{'fraction_non_alphanumeric': 0.026810217437196537, 'fraction_numerical': 0.011751460136513969, 'mean_word_length': 5.019483269800932, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Analysis of Convolutional Neural Network based Image Classification Techniques

With the rapid urbanization and people moving from rural areas to urban time has become a very huge commodity. As a result of this change in people's lifestyles, there is a growing need for speed and efficiency. In the supermarket industry, item identification and billing are generally done manually, which takes a lot of time and effort. The lack of a bar code on the fruit products slows down the processing time. Before beginning the billing process, the seller may need to weigh the items in order to update the barcode, or the biller may need to input the item's name manually. This doubles the effort and also consumes a significant amount of time. As a result, several convolutional neural network-based classifiers are proposed to identify the fruits by visualizing via the camera for establishing a quick billing procedure in order to overcome this difficulty. The best model among the suggested models is capable of classifying pictures with start-of-art accuracy, which is superior than that of previously published studies.


Figure 1. Basic Architecture of Proposed Computer Vision System
The system is attached to a camera to capture an image, which can be read by a systemprogrammed algorithm. Aside from smartphone cameras, computer system screen images are used as visual scenes for computer vision based systems [1].
Computer Vision has become more prominent among medical researchers. A study [2] uses thermal pictures to propose a computer-aided diagnosis method based on convolutional neural networks (CNN). When compared to other techniques, CNNs are faster, more reliable and robust. The proposed models have outperformed various state-of-the-art architectures, including ResNet50, SeResNet50, and Inception, in terms of accuracy (92%) and F1-score (92%). Researchers describe a fully autonomous brain tumor segmentation and classification model based on a Deep Convolutional Neural Network with a multiscale approach in this publication [3]. The suggested neural model can evaluate MRI scans with three types of tumors: meningioma, glioma, and pituitary tumor, in sagittal, coronal, and axial perspectives, pedestrian navigation, researchers suggested a hybrid structure with a convolutional neural network (CNN) and local image characteristics in this paper [4]. AlphaMEX, a revolutionary end-to-end trainable global pooling operator was created to increase the CNN scene Computer Vision has also been employed in biometric authentication. This article [6] has presented a facial recognition system, which facilitates a simple and quick searching for offenders by saving more time, and also it assists the police and administration more effectively. Face identification from the video has been performed by using a pre-trained model called FaceNet (FN) in this work. FN can gain 98.47 percent accuracy. This research [7] discusses about the facial biometrics system, in which multiple classifiers are utilized within the framework of face recognition. Multiple algorithms are employed in the process.
All of them performed quite well, with an average accuracy of above 90%, while PCA+LDA+1N had the greatest average accuracy of 98%. The goal of this study [8]  and a combination of them, according to experimental findings obtained from three databases. This paper [9] provides a realistic alternative that uses the bare minimum of model design while matching the performance of deep learning techniques at the cutting edge. Their technique surpasses deep learning-based methods in various benchmark datasets and even excels those based on large data. The origins of ear modeling are review in this article [10].
The classification approach of deep learning utilizing CNN was compared to the frequently used machine-learning techniques. The average ear recognition rate for both the left and right ear was 92% in this article, thanks to deep learning utilizing Convolution Neural Network In this period of pandemic, deep learning-based solutions are heavily researched to overcome COVID-19. Researchers suggest CoroNet, a deep CNN model for automatically detecting COVID-19 infection from chest X-ray images, in this work [11]. The suggested model is built on the Xception architecture, which has been pre-trained on the ImageNet dataset, as well as the COVID-19 dataset and additional chest pneumonia X-ray imaging datasets. For 4class instances, the suggested model had an overall accuracy of 89.6%, while for 3-class cases; it had an accuracy of 95%. This article [12] describes an automated COVID-19 detection method based on chest X-ray images that may be used in conjunction with the RT-PCR test to enhance diagnostic rates. Textural characteristics are retrieved from chest X-ray pictures and local binary pattern (LBP) based images in the suggested method. To test the robustness of the proposed technique, 2905 chest X-ray pictures of normal, pneumonia and COVID-19 infected people were analyzed on various class combinations. The created technique performs at a high level. Using two datasets comprising normal and COVID-19 positive pictures, Haque [13] presented a unique convolutional neural network model to detect COVID-19 patients. Using a second dataset, the suggested model has an accuracy of 98.3%. However, the model only considers binary categorization; it is unable to distinguish between COVID and non-COVID pneumonia cases.
In other domains also, deep learning and computer vision is used. Capsule networks using structured data exhibit optimal performance in visual inference areas. The categorization of hierarchical multi-label text is accomplished in this study [14] by using a basic capsule type of learning algorithm that may give a high recognition rate in a short amount of time. To complete the classification job, the ELM technique was created with the existence of a sigmoidal function of biases in the hidden nodes in this study article [15]. In addition, the suggested enhanced version of ELM improves accuracy and effectiveness in classification and regression issues. In this paper [16], a unique and consistent oversampling technique is presented to improve the classification performance particularly on binary unbalanced datasets. It is called as NMOTe, and it is considered as an improved and superior alternative to the existing approaches. In this paper, the performance of NMOTe on various typical datasets that are examined to get a statistical understanding of why it has surpassed the current state-of-the-art as the most resilient approach for tackling the two-class data imbalance problem. Deep Neural Networks (DNN) have recently shown a wide range of capabilities in the pattern recognition paradigm. Depth layer networks, filters, training, and testing datasets become as a part of this study [17] on DNN. When compared to current discoveries in neuroscience research, the suggested study outperforms them. For image analysis, image fusion has acquired a lot of traction in the medical and satellite imaging fields. Researchers have attempted to enhance the WOA algorithm by changing the WOA algorithm in this study [18]. Other metaheuristic optimization techniques develop a comparison based on the simulation and synthesis results. This study [19] incorporates the Support Vector Machine-based Cuckoo Search Algorithm to develop an accurate monitoring and reduce maintenance costs in the sector of energy generation by utilizing windmills. The results demonstrate that, the SVM-based CSO is more accurate than other current models that are involved in predicting the fault models.
Finally, Computer Vision is also used to detect different types of fruits. To improve the accuracy of automatic vegetable recognition and classification, this paper [20] presents a deep learning-based method for recognizing and classifying the vegetable images. The improved VGG network model was used to train the vegetable image dataset by using the open-source Caffe deep learning framework. The output characteristic of the first two completely linked layers has been proposed to be combined (VGG-M). The experimental verification indicated that the proposed method has achieved a recognition accuracy rate of 96.5% in the test dataset, which is much higher than the VGG network (92.1%) and AlexNet network (86.3%). Researchers suggested a realistic approach for performing fruit recognition in this study [21] by utilizing two newly developed classifiers, which are successful and efficient. They used EfficientNet and MixNet from two deep neural network families, which intend to create an expert system to reliably and quickly recognize the fruits. The results of the experiments demonstrate that, by using EfficientNet and MixNet on the dataset in question, it significantly increases the overall prediction accuracy when compared to a wellestablished baseline. This study [22]  recognition models were used in this study [23] to perform recognition trials on red and green apples under three lighting and two picture sharpness conditions along with transfer learning in order to speed up the training process. The findings revealed that, among the four detection models, the enhanced YOLOv3 model had obtained the best recognition impact.

