## 5 Conclusions  
This work brings out a new way of improving the accuracy of compressive domain learning using a novel CS sensing matrix learning method. In doing so, a very simple compressive sensing process is used which can be easily implemented using a standard digital camera and a pixel retaining mask function. By making the CNN architecture standard and simple, the strength of the sensing matrix learning process is brought out. The proposed GA-based learning of sensing matrix guarantees an increase in the training accuracy of the CNN by 8% for datasets consisting of natural images with more features for compression ratio of 90%. This gives a new direction to integrate this learning methodology with sophisticated CNN architecture customized to certain applications. The sensing matrix retains the image information in the CS measurements and thus does not need any pre-processing or reconstruction operation in the training and testing phase. In the numerical experiments we conducted, lot of intermediate results are obtained which throw light on compressive domain learning. The compressive sampling in spatial domain is achieved by applying binary mask to the dataset images to retain only P% of the pixels for a two-class image and the performance of CNN is thoroughly studied. The study reveals that the CNN performance is better when compressive sampling is applied to both training and testing dataset. The training accuracy achieved for the popular dog and cat dataset for the case of retaining only 10% of the pixels both in training and testing dataset is 77% with validation accuracy of 61%. The proposed novel GACL algorithm adopted in this research to learn the best binary mask and a training accuracy of 80% is achieved for the case of 10% pixels retained (CR = 90%) in both training and testing dataset using vertical crossover of the mask function. Much more improved training accuracy of 85% is achieved in GACL for the case of 10% pixels retained in both training and testing dataset using diagonal crossover of the mask function which is much entertaining and the concept can be deployed in IoT frame work where huge image data are transferred for analytics purpose. To examine the robustness of the proposed GACL-based compressive sampling, we extended the experiment to a user-defined small multiclass dataset and found both training and validation accuracies improve with GACL. The complete work is limited in the spatial domain and extending the implementation in frequency domain such as DCT, Wavelet will opens up new door in this direction. The proposed GA algorithm fit well with the existing GA-based tuning of hyper parameters of CNN. The binary mask that is applied to the dataset images can also be considered as a standard parameter of CNN architecture. Applying more computationally intensive advanced heuristic algorithms on a real valued masks can be explored in future.  
Ideation and concept, B.F.A.B.H. and P.R.; methodology, B.F.A.B.H. and P.R.; writing--original manuscript preparation, B.F.A.B.H. and P.R.; review P.R.; editing, B.F.A.B.H. and P.R.; supervision, P.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.