Explore the vast range of titles available.

High-performance concrete (HPC) is commonly utilized in the construction industry because of its strength and durability. The mechanical properties of HPC, specifically its compressive and tensile strength, are crucial indicators. Accurate prediction of concrete strength is crucial for optimizing the design as well as the performance of concrete structures. In this investigation, a novel approach for strength prediction of HPC is proposed, employing the Support Vector Regression (SVR) algorithm in conjunction with three optimizers: the Slime Mold Algorithm (SMA), Adaptive Opposition Slime Mold Algorithm (AOSM), and Equilibrium Slime Mold Algorithm (ESMA). The SVR algorithm is a robust machine-learning technique that has displayed promising results in various prediction tasks. The utilization of SVR allows for the effective modeling and prediction of the complex relationship between the strength properties of HPC and the influencing factors. To achieve this, a dataset comprising 344 samples of high-performance concrete was collected and utilized to train and assess the SVR algorithm. However, the choice of suitable optimization algorithms becomes crucial to enhance prediction accuracy and convergence speed. Through extensive experimentation and comparative analysis, the proposed framework’s performance is evaluated using real-world HPC strength data. The results demonstrate that combining SVR with AOSM, ESMA, and SMA outperforms traditional prediction accuracy and convergence speed optimization methods. The suggested framework provides an effective and reliable solution for accurately predicting the compressive strength (CS) of HPC, enabling engineers and researchers to optimize the design and construction processes of HPC structures.

Soil resistance characteristics, particularly the California Bearing Ratio (CBR), play a pivotal role in pavement and subgrade design. However, conventional laboratory-based CBR testing is often time-consuming, labor-intensive, and costly. This study presents a novel machine learning framework that combines Support Vector Regression (SVR) with three recent metaheuristic optimization algorithms—Dingo Optimization Algorithm (DOA), Alibaba and the Forty Thieves Optimization (AFT), and Adaptive Opposition Slime Mold Algorithm (AOSMA)—to predict CBR values efficiently and accurately. A dataset consisting of 220 soil samples with eight geotechnical input parameters was used to develop and evaluate the hybrid models. The predictive performance of each model was assessed using multiple evaluation metrics, including R², RMSE, MSE, RSR, and WAPE. Results indicate that the SVR–AFT (SVAF) hybrid model outperformed the others, achieving an R² of 0.9968 and an RMSE of 0.7946 in the testing phase, demonstrating high generalization ability and predictive precision. The integration of SVR with metaheuristic algorithms significantly enhances model robustness and accuracy, offering a practical and cost-effective alternative to empirical CBR testing methods. This work highlights the potential of hybrid AI models in solving complex geotechnical prediction problems and contributes to the growing body of research at the intersection of civil engineering and artificial intelligence.

As an extensively used experiment, the California Bearing Ratio (CBR) tests the resistance of soils in subgrade layers and superstructure foundations often used to design flexible pavements. Practically, since CBR tests are time-consuming and costly, only a limited number of them could be performed over a road construction project. In these cases, artificial-based prediction methods will be helpful as they are quick and cheap. Artificial neural networks (ANNs), including Radial Basis Function (RBF), are powerful tools in prediction procedures employing modeling philosophy. On the other hand, recently, because meta-heuristics are very efficient, academics have focused more on optimization utilizing them, reasonable execution time, and significant convergence acceleration rate in solving real-world problems. In this study, three different hybrid models are introduced comprising the neural network approach along with three optimizers [including adaptive opposition slime mold algorithm (AOSMA), gradient-based optimizer (GBO), and Sine cosine algorithm (SCA)]. Predicted values of CBR in two categories of training and testing models have been compared with measured values of CBR tests. Finally, through some evaluators, the efficiency of hybrid models was evaluated, and the best-proposed model was presented for practical applications. In addition, RBAO obtained the most suitable prediction values compared to other developed models.

