A Hybrid Al-Biruni Earth Radius–Random Forest Model for Accurate and Efficient Student Performance Classification

Mohamed E.

doi:https://doi.org/10.54216/JAIM.110203

International Journal of BIM and Engineering Science | 2018

BIM Performance Improvement Framework for Syrian AEC Companies

The Architectural, engineering, and construction (AEC) industry projects in Syria struggled with myriad problems. However, Building Information Modelling (BIM) technology worldwide proves its capability to solve these issues, Syrian AEC companies are rarely using BIM. Therefore, the aim of this study is to improve the BIM performance in Syrian AEC companies which are already in the BIM zero level and to provide strategies to the companies which do not use BIM for BIM adoption in their projects. An extensive literature review has been conducted to investigate the latest strategies and frameworks to implement and improve BIM performance. In addition to, an online questionnaire analysed by SPSS software and Excel to develop the suggested framework. Furthermore, the General Company for Engineering Studies and Consultations (GCEC) is selected as a case study to validate the framework. This study assessed and enabled the company to improve its BIM performance by using BIM maturity matrix (BIM3) through three stages: 1) Identified BIM and its performance, 2) Performance measurement, 3) Performance improvement. This study provides a new and novel companies’ BIM performance improvement framework which consisted of three fields: policy, process, and technology. The results of this study assisted to identify, obtain, and improve BIM interactions between individuals and companies to enhance the collaboration between all project participants. The future research will attempt to test and validate the proposed framework for private sector companies.

groups

Sonia Ahmed mail -

Petr DlasK mail -

Omar Selim mail -

Ashraf Elhendawi mail

link https://doi.org/10.54216/IJBES.010102

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2026

Intelligent Data Analysis of Asymmetric Oil Price Transmission and Financial Development: Evidence from an Emerging Market Economy

This article provides a data analysis framework to study asymmetric macro-financial relationships in an emerging market economy with significant energy dependence. Using annual observations for Egypt over 1990–2024, we estimate a nonlinear autoregressive distributed lag (NARDL) error-correction model in which changes in Brent crude prices are algorithmically decomposed into positive and negative cumulative partial-sum series. A composite financial development index, constructed from banking-sector depth indicators, enters the model both as a direct regressor and as an interaction term with each shock component. The results show that positive oil-price shocks carry a substantially larger long-run penalty on real GDP growth than negative shocks of equal magnitude—consistent with the cost-side exposure of a net oil-importing economy. Financial deepening conditions the transmission of these shocks but does not neutralise them; the allocation of credit toward productive private-sector activity, rather than the aggregate volume of intermediation, determines the direction of the moderating effect. Rolling-window and dynamic multiplier analyses confirm structural instability in the oil–growth relationship across sub-periods, validating the nonlinear modelling approach over standard linear alternatives. Unit root tests with structural breaks, NARDL bounds tests, and a battery of diagnostic checks support the robustness of the estimated long-run relationships. The findings carry direct implications for energy-risk management, financial-sector reform, and growth-stability policy in emerging market settings.

groups

Heba Moselhy mail -

Noura Metawa mail

link https://doi.org/10.54216/AJBOR.140206

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2026

DTOSFS–CatBoost: A Hybrid Metaheuristic Framework for Accurate and Interpretable Unemployment Forecasting

The fact that educational, demographic, and macroeconomic variables interact nonlinearly has remained a thorn in the flesh of socio-economic analytics to date, making it challenging to forecast unemployment with sufficient precision. To address this, the current study presents a hybrid metaheuristic, Dipper Throated Optimization with Stochastic Fractal Search (DTOSFS), coupled with the Category Boosting (CatBoost) algorithm to improve predictive modelling. The suggested DTOSFS-CatBoost system combines the general exploratory search of DTO with SFS refinement to stochastic local optimization of hyperparameters, and alleviates overfitting. Empirical experiments have shown that whereas the original CatBoost gave results with a Mean Squared Error (MSE) of 0.0256 and Root Mean Squared Error (RMSE) of 0.1601 with a correlation coefficient of 0.873, the CatBoost optimized by DTOSFS had drastically better results with an MSE of 0.00033, RMSE of 0.00207, and a correlation coefficient of 0.930. These results confirm an increased exploration-to-exploitation ratio in DTOSFS and yield small, powerful designs that substantially enhance model stability, precision, and convergence speed. These results show that educational attainment (at least tertiary and primary enrollment) and demographics (at least the birth rate) are influential factors in unemployment variation. This addition to predictive performance is not the only one, and it provides a predictive data-driven labor-market optimization paradigm that can be replicated and interpreted. The research observes that hybrid metaheuristics and gradient boosting can be used to drive next generation economic intelligence systems for adaptive policy formulation and to enhance online, privacy conscious, and cross-domain unemployment prediction.

