A large number of organizations have been increasingly adopting hybrid cloud computing models due to the significant acceleration of digital business transformations. These hybrid clouds offer organizations scalability and flexibility from public cloud platforms combined with control and compliance from their on-premises infrastructure. Hybrid cloud models also present organizations with several advantages such as greater operational agility; lower costs; and greater workload scalability. However, hybrid clouds create a variety of new and complex cybersecurity issues related to identity management; cross-cloud visibility; data governance; regulatory compliance; and distributed attack surfaces. The objective of this research is to analyze and compare several of the most recent data security methods used by hybrid cloud systems for digital businesses. In order to achieve the objective, the research will employ a multi-faceted approach consisting of literature synthesis; technical framework analysis; a metric based assessment model; and a simulated threat modeling process. Advanced security methods such as Zero Trust Architecture (ZTA); Cloud Security Posture Management (CSPM); Cloud Workload Protection Platforms (CWPP); Homomorphic Encryption; Secure Multi-Party Computation (SMPC); Trusted Execution Environments (TEEs) and AI-driven Security Operations (SecOps) were selected for examination. The proposed evaluation method examines each identified technique using a set of multiple operational and security metrics such as confidentiality; integrity; availability; scalability; compliance alignment; performance overhead and incident response efficiency. Evaluation results indicate that organizations can increase their hybrid cloud ecosystem's security resilience through the use of integrated security frameworks incorporating elements of ZTA; automated CSPM/CWPP components and hardware-enforced trust technologies. The proposed framework can assist decision makers at organizations in determining how best to implement their hybrid cloud security solutions in accordance with current cybersecurity regulations and standards.

The high growth rate of digital financial ecosystems has greatly amplified the magnitude, speed, and sophistication of fraudulent transactions that have presented major challenges to the conventional fraud detection systems. The traditional rule-based and statistical models usually have high false positive rates, slow detection, and minimal capability to adjust to changing patterns of fraud. In this study, it is proposed to develop a deep learning-based enterprise risk analytics framework that would allow detecting financial fraud in the real-time and meet the regulatory compliance requirements. The architecture combines sophisticated deep learning models, such as Long Short-Term Memory (LSTM) networks, autoencoders and graph neural networks, with business risk management applications, such as dynamic risk scoring, anomaly detection pipelines, and compliance monitoring layers. The proposed system is tested using the publicly available transactional datasets, like the European card fraud data, on the basis of the most important performance indicators, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (ROC-AUC). The results show that deep learning models by far exceed traditional machine learning methods by being more accurate in detection and significantly lowering false positives in highly imbalanced datasets. Additionally, explainable AI methods improve model transparency, which can be easily accepted by regulators and audited. The research will add to the body of knowledge by filling in the gap between superior artificial intelligence methods and risk governance models on an enterprise level and provide a flexible and scalable answer to the contemporary financial institutions. The offered framework both enhances the ability to detect fraud and facilitate proactive risk management and compliance in more sophisticated financial settings.

The rapid expansion of the Internet of Things (IoT) has enabled seamless connectivity among billions of resource-constrained devices, creating new opportunities in smart healthcare, industrial automation, smart cities, and cyber-physical systems. However, this connectivity introduces serious security challenges, particularly in the area of device authentication and secure communication. Mutual authentication is a fundamental requirement to ensure that both communicating entities verify each other before exchanging sensitive data. Traditional authentication mechanisms are often unsuitable for IoT environments due to their computational overhead, energy consumption, and communication latency. This paper proposes a lightweight mutual authentication protocol designed specifically for resource-constrained IoT environments. The proposed framework leverages efficient cryptographic primitives and physical unclonable functions (PUFs) to achieve secure, scalable, and low-overhead authentication. The protocol is evaluated conceptually against common IoT threats such as impersonation attacks, replay attacks, and man-in-the-middle attacks. Comparative analysis with existing state-of-the-art approaches demonstrates that the proposed solution significantly improves efficiency while maintaining strong security guarantees. The study also explores integration scenarios in Internet of Medical Things (IoMT) and edge-enabled IoT architectures.

