With the advent of the Internet of Things (IoT), smart devices have now changed their dimensions to provide applications in different domains such as medical, agriculture, and Industry 4.0. Although IoT provides more diversified applications, enhancing the security in IoT remains on the darker side of the research. Traditional IoT systems involve a third party to secure sensitive data during transmission in an IoT environment which can lead to complex and serious problems. To overcome security issues and eradicate third-party involvement, Blockchain technology is the modern-day solution in an IoT environment. In the context of a Secured IoT system, we proposed a novel chaotic encryption-based blockchain-IoT architecture to clinch the security and privacy of data. Since smart sensors and image sensors are used widely in an IoT environment, the proposed scheme was tested with different image sets to evaluate performance metrics such as Number of Pixel Change Rate (NPCR), Unified Averaged Changed Intensity (UACI), Correlation Coefficients, and entropy under different attack scenarios. We obtained an NPCR of 99.65%, a UACI of 34%, and an entropy value close to 8. These values incite that the novel chaotic encryption-based blockchain-IoT architecture will be safe from IoT attacks. Results showed that integrating chaotic encrypted blockchain architecture with IoT could be more effective in defending attacks.

With the SARS-CoV-2's exponential growth, intelligent and constructive practice is required to diagnose the COVID-19. The rapid spread of the virus and the shortage of reliable testing models are considered major issues in detecting COVID-19. This problem remains the peak burden for clinicians. With the advent of artificial intelligence (AI) in image processing, the burden of diagnosing the COVID-19 cases has been reduced to acceptable thresholds. But traditional AI techniques often require centralized data storage and training for the predictive model development which increases the computational complexity. The real-world challenge is to exchange data globally across hospitals while also taking into account of the organizations' privacy concerns. Collaborative model development and privacy protection are critical considerations while training a global deep learning model. To address these challenges, this paper proposes a novel framework based on blockchain and the federated learning model. The federated learning model takes care of reduced complexity, and blockchain helps in distributed data with privacy maintained. More precisely, the proposed federated learning ensembled deep five learning blockchain model (FLED-Block) framework collects the data from the different medical healthcare centers, develops the model with the hybrid capsule learning network, and performs the prediction accurately, while preserving the privacy and shares among authorized persons. Extensive experimentation has been carried out using the lung CT images and compared the performance of the proposed model with the existing VGG-16 and 19, Alexnets, Resnets-50 and 100, Inception V3, Densenets-121, 119, and 150, Mobilenets, SegCaps in terms of accuracy (98.2%), precision (97.3%), recall (96.5%), specificity (33.5%), and F1-score (97%) in predicting the COVID-19 with effectively preserving the privacy of the data among the heterogeneous users.

Every year India losses the significant amount of annual crop yield due to unidentified plant diseases. The traditional method of disease detection is manual examination by either farmers or experts, which may be time-consuming and inaccurate. It is proving infeasible for many small and medium-sized farms around the world. To mitigate this issue, computer aided disease recognition model is proposed. It uses leaf image classification with the help of deep convolutional networks. In this paper, VGG16 and Resnet34 CNN was proposed to detect the plant disease. It has three processing steps namely feature extraction, downsizing image and classification. In CNN, the convolutional layer extracts the feature from plant image. The pooling layer downsizing the image. The disease classification was done in dense layer. The proposed model can recognize 38 differing types of plant diseases out of 14 different plants with the power to differentiate plant leaves from their surroundings. The performance of VGG16 and Resnet34 was compared.  The accuracy, sensitivity and specificity was taken as performance Metrix. It helps to give personalized recommendations to the farmers based on soil features, temperature and humidity

The hypothesis of interlingual interference was tested by employing the network model of the semantic memory. Ninety-three true-false propositions were presented to English monolinguals and Spanish-English bilingual subjects. Two variables were measured: the reaction time to the true-false items, and the semantic judgment. It was found that subjects operating in a monolingual context performed equally well. However, the performance of subjects operating in a dual language context was significantly impaired. Difference in hierarchical organization of the semantic memory for the two languages was an important factor in determining interlingual interference.

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