Princy As

Agricultural enterprises have financial uncertainties due to market volatility, climate disruptions, and changes in policies; therefore, farming operations must use timely and accurate forecasts, as they are particularly vulnerable to external economic shocks and environmental variability. Standard forecasting methods usually cannot capture nonlinear dependencies and dynamic shifts in risk profiles; therefore, there is a need to consider intelligent, adaptive systems. Research proposes a novel financial risk prediction model using the Sooty Tern Optimization Algorithm Attention-Based Long Short-Term Memory (STOA-Att-LSTM). Financial risk data were collected, which included agricultural enterprise financial records, national weather databases, and commodity market indices. To ensure data integrity and modelling efficiency, two essential pre-processing techniques were employed. Handling missing values was performed using linear interpolation to reconstruct incomplete sequences, particularly in time-series financial and climatic data, to standardize variables, facilitating efficient model training and convergence. The STOA algorithm was used to optimize the hyper-parameters of the Att-LSTM model, enhancing its generalization and predictive accuracy. The attention mechanism enabled the model to dynamically focus on critical time-dependent features influencing financial risk. Dynamic state analysis further strengthened the framework by capturing temporal shifts in enterprise conditions. Model evaluation using Python-based implementation of error metrics and classification accuracy (0.9899) showed better results compared to traditional and baseline deep learning (DL) models. The proposed framework offers a robust, adaptive tool for proactive financial risk assessment in agricultural enterprises, supporting sustainable decision-making in uncertain environments.

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.