# Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System
## 2 Related Work
A smart humidity monitoring system is proposed in [19], which makes provision for the neighboring correlation between contagious disease and humidity and proposes a solution which can preserve the relative humidity of a room, in this manner restraining an outbreak of infectious diseases. This system has utilized a sensor-based IoT unit, which takes into account the positive effects of humidity retention and observes the relative humidity of a room and gives specific information to the occupants of it. Specifically, the relative humidity of the air is monitored by the sensor-based hardware module, which transmits the data to the user in a separate room or even at a remote location through the internet. This module can easily be programmed to sustain relative humidity levels in a room determined by the user's needs. The system also controls the air conditioning and humidifying devices of the room to maintain optimal humidity levels. The developed system is a cost-effective application that can be incredibly valuable in hospitals because the risk and danger of infectious disease outbreak is high in such buildings. Due to its low cost, it could also be utilized in schools, workplaces, and homes. A well-promising Deep Learning (DL) approach for antibiotic discovery which can be proved very helpful for the branch of medicine is proposed in [20], where a Deep Neural Network (DNN) has been trained to be able to forecast molecules with antibacterial action. Virtual Screening (VS) is regarded to be an important tool for drug development, while  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & \multirow{2}{*}{**Proposal**} & \multirow{2}{*}{**Proposal’s**} & \multicolumn{2}{c}{**Diagnose/**} & \multirow{2}{*}{ \begin{tabular}{c} **Data** \\ **Test/Detect/** \\ **Prevent** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Integrated** \\ **Technologies** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Analysis** \\ **System** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Energy** \\ **Efficient** \\ **Speranio** \\ \end{tabular}  } \\ \hline \multirow{8}{*}{[PERSON] \& [PERSON] [24]} & Fast compound classification method based on a deep neural network for Virtual & \multirow{8}{*}{ \begin{tabular}{c} Outperforms \\ state-of-the-art machine \\ learning techniques with an \\ overall accuracy more \\ than 99\%. \\ \end{tabular}  } & \multirow{8}{*}{ \begin{tabular}{c} \(\bullet\) Detect \\ Prevent \\ \end{tabular}  } & \multirow{8}{*}{No} & \multirow{8}{*}{No} \\  & & & & & & & \\ \cline{1-1} \cline{4-10}  & & & & & & & \\ \cline{1-1} \cline{4-10}  & Fast compound classification method based on a deep neural network for Virtual & \multirow{8}{*}{ \begin{tabular}{c} Outperforms \\ state-of-the-art machine \\ learning techniques with an \\ overall accuracy more \\ than 99\%. \\ \end{tabular}  } & \multirow{8}{*}{\ \begin{tabular}{c} \(\bullet\) Detect \\ Prevent \\ \end{tabular}  } & \multirow{8}{*}{ \begin{tabular}{c} \(\bullet\) Pretural \\ Screening \\ \end{tabular}  } & \multirow{8}{*}{No} & \multirow{8}{*}{No} \\  & & & & & & & \\ \cline{1-1} \cline{4-10}  & & & & & & & \\ \cline{1-1} \cline{ML techniques are considered to be an effective and efficient method of performing VS for lead drugs. ML-based techniques are used for VS and applications in the discovery of drugs for Alzheimer's disease in [21]. Generally, ML for VS includes assembling a filtered training group of compounds, consisting of well-known active and inert compounds. After model training, it is confirmed and, if it is precise enough, used in formerly invisible databases to test unique compounds with favored drug target binding activity.