Enhancing Gamma–Hadron Separation in Imaging Atmospheric Cherenkov Telescopes Using Attention-Guided Deep Learning and Adaptive Balanced Greylag Goose Optimization

Ebrahim A.; S. K.

doi:https://doi.org/10.54216/JAIM.110105

International Journal of BIM and Engineering Science | 2026

Quantifying the ISO 19650 Dividend: Developing Practical KPIs for BIM Implementation ROI

Purpose – The current global transformation in the construction industry, through the use of Building Information Modeling (BIM) and ISO 19650 information management, is hindered by a missing financial Return on Investment (ROI) on the ISO 19650 information management. This is a hindrance for investment and decision-making. The research seeks to solve the problem through the establishment of a Key Performance Indicator (KPI) for the realization of the “ISO 19650 Dividend.” Design/methodology/approach – A sequential explanatory mixed-methods approach was adopted, integrating a systematic literature review, the analysis of existing data (n = 104), a cross-sectional study involving a primary survey of a targeted cohort in the UK and Saudi Arabia (n = 187), and in-depth expert interviews (n = 15). Quantitative data were analysed using weighted mean, gap, and path analyses, while qualitative data were examined through thematic analysis. Findings – The paper pinpoints the attainment of operational efficiency and cost competitiveness as the key priority level for the value drivers, while pointing out the substantial gap in measuring the intangible value, such as organization capital and sustainability. Commitment to the organization by the leaders stands as the key critical success factor. The key outcome of this paper includes the development of the four-leveled KPI Framework and the conceptual model focusing on the adoption and successful measurement, resulting in the ROIa. Practical implications – The framework offers a structured roadmap or a step-by-step process change that enables organizations to move from basic process compliance measurement metrics to financial metrics measurement in their digital projects. This framework provides professionals in this domain a way in which benefits realized from collaboration are converted into a proxy measures. Originality/value – Instead, this research breaks the mold of general sets of BIM benefits in offering the first-ever integrated measurement framework that specifically sets out to quantify the ROI of implementing ISO 19650, by synthesizing performance metrics with qualitative knowledge of leadership and change management in a holistic approach for the realization of digital promise and profit.

groups

Ashraf Elhendawi mail -

Muhaideb AlMuhaideb mail -

Abdul Salam Darwish mail

link https://doi.org/10.54216/IJBES.120102

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Robust Forgery Detection in Digital Images Utilizing the Multiple Image Splicing Data Set (MISD)

In the area of digital information, establishing the authenticity of an image has grown to have greater significance as more and more persons have access to sophisticated image editing technologies. There is however a challenge in detecting such a forgery since it is usually very realistic and it is hard to know the difference between the real images and the fake ones. This paper aims at creation of a mechanism of identifying forged images based on Multiple Image Splicing Dataset (MISD) as a reference point. The suggested system will help to improve the results of the forgery detection, paying particular attention to the images processing during some of the pre-processing steps Firstly, converting colors into the hue-based histograms and RGB histograms, and hue-based histograms in an HSV, in comparison between the original and forged image, its HSV histogram, and its grayscale histogram, etc. Lastly, compute MSE and SSIM original and forged image. The implementation results showed that average value of MSE and SSIM metrics on Multiple Image Splicing Dataset (MISD) equal to 184.82 and 0.65 respectively that means the suggested method proved the efficiency of the technique to identify forged images as quickly as possible but still retain accuracy.

groups

Heba Adnan Raheem mail

link https://doi.org/10.54216/JISIoT.170129

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2026

A Comparison between Elastic Net Logistic and a Set of Machine Learning Algorithms in Predicting Breast Cancer

Breast cancer is a common type of cancers and the main reason of increased death of women universally. Recently, ML methods have become important in varying fields, such as Logistic Regression, Elastic Net Logistic, Decision Tree, Random Forest, Boosting, Naive Bayes and K Nearest Neighbor. The aim of the current study is to know and predict the type of cancerous tumor whether it is benign or malignant. These above techniques are expected to be helpful. Breast tumor type diagnosis using numerous performance metrics i.e. accuracy, classification error, sensitivity and specificity, both certified and trained models were assessed. The models were developed to determine which model would provide the best performance and comparisons were done. A separate data set from the one used to create the models was utilized to confirm every model. According to the analysis, the findings showed that elastic net logistic model had the highest performance in accurate classification rate (accuracy), classification error and sensitivity. Making it the best classifier for predicting the kind of breast cancer among all other models, privacy and it was also distinguished by reduce the high dimensionality and multicollinearity problems.

