Angad Tiwary

Risk prediction is crucial in corporate financial management, influencing strategic decision-making, investment optimization, and proactive mitigation. Traditional models struggle to handle modern financial data's complexity, nonlinearity, and temporal volatility. To address these limitations, this research proposes an advanced, intelligent risk prediction framework based on a Siberian Tiger Optimization-driven Temporal Convolutional Neural Network (STO-TCN). The framework is specifically designed to improve predictive accuracy, adaptability, and robustness in fluctuating financial environments. Research utilizes a comprehensive dataset comprising publicly available corporate financial statements, stock exchange disclosures, and macroeconomic indicators across diverse industry sectors. To enhance data integrity and model performance, two preprocessing techniques were applied: z-score standardization to ensure uniform data scaling and outlier detection to minimize the distortion caused by anomalous data entries. The TCN component effectively captures sequential patterns in financial time-series data, while the STO algorithm optimizes model hyperparameters and network weights, accelerating convergence and reducing overfitting. Experimental results demonstrate that the STO-TCN framework significantly outperforms traditional models with an accuracy of 0.9844, particularly in highly dynamic market scenarios using Python. This predictive framework offers a scalable and adaptive solution for corporate financial risk assessment, with practical applications in investment planning, regulatory compliance, financial governance, and enterprise sustainability. Further investigation incorporates real-time data streams and evaluates performance in small and medium-sized enterprises (SMEs) to broaden its applicability.

Introduction: Effective communication mechanisms are vital for organizational success, influencing employee engagement and overall performance. Despite their importance, many organizations face challenges due to unclear or infrequent communication, which undermines trust and productivity. This research investigates the influence of communication mechanisms—including frequency, medium, formality, and clarity—on employee engagement and organizational effectiveness, with employee trust as a mediating factor.Methodology: Data were collected through a structured survey administered to 171 employees across various sectors. The survey measured communication practices, employee trust, and engagement using a 5-point Likert scale. Statistical analyses, including regression analysis, were conducted using IBM SPSS version 25 to examine the relationships among variables.Results: The analysis revealed that communication frequency and clarity significantly enhance Economic success (ES), and Operational efficiency (OE). Regression results indicated a strong positive correlation between clear communication and employee engagement (p < 0.01). Two-way communication (TWC), Transparency of communication (TC), Communication consistency (CC)and Employee engagement (EE) also contributed to improved trust and engagement.Conclusion: Findings highlight that communication clarity and frequency are critical drivers of employee engagement, facilitated by trust in communication. Organizations should prioritize enhancing communication quality and consistency to build trust, thereby fostering higher engagement and improved organizational effectiveness.