Harnessing TLBO-Enhanced Cheetah Optimizer for Optimal Feature Selection in Cancer Data

Metaheuristic optimization methods are iterative search processes that aim to efficiently solve complex optimization problems. These basically find the solution space very efficiently, often without utilizing the gradient information, and are inspired by the bio-inspired and socially motivated heuristics. Metaheuristic optimization algorithms are increasingly applied to complex feature selection problems in high-dimensional medical datasets. Among these, Teaching-Learning-Based optimization (TLBO) has proven effective for continuous design tasks by balancing exploration and exploitation phases. However, its binary version (BTLBO) suffers from limited exploitation ability, often converging prematurely or getting trapped in local optima, particularly when applied to discrete feature selection tasks. Previous studies reported that BTLBO yields lower classification accuracy and higher feature subset variance compared to other hybrid methods in benchmark tests, motivating the development of hybrid approaches. This study proposes a novel hybrid algorithm, BTLBO-Cheetah Optimizer (BTLBO-CO), which integrates the global exploration strength of BTLBO with the local exploitation efficiency of the Cheetah Optimization (CO) algorithm. The objective is to enhance the feature selection process for cancer classification tasks involving high-dimensional data. The proposed BTLBO-CO algorithm was evaluated on six benchmark cancer datasets: 11 tumors (T), Lung Cancer (LUC), Leukemia (LEU), Small Round Blue Cell Tumor or SRBCT (SR), Diffuse Large B-cell Lymphoma or DLBCL (DL), and Prostate Tumor (PT). The results demonstrate superior classification accuracy across all six datasets, achieving 93.71%, 96.12%, 98.13%, 97.11%, 98.44%, and 98.84%, respectively. These results validate the effectiveness of the hybrid approach in addressing diverse feature selection challenges using a Support Vector Machine (SVM) classifier.

Deep Learning in Medical Image Analysis: A Comprehensive Review of Algorithms, Trends, Applications, and Challenges

Medical image analysis has become a cornerstone of modern healthcare, driven by the exponential growth of data from imaging modalities such as MRI, CT, PET, ultrasound, and X-ray. Traditional machine learning methods have made early contributions; however, recent advancements in deep learning (DL) have revolutionized the field, offering state-of-the-art performance in image classification, segmentation, detection, fusion, registration, and enhancement. This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks, highlighting both foundational models and recent innovations. The article begins by introducing conventional techniques and their limitations, setting the stage for DL-based solutions. Core DL architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Vision Transformers (ViTs), and hybrid models, are discussed in detail, including their advantages and domain-specific adaptations. Advanced learning paradigms such as semi-supervised learning, self-supervised learning, and few-shot learning are explored for their potential to mitigate data annotation challenges in clinical datasets. This review further categorizes major tasks in medical image analysis, elaborating on how DL techniques have enabled precise tumor segmentation, lesion detection, modality fusion, super-resolution, and robust classification across diverse clinical settings. Emphasis is placed on applications in oncology, cardiology, neurology, and infectious diseases, including COVID-19. Challenges such as data scarcity, label imbalance, model generalizability, interpretability, and integration into clinical workflows are critically examined. Ethical considerations, explainable AI (XAI), federated learning, and regulatory compliance are discussed as essential components of real-world deployment. Benchmark datasets, evaluation metrics, and comparative performance analyses are presented to support future research. The article concludes with a forward-looking perspective on the role of foundation models, multimodal learning, edge AI, and bio-inspired computing in the future of medical imaging. Overall, this review serves as a valuable resource for researchers, clinicians, and developers aiming to harness deep learning for intelligent, efficient, and clinically viable medical image analysis.

A Novel Cascaded TID-FOI Controller Tuned with Walrus Optimization Algorithm for Frequency Regulation of Deregulated Power System

This paper presents an innovative and effective control strategy tailored for a deregulated, diversified energy system involving multiple interconnected area. Each area integrates a unique mix of power generation technologies: Area 1 combines thermal, hydro, and distributed generation; Area 2 utilizes a blend of thermal units, distributed solar technologies (DST), and hydro power; and Third control area hosts geothermal power station alongside thermal power generation unit and hydropower units. The suggested control system employs a multi-layered approach, featuring a blended methodology utilizing the Tilted Integral Derivative controller (TID) and the Fractional-Order Integral method to enhance performance and stability. The parameters of this hybrid TID-FOI controller are finely tuned using an advanced optimization method known as the Walrus Optimization Algorithm (WaOA). Performance analysis reveals that the combined TID-FOI controller significantly outperforms the TID and PID controllers when comparing their dynamic response across various system configurations. The study also incorporates investigation of redox flow batteries within the broader scope of energy storage applications to assess their impact on system performance. In addition, the research explores the controller’s effectiveness under different power exchange scenarios in a deregulated market, accounting for restrictions on generation ramp rates and governor hysteresis effects in dynamic control. To ensure the reliability and resilience of the presented methodology, the system transitions and develops across a broad range of varying parameters and stochastic load fluctuation. To wrap up, the study offers a pioneering control approach—a hybrid TID-FOI controller optimized via the Walrus Optimization Algorithm (WaOA)—designed for enhanced stability and performance in a complex, three-region hybrid energy system functioning within a deregulated framework.

Experimental Study on Self-Curing Concrete Using Sodium Polyacrylate

Harnessing Longevity Genes for Lifespan Extension: Insights from Naked Mole Rats

Removal of ovarian cyst by individualised homoeopathic remedy: A case report

Individualised homoeopathic treatment of diabetes mellitus: A case report

Restoring equilibrium: Homoeopathy’s influence on irritable bowel syndrome

Effect of basal cut and spike dipping in preservative solution on vase life of gladiolus (Gladiolus grandiflorus L.) cv. Saffron

Assessment of quality of irrigation water in the central Narmada valley zone of Madhya Pradesh, India using GIS