groups

Hadeel Imad Naser mail -

Wakaa Ali Hadba mail

link https://doi.org/10.54216/GJMSA.130101

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2025

A Short Note on Interval-Valued Bipolar Fuzzy SuperHyperGraphs

Hypergraphs extend classical graphs by allowing hyperedges to connect arbitrary nonempty subsets of vertices, thereby capturing higher-order, group-level interactions. Superhypergraphs further broaden this setting by iterating the powerset construction, which yields layered supervertices and supports multi-level relational structure. An interval-valued bipolar fuzzy graph assigns positive and negative membership intervals to vertices and edges while satisfying bipolar consistency constraints. In this paper, we extend interval-valued bipolar fuzzy graphs to the settings of hypergraphs and superhypergraphs.

groups

Takaaki Fujita mail -

Ajoy Kanti Das mail -

Sankar Prasad Mondal mail -

Suman Das mail

link https://doi.org/10.54216/GJMSA.120204

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2026

Enhancing Financial Decision-Making in SMEs: Improving Forecasting Accuracy for Sustainable Growth

The growing complexity of financial decision-making in Small and Medium-Sized Enterprises (SMEs) necessitates advanced predictive models capable of accurately forecasting financial outcomes such as revenue, profit margins, and cash flow. Despite the availability of various machine learning models, there remains a need for optimization techniques that enhance model accuracy, generalization, and efficiency. This paper addresses this gap by applying metaheuristic optimization strategies to improve the performance of baseline financial forecasting models, particularly the Logarithmic Transformation (LogTrans) model. We propose the integration of several state-of-the-art metaheuristic algorithms, including Simulated Simulated Annealing (SSO), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WAO), and others, to optimize hyperparameters and perform feature selection. Our results demonstrate that the optimized SSO + LogTrans configuration outperforms all other models, achieving a remarkable Mean Squared Error (MSE) of 1.95E-07, a Root Mean Squared Error (RMSE) of 4.42E-04, and a high R-squared (R²) value of 0.966. These findings indicate that metaheuristic-driven optimization significantly improves predictive accuracy and generalization capability in SME financial decision-making models. The implications of this study extend beyond SMEs, offering potential applications in industries such as banking, investment, and insurance, where precise financial forecasting is critical. Furthermore, our approach highlights the importance of metaheuristics in the automated optimization of machine learning models, paving the way for further advancements in real-time decision support systems for dynamic financial environments.

groups

Sayed Elkenawy mail

link https://doi.org/10.54216/AJBOR.140107

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2026

A Deep Learning and Metaheuristic Optimization Framework for Short-Term Electricity Consumption Forecasting Using High-Resolution SCADA Data

Accurate prediction of electricity consumption is a critical requirement for improving operational efficiency, enhancing grid reliability, and supporting sustainability objectives in urban power distribution systems, particularly in regions experiencing steady population growth and increasing demand pressure. Motivated by the limitations of conventional statistical and physics-inspired forecasting approaches, as well as the strong sensitivity of deep learning architectures to hyperparameter configuration, t his s tudy p roposes a robust data-driven framework that integrates deep learning with advanced metaheuristic optimization for high-precision short-term electricity consumption forecasting. The main contribution of this work lies in the systematic development and evaluation of hybrid metaheuristic–Bidirectional Long Short-Term Memory (BiLSTM) models, in which multiple state-of-the-art optimization algorithms are employed to tune model hyperparameters. Particular emphasis is placed on the integration of the Ninja Optimization Algorithm with BiLSTM (NijOA + BiLSTM), which is designed to effectively navigate complex, high-dimensional hyperparameter search spaces encountered in deep learning–based load forecasting tasks. Baseline experiments demonstrate that BiLSTM outperforms other deep learning models, including Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), achieving a baseline Root Mean Squared Error (RMSE) of 0.0964 and a coefficient of determination (R2) of 0.854. These results confirm t he a dvantage o f b idirectional t emporal l earning in capturing the nonlinear and time-dependent characteristics of electricity consumption recorded at high temporal resolution from SCADA systems. Following metaheuristic optimization, the NijOA + BiLSTMmodel delivers a substantial improvement in predictive performance. The optimized configuration reduces RMSE to 0.0038, Mean Squared Error (MSE) to 1.45 × 10−5, and Mean Absolute Error (MAE) to 0.00019, while increasing the correlation strength to r = 0.973 and the explanatory power to R2 = 0.97. Comparative analysis across different optimization strategies further confirms t he s uperiority o f t he NijOA + BiLSTM hybrid model over alternative configurations, including WAO + BiLSTM, BBO + BiLSTM, GA + BiLSTM, SFS + BiLSTM, DE + BiLSTM, and JAYA + BiLSTM. The implications of these findings are significant for real-world urban electricity distribution applications. The proposed framework enables highly accurate and reliable short-term electricity consumption forecasting, making it well suited for deployment within smart grid and distribution management systems. Such predictive capability can support informed operational decision-making, improve demand-side management strategies, reduce uncertainty in short-term planning, and contribute to the long-term sustainability and resilience of urban power distribution networks.

