Rice, a worldwide grown grain, frequently suffers production issues caused by nutrient imbalances, particularly potassium, nitrogen, and phosphorus. Identifying nutrient deficiencies in rice plants proves challenging due to variations in leaf colour and form. Visually classifying nutritional shortages based on leaf characteristics, such as colour and shape, becomes a complex and resource-intensive task. The intricacies involved make the identification of nutrient deficiencies in rice both time-consuming and expensive. This study presents a computer vision-based deep learning system termed CAR-CapsNet, an upgraded capsule network (CapsNet) that uses contextual attention routing (CAR) to classify rice crop nutrient deficiencies. CAR-Capsnet’s innovative use of contextual attention routing significantly enhances the model’s ability to navigate and interpret complex visual features and patterns, leading to improved classification accuracy compared to previous routing methods. The training and evaluation datasets are sourced from Kaggle, a freely accessible data platform. The dataset consists of 1,155 images of rice leaves, divided into three distinct classes representing deficiencies in nitrogen, phosphorus, and potassium. The dataset undergoes pre-processing using a Wiener filter and adaptive Otsu segmentation. The proposed model was evaluated against CNN and the original CapsNet. CAR-CapsNet outperformed both baseline models in the experiments. CAR-CapsNet classifies rice crop nutrient deficiencies with 97.1% accuracy. Additionally, the model exhibits an impressive recall of 96.9%, an exceptional Kappa score of 95.4%, and an F1-score of 96.9%, highlighting its overall effectiveness. The classifier’s performance was compared with three prior approaches, including Random Forest Regression with an accuracy of 81.82%, SVM with C-means clustering at 92%, and VGG19 at 91.8%. The results demonstrate that the proposed method more effectively classifies rice crop nutrient deficiencies than these methods.

Security has emerged as the most concerned aspect of cloud computing environment and a prime challenge for the cloud users. The stored data can be retrieved by the user whenever and wherever required. But there is no guarantee that the data stored in the cloud server has not been accessed by any unauthorized user. The current cloud framework does not allow encrypted data to be stored due to the space and storage cost. Storing secret data in an unencrypted form is vulnerable to external attacks by both illegitimate customers and a Cloud Service Provider (CSP). Traditional encryption techniques require more computation and storage space. Hence, protecting cloud data with minimal computations is the prime task. Secured Document Sharing Using Visual Cryptography (SDSUVC) technique proposes an efficient storage scheme in a cloud for storing and retrieving a document file without any mathematical computations and also ensures data confidentiality and integrity.

The past health survey shows death of few thousand people due to their heavy workload and mental pressure. Death of more patients is unavailability of doctors in critical situation. With the advancement in technology, the proposed system can monitor the patient's body condition uninterruptedly from anywhere in the world. The researcher is planned to make an inter-net of things for health care system which can monitor health of human being. In emergency situation notification will be send to doctors immediately. It takes the body parameters like Temperature, humidity, and pulse sensor and body movements. Analysis of patients’ body data is done against the normal situation to track abnormal physiological parameters.

Cloud computing model allows the customers to access the secured information from the cloud server without the help of using the hardware or the software. For the effective usage of the data from the cloud provider, the data owner encrypts the data and then its ends to the cloud server. To secure the data in the cloud we have dealt through lots of security issues earlier. In this paper we have proposed the idea how the data could be made more secured using visual cryptographic techniques. This model not only increases the trust among the users to send their information with full reliability and also helps in the authorization of the users.