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Water flow prediction and planning significantly help decision-makers determine the most suitable irrigation strategy and crop type and help avoid risks from flooding, among other benefits. Conventional statistical and physical models are often challenged by the highly dynamic and nonlinear nature of hydrological processes. Recent advances in machine learning (ML), including artificial neural networks (ANNs), provide powerful tools for modeling these complex relationships. However, the performance of these models depends on the optimal parameter tuning. By combining ANN with Crow Search Optimization, we aim to improve prediction accuracy and robustness while providing a clever, adaptable, and reliable solution to real-world water flow forecasting problems. The Crow Search Algorithm (CSA) is one of the most recent metaheuristic algorithms used as a training algorithm for neural network models to achieve higher performance. This research provides an evolutionary-based model to predict the flow of the Black River, a well-known river in the USA. The adopted ANN model was used to train and predict daily flows at the initial Black Water River station (No. 02047500) near Dendron, Virginia. Among the well-known metaheuristic algorithms employed in this study for comparison are the Salp Swarm Algorithm (SSA), Particle Swarm Optimization (PSO), and the Dandelion Optimizer (DO). Based on comparative research, the CSA algorithm outperforms other training algorithms in predicting river flow, achieving an average fitness value of 0.0048926, which is 41% better than SSA, 81% better than PSO, and 49% better than DO. Furthermore, CSA has achieved a superior convergence curve, and high variance accounts for VAFs of up to 99.06% on the training data and 98.45% on the test data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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Tomato plant diseases pose a significant threat to agricultural productivity, resulting in substantial economic losses. Early and accurate diagnosis is crucial for effective disease management. This paper describes the design and implementation of expert systems for tomato disease detection using the CLIPS (C Language Integrated Production System) platform. The tool is designed to help farmers and agronomists accurately identify diseases affecting tomato crops by simulating knowledge from professional experts. We carefully developed a set of rules to distinguish leaf blight symptoms from those of other tomato diseases and provided recommendations to minimize crop losses and maximize yields. The expert system was developed using a forward-chaining inference engine, and its performance was evaluated through a set of real-world test cases, demonstrating a high level of accuracy and consistency in decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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Cardiovascular diseases (CVDs) are still one of the foremost causes of death globally. An urgent need for an accurate premature diagnosis is essential for efficient treatment and avoidance. The goal of this research is to introduce our initial idea for developing a hybrid intelligent diagnosis model that integrates the Extreme Learning Machine (ELM) with Particle Swarm Optimization (PSO) to enhance diagnosis accuracy for heart disease. To address the problems of random initialization, the PSO algorithm is used to optimize the ELM’s input biases and weights. Experiments using the Cleveland Heart Disease dataset evaluated the performance of traditional ELM and the proposed PSO-ELM model. Results indicate that integrating PSO for parameter tuning yields significant improvements across evaluation metrics. The traditional ELM recorded an accuracy of 0.8595 on the training set and 0.8525 on the test set, while the PSO-ELM model achieved up to 0.9174 in training and 0.8852 in testing. Enhancements were also noted in precision, recall, and F1-score, with the F1-score increasing from 0.8768 for traditional ELM to over 0.92 for several PSO configurations.
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