## 3 Results and Discussion
### Advantages of the Proposed Algorithm  
From the above sections, the advantages of the proposed algorithm are quite evident and listed as follows:  
* The proposed technique is relatively easy to understand and implement. In addition, its identification accuracy meets our expectations for the present dataset. * It can be successfully applied to a few grayscale image samples, whereas deep learning approaches such as Custom Object CNN, YOLO and RCNN suffer from poor accuracy in the presence of a small image dataset. These famous image classification models are trained on massive datasets. Among these datasets, the top three used for training are as follows: \(\odot\) ImageNet--1.5 million images with 1000 object categories/classes, \(\odot\) Microsoft Common Objects in Context (COCO)--2.5 million images, 91 object categories, \(\odot\) PASCAL VOC Dataset--500K images, 20 object categories. * The proposed algorithm can be successfully applied to a small image dataset, such as 300 images, because it does not require training for object identification. * Further referring to our limitations of the size of the dataset and quality of the images, this technique can use samples with a low divergence and lower rank hardware resource for computing on the edge. As we know, deep CNN models depend on hyper-parameter choices such as the filter size, regularisation chosen and quantisation levels [38; 42; 43]. Some of the hyper-parameters we finetune in deep learning methods that can impact the accuracy gain and robustness of the models are epochs (min 1), batch size (min 1), loss function (cross entropy, L1 loss, mean square loss, negative likelihood), optimiser algorithm. learning rate, weight decay, rho, lambda, alpha, epsilon, momentum and learning rate decay. These parameter (more than 15) settings may have an infinite combination and may lead to another combinatorial optimization problem in this case [43], 320 random hyper-parameter settings (in this case) and hence it can swing an average random accuracy by as much as 41.8 \(\pm\) 24.3 [43]. This result shows the amount of variation parametric choices can have over the results. Such high-dimension problems are then reduced in trials with extensive domain expertise, which again becomes very close to an empirical approach such as ours. In comparison, the proposed model has less than five parameters (\(\alpha,\ \beta,\ \gamma,\ \delta,\ and\ I\)) to optimise in our proposed approach. Hence, it is a much simpler and straightforward comparison. * We have tried deep learning in our separate experiments on the Advance Drive Assistance System (ADAS) and for various COVID-19 related apps that run deep learning on-the edge computing. We found that the accuracy levels have drastically reduced the inference of the full Resnet and YOLO-based object detection model after model quantisation. There is a limit on the capacity of deep learning models, whereas our proposed model can easily make inferences on edge (IoT) hardware such as Jetson Nano and even Raspberry Pi. * Our proposed model can be used to identify objects without fixed geometric shapes and sizes (such as pores and fractures). In contrast, deep CNN models are inefficient in this scenario because of an insufficient understanding of the necessary shape-related features required to classify any correctly. * Furthermore, though being scientifically regressive, back-propagation (BP) with the gradient descent (GD) has the inherent issue that it frequently gets stuck in local optima [44]. This result was further supported by [PERSON] et al. [45], who found that BP with GD can be guaranteed to find global minima only if the problem is linearly classified. As a learning algorithm, BP, unless supported by empirical techniques such as adaptive learning rate, momentum, noise randomisation and weight spawning, cannot find guaranteed global minima using current deep learning techniques, as discussed above.