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Baby corn (Zea mays) is young, finger‐like, unfertilized cobs of maize with one to three centimeters of emerged silk, preferably harvested within 24 hours of silk emergence depending upon the growing season. It is a very perishable vegetable and hence the purpose of this present study is to standardize the freezing method for extending the shelf life of baby corn and to study the effect of frozen storage of 90 days on its nutritional composition.

Frozen baby corn was analysed for various nutritional parameters, namely proximate composition, minerals, in vitro starch and protein digestibility and vitamin, at regular intervals of 30 days for nine months.

Moisture, crude protein, crude fat and crude fibre content of baby corn showed no significant change during 90 days of frozen storage. A significant reduction was observed in calcium, magnesium, zinc and iron content of frozen baby corn. In vitro starch and protein digestibility showed a non‐significant change during frozen storage. Ascorbic acid and beta‐carotene content of frozen baby corn decreased significantly by 11.60 and 10.75 percent, respectively, by the end of 90 days of storage.

The present study indicates that freezing is an effective processing technology to enhance the storage life of baby corn.

The purpose of this paper is to study nutritional composition of HM‐4 variety of baby corn.

Baby corn was analysed for proximate composition, available carbohydrates, dietary fiber constituents, in vitro digestibility, minerals, anti‐nutrients, vitamin and amino acids.

Baby corn contained 90.03, 17.96, 2.13, 5.30 and 5.89 percent moisture, protein, fat, ash and crude fibre, respectively. Total soluble sugars content was 23.43 g/100 gm and reducing sugars was 1.96 g/100 g. It contained 8.10 g/100 g of cellulose and 5.41 g/100 g of lignin. In vitro starch and protein digestibility was 28.80 mg maltose released per gram and 72.18 percent, respectively. Baby corn contained 5.43 mg/100 g of ascorbic acid and 670 μg/100 g of β‐carotene. Calcium, magnesium and phosphorus content of baby corn was 95.00, 345.00 and 898.62 mg/100 g, respectively, baby corn contained 0.05, 2.85 and 0.675μg/g of methionine, isoleucine and leucine, respectively.

The study indicated that baby corn is good source of various nutrients like protein, crude fibre, carbohydrates and dietary fibres and its nutritional quality is at par or even superior to many other commonly used vegetables.

The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud.

This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer.

The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia.

One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked.

Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.