Rice is grown almost everywhere in the world, especially in Asian countries, because it is part of the diets of about half of the world's population. However, farmers and planting experts have faced several persistent agricultural obstacles for many years, including many rice diseases. Severe rice diseases might result in no grain harvest; hence, in the field of agriculture, a fast, automatic, less expensive, and reliable approach to identifying rice diseases is widely needed. This paper focuses on how to build a lightweight deep learning model to detect rice plant diseases more precisely. To achieve the above objective, we created our own CNN model "LW17" to detect rice plant disease more precisely in comparison to some of the pre-trained models, such as VGG19, InceptionV3, MobileNet, Xception, DenseNet201, etc. Using the proposed methodology, we took UCI datasets for disease detection and tested our model with different layers, different training–testing ratios, different pooling layers, different optimizers, different learning rates, and different epochs. The Light Weight 17 (LW17) model reduced the complexity and computation cost compared to other heavy deep learning models. We obtained the best accuracy of 93.75% with the LW17 model using max pooling with the "Adam" optimizer at a learning rate of 0.001. The model outperformed the other state-of-the-art models with a limited number of layers in the architecture.

The detection of neurological disorders and diseases is aided by automatically identifying brain tumors from brain magnetic resonance imaging (MRI) images. A brain tumor is a potentially fatal disease that affects humans. Convolutional neural networks (CNNs) are the most common and widely used deep learning techniques for brain tumor analysis and classification. In this study, we proposed a deep CNN model for automatically detecting brain tumor cells in MRI brain images. First, we preprocess the 2D brain image MRI image to generate convolutional features. The CNN network is trained on the training dataset using the GoogleNet and AlexNet architecture, and the data model's performance is evaluated on the test data set. The model's performance is measured in terms of accuracy, sensitivity, specificity, and AUC. The algorithm performance matrices of both AlexNet and GoogLeNet are compared, the accuracy of AlexNet is 98.95, GoogLeNet is 99.45 sensitivity of AlexNet is 98.4, and GoogLeNet is 99.75, so from these values, we can infer that the GooGleNet is highly accurate and parameters that GoogLeNet consumes is significantly less; that is, the depth of AlexNet is 8, and it takes 60 million parameters, and the image input size is 227 × 227. Because of its high specificity and speed, the proposed CNN model can be a competent alternative support tool for radiologists in clinical diagnosis.

The objective of this paper is to provide a better rendition of Generation Z purchase intentions of retail products through Facebook. The study gyrated around the favorable attitude formation of Generation Z translating into intentions to purchase retail products through Facebook. The role of antecedents of attitude, namely enjoyment, credibility, and peer communication was also explored. The main purpose was to analyze the F-commerce pervasiveness (retail purchases through Facebook) among Generation Z in India and how could it be materialized effectively. A conceptual façade was proposed after trotting out germane and urbane literature. The study focused exclusively on Generation Z population. The data were statistically analyzed using partial least squares structural equation modelling. The study found the proposed conceptual model had a high prediction power of Generation Z intentions to purchase retail products through Facebook verifying the materialization of F-commerce. Enjoyment, credibility, and peer communication were proved to be good predictors of attitude ( $R^{\mathrm{2}}$=0.589) and furthermore attitude was found to be a stellar antecedent to purchase intentions ( $R^{\mathrm{2}}$=0.540).