groups

Wei Hong Lim mail -

Amel Ali Alhussan mail

link https://doi.org/10.54216/JAIM.110101

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2026

An Intelligent Metaheuristic-Optimized Deep Learning Approach for Heart Disease Diagnosis and Patient Stratification

The growing heterogeneity of cardiovascular disease presentations poses significant c hallenges for clinical decision support systems, particularly in identifying patient similarities and developing robust predictive models capable of supporting personalized treatment strategies, which motivates the need for advanced data-driven frameworks that can jointly exploit unsupervised learning, deep learning, and intelligent optimization. In this study, we propose a comprehensive hybrid framework that integrates unsupervised patient clustering with deep learning classification, enhanced through Fitness Greylag Goose Optimization (FGGO), where clustering is first employed to uncover latent patient subgroups and inform downstream learning, followed by the use of a Deep Learning Framework Distilled by Gradient Boosting Decision Trees (DeepGBM) as the core predictive model, and finally optimized via FGGO for automated hyperparameter tuning. The primary contribution of this work lies in the design of an FGGO-optimized DeepGBM framework that systematically improves learning stability, feature interaction modeling, and predictive robustness, while also providing a rigorous comparative evaluation against other state-of-the-art metaheuristic optimizers, including Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Dipper Throated Optimization (DDTO), and Multiverse Optimization (MVO). Experimental results demonstrate that, at the baseline stage without optimization, DeepGBM achieves an accuracy of 0.9032, sensitivity of 0.8824, specificity of 0.9195, and F-score of 0.8889, indicating strong but improvable performance on heart disease patient data. After metaheuristic optimization, the proposed FGGO + DeepGBM model exhibits a substantial performance enhancement, reaching an accuracy of 0.9795, sensitivity of 0.9747, specificity of 0.9831, positive predictive value of 0.9776, negative predictive value of 0.9809, and an F-score of 0.9761, consistently outperforming PSO + DeepGBM, GWO + DeepGBM, DDTO + DeepGBM, and MVO + DeepGBM across all evaluation metrics. These results highlight the robustness and convergence consistency of FGGO-based optimization and confirm i ts e ffectiveness in navigating complex hyperparameter search spaces. The implications of this work extend to clinical practice and intelligent healthcare systems, as the proposed framework offers a reliable and scalable solution for patient stratification and heart disease prediction, supporting more accurate, interpretable, and data-driven clinical decision-making while paving the way for future integration into personalized and precision medicine applications.

groups

Khaled Sh. Gaber mail -

Amal H. Alharbi mail

link https://doi.org/10.54216/JAIM.110102

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2026

A Metaheuristic-Optimized Deep Learning Framework for Accurate Classification of Obsessive–Compulsive Disorder Using Clinical Data Based on the Ninja Optimization Algorithm

The growing prevalence and clinical complexity of Obsessive–Compulsive Disorder (OCD) motivate the need for reliable, data-driven decision-support systems capable of improving diagnostic accuracy and robustness beyond traditional assessment methods. In this study, we propose an optimized deep learning framework that integrates a Deep Learning framework distilled by Gradient Boosting Decision Trees (DeepGBM) with a novel metaheuristic optimizer, the Ninja Optimization Algorithm (NiOA), to enhance OCD-related classification using structured demographic and clinical data. The main contribution of this work lies in the design of a unified optimization pipeline in which NiOA is employed for automated hyperparameter tuning of DeepGBM, and in the comprehensive comparison of this approach against baseline deep learning models and alternative metaheuristic optimizers, including Multiverse Optimization (MVO), Bat Algorithm (BA), and Particle Swarm Optimization (PSO). Experimental evaluation demonstrates that, at the baseline stage, DeepGBM outperforms Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Bidirectional Long Short-Term Memory networks (BiLSTM), achieving an accuracy of 0.8970 and an F-score of 0.8935. Following optimization, the proposed NiOA+DeepGBM framework achieves substantial performance gains, reaching an accuracy of 0.9779, sensitivity of 0.9763, specificity of 0.9793, and an F-score of 0.9770, consistently surpassing MVO+DeepGBM, BA+DeepGBM, and PSO+DeepGBM across all evaluation metrics. These results confirm the superior capability of NiOA in navigating complex hyperparameter spaces and enhancing both predictive accuracy and generalization. The implications of this work are significant for intelligent mental health assessment, as the proposed NiOA-optimized DeepGBM model offers a robust, clinically relevant decision-support tool that can assist clinicians in improving diagnostic reliability, reducing uncertainty, and supporting the development of scalable, AI-driven mental healthcare systems.

