Gopal Krishna Shyam

Gopal Krishna Shyam, Ila Chandrakar

Crop yield prediction is one of the most important challenge, which is crucial to world food security and policy-making decisions. The conventional forecasting techniques are limited in their accuracy with reference to the fact that they utilize static data sources that do not reflect the dynamic and intricate relationships that exist between the variables of the environment over time [5,13]. This paper presents Attention-Based Multi-Modal Deep Learning Framework (ABMMDLF), which is suggested to be used in high-accuracy spatio-temporal crop yield prediction. The model we use combines multi-year satellite imagery, high-resolution time-series of meteorological data and initial soil properties as opposed to the traditional models which use only one of the aforementioned factors [12, 21]. The main architecture involves the use of Convolutional Neural Networks (CNN) to extract spatial features and a Temporal Attention Mechanism to adaptively weight important phenological periods targeted by the algorithm to change over time and condition on spatial features of images and video sequences. As can be experimentally seen, the proposed research work provides an R^2 score of 0.89, which is far better than the baseline models do.

Gopal Krishna Shyam, Priyanka Bharti

In modern distributed cloud environments, efficient resource allocation is required as traditional scaling mechanisms are often subject to cloud thrashing due to network-induced latencies. In this paper, we propose C-SAS (Complex-Stability Aware Scaling), an intelligent autonomous orchestration framework that leverages complex analytic methods to achieve system-wide equilibrium. In contrast to heuristic-based models, C-SAS acts as a stability-aware agent, converting telemetry noise into a deterministic "Safety Envelope" on the $s$-plane using the Argument Principle and Rouché's Theorem. The algorithm smartly suppresses oscillatory scaling operations that would otherwise degrade performance, by computing a real-time Analytic Stability Index (ASI). The experimental results show that C-SAS reduces VM flapping by 94\%, and achieves 96\% resource efficiency, significantly outperforming standard PID and ML-based autonomous agents. Our results suggest that future resilient autonomous cloud infrastructures will require AI-driven orchestrators with built-in formal stability constraints.