This research presents a novel approach to evaluating student academic performance at Nalla Narasimha Reddy Group of Institutions (NNRG) by implementing a Student Training Based Optimization (STBO) algorithm. The proposed method draws inspiration from the structured training and adaptive learning behavior of students, simulating their progression through knowledge acquisition, skill refinement, and performance enhancement phases. The STBO algorithm is applied to optimize academic performance assessment by identifying key parameters such as attendance, internal assessments, learning pace, participation, and project outcomes. By modelling student development as a dynamic optimization process, the algorithm effectively predicts academic outcomes and recommends personalized strategies for improvement. Experimental evaluation on real academic datasets from NNRG CSE, CSE (Data Science), and CSE (AIML) Students demonstrates that the STBO algorithm achieves higher prediction accuracy and adaptive feedback generation when compared to traditional statistical and machine learning techniques. This approach also facilitates early identification of at-risk students and promotes data-driven decision-making for faculty and administration. Overall, the STBO-based framework not only enhances performance assessment but also contributes to academic excellence by aligning learning strategies with individual student needs.

Scheduling is an NP-hard problem, and heuristic algorithms are unable to find approximate solutions within a feasible time frame. Efficient Task Scheduling (TS) in Cloud-Fog Computing (CFC) environments is crucial for meeting the diverse resource demands of modern applications. This paper introduces the Sewing Training-Based Optimization (STBO) algorithm, a novel approach to resource-aware task scheduling that effectively balances workloads across cloud and fog resources. STBO categorizes Virtual Machines (VMs) into low, medium, and high resource utilization queues based on their computational power and availability. By dynamically allocating tasks to these queues, STBO minimizes delays and ensures that tasks with stringent deadlines are executed in optimal environments, enhancing overall system performance. The algorithm leverages processing delays, task deadlines, and VM capabilities to assign tasks intelligently, reducing response times and improving resource utilization. Experimental results demonstrate that STBO outperforms existing scheduling algorithms in reducing makespan by 21.6%, improved energy usage by 31%, and maximizing throughput by 27.8%, making it well-suited for real-time, resource-intensive applications in CFC systems.

Scheduling is an NP-hard problem, and heuristic algorithms are unable to find approximate solutions within a feasible time frame. Efficient task scheduling in Cloud Computing (CC) remains a critical challenge due to the need to balance energy consumption and deadline adherence. Existing scheduling approaches often suffer from high energy consumption and inefficient resource utilization, failing to meet stringent deadline constraints, especially under dynamic workload variations. To address these limitations, this study proposes an Energy-Deadline Aware Task Scheduling using the Water Wave Optimization (EDATSWWO) algorithm. Inspired by the propagation and interaction of water waves, EDATSWWO optimally allocates tasks to available resources by dynamically balancing energy efficiency and deadline adherence. The algorithm evaluates tasks based on their energy requirements and deadlines, assigning them to virtual machines (VMs) in the multi-cloud environment to minimize overall energy consumption while ensuring timely execution. Google Cloud workloads were used as the benchmark dataset to simulate real-world scenarios and validate the algorithm's performance. Simulation results demonstrate that EDATSWWO significantly outperforms existing scheduling algorithms in terms of energy efficiency and deadline compliance. The algorithm achieved an average reduction of energy consumption by 21.4%, improved task deadline adherence by 18.6%, and optimized resource utilization under varying workloads. This study highlights the potential of EDATSWWO to enhance the sustainability and efficiency of multi-cloud systems. Its robust design and adaptability to dynamic workloads make it a viable solution for modern cloud computing environments, where energy consumption and task deadlines are critical factors.