bias
\item[$\hat b_{t+1}$] Updated hidden layer dynamic bias
\item[$RelU$] Rectified linear unit activation function
\item[$\circ$] Hadamard product
\item[RMSE] Root mean square error
\item[$r$] Correlation coefficient
\item[MAPE] Mean absolute percentage error
\item[$R_t$] Real value
\item[$F_t$] Forecast value
\item[$\mu _R$] Mean of real values
\item[$\mu_F$] Mean of forecasted values
\end{IEEEdescription}
\end{document} 

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