groups

Safaa Zaman mail -

El-Sayed M. El-Kenawy mail

link https://doi.org/10.54216/JAIM.110103

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2026

A Human-Inspired Metaheuristic Optimization Framework for Accurate Liver Disease Prediction Using Clinical Laboratory Data

The rapid increase in liver disease prevalence worldwide, particularly in developing regions, necessitates accurate and reliable diagnostic systems capable of supporting early clinical decision-making based on routine laboratory data. Traditional diagnostic approaches and unoptimized machine learning models often struggle to fully capture the complex, nonlinear relationships among biochemical liver indicators, leading to suboptimal predictive reliability. Motivated by these challenges, this study proposes a human-inspired metaheuristic optimization framework that integrates the iHow Optimization Algorithm (iHOW) with the Extreme Gradient Boosting model (XGBoost) to enhance liver disease prediction performance. The main contribution of this work lies in the development of an optimized diagnostic pipeline that systematically tunes XGBoost hyperparameters using iHOW and rigorously benchmarks its effectiveness against established metaheuristic optimizers, including Genetic Algorithm (GA), Particle Swarm Optimizer (PSO), Grey Wolf Optimizer (GWO), and Greylag Goose Optimization (GGO). Experimental evaluation is conducted on a clinically sourced liver disease dataset using multiple diagnostic metrics. In the baseline stage, the unoptimized XGBoost model achieves an accuracy of 0.921875, sensitivity of 0.920245399, specificity of 0.923566879, and F-Score of 0.923076923. After hyperparameter optimization, the proposed iHOW+XGBoost framework demonstrates substantial performance enhancement, attaining an accuracy of 0.983696458, sensitivity of 0.983391608, specificity of 0.984012066, and F-Score of 0.983965015, outperforming GA+XGBoost, PSO+XGBoost, GWO+XGBoost, and GGO+XGBoost across all evaluated metrics. These results confirm the effectiveness of human-inspired optimization in navigating complex hyperparameter search spaces and improving diagnostic robustness. The findings of this study highlight the practical implications of integrating advanced metaheuristic optimization with ensemble learning models, offering a highly accurate, reliable, and scalable decision-support framework that can be leveraged for early liver disease screening and extended to other medical diagnostic and predictive healthcare applications.

groups

Benyamin Abdollahzadeh mail -

Marwa M. Eid mail

link https://doi.org/10.54216/JAIM.110104

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2026

Enhancing Gamma–Hadron Separation in Imaging Atmospheric Cherenkov Telescopes Using Attention-Guided Deep Learning and Adaptive Balanced Greylag Goose Optimization

Gamma–hadron discrimination remains a fundamental challenge in very-high-energy gamma-ray astronomy due to the strong overlap between gamma-ray–initiated and hadron-induced air showers recorded by imaging atmospheric Cherenkov telescopes, particularly at low energies where background contamination is severe. Traditional cut-based and non-optimized machine learning approaches often struggle to fully exploit the nonlinear and correlated nature of Cherenkov image parameters, leading to suboptimal background suppression and reduced telescope sensitivity. To address these limitations, this paper proposes a unified deep learning and metaheuristic optimization framework that combines an enhanced attention-based long short-term memory network (EALSTM) with advanced optimization strategies. In particular, a novel Adaptive Balanced Greylag Goose Optimization algorithm (ABGGO) is employed to jointly perform feature selection and hyperparameter optimization, enabling effectiveexploration–exploitation balancing while preserving physically meaningful feature representations. The proposed ABGGO+EALSTM framework is systematically evaluated against baseline deep learning models, including artificial neural networks (ANN), convolutional neural networks (CNN), and standard long short-term memory networks (LSTM), under identical experimental conditions. Experimental results on a Monte Carlo–generated Cherenkov telescope dataset demonstrate clear and consistent performance gains at every stage of the analysis. In the baseline evaluation stage, EALSTM achieves an accuracy of 0.9294 and an F-score of 0.9266, outperforming ANN, CNN, and LSTM. Following metaheuristic optimization, the proposed ABGGO+EALSTM model attains a peak accuracy of 0.9718, sensitivity of 0.9694, specificity o f 0 .9740, a nd F-score o f 0 .9705, representing absolute improvements exceeding 4% over the baseline EALSTM configuration and outperforming GA+EALSTM, GWO+EALSTM, and PSO+EALSTM variants. These results demonstrate that integrating attention-based deep learning with adaptive metaheuristic optimization significantly enhances gamma–hadron discrimination, leading to improved background suppression and signal retention. The proposed framework offers a scalable and robust solution for current and next-generation Cherenkov observatories, with strong potential for real-time event filtering, multi-telescope analysis, and future deployment on real observational data.

groups

Ebrahim A. Mattar mail -

S. K. Towfek mail

link https://doi.org/10.54216/JAIM.110105

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new