groups

Ghassan AL-Thabhawee mail -

Hussein Alkattan mail

link https://doi.org/10.54216/MOR.060101

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2026

Optimizing Digital Marketing Revenue Forecasting Using an XGBoost–Dipper Throated Optimization Hybrid Model

The explosive growth of digital marketing data and the increasing need for accurate revenue forecasting have driven the adoption of advanced Machine Learning (ML) techniques capable of modeling complex, nonlinear relationships in dynamic environments. Motivated by the limitations of traditional linear forecasting methods, this study proposes an optimized predictive framework that integrates the Extreme Gradient Boosting (XGBoost) algorithm with a novel metaheuristic, Dipper Throated Optimization (DTO), to enhance model performance on temporal marketing data. The key contribution of this work lies in combining ensemble learning with bio-inspired optimization to achieve superior predictive accuracy and stability in Time-Series forecasting tasks. As the experiments of the Digital Marketing Metrics dataset demonstrate, the original XGBoost model achieved a Mean Squared Error (MSE) of 0.0905 and a coefficient of determination (R2) of 0.8007, and the optimized XGBoost+DTO model has significantly improved results, with an MSE of 0.0010 and a coefficient of determination (R2) of 0.9002. These results support the argument that DTO is effective in hyperparameter optimization and reducing generalization errors. The results of this paper are not unique to digital marketing, and the authors have presented a scalable, interpretable optimization model that can be generalized to other data intensive fields, such as financial analytics, demand forecasting, and customer behavior modelling. The study is a good step in the right direction of creating more accurate, adaptive and data-driven decision-making in the digital economy by integrating ML and nature-inspired optimization.

groups

Mohamed Rabehi mail -

Abdelaziz Rabehi mail

link https://doi.org/10.54216/MOR.060102

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2026

Enhanced Stock Price Forecasting: Time Series Analysis with ARIMA and FGGO Optimization

Forecasting financial markets remains a persistent challenge due to the nonlinear, stochastic, and nonstationary nature of stock price dynamics. This study is motivated by the need to enhance the robustness and adaptability of traditional statistical forecasting models through intelligent optimization. We propose an advanced hybrid framework that integrates the AutoRegressive Integrated Moving Average (ARIMA) model with the Fitness Greylag Goose Optimization (FGGO) algorithm—a refined metaheuristic inspired by collective behavioral intelligence and adaptive search strategies. The primary contribution of this research lies in the methodological fusion of classical time series modeling with dynamic metaheuristic optimization to improve predictive accuracy, convergence stability, and resistance to local optima. Comparative experiments on the historical stock prices of PT Bank Central Asia Tbk (BBCA.JK) demonstrate a substantial performance uplift: the baseline ARIMA model achieved a Mean Squared Error (MSE) of 0.0333, whereas the FGGO-optimized ARIMA reduced the MSE dramatically to 0.0038, outperforming other optimization techniques such as the Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO). These results confirm that FGGO significantly enhances ARIMA’s capacity to capture intricate temporal dependencies and volatile market structures. The implications of this study extend beyond finance, offering a scalable, explainable, and high performance optimization paradigm for diverse time series forecasting applications in economics, engineering, and intelligent decision-support systems.

groups

Laith Farhan mail -

Raad S. Alhumaima mail

link https://doi.org/10.54216/MOR.060103

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2026

Hybrid Metaheuristic–Ensemble Pipeline for Student Mental Health: Waterwheel Plant Algorithm with Random Forest

