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 correct diagnosis of heart disease can save lives, while the incorrect diagnosis can be lethal. The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches, including deep learning. We used a dataset with 13 primary characteristics to carry out the research. Support vector machine and logistic regression algorithms are used to process the datasets, and the latter displays the highest accuracy in predicting coronary disease. Python programming is used to process the datasets. Multiple research initiatives have used machine learning to speed up the healthcare sector. We also used conventional machine learning approaches in our investigation to uncover the links between the numerous features available in the dataset and then used them effectively in anticipation of heart infection risks. Using the accuracy and confusion matrix has resulted in some favorable outcomes. To get the best results, the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine (SVM) classification.

We live in an age where everything around us is being created. Data generation rates are so scary, creating pressure to implement costly and straightforward data storage and recovery processes. MapReduce model functionality is used for creating a cluster parallel, distributed algorithm, and large datasets. The MapReduce strategy from Hadoop helps develop a community of non-commercial use to offer a new algorithm for resolving such problems for commercial applications as expected from this working algorithm with insights as a result of disproportionate or discriminatory Hadoop cluster results. Expected results are obtained in the work and the exam conducted under this job; many of them are scheduled to set schedules, match matrices’ data positions, clustering before determining to click, and accurate mapping and internal reliability to be closed together to avoid running and execution times. Mapper output and proponents have been implemented, and the map has been used to reduce the function. The execution input key/value pair and output key/value pair have been set. This paper focuses on evaluating this technique for the efficient retrieval of large volumes of data. The technique allows for capabilities to inform a massive database of information, from storage and indexing techniques to the distribution of queries, scalability, and performance in heterogeneous environments. The results show that the proposed work reduces the data processing time by 30%.