Computer Engineering, Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research
44
Scopus Publications
Scopus Publications
Infection Aware Hyper-Heuristic Framework for Hospital Room–Patient Matching Kassem Danach, Wael Hosny Fouad Aly, Chadi Fouad Riman Algorithms, 2026 The assignment of hospital rooms to patients is a critical operational decision that has a direct impact on patient safety, infection control, and staff workload. This study introduces HRPM–IRC, an epidemiology-aware hyper-heuristic framework developed to optimize room–patient matching by minimizing the risk of nosocomial infections, reducing travel and specialty mismatch costs, and promoting equitable nurse workload distribution. A mixed-integer linear programming model is formulated to capture infection transmission probabilities, isolation and cohorting requirements, and multi-ward capacity constraints. On top of this model, a bio-inspired hyper-heuristic adaptively selects and refines low-level heuristics, including cohort-first greedy allocation, risk-gradient swaps, and pathogen-aware local MILP refinement, on the basis of contextual epidemiological indicators and reinforcement learning. The framework was validated using a real-world dataset obtained from a tertiary hospital in Lebanon, comprising 142 anonymized patient admissions, 35 rooms, and six nursing teams. Results demonstrate that HRPM–IRC consistently reduces modeled infection risk and workload imbalance by up to forty percent compared to conventional assignment heuristics while maintaining near-real-time decision-making capabilities suitable for dynamic hospital operations. These findings underscore the effectiveness of epidemiology-aware hyper-heuristics in enhancing hospital resilience, improving infection prevention, and supporting fair resource utilization in data-limited healthcare environments typical of Lebanon and other middle-income countries.
AI-powered hub optimization: a reinforcement learning and graph-based approach to scalable blockchain networks Kassem Danach, Hassan Rkein, Alaaeddine Ramadan, Hassan Harb, Bassam Hamdar Iaes International Journal of Artificial Intelligence, 2026 Blockchain networks face persistent scalability challenges, including network congestion, high latency, and transaction costs. To address these limitations, this study proposes an AI-driven hub location optimization framework that integrates reinforcement learning (RL), mixed integer linear programming (MILP), and graph neural networks (GNNs). The RL-based hub selection dynamically identifies optimal supernode placement, while MILP ensures cost-efficient transaction routing, and GNNs predict flow patterns for proactive congestion management. Experimental results on Ethereum and Bitcoin datasets demonstrate significant improvements, including a 58.6% reduction in transaction latency, 28.9% gas fee savings, and 41.5% congestion reduction. Beyond performance gains, statistical tests confirm the significance of these improvements, and ablation studies highlight the complementary role of each component.
Digital Dewaxing of Raman Hyperspectral Images: Application to Paraffin-Embedded Skin Biopsies Abbas Rammal, Rabih Assaf, Kassem Danach Journal of Raman Spectroscopy, 2026 Raman spectral imaging is a powerful tool for studying the molecular composition of biological samples, typically preserved in paraffin to protect their molecular structures. However, the intense signal from paraffin can interfere with the analysis, necessitating the removal of pure paraffin pixