The relatively short timeframe of the data-oriented approach has made cloud architecture the basis for flexible and effective data analysis and data science projects. This paper presents the design strategies and considerations of cloud architectures for data science platforms that compliments modern analytics and machine learning workloads. Sub-processes like data acquisition, management, analysis, and coordination are discussed, as well as their part in supporting moment and science driven decision-making. Responsiveness is given on the use of tools and platforms that are built natively on cloud to help in getting better collaboration, better costs and high performance. Security and compliance issues are discussed in order to create a viable base for the protection of potentially private and confidential information in risky business sectors. Also, there are emerging trends like edge computing and AI analytics that describe in detail what may be expected in cloud computing for data science in the future. In the framework of this paper, the best practices and case examples serve as recommendations for developing and improving cloud architecture for innovative data capabilities within an organization.

As the global population ages, the demand for long-term care (LTC) services has risen significantly. Long-term care facilities that support residents with chronic illnesses, disabilities, or age-related conditions face numerous challenges in delivering effective and efficient care. Traditional methods of monitoring residents' health often rely on periodic check-ups, which may overlook subtle changes in health, contributing to delayed interventions and escalating healthcare costs.

AI-enabled remote health monitoring represents a transformative solution to these challenges. By leveraging artificial intelligence (AI) technologies, such as wearable devices, smart sensors, and predictive analytics, healthcare providers can continuously monitor vital signs, detect health issues early, and personalize care plans for each resident. This white paper explores the current landscape of AI-driven monitoring in LTC, its benefits, challenges, and future potential.

The rapid growth of digital commerce has intensified the need for secure, efficient, and user-friendly authentication methods. Traditional authentication techniques, such as passwords and PINs, are increasingly vulnerable to cyber threats, prompting the exploration of more advanced solutions. This research investigates the integration of big data with biometric authentication systems to improve real-time identity verification processes in digital commerce. By leveraging big data analytics, biometric algorithms can achieve higher accuracy, scalability, and adaptability, providing more robust security and a seamless user experience.

The study highlights how big data enables the enhancement of biometric algorithms through continuous learning, real-time data processing, and the combination of multiple biometric modalities (e.g., fingerprint, facial recognition, and voice). It explores the potential of machine learning and artificial intelligence to fine-tune biometric systems, addressing challenges such as false acceptance/rejection rates and improving system responsiveness. Furthermore, the research identifies key challenges, including data privacy concerns, algorithmic bias, ethical issues, and technical constraints related to processing large volumes of biometric data in real-time.

The research also examines the future trajectory of biometric authentication in digital commerce, emphasizing advancements in multi-modal biometrics, quantum encryption, and continuous authentication. As these technologies evolve, they promise to make biometric systems more secure, efficient, and accessible. Despite the challenges, the integration of big data in biometric authentication holds great potential to redefine the landscape of digital commerce by offering a safer, more user-friendly experience for consumers worldwide.

PEBSA is a machine learning (ML) model that aims to analyze public sentiment towards America's economic performance and predict its future behavior. By collecting public comments and posts from several social media platforms, sentiment analysis techniques will be applied to classify comments based on positive or negative outlooks. The ML model will then incorporate macroeconomic reasoning to forecast future economic behavior. The project will focus on fair and unbiased testing by employing scientific sampling techniques during data collection processes. The outcomes of this research will provide valuable insights for policymakers, businesses, and investors, facilitating informed decision-making in the realm of economic performance analysis.

This article analyzes the vulnerabilities of the Content Security Policy (CSP) standard with the aim of improving website security. It highlights that this standard has vulnerabilities that enable attackers to successfully execute attacks by injecting malicious code into web pages. The relevance of studying CSP vulnerabilities to enhance website protection is substantiated. The primary aspects of implementing web resource protection using the CSP standard are discussed. The article outlines the most well-known techniques for bypassing the directives of this standard. It describes the process of executing an XSS attack using the "unsafe-inline" directive. It is revealed that such an attack organization allows the interception of user data without detection by users or security administrators. The conclusion is made that the use of the unsafe-inline directive by developers does not provide an adequate level of protection against XSS attacks. As an alternative, the implementation of a more effective CSP policy, configured in accordance with the recommendations of information security specialists, is proposed.