Abstract Blockchain technology offers a secure solution for managing sensitive data with Artificial Intelligence, supply chain, cloud computing, and healthcare applications. Its key features, confidentiality, decentralization, security, and privacy, enhance healthcare systems, especially when integrated with Internet of Things (IoT) devices. This integration improves communication between healthcare systems and IoT devices, increasing security, privacy, and operational efficiency. However, traditional healthcare systems face challenges like phishing, identity theft, and masquerading attacks. We propose a blockchain-based decentralized application to mitigate these risks and generate, maintain, and validate healthcare medical certificates. The application enables secure communication between hospitals, patients, doctors, and IoT devices using smart contracts for confidentiality and authentication. Our architecture utilizes Non-Interactive Zero-Knowledge Proof to maintain data integrity and privacy. We further integrate Blockchain Data Storage and the Inter-Planetary File System to reduce storage costs and enhance security through Ethereum smart contracts. An Intrusion Detection System monitors IoT traffic to detect potential security threats. Performance analysis demonstrates that this solution addresses key security and privacy challenges, offering an efficient and scalable framework for healthcare data management.

Detecting human personality traits is a critical task across various domains, including healthcare, education, and psychology. Recent advancements in artificial intelligence have greatly enhanced the automatic detection of personality traits using writing styles and handwriting analysis. However, existing datasets and studies predominantly focus on English, limiting the generalizability to multilingual contexts. To bridge this gap, this study presents HiEnWrite, a novel Hindi–English bilingual dataset comprising handwritten samples collected from over 400 authors, annotated with Big Five personality scores. The dataset consists of approximately 800 images, annotated through rigorous Big Five questionnaires. We propose and evaluate an end-to-end convolutional neural network (CNN) tailored to detect five core personality traits. Additionally, extensive experimentation is conducted using transfer learning-based approaches with multiple CNN architectures, achieving a maximum correlation of 0.411 with VGG19. The model performance is thoroughly assessed using metrics like root mean square error (RMSE), training/testing durations, and residual analysis, demonstrating the efficacy of the proposed approach.

Summary Recommender systems have become a core part of the retail experience. Retailers often rely on recommender systems to help them drive more conversions through targeted communication and advertisements. However, recommender systems are not one size fits all. Specialized retailers require specialized recommender systems to consider various features, attributes, and dynamics about the product category. In this paper, we have proposed a novel fruit recommender system that generates dynamic recommendations while remediating the problem of data sparsity. We have developed a novel fruit recommender system that considers the temporal dynamics in the fruit market, like price fluctuations, fruit seasonality, and quality variations that occur throughout the year. To perform this task, we have used Recurrent Recommender Network (RRN), which uses the deep learning method Long Short‐Term Memory (LSTM) to implement the system model. To ensure that our work and results obtained are practical, we have worked in a real‐world setting, by tying up with a specialty fruit retailer based in New Delhi to get the real‐world Point‐of‐Sale (POS) data of consumers. The result of the study suggests our algorithm performs better than other benchmark algorithms along NDCG and RMSE metrics.

Summary Since their launch in the early 1990's, recommender systems (RSs) have played an essential role in information filtering and providing personalized information to users by utilizing the web, mobile, context, personalized, and practical applications. Many research articles have been published in various application domains such as movies, books, documents, news, images, music, shopping, TV programs, tourism, group, social, taxi, and others. However, from these domains, taxi recommendation is a new and thrust area of research, as publications in this domain are few and recently published. Taxi recommendations are associated with drivers‐passengers with their: behavior, hotspots, trajectory, point of interest, route planning or prediction, taxi finding, anomaly or fraud detection, urban computing, mobility patterns and so forth. Due to this diversity and various agendas in taxi recommendations, more exploration is required, as it is a less mature and growing field than other domains. So we have done a literature review in the taxi domain by classification, incorporating reputed articles published from 2001 to August 2022. We include the reputed papers which highlight, analyze and perform studies only on the taxi domain. This article categorizes and discusses the research and development in taxi recommendations as nature, approaches, algorithms, methods, datasets, models, devices, information, and evaluation techniques. This survey article will actively help the researchers and professionals understand the current situations in taxi RS and resolve future trends, opportunities, and research.

Various opportunities are available to depict different domains due to the diverse nature of social networks and researchers' insatiable. An opinion leader is a human entity or cluster of people who can redirect human assessment strategy by intellectual skills in a social network. A more comprehensive range of approaches is developed to detect opinion leaders based on network-specific and heuristic parameters. For many years, deep learning–based models have solved various real-world multifaceted, graph-based problems with high accuracy and efficiency. The Graph Neural Network (GNN) is a deep learning–based model that modernized neural networks’ efficiency by analyzing and extracting latent dependencies and confined embedding via messaging and neighborhood aggregation of data in the network. In this article, we have proposed an exclusive GNN for Opinion Leader Identification (GOLI) model utilizing the power of GNNs to categorize the opinion leaders and their impact on online social networks. In this model, we first measure the n-node neighbor's reputation of the node based on materialized trust. Next, we perform centrality conciliation instead of the input data's conventional node-embedding mechanism. We experiment with the proposed model on six different online social networks consisting of billions of users’ data to validate the model's authenticity. Finally, after training, we found the top-N opinion leaders for each dataset and analyzed how the opinion leaders are influential in information diffusion. The training-testing accuracy and error rate are also measured and compared with the other state-of-art standard Social Network Analysis (SNA) measures. We determined that the GNN-based model produced high performance concerning accuracy and precision.

Abstract Over the years, various document‐clustering techniques were developed to group the textual data. The performance of document clustering systems heavily relies on the optimal use of text representations. Vector space model is an extensively used technique by existing clustering algorithms to present the text in a structured form. However, such representations suffer from a lack of semantic associations, high dimensionality, and sparsity. In order to enrich the document representation by retaining the semantic and morphological associations, this paper introduced a word cluster‐based modified term frequency‐inverse document frequency (WC_MTI) model, in which semantically associated word embeddings from the word2vec are supplemented with morphological information using kernel principal component analysis. In addition, to address high dimensionality and sparsity issues and improve the clustering, we use a self‐training technique that learns discriminative features using the WC_MTI model and autoencoder (AE) and then updates the encoder network weights using assignments from a clustering algorithm as supervision. The proposed model organizes documents into topically compatible clusters by maintaining the semantic and morphological similarity between terms using the skip‐gram with negative sampling (SGNS) and low‐dimensional vector representations. We evaluate the proposed approach against the existing text representation methods. Experimental findings suggest that the proposed approach enhanced the average accuracy by 89.62%.

With the introduction of Web 2.0, there has been an extreme increase in the popularity of social bookmarking systems and folksonomies. In this paper, our motive is to develop a recommender system that is based on user assigned tags and content present on web pages. Although the tag recommendations in social tagging systems can be very accurate and personalized, there exists an issue of risk to the privacy of user's profile, since the social tags are given by a user expose his preferences to other users in contact. To overcome this problem, we have incorporated obfuscation privacy strategies with the well-known Delicious dataset in social tagging based recommender system. We have applied the popular supervised machine-learning algorithm, K-Nearest Neighbours classifier to the dataset that recommends relevant tags to the user. Privacy has been introduced in our tag-based recommender system by hiding some of the necessary tags, bookmarks of a user and replacing them with some random tags and bookmarks. Our experiment results indicate that the recommender system being implemented is highly efficient in terms recall and privacy measure for different values of k. The results and comparisons indicate that we have successfully employed an effective tag recommender system, which also protects the user's privacy without any significant fall in the quality of recommendation.