PROPOSED WORK
The purposed deep learning-based image classification techniques' performance is analyzed with several classifiers for achieving its better outcome. The images utilized in this work are download from the Kaggle Fruits 360 dataset, which consists of 3334 images in the training folder with a group of Avocado, Banana, Cherry, Cocos, Kiwi, Mango, and Orange images.
Similarly, 1121 images are available in test folders. Figure 2 shows few sample images that are used for training.  The proposed method classifies the fruit by detecting the most important features of the images by applying filters or feature detectors to the input image in order to generate the feature maps or the activation maps by using the activation function. Feature detectors or filters aid in the identification of various features present in a picture, such as edges, vertical lines, horizontal lines, bends, and so on.    Unit. If x is positive, it outputs x, otherwise, it outputs zero. It can be mathematically summarised as in equation (3). Throughout the design, the convolution and max pool layers are arranged in the same way. It features two FC (completely connected layers) in the end, followed by a softmax for output.
The 16 in VGG16 alludes to the fact that it contains 16 layers with different weights. As a result, the sole distinction between VGG16 and VGG19 represents the number of layers. Figure 5 and Figure 6 represents the architecture of the VGG16 and VGG19 that are used in this paper.  other enhancements (along with the use of batch normalization for layers in the side head).
Since the utilized Inception-V3 model architecture is too large, it is not included in this paper.
Totally, it includes 159 layers, which consists of convolution, batch normalization, max pooling, concatenation, average pooling, and dense layer. The concatenation transfers information from one layer to another.
The name DenseNet [26] comes from the fact that each layer in a DenseNet design is linked to every other layer. L(L+1)/2 direct connections exist for L layers. The feature maps of all previous layers are utilized as inputs for each layer, and their feature maps are used as inputs for subsequent layers. DenseNets simply connect every layer to every other layer, as easy as it may appear. This is the centralized concept, and it is also incredibly strong. The concatenation of feature maps from preceding layers is considered as the input of a layer within DenseNet. From Figure 7, it can be seen that each layer is interconnected with the other. There are a total of 121 layers in the proposed DenseNet architecture. The proposed models are developed by using all these layers. Since our task is multi-class classification, the final model should be able to output the probable class of the fruit. To achieve this, the final output of the model is converted to a list of probabilities by using the softmax activation function. Softmax returns a vector, which includes the probability distribution of a set of possible outcomes. The mathematical representation of the softmax is given in equation (4).
The main objective of the proposed model is to learn patterns from the given dataset. This will finally enable the model to correctly classify the new images into true class. Confusion Matrix is the visual representation of the performance of a statistical classification model. Figure 8 is the basic overview of the confusion matrix. 1 − = 2 * Table 3.Confusion Matrix of VGG19 Table 4.Confusion Matrix of InceptionV3 From Table 5 and Figure 9, it can be observed that, DenseNet has classified the image with the highest metrics. The VGG19 based model performs poorly in all metrics along with the accuracy, precision, recall, and F1-Score value of 87.81, 91, 88, and 86 respectively. The confusion matrix also clearly shows the perfectness of the DenseNet in classifying the testing dataset. Other models also have the difficulty in classifying the multiple classes properly. DenseNet outperforms the previous models by eliminating the vanishing-gradient problem, which improves feature propagation, encourages feature reuse, and significantly reduces the number of parameters. The model can also be trained with fewer parameters. The smooth flow of data is ensured via dynamic feature propagation.

CONCLUSION
The proposed deep learning-based image classification system suggests DenseNet based model to classify the images effectively than the other classifiers by obtaining a training and testing accuracy of 99.25% and 100% respectively. The proposed methodology can be merged with a camera system in industries and factories to detect fruit types based on the gained image. The model performance can be optimized by employing a huge number of training and testing images. Nevertheless, the DenseNet based model has delivered a start-ofart performance, which can be employed in both industries and factories.