Abstract The utilization of automated software tools is imperative to enhance the efficiency of lung diseases through the analysis of X-ray images. The main objective of this study is to employ an analysis of chest X-ray images to diagnose lung disease. This study presents an Optimized Convolutional Neural Network (CNNFPA) designed to automate the diagnosis of lung disease. The Flower pollination technique is employed to optimize the hyperparameters associated with the training of the layers of the Convolutional Neural Network (CNN). In this paper, a novel model called RCNNFPA model is proposed, which makes use of a pre-trained ResNet50 with its layers frozen. Subsequently, CNNFPA architecture is integrated on top of the frozen ResNet-50 layers. This approach allowed us to leverage the knowledge captured by the ResNet-50 model on a large-scale dataset. To assess the efficacy of the proposed model and perform a comparison study using several classification methodologies, various publicly available datasets comprising images of COVID-19, Viral Pneumonia, Normal, and Tuberculosis are employed. As optimized and elaborated upon in this study, the CNN model is juxtaposed with existing state-of-the-art models. The proposed novel RCNNFPA model demonstrates considerable potential in facilitating the automated screening of individuals affected by different lung diseases.

Abstract The aptness of machine learning (ML) to learn from large datasets, discover trends, and make predictions has demonstrated its potential to metamorphose the medical field. Medical data analysis with ML algorithms can improve patient outcomes in terms of both treatment and diagnosis. This paper investigates the numerous possibilities of ML in the medical industries, including radiology, pathology, genomics, and clinical decision‐making. It also goes over the benefits and drawbacks of ML in various sectors as well as the limitations that come with its application. It illustrates the potential advantages of ML, such as better accuracy and efficiency in diagnosis and individualized treatment programs, through a review of previous studies. Lastly, it provides perspectives on prospective advancements and prospects for the discipline. This study also intends to investigate the applications of deep learning (DL) in the medical field. DL algorithms have performed exceptionally satisfactorily in several healthcare‐related fields. The main conclusions of the study are summarized, and their ramifications for the healthcare sector are discussed in this paper's conclusion. This paper intends to contribute to a greater understanding of the prevailing state of the discipline and the possibility for future developments by emphasizing the prospects of these methodologies to alter medical study and clinical practice.

Abstract Web is becoming rich in data. Some of the sources from where these data are originating includes Blogs, YouTube, Twitter, Emails, E‐commerce, Banking, sensors, and the Internet of Things. But these data are structured in a very poor fashion. The content of the web is becoming heterogeneous in nature both in terms of compendium and structure. It can be said that these data are human‐readable data but the main motive is to draw inferences from these data which is only possible if it can be made machine‐accessible. Clustering is considered an important task to organize these data and draw meaningful inferences from these data. In this paper, a clustering approach is proposed that can be applied to knowledge graphs and the possibility of applying Locality Sensitive Hashing is explored. Given the size of linked data, it is observed that this approach can be effective and scalable in comparison to other clustering approaches such as Hierarchical clustering, K‐Means clustering, and K‐Medoid clustering in discovering different communities that are defined by the link structure of the graph. The experimental results on different types of Linked Data sources justify the efficacy of the proposed model in terms of scalability and efficiency.

Abstract With the advent of the internet, the recommender system escorts the users in a customized way to nominate items from a massive set of possible alternatives. The emergence of overspecification in recommender system has emphasized negative effects on the context of prediction. The drift of user interest over time is one of the challenging affairs in present personalized recommender system. In this paper, we present a neural network model to improve the recommendation performance along with usage of fuzzy-based clustering to decide membership value of users and matching imputation to cutback sparsity to some extent. We evaluate our model on the MovieLens dataset and show that our model not only elevates accuracy, but also considers the order in which recommendation should be given. We compare the proposed model with a number of state-of-the-art personalization methods and show the dominance of our model using accuracy metrics such as root-mean-square error and mean absolute error.