The accurate prediction of unconfined compressive strength (UCS) in rock samples is critical for the successful planning, design, and implementation of mining and civil engineering projects. UCS is crucial in assessing the stability and durability of rock masses, which directly influences the safety, efficiency, and cost-effectiveness of construction and excavation operations. Here’s a refined version of your text for enhanced clarity and flow: in this part, the execution of the proposed model was compared for both single and hybrid configurations. Hybrid models included support vector regression (SVR) combined with the Seahorse Optimizer (SVSH) and SVR combined with the COOT optimization algorithm (SVCO). For training, 70% of the UCS dataset was utilized, while the remaining 30% was equally divided between testing (15%) and validation (15%). For the model evaluation, several metrics were considered in this work, including the R 2 , RMSE, WAPE, MAE, and RAE, which ensure fairness in the analysis. The closer the R 2 value comes to 1, the better the performance. The error metrics should be close to 0 for better accuracy. From Table 2, one can observe that the result of the standalone SVR model gave an RMSE of 6.213 during training and 9.454 during testing, hence showing poor performance. However, the inclusion of optimization algorithms significantly improved the performance of the SVR framework. Among the hybrid models, the SVSH model had the best performance, with an R 2 value of 0.998 and an RMSE of 1.261 during training. The SVCO model performed moderately, with an R 2 value of 0.988 during training.

In geotechnical engineering, it is crucial to make sure that the undrained shear strength (USS) of soft, sensitive clays is accurately assessed. The accuracy in forecasting USS is pivotal for ensuring the structural integrity and stability of foundations and earthworks. Addressing this concern, advanced data-driven NB techniques are utilized to disclose the complex interactions of USS with basic soil parameters. This paper presents a novel methodology for the USS prediction in soft clays using machine learning techniques, and particularly it highlights the attention on the following five important input variables: pre-consolidation stress (PS), vertical effective stress (VES), liquid limit (LL), plastic limit (PL), and natural water content (W). These are selected in view of their well-understood impact on the USS. This study reports an innovative effort to use SHO and AOSM for the model's hyperparameter tuning, reducing heuristic methods and computationally expensive brute-force searches. This will provide a neat methodology for improving accuracy in USS predictions and maintaining the optimality of the model performance. The results, therefore, provide geotechnical engineers and researchers with considerable benefits. They give a sound basis that is data-driven for the assessment of USS in soft sensitive clays and advance the safety and stability of civil engineering projects.

Student performance prediction systems are crucial for improving educational outcomes in various institutions, including universities, schools, and training centers. These systems gather data from diverse sources such as examination centers, registration departments, virtual courses, and e-learning platforms. Analyzing educational data is challenging due to its vast and varied nature, and to address this, machine learning techniques are employed. Dimensionality reduction, enabled by machine learning algorithms, simplifies complex datasets, making them more manageable for analysis. In this study, the Support Vector Classification (SVC) model is used for student performance prediction. SVC is a powerful machine-learning approach for classification tasks. To further enhance the model's efficiency and accuracy, two optimization algorithms, the Sea Horse Optimization (SHO) and the Adaptive Opposition Slime Mould Algorithm (AOSMA), are integrated. Machine learning (ML) reduces complexity through techniques like feature selection and dimensionality reduction, improving the effectiveness of student performance prediction systems and enabling data-informed decisions for educators and institutions. The combination of SVC with these innovative optimization strategies highlights the study's commitment to leveraging the latest advancements in ML and bio-inspired algorithms for more precise and robust student performance predictions, ultimately enhancing educational outcomes. Based on the obtained outcomes, it reveals that the SVSH model registered the best performance in predicting and categorizing the student performance with Accuracy=92.4%, Precision=93%, Recall=92%, and F1_Score=92%. Implementing SHO and AOSMA optimizers to the SVC model resulted in improvement of Accuracy evaluator outputs by 2.12% and 0.89%, respectively.

