Heart disease diagnosis using decision trees with feature selection method - Sheta, A.; El-Ashmawi, W.; Baareh, A. (International Arab Journal of Information Technology, 2024)
The advancement in treating medical data grows significantly daily. An accurate data classification model can help determine patient disease and diagnose disease severity in the medical domain, thus easing doctors' treatment burdens. Nonetheless, medical data analysis presents challenges due to uncertainty, the correlations between various measurements, and the high dimensionality of the data. These challenges burden statistical classification models. Machine Learning (ML) and data mining approaches have proven effective in recent years in gaining a deeper understanding of the importance of these aspects. This research adopts a well-known supervised learning classification model named a Decision Tree (DT). DT is a typical tree structure consisting of a central node, connected branches, and internal and terminal nodes. In each node, we have a decision to be made, such as in a rule-based system. This type of model helps researchers and physicians better diagnose a disease. To reduce the complexity of the proposed DT, we explored using the Feature Selection (FS) method to design a simpler diagnosis model with fewer factors. This concept will help reduce the data collection stage. A comparative analysis has been conducted between the developed DT and other various ML models, such as Logistic Regression (LR), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB), to demonstrate the effectiveness of the developed model. The results of the DT model establish a notable accuracy of 93.78% and an ROC value of 0.94, which beats other compared algorithms. The developed DT model provided promising results and can help diagnose heart disease. © 2024, Zarka Private University. All rights reserved.
Keywords: Machine learning; classification; decision tree; dept.csc; diagnosis; heart disease; peer-reviewed

Modeling Hot Rolling Process Forces Using Bio-Inspired Metaheuristic Search Algorithm - Sheta, A.; Braik, M.; Oznergiz, E.; Elashmawi, W.H.; Rausch, P.; Othman, E.S. (2024)
This research introduces the application of an innovative bio-inspired metaheuristic technique, termed the Crow Search Algorithm (CSA), to model a crucial industrial process - hot rolling manufacturing. Inspired by the foraging patterns of crows, the CSA algorithm has demonstrated its prowess in solving diverse optimization challenges. In the context of this study, the CSA algorithm is harnessed to fine-tune the parameters of a simulation model focused on predicting the force exerted during a hot rolling procedure. The proposed model takes into consideration a range of influential factors, including the initial temperature (Ti), width (Ws), carbon equivalent (Ce), gauge (hi), draft (i), and roll diameter (R). The findings underscore the CSA's capability to deliver an exceptional modeling performance characterized by swift convergence and high solution quality. By getting along very well with the proposed model with the CSA algorithm, a robust and efficient avenue to optimize the hot rolling process emerges, with the potential for expansion into other manufacturing domains. The computational and simulation results demonstrated that the proposed approach-based CSA outperformed different meta-heuristic search algorithms, such as the Salp Swarm Algorithm (SSA), Dandelion Optimizer (DO), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), and Moth-Flame Optimization (MFO), in all test cases. The CSA has achieved the highest coefficient of determination (R2), equal to 0.97244, and the lowest mean squared error (MSE), equal to 1904.97, compared to its opponent algorithms. © 2024 IEEE.
Keywords: Crow Search Algorithm; Hot Rolling Process; Meta-heuristic search algorithms; dept.csc

Predicting Surface Ozone Levels in Eastern Croatia: Leveraging Recurrent Fuzzy Neural Networks with Grasshopper Optimization Algorithm - Braik, M.; Sheta, A.; Kovač-Andrić, E.; Al-Hiary, H.; Aljahdali, S.; Elashmawi, W.H.; Awadallah, M.A.; Al-Betar, M.A. (Water, Air, and Soil Pollution, 2024)
Urban air pollution, a combination of industry, traffic, forest burning, and agriculture pollutants, significantly impacts human health, plants, and economic growth. Ozone exposure can lead to mortality, heart attacks, and lung damage, necessitating the creation of complex environmental safety regulations by forecasting ozone concentrations and associated pollutants. This study proposes a hybrid method, RFNN-GOA, combining recurrent fuzzy neural network (RFNN) and grasshopper optimization algorithm (GOA) to estimate and forecast the daily ozone (O3) in specific urban areas, specifically Kopački Rit and Osijek city in Croatia, aiming to improve air quality, human health, and ecosystems. Due to the intricate structure of atmospheric particles, modeling of O3 likely poses the biggest challenge in air pollution today. The dataset used by the proposed RFNN-GOA model for the prediction of O3 concentrations in each explored area consists of the following air pollutants, NO, NO2, CO, SO2, O3, PM10, and PM2.5; and five meteorological elements, including temperature, relative humidity, wind direction, speed, and pressure. The RFNN-GOA method optimizes membership functions’ parameters and the rule premise, demonstrating robustness and reliability compared to other identifiers and indicating its superiority over competing methods. The RFNN-GOA method demonstrated superior accuracy in Osijek city and Kopački Rit area, with variance-accounted for (VAF) values of 91.135%, 83.676%, 87.807%, 79.673% compared to the RFNN method’s corresponding values of 85.682%, 80.687%, 80.808%, 74.202% in both training and testing phases, respectively. This reveals that RFNN-GOA increased the average VAF in Osijek city and Kopački Rit area by over 5% and 8%, respectively. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Keywords: Auto-regressive integrated moving average; Grasshopper optimization algorithm; Multiple linear regression; Recurrent fuzzy neural network; dept.csc; peer-reviewed