## 7 Conclusions and Future Work  
In current research work we proposed an android based wound care solution to detect environmental feasibility level for wound healing, as wound healing effected by many factors, \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Advantages** \\ \hline
**Cost** & Proposed approach provide low cost solution to patients as all used sensors are cheap and designed circuit is affordable for patients. \\ \hline
**Availability** & Proposed approach provided solution which is always available for wound monitoring while medical experts not always available to monitor environmental factors. \\ \hline
**Location** & Proposed solution is patient centric which increases patient wound monitoring trend and ultimately help to enhance wound healing rate. \\ \hline \hline
**Issue** & **Limitation** \\ \hline
**Weather** & Proposed approach considered weather conditions of normal zone, extreme weather conditions didn’t consider for analysis by proposed system. \\ \hline
**Skin Diseases** & Proposed approach analyzes environment effect without considering skin special characteristics i.e., Skin disease \\ \hline
**Classification Approach** & Proposed approach apply classification by NN, it didn’t compared working of other classifier. \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Limitations of Proposed Approach. \begin{table}
\begin{tabular}{c c} \hline \hline
**Comparison Factor** & **Neural Network** & **Machine Learning Techniques** \\ \hline
**Data Size** & Ability to handle large data sets & Suitable to handle small data sets. \\
**Performance** & Show improvement in performance with large training data & Consistent performance \\
**Configuration** & NN didn’t have parameters so easy to configure input, layers and output & Parameter adjustment required. \\
**Computational power** & NN consider more powerful as NN have ability to handle larger data & Computationally less powerful \\
**Problem Type** & Can handle non-linear problems & Algorithms as compare to NN \\
**Error** & As NN is non-parametric so it didn’t face & Error could occur in parametric \\  & error of parametric estimation & estimation. \\
**Learning type** & Black box, it cannot interpret relationship & Have capability to interpret input \\  & between input and output & relationship with output. \\
**Working Focus** & Suitable for output centric problems. & Some may focus on input output \\  & & relationship rather than just output. \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Comparison with Machine Learning Techniques. environment is one such factor which play active role in healing. It is necessary to monitor wound environment like other wound characteristic i.e., wound color, size and depth. Like other wound characteristic wound environment may not need clinical visit for assessment. This environmental factor is somehow measurable at patient site, which may enable patient to assure healthy wound environment for speedy recovery. Therefore, we proposed simple wound care solution to monitor current environment feasibility for open skin wound healing. Given solution have two components i.e., EEMS and NN Model. We used Exterior environment monitoring system to record current environment readings with help of Arduino UNO based circuit connected with three sensors DHT22, MQ2 and PMS5003 Dust sensor. We programmed Arduino Controller to record Temperature, humidity, Air quality and dust particles from environment and record the values in Excel. Next component of Presented model used NN trained Model of MATLAB which take designed standard training dataset to train in 7 rounds and the further used this trained NN to predict Environment feasibility of dataset which are taking Real time environment reading by EEMS.