Modular Multilevel Converters (MMCs) have emerged as one of the most significant power electronic converter topologies for medium- and high-voltage applications due to their scalability, modularity, superior output waveform quality, and reduced harmonic distortion. Despite their advantages, conventional MMC control strategies typically depend on continuous capacitor voltage measurement for balancing energy among submodules. Such requirements increase system complexity, sensing costs, communication burden, and susceptibility to measurement inaccuracies. This study presents a comprehensive review and conceptual research framework for an advanced control strategy applied to a 21-level MMC employing Nearest Level Modulation (NLM) without direct capacitor voltage sensing. The proposed framework integrates modulation optimization, circulating current suppression, sensorless balancing mechanisms, and adaptive control principles to achieve stable converter operation while reducing hardware requirements. A detailed synthesis of existing MMC control approaches is conducted, emphasizing modulation methods, predictive control techniques, voltage balancing mechanisms, and renewable-energy-oriented MMC applications. Based on identified research gaps, a sensorless NLM-based control architecture is formulated and analyzed. The framework demonstrates how switching state selection, energy distribution estimation, and current-based balancing algorithms can maintain capacitor voltage equilibrium without dedicated voltage sensors. The study further discusses expected performance improvements in switching losses, harmonic quality, computational efficiency, reliability, and scalability. Findings indicate that sensorless NLM control can provide a practical and economically viable alternative for next-generation MMC systems deployed in smart grids, HVDC transmission, renewable energy integration, and industrial drive applications. The research contributes a structured theoretical foundation for future implementation and validation of advanced MMC control systems.

Smart buildings are increasingly being promoted as a solution to rising global energy demand, yet many of them still suffer from inefficiencies in energy usage due to poor forecasting, suboptimal control strategies, and limited coordination between energy generation and consumption systems. At the same time, the integration of renewable energy sources such as solar, wind, and hybrid storage systems has introduced additional complexity because of their intermittent and unpredictable nature. In this context, artificial intelligence offers promising capabilities for improving energy optimization through demand prediction, adaptive control, and intelligent scheduling of energy resources. However, most existing studies focus either on AI-based energy management or renewable integration in isolation, with limited attention given to how these systems can be effectively incorporated into construction project management processes. This gap is particularly important during the design and planning phases, where early decisions significantly influence long-term building performance. This paper proposes a conceptual framework that integrates AI-driven energy optimization with renewable energy systems from a construction lifecycle perspective. The framework emphasizes data-driven decision support, lifecycle energy planning, and sustainability-aware project management. The key contribution lies in connecting energy modeling, AI techniques, and construction project decision-making into a unified approach aimed at improving both operational efficiency and environmental performance of smart buildings.

Legacy healthcare information systems often operate through fragmented data models, monolithic applications, limited interoperability, and manually governed deployment practices that restrict scalability, security, and clinical responsiveness. This technical paper proposes an AWS-native modernization architecture that transforms legacy healthcare platforms into secure, interoperable, microservices-based systems supported by automated DevOps pipelines. The paper synthesizes research on electronic health record interoperability, FHIR-based integration, blockchain-enabled health data exchange, semantic data mapping, cloud-native systems, cybersecurity, healthcare analytics, and artificial intelligence to develop a structured modernization framework. The proposed model combines domain-oriented microservices, API-driven interoperability, container orchestration, automated CI/CD workflows, identity and access management, observability, and compliance-aware data governance. The analysis indicates that healthcare modernization should not be treated merely as infrastructure migration but as a socio-technical redesign of data, workflow, security, and operational governance. Findings suggest that AWS-native services can improve deployment reliability, horizontal scalability, auditability, and system resilience when combined with standardized clinical terminologies and secure interoperability layers. However, modernization also introduces risks related to vendor dependency, migration complexity, data quality, regulatory accountability, and organizational readiness. The paper concludes that a phased, security-by-design, interoperability-first architecture provides a practical pathway for healthcare institutions seeking to modernize legacy systems without disrupting clinical continuity.