With the advancement of technology and time, people have always sought to solve problems in the most efficient and quickest way possible. Since the introduction of the cloud computing environment along with many different sub-substructures such as task schedulers, resource allocators, resource monitors, and others, various algorithms have been proposed to improve the performance of the individual unit or structure used in the cloud environment. The cloud is a vast virtual environment with the capability to solve any task provided by the user. Therefore, new algorithms are introduced with the aim to improve the process and consume less time to evaluate the process. One of the most important sections of cloud computing is that of the task scheduler, which is responsible for scheduling tasks to each of the virtual machines in such a way that the time taken to execute the process is less and the efficiency of the execution is high. Thus, this paper plans to propose an ideal and optimal task scheduling algorithm that is tested and compared with other existing algorithms in terms of efficiency, makespan, and cost parameters, that is, this paper tries to explain and solves the scheduling problem using an improved meta-heuristic algorithm called the Hybrid Weighted Ant Colony Optimization (HWACO) algorithm, which is an advanced form of the already present Ant Colony Optimization Algorithm. The outcomes found by using the proposed HWACO has more benefits, that is, the objective for reaching the convergence in a short period of time was accomplished; thus, the projected model outdid the other orthodox algorithms such as Ant Colony Optimization (ACO), Quantum-Based Avian Navigation Optimizer Algorithm (QANA), Modified-Transfer-Function-Based Binary Particle Swarm Optimization (MTF-BPSO), MIN-MIN Algorithm (MM), and First-Come-First-Serve (FCFS), making the proposed algorithm an optimal task scheduling algorithm.

Cervical cancer is a significant health problem across the globe, especially in nations with low or middle incomes, where access to early screening is limited and contributes to high mortality rates. Traditional diagnostic methods, including Papanicolaou smears (Pap smears) and Human Papillomavirus testing (HPV testing), often suffer from accessibility constraints, lower accuracy, and scalability issues. Recent advancements in artificial intelligence (AI) and Machine Learning (ML) have exhibited significant efficacy by improving detection accuracy and streamlining the diagnostic procedure. This review discusses AI-driven methodologies, including deep learning models (EfficientNetB0, Cervical Net), hybrid frameworks, and ensemble techniques. This study examines important obstacles related to class imbalance, data privacy, and model interpretability while also presenting a solution known as the Adaptive Synthetic Minority Over-Sampling Technique combined with Tomek Links (Adaptive SMOTE Tomek) for data augmentation and federated learning (FL) for secure, distributed AI model training. The outcomes emphasize AI’s vital role in improving cervical cancer screening and correspond with the World Health Organization’s (WHO) 2030 goals for cervical cancer eradication. Future research should focus on real-world validation, improved generalization, and the integration of AI in clinical settings to ensure widespread usage and reliability. This work is useful for medical professionals in cervical cancer diagnosis, in addition to traditional screening methods.