The problem of depression among college students has become a burning research problem, as the number of psychosocial stress factors, academic loads, and lifestyle disorders that lead to the worsening of mental health has increased. Driven by the increasing need for innovative, data-driven, and interpretable diagnostic models, this paper presents a combined Machine Learning (ML) and metaheuristic optimization model for predicting depression using multidimensional psychosocial and academic data collected from 100 Computer Science students. The suggested hybrid model combines a Random Forest (RF) classifier with the Waterwheel Plant Algorithm (WWPA), a nature-inspired mechanism for optimizing hyperparameter settings and feature selection. Experimentation using the Random Forest baseline model yielded a baseline accuracy of 0.9081, a Sensitivity (True Positive Rate) of 0.8936, and an F-Score of 0.9032. The hybrid WWPA+Random Forest model showed significant gains after introducing the WWPA optimization method, achieving a high accuracy of 0.9577, a sensitivity of 0.9502, a specificity of 0.9644, and an F-score of 0.9553. These findings validate the high-quality performance of the proposed model in achieving balanced, high-precision classification and in resisting overfitting. The results highlight the potential to integrate ensemble learning with bio-inspired optimization to advance depression prediction, providing a scalable, explainable, and ethically appropriate framework for predicting depression early in a person’s life. This work opens the way to creating a proactive digital mental health system that will enable educational organizations to identify at-risk students early and offer timely, individualized support for well-being and academic achievement.

groups

Amel Ali Alhussan mail -

Abdelaziz A. Abdelhamid mail

link https://doi.org/10.54216/MOR.060104

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2026

A Comparative Deep Learning Approach for Short-Term Wind Power Generation Prediction

Accurate wind power forecasting is essential for reliable renewable energy integration, grid stability, reserve scheduling, and wind farm operation because turbine output is highly variable and strongly influenced by meteorological conditions. However, forecasting wind power remains challenging due to the nonlinear relationship between weather variables and power generation, the temporal dependency of hourly observations, and the circular nature of wind direction data. This study aims to develop and compare deep learning models for predicting normalized wind turbine power output using a field-based hourly dataset collected from an operational wind energy site starting from January 2, 2017. The dataset includes temperature, relative humidity, dew point, wind speed at 10 m and 100 m, wind direction at 10 m and 100 m, wind gusts, and normalized turbine output. Five predictive models, namely LSTM, RNN, GRU, CNN, and Dense neural networks, were trained and evaluated after applying data preprocessing procedures, including missing-value handling, feature scaling, temporal alignment, and wind-direction transformation. Model performance was assessed using MSE, RMSE, MAE, MBE, correlation coefficient (r), coefficient of determination (R2), RRMSE, NSE, and WI. The empirical results showed that recurrent architectures outperformed the CNN and Dense models, confirming the importance of temporal learning in hourly wind power forecasting. Among all models, LSTM achieved the best overall performance, with MSE = 0.0008, RMSE = 0.0282, MAE = 0.0106, MBE = -0.0006, r = 0.9940, R2 = 0.9880, RRMSE = 0.0861, NSE = 0.9880, and WI = 0.9970. These findings demonstrate that LSTM can effectively capture nonlinear and sequential relationships between meteorological variables and turbine power generation, providing a reliable forecasting approach for operational wind energy management and supporting more stable integration of wind power into modern electricity systems.

groups

Mona Ahmed Yassen mail -

Mohamed G. Abdelfattah mail -

Islam Ismail mail -

Hossam El-Din Moustafa mail

link https://doi.org/10.54216/MOR.060105

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2026

Enhancing Identity Verification Reliability in Digital Environments Using Keystroke Dynamics and Dipper Throated Optimization

Keystroke Dynamics Analysis (KDA) is a prominent behavioral biometric technique for continuous user authentication in digital environments. Yet, keystroke timing prediction remains challenging due to individual typing variability, temporal inconsistencies, and the tendency of machine learning models to overfit in high dimensional spaces when hyperparameters are poorly tuned. This study formulates the task as predicting keystroke timing intervals—dwell times, keydown–keydown latencies, and keyup–keydown latencies—for a fixed password sequence. We introduce a predictive framework that integrates the Dipper Throated Optimizer (DTO) with regression modeling, using a sequential dual optimization strategy: binary DTO (bDTO) first selects informative feature subsets, followed by standard DTO to fine-tune the hyperparameters of a Gradient Boosting Regressor (GBR). This design balances exploration and exploitation to address the complexity of optimization in behavioral biometric data. Experimental validation on the Keystroke Dynamics Benchmark Dataset demonstrates stepwise performance gains: the baseline GBR achieved an MSE of 0.014244, reduced to 0.004768 after bDTO based feature selection (66.5% improvement), and further refined to an MSE of 0.000003 with DTO hyperparameter tuning (99.97% relative improvement), a result interpreted with caution due to potential overfitting risks. The optimized model also attained R2 = 0.9824, Nash–Sutcliffe Efficiency = 0.9786, and Willmott Index = 0.9810, underscoring strong predictive agreement between observed and predicted timing intervals.

