With the increasing adoption of cloud computing, ensuring data privacy and security has become a critical challenge, particularly in the context of anomaly detection. This study proposes a lightweight homomorphic encryption-based approach to enhance cloud security by enabling privacy-preserving anomaly detection. Homomorphic encryption allows computations to be performed on encrypted data without the need for decryption, ensuring that sensitive information remains protected throughout the detection process. The paper explores the implementation of a lightweight encryption scheme that balances both computational efficiency and strong security, making it suitable for real-time anomaly detection in cloud environments. The proposed method is evaluated against traditional encryption approaches, demonstrating its capability to detect anomalous behaviors without exposing raw data to cloud service providers. Results indicate that the lightweight homomorphic encryption method maintains high levels of accuracy in detecting anomalies while ensuring minimal performance overhead, making it a promising solution for secure cloud-based anomaly detection systems. This work contributes to advancing privacy-preserving techniques in cloud security and paves the way for more secure and efficient cloud computing applications.

This article examines the design challenges that arise when a primary care organization transitions from a commercial electronic health record (EHR) platform to a custom-built system, focusing on three interconnected dimensions: clinical workflow integration, interface usability, and structural data integrity. As of 2024, 91% of U.S. office-based physicians have adopted a certified EHR, yet EHR-related physician burnout remains associated with interface complexity, documentation burden, and coding inaccuracies. The study combines systematic literature analysis with an original engineering case at Oak Street Health to evaluate how purpose-built encounter note design, specifically the Assessment and Plan section with real-time ICD-10 search, structural CPT order linkage to diagnoses, and a pre-signing Superbill review panel,addresses these problems in practice. The proposed architecture is designed to reduce exposure to orphaned ICD or CPT codes by enforcing diagnosis-to-order binding at the application layer and to provide a structured checkpoint for identifying coding inconsistencies before claim submission. Design-stage API optimization also eliminated redundant calls, with a projected reduction of approximately 3,000 server requests per minute at peak load; this estimate requires production validation. Results are relevant for healthcare engineering teams, clinical informaticists, and health system administrators planning EHR modernization initiatives.

This study investigates the use of artificial intelligence (AI) and machine learning (ML) models to predict, detect, and mitigate cybersecurity threats, including zero-day attacks, ransomware, and insider threats. Using a comprehensive dataset of network logs and attack signatures, we evaluated models such as Logistic Regression, Random Forest, XGBoost, CNN, and LSTMOur results demonstrate that deep learning models, particularly CNN (97.3% AUC-ROC) and LSTM (96.8% AUC-ROC), significantly outperform traditional methods, excelling in real-time threat detection and minimizing false positives. This study highlights the practical applicability of AI and ML in enhancing cybersecurity frameworks, paving the way for more efficient and scalable solutions against evolving threats.

The global transportation ecosystem is undergoing a profound structural transformation driven by the convergence of sustainability imperatives, rapid urbanization, digital infrastructure maturation, and evolving mobility consumption patterns. Within this context, Fleet-as-a-Service (FaaS) has emerged as a unifying operational and conceptual paradigm that reconceptualizes vehicle ownership, deployment, testing, and lifecycle management as service-oriented processes rather than asset-bound activities. This research article develops an extensive theoretical and analytical examination of FaaS architectures as they apply to sustainable vehicle testing, shared mobility operations, and first- and last-mile transportation systems. Drawing upon interdisciplinary scholarship from transportation engineering, computer systems, real-time scheduling, cloud and serverless computing, and intelligent transportation systems, the article situates FaaS as a socio-technical infrastructure rather than a narrowly defined logistics solution. Particular emphasis is placed on how FaaS reshapes the testing and validation of emerging vehicle technologies, including automated, connected, and shared vehicles, while simultaneously enabling scalable operational models aligned with sustainability objectives (Deshpande, 2024).

The study synthesizes prior work on shared mobility optimization, demand-responsive transit, feeder services, and micro-simulation modeling to demonstrate how FaaS platforms enable dynamic fleet orchestration across heterogeneous urban environments (Goswami et al., 2021; Huang et al., 2021). Beyond transportation theory, the article critically integrates insights from real-time systems scheduling, serverless computing, and edge-cloud architectures to argue that modern FaaS deployments are computationally intensive systems whose performance, reliability, and ethical accountability depend on sophisticated task partitioning, latency guarantees, and workload management strategies (Baruah, 2013; Nguyen et al., 2019). Through an expansive qualitative methodology grounded in comparative literature analysis and conceptual modeling, the article identifies persistent gaps in existing research, particularly concerning the ethical governance, computational sustainability, and cross-domain integration of FaaS systems.

The results of this analysis indicate that FaaS is not merely an operational efficiency mechanism but a foundational infrastructure capable of aligning mobility innovation with environmental stewardship, equitable access, and technological accountability. The discussion extends these findings by engaging competing scholarly perspectives, addressing limitations of current models, and outlining future research trajectories that bridge transportation planning with computing systems design. By offering a deeply elaborated, citation-rich, and theoretically grounded contribution, this article advances the academic discourse on sustainable mobility systems and positions FaaS as a critical lever for the next generation of intelligent transportation ecosystems (Deshpande, 2024; Grahn et al., 2021).