Enhancing accessibility: Development and usability testing of mobile application mitigating sexual harassment for visually impaired masseurs.

This study aimed to assess the development and usability of the Visually Impaired Masseur Assistance Application (VIMAA) designed to respond to signs of danger or instances of sexual harassment experienced by Visually Impaired Masseurs (VIMs). It harmonizes Rapid Application Development (RAD) method and qualitative in-depth interviews. RAD was implemented with emphasis on four core stages: requirement identification, design workshop, construction, and implementation, while Qualitative in-depth interviews were conducted utilizing thematic analysis for usability testing.

Machine learning methods to predict particulate matter PM .

Introduction Pollution of air in urban cities across the world has been steadily increasing in recent years. An increasing trend in particulate matter, PM , is a threat because it can lead to uncontrollable consequences like worsening of asthma and cardiovascular disease. The metric used to measure air quality is the air pollutant index (API).

Utilizing data sampling techniques on algorithmic fairness for customer churn prediction with data imbalance problems.

Customer churn prediction (CCP) refers to detecting which customers are likely to cancel the services provided by a service provider, for example, internet services. The class imbalance problem (CIP) in machine learning occurs when there is a huge difference in the samples of the positive class compared to the negative class. It is one of the major obstacles in CCP as it deteriorates performance in the classification process.

Improving the support for XML dynamic updates using a hybridization labeling scheme (ORD-GAP).

As the standard for the exchange of data over the World Wide Web, it is important to ensure that the eXtensible Markup Language (XML) database is capable of supporting not only efficient query processing but also capable of enduring frequent data update operations over the dynamic changes of Web content. Most of the existing XML annotation is based on a labeling scheme to identify each hierarchical position of the XML nodes. This computation is costly as any updates will cause the whole XML tree to be re-labelled.

An empirical study on Resource Description Framework reification for trustworthiness in knowledge graphs.

Knowledge graph (KG) publishes machine-readable representation of knowledge on the Web. Structured data in the knowledge graph is published using Resource Description Framework (RDF) where knowledge is represented as a triple (subject, predicate, object). Due to the presence of erroneous, outdated or conflicting data in the knowledge graph, the quality of facts cannot be guaranteed.

A hybrid recommender system based on data enrichment on the ontology modelling.

A recommender system captures the user preferences and behaviour to provide a relevant recommendation to the user. In a hybrid model-based recommender system, it requires a pre-trained data model to generate recommendations for a user. Ontology helps to represent the semantic information and relationships to model the expressivity and linkage among the data.

Improving the data access control using blockchain for healthcare domain.

Unauthorized access to data is one of the most significant privacy issues that hinder most industries from adopting big data technologies. Even though specific processes and structures have been put in place to deal with access authorization and identity management for large databases nonetheless, the scalability criteria are far beyond the capabilities of traditional databases. Hence, most researchers are looking into other solutions, such as big data management.

Neural matrix factorization++ based recommendation system.

In recent years, Recommender System (RS) research work has covered a wide variety of Artificial Intelligence techniques, ranging from traditional Matrix Factorization (MF) to complex Deep Neural Networks (DNN). Traditional Collaborative Filtering (CF) recommendation methods such as MF, have limited learning capabilities as it only considers the linear combination between user and item vectors. For learning non-linear relationships, methods like Neural Collaborative Filtering (NCF) incorporate DNN into CF methods.