Accurate forecasting of Gross Domestic Product (GDP) is essential for informed policy-making and strategic economic planning. This paper proposes a hybrid deep learning model that integrates consumer sentiment data with traditional economic indicators to predict GDP growth. Utilizing a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, the model captures both semantic sentiment features and temporal economic patterns. Experimental results show that the hybrid model outperforms baseline machine learning algorithms and standalone deep learning approaches in terms of prediction accuracy. This research highlights the potential of sentiment-aware deep learning frameworks in enhancing macroeconomic forecasting and decision-making.

In the aerospace supply chain, a complex, high-stakes ecosystem is at risk of multiple risk categories such as component shortage, cyber threats, and noncompliance with regulations. Traditional risk mitigation strategies are not enough. They are now offered as measures reactive to risks and static contingency plans. This paper investigates how AI-driven predictive risk modeling can break these limitations of the current risk management practices and allow risk management to change from reactionary to proactive across the aerospace supply chain. These models leverage the power of machine learning by poring over structured and unstructured data (telemetry data, supplier log files) and searching for patterns that predict future disruptions. Core technologies that can ingest and process data in real-time, like Apache Kafka and Apache Spark, support dynamic risk calculation. Combining with the domain expertise, they provide precision to the model and compliance framework (FAA, ITAR, AS9100) for legal compliance. The document also mentions some architectural shifts from monolith to microservice systems and the use of design patterns such as CQRS, the Strangler pattern, and ModelOps in the model deployment. Quantifiable benefits, as shown in a case study in a major aerospace OEM, include reduced downtime, decreased procurement times, and better prediction. Results suggest that stakeholders must be involved, ethical AI governance should be implemented, and iterative validation should be used to build trust and alignment in the system. Edge AI, blockchain, and quantum computing are moving in the right direction in the industry and predictive analytics. The guide is a strategic tool for converting their operation to systems with resilient and intelligent supply chains that the aerospace industry’s professionals aspire to embrace.

In recent years, the banking industry has witnessed a surge in digital feedback channels, where customers regularly share their experiences and opinions. Extracting meaningful insights from this unstructured data is vital for enhancing customer satisfaction and service quality. This study presents a comparative analysis of two advanced deep learning models—Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT)—to classify the sentiment of bank customer reviews into positive, negative, and neutral categories. A cleaned and preprocessed dataset consisting of real-world customer reviews was used for model training and evaluation. The LSTM model was able to capture sequential patterns effectively, achieving competitive results in sentiment prediction. However, BERT outperformed LSTM across all evaluation metrics, achieving higher accuracy, precision, recall, and F1-score. Detailed confusion matrix analysis further confirmed BERT’s superiority in handling ambiguous and context-rich sentiment expressions. The findings highlight the practical implications of using transformer-based models in financial text analytics and provide a reliable framework for future sentiment analysis applications in the banking sector.

Employee retention has become a critical issue for many organizations in the modern business environment. Poor retention rates can have significant implications for organizational performance and employee engagement. This study investigates the effects of low employee retention on the overall performance of organizations and the level of engagement among employees. Using a mixed-methods approach, we surveyed 500 employees across different industries, conducted interviews with HR managers, and analyzed secondary data on organizational performance. The findings suggest that high employee turnover negatively impacts productivity, customer satisfaction, and organizational culture. Additionally, poor retention leads to lower employee engagement levels, reduced morale, and increased job dissatisfaction. The paper concludes with strategic recommendations for improving retention practices and fostering a more engaged workforce.

Advanced technological solutions need to address critical data security problems and interoperability issues that emerge from EHR systems developing in complexity. This document evaluates the combination of Blockchain technology and Artificial Intelligence tools for EHR system transformation from a business analytical standpoint. Blockchain technology maintains a secure environment through its indestructible record system and distributed computing framework that locks out breaches and unlawful data modifications thus meeting healthcare requirements of HIPAA and GDPR. AI-driven predictive analytics together with automation enables healthcare system interoperability through data organization of unstructured clinical information with optimized patient record access while providing real-time communication capabilities. This research uses quantitative methods to review securing and streamlining EHR data practices by analyzing how AI and Blockchain techniques work in current healthcare operations. The joint usage of Blockchain technology with AI creates administrative cost reductions of 30% coupled with data retrieval speed improvements totaling 40% while managing to defend healthcare facilities against over 50% of cybersecurity attacks in hazardous environments. This study outlines major challenges for extended adoption which include expensive setup costs together with problems related to standards and barriers raised by regulations. This paper connects empty research areas to deliver operational guidelines for healthcare organizations and their executive leaders and government officials seeking secure EHRs with interoperable capabilities through AI-Blockchain infrastructure deployment. The evaluation indicates Blockchain technology and AI principles hold great transformative power through strategic investments into alignment with regulations and development of infrastructure and automation powered by AI. Research must address the ongoing development of AI-Blockchain systems which will assess their effect on patients' real-time clinical care solutions and personalized medical treatments.

Healthcare cost optimization has improved through the implementation of Artificial Intelligence in remote patient monitoring because it increases operational efficiency and predictive analytics and allows immediate patient care management. This paper examines AI-powered RPM's economic value by showing its capacity to decrease hospital admissions together with reducing healthcare expenses while directing resources effectively. Business intelligence tools with AI help RPM systems create predictive intervention strategies for better patient results without affecting operational costs. This research uses quantitative methods alongside real-world healthcare institution data, financial calculations along with cost-benefit assessment for examining AI-driven RPM sustainability. The economic advantages of Artificial Intelligence are measured through analytical methods which combine cost-effectiveness analysis and machine learning predictive models together with regression analyses. Ai-powered RPM implementation cuts hospital stay expenses by a minimum of thirty percent while diminishing emergency room usage by 25 percent and enables better healthcare workforce allocation that enhances employee productivity and decreases operational expenses. This investigation brings originality through its combination of business intelligence methodology and predictive analytics with AI-driven RPM for better organizational choices. The paper fills knowledge gaps in the literature by using empirical data to prove the extended financial advantages of AI-based healthcare solutions. AI-based RPM systems demonstrate both technological advancement and financial practicability which enables healthcare policymakers together with hospital administrators and insurers to make well-informed investment choices. Researchers must study ways to scale AI-driven RPM systems which will enable their successful implementation throughout healthcare systems with various characteristics.

Quality Assurance (QA) in the retirement services industry is undergoing a paradigm shift as firms modernize legacy Omni-based recordkeeping systems. This paper explores the transition from manual mainframe testing to intelligent automation for Omni-based retirement platforms. We discuss the challenges posed by legacy mainframe environments and the strategies to modernize QA using tools like IBM Personal Communications (PCOMM) for terminal automation, Job Control Language (JCL) for batch processing, and OmniScript for custom business logic. Emphasis is placed on maintaining compliance with stringent retirement regulations and ensuring data accuracy and system performance. We present case studies of organizations that have successfully migrated from labor-intensive, error-prone QA processes to robust automated frameworks. Diagrams and figures illustrate the legacy vs. modern architecture and quantify improvements such as reduced testing cycle time and increased coverage. The results demonstrate that intelligent automation in Omni-based systems not only accelerates testing and enhances quality but also ensures greater reliability and compliance in a highly regulated domain. The findings aim to guide retirement services providers in upgrading their QA practices for better efficiency, accuracy, and risk management.