The slime mold algorithm (SMA) is a swarm-based metaheuristic algorithm inspired by the natural oscillatory patterns of slime molds. Compared with other algorithms, the SMA is competitive but still suffers from unbalanced development and exploration and the tendency to fall into local optima. To overcome these drawbacks, an improved SMA with a dynamic quantum rotation gate and opposition-based learning (DQOBLSMA) is proposed in this paper. Specifically, for the first time, two mechanisms are used simultaneously to improve the robustness of the original SMA: the dynamic quantum rotation gate and opposition-based learning. The dynamic quantum rotation gate proposes an adaptive parameter control strategy based on the fitness to achieve a balance between exploitation and exploration compared to the original quantum rotation gate. The opposition-based learning strategy enhances population diversity and avoids falling into the local optima. Twenty-three benchmark test functions verify the superiority of the DQOBLSMA. Three typical engineering design problems demonstrate the ability of the DQOBLSMA to solve practical problems. Experimental results show that the proposed algorithm outperforms other comparative algorithms in convergence speed, convergence accuracy, and reliability.

Problem Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manmade visual details that are complex to generalize across ultrasound images utilizing distinct techniques. Distinct imaging tools have been utilized in previous works such as mammography and MRI. However, these imaging tools are costly and less portable than ultrasound imaging. Also, ultrasound imaging is a non-invasive method commonly used for breast cancer screening. Hence, the paper presents a novel deep learning model, BCDNet, for classifying breast tumors as benign or malignant using ultrasound images. Aim The primary aim of the study is to design an effective breast cancer diagnosis model that can accurately classify tumors in their early stages, thus reducing mortality rates. The model aims to optimize the weight and parameters using the RPAOSM-ESO algorithm to enhance accuracy and minimize false negative rates. Methods The BCDNet model utilizes transfer learning from a pre-trained VGG16 network for feature extraction and employs an AHDNAM classification approach, which includes ASPP, DTCN, 1DCNN, and an attention mechanism. The RPAOSM-ESO algorithm is used to fine-tune the weights and parameters. Results The RPAOSM-ESO-BCDNet-based breast cancer diagnosis model provided 94.5 accuracy rates. This value is relatively higher than the previous models such as DTCN (88.2), 1DCNN (89.6), MobileNet (91.3), and ASPP-DTC-1DCNN-AM (93.8). Hence, it is guaranteed that the designed RPAOSM-ESO-BCDNet produces relatively accurate solutions for the classification than the previous models. Conclusion The BCDNet model, with its sophisticated feature extraction and classification techniques optimized by the RPAOSM-ESO algorithm, shows promise in accurately classifying breast tumors using ultrasound images. The study suggests that the model could be a valuable tool in the early detection of breast cancer, potentially saving lives and reducing the burden on healthcare systems.

To ascertain the optimal and most efficient reservoir temperature of a geothermal source, long-term field studies and analyses utilizing specialized devices are essential. Although these requirements increase project costs and induce delays, utilizing machine learning techniques based on hydrogeochemical data can minimize losses by accurately predicting reservoir temperatures. In recent years, applying hybrid methods to real-world challenges has become increasingly prevalent over traditional machine learning methodologies. This study introduces a novel machine learning approach, named AOSMA-MLP, integrating the adaptive opposition slime mould algorithm (AOSMA) and multilayer perceptron (MLP) techniques, specifically designed for predicting the reservoir temperature of geothermal resources. Additionally, this work compares the basic artificial neural network and widely recognized algorithms in the literature, such as the whale optimization algorithm, ant lion algorithm, and SMA, under equal conditions using various evaluation regression metrics. The results demonstrated that AOSMA-MLP outperforms basic MLP and other metaheuristic-based MLPs, with the AOSMA-trained MLP achieving the highest performance, indicated by an R2 value of 0.8514. The proposed AOSMA-MLP approach shows significant potential for yielding effective outcomes in various regression problems.