groups

Ebrahim A. Mattar mail

link https://doi.org/10.54216/JAIM.110201

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2026

A Hybrid Al-Biruni Earth Radius–Stochastic Fractal Search Optimized XGBoost Model for Accurate Student Performance Prediction

The ability to accurately predict student academic performance is now an established pillar of contemporary educational data mining, driven by the desire for data-driven interventions and personalized learning. This paper presents a mixed-methodology optimization and machine learning model that combines the Al-Biruni Earth Radius-Stochastic Fractal Search (BER-SFS) algorithm with eXtreme Gradient Boosting (XGBoost) to improve predictive accuracy and model stability in forecasting student performance. The proposed BER-SFS + XGBoost model is an optimized system that systematically optimises the hyperparameter based on dual exploration-exploitation mechanism to reduce the Mean Squared Error (MSE) of the baseline XGBoost model of 0.0226 to 0.00029 and the Root Mean Squared Error (RMSE) of the baseline XGBoost model to 0.1504 to 0.00194 and the coefficient of determination R2 of 0.9019 to Comparison to the other metaheuristics, including theGrey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) proved the superiority of the suggested hybrid model in all metrics of the evaluation. These findings support the potential to combine the dynamics of geometric exploration provided by BER with those of diffusion-based refinement offered by SFS to achieve high generalization and minimal bias. The implications extend beyond predictive analytics, demonstrating that the hybrid metaheuristic optimization approach can serve as a scalable, explainable method for adaptive educational systems, enabling early detection of at-risk learners and the development of data-driven academic support mechanisms.

groups

Abdelaziz A. Abdelhamid mail -

Amal H. Alharbi mail

link https://doi.org/10.54216/JAIM.110202

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2026

A Hybrid Al-Biruni Earth Radius–Random Forest Model for Accurate and Efficient Student Performance Classification

The growing availability of educational data has prompted the use of machine learning methods to predict student academic performance and support data-driven decision-making in education. Nevertheless, such models for predicting performance rely heavily on proper data preprocessing, model selection, and optimal hyperparameter settings. This research proposes a hybrid predictive architecture that combines machine learning classifiers with bio-inspired metaheuristic optimization algorithms to improve classification efficiency in educational data mining. It is based on the xAPI-Edu.A dataset of 480 students’ demographic, academic, and behavioral characteristics is used to first analyze a set of baseline machine learning models, including Random Forest, XGBoost, Support Vector Machine, Multilayer Perceptron, K-Nearest Neighbors, and Gaussian Naive Bayes, using standard classification metrics. The initial experimental findings on the baseline layer show that the Random Forest classifier outperforms the other models before optimization, achieving accuracies of 0.8889 and 0.8814, and F-scores of 0.8889 and 0.8814, respectively, indicating strong generalization and equal discrimination among the classes. To further improve the predictive performance, the state-of-the-art metaheuristic algorithms, i.e., the Al-Biruni Earth Radius Optimizer (BER), the Gray Wolf Optimizer (GWO), the Particle Swarm Optimization (PSO), the Genetic Algorithms (GA) and the Whale Optimization Algorithms (WOA) are adopted to optimize the hyperparameters of the Random Forest. It has been experimentally demonstrated that every optimization approach provides a measurable performance increase, but the BER-optimized Random Forest consistently performs better. In particular, the BER-Random Forest model achieves an F-score of 0.9477 and an accuracy of 0.9439, both of which are much higher than the baseline configuration. Full statistical and visual analyses, such as kernel density estimation, Z-score heatmaps, and swarm plots, also support the strength, stability and superiority of the proposed BER-based optimization framework. Such findings demonstrate the effectiveness of metaheuristic-based hyperparameter optimization in educational predictive analytics and provide significant insights into the creation of intelligent, efficient, and data-driven systems of academic assistance.

groups

Mohamed E. Ghoneim mail

link https://doi.org/10.54216/JAIM.110203

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new