Although software developers of mHealth apps are responsible for protecting patient data and adhering to strict privacy and security requirements, many of them lack awareness of HIPAA regulations and struggle to distinguish between HIPAA rules categories. Therefore, providing guidance of HIPAA rules patterns classification is essential for developing secured applications for Google Play Store. In this work, we identified the limitations of traditional Word2Vec embeddings in processing code patterns. To address this, we adopt multilingual BERT (Bidirectional Encoder Representations from Transformers) which offers contextualized embeddings to the attributes of dataset to overcome the issues. Therefore, we applied this BERT to our dataset for embedding code patterns and then uses these embedded code to various machine learning approaches. Our results demonstrate that the models significantly enhances classification performance, with Logistic Regression achieving a remarkable accuracy of 99.95%. Additionally, we obtained high accuracy from Support Vector Machine (99.79%), Random Forest (99.73%), and Naive Bayes (95.93%), outperforming existing approaches. This work underscores the effectiveness and showcases its potential for secure application development.

Blockchain technology to build safe, decentralized systems for managing individual health records has become increasingly prevalent. As the number of personal health records (PHRs) increases, concerns about data privacy, security, and interoperability emerge, highlighting the need for standardized data integration with secured record linkage, privacy preservation, and quality control in research and analytical platforms. To address these challenges, we propose a novel Blockchain-based Patient Record Linkage System (BlockPRLS) for Big Data Analytics, leveraging confidentiality, integrity, and availability with a secured shared mechanism of PHRs. Moreover, the proposed solution significantly mitigates data gaps among operational health information systems (HIS) and clinical research infrastructure in low- and mid-income countries; where the international data standardization methods (e.g., LOINC, SONMED CT, OMOP Common Data Model) are not followed. Our extended framework presents a detailed architecture of a blockchain platform connecting with a mobile app- B2MAppH that connects patients, doctors, telehealth, and HIS, potentially able to transfer anonymous health records into the Central Data Repository (CDR) of a big data infrastructure. B2MappH is integrated with CDR, maintaining the privacy of the patient. It has been implemented in a private blockchain environment. Therefore, it provides an efficient solution for storing, linking, and sharing patient data, enabling better care coordination, big data research, and outcomes. Experimental evaluation shows the effectiveness of the system for both personal healthcare and big data analytics support.

This paper introduces a novel concept of understanding cerebral dynamics by exploring the acoustic signals generated by blood flow in the brain (BFB) and mechanical resonant frequencies produced by the brain. The concept of this paper will be a groundbreaking approach to brain signal analysis through acoustic signals of BFB. Traditional methods for brain wave capture and analysis mostly depend on fMRI and EEG signals, which are now very popular and have some limitations in accessibility, cost, and real-time analysis capabilities. In this study, we seek the existing gaps in the current brain signal-capturing methods, and our theoretical underpinnings hypothesize that sound produced by blood flow in the brain (BFB) can be an innovative approach to capture the brain signal in a more user-friendly and accessible way. The feasibility of capturing and analyzing these BFB-sound is also discussed in this paper. We proposed a theoretical framework to capture the BFB sound through the human ear. The successful completion of this concept architecture will serve in different applications, from diagnosing neurological disorders to monitoring brain health, underscoring its potential to revolutionize non-invasive brain diagnostics. By synthesizing current knowledge and proposing innovative techniques, this paper aims to pave the way for new frontiers in understanding brain function through the brain sound generated by blood flow and captured from the human ears.

-The integration of Artificial Intelligence (AI) in mobile health (mHealth) devices offers significant advancements in patient care but also raises complex security and privacy concerns for sensitive health data. This paper explores the evolving landscape of Health Insurance Portability and Accountability Act (HIP AA) compliance within the context of AI-based mHealth devices, focusing on anticipated challenges in 2024. It examines the current state of HIP AA compliance in AI-driven mHealth, identifying potential vulnerabilities and gaps among mHealth devices in considering privacy and security. The study outlines key security and privacy considerations specific to AI-powered mHealth technologies, emphasizing risks such as data breaches, ransomware attack and unauthorized access. The paper concludes by highlighting the importance of continual adaptation to the dynamic nature of AI technologies and offers insights for stakeholders to navigate the regulatory landscape and ensure the responsible deployment of AI in healthcare.