TfELM: Extreme Learning Machines framework with Python and TensorFlow

—The World Health Organization reports that cardiac disorders result in approximately 1.02 million deaths. Over the last years, heart disorders, also known as cardiovascular diseases, have significantly influenced the medical sector due to their immense global impact and high level of danger. Unfortunately, accurate prognosis of heart problems or CD, as well as continuous monitoring of the patient for 24 hours, is unattainable due to the extensive expertise and time required. The management and identification of cardiac disease pose significant challenges, particularly in impoverished or developing nations. Moreover, the absence of adequate medical attention or prompt disease management can result in the individual's demise. This study presents a novel optimization technique for diagnosing cardiac illness utilizing Support Vector Machine (SVM) and Grasshopper Optimization Algorithm (GOA). The primary objective of this approach is to identify the most impactful characteristics and enhance the efficiency of the SVM model. The GOA algorithm, which draws inspiration from the natural movements of grasshoppers, enhances the search for features in the data and effectively reduces the feature set while maintaining prediction accuracy. The initial stage involved pre-processing the ECG data, followed by its classification using several algorithms such as SVM and GOA. The findings demonstrated that the suggested approach has markedly enhanced the effectiveness and precision of heart disease diagnosis through meticulous feature selection and model optimization. This approach can serve as an efficient tool for early detection of heart disease by simplifying the process and enhancing its speed.

Performance Optimization of Support Vector Machine with Adversarial Grasshopper Optimization for Heart Disease Diagnosis and Feature Selection

Nature has evolved sophisticated optimization strategies over billions of years, yet computational algorithms inspired by plants remain remarkably underexplored. We present a comprehensive systematic review following PRISMA 2020 guidelines, analyzing 175 studies (2000–2025) of plant-inspired metaheuristic optimization algorithms and their predominantly animal-inspired counterparts. Despite constituting only 9.7% of bio-inspired optimization literature, plant-inspired algorithms demonstrate competitive and often superior performance compared to animal-inspired approaches. Through a meta-analysis of empirical studies, we document that algorithms like Phototropic Growth and Binary Plant Rhizome Growth achieve 97% superiority on CEC2017 benchmarks and 81% accuracy on high-dimensional feature-selection tasks—significantly exceeding established animal-inspired methods like Particle Swarm Optimization and Genetic Algorithms (p &lt; 0.05). However, our review reveals a critical gap: the majority of these algorithms lack the formal theoretical foundations of their counterparts. This paper systematically documents these theoretical deficiencies and positions them as a key area for future research. Our framework maps botanical processes to computational operators, providing structured guidance for future algorithm development. Plant-inspired approaches excel particularly in distributed optimization, resource allocation, and multi-objective problems by leveraging unique mechanisms evolved for survival in sessile, resource-limited environments. These findings establish plant-inspired approaches as a promising yet severely underexplored frontier in optimization theory, with immediate applications in sustainable computing, resilient network design, and resource-constrained artificial intelligence. 

A Systematic Review of Bio-Inspired Metaheuristic Optimization Algorithms: The Untapped Potential of Plant-Based Approaches

This work presents a research on Nature Inspired Metaheuristic Algorithms (MA) used as optimizers in training process of Machine Learning method called Extreme Learning Machine (ELM). We tested 19 MA optimizers measuring their performance directly on sample datasets. The impact of input parameters such as number of hidden layer units, optimization stopping conditions and population size on the accuracy results, training and prediction time is evaluated here. Significant differences in performance of applied methods and their parameters’ values are detected. The most meaningful outcome of this paper shows that an increase of the number of MA iterations does not yield significant boost in accuracy with a huge increase in training time. Indeed a cap on number of MA iterations ranging from 1 to 5 is sufficient for analyzed machine learning tasks. In our research the best results are obtained for population size ranging between 50 and 100. Hybridized ELM outperforms classical implementation of ELM as higher accuracy is reached for the same number of neurons. 

Performance of Selected Nature-Inspired Metaheuristic Algorithms Used for Extreme Learning Machine

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Performance of Selected Nature-Inspired Metaheuristic Algorithms Used for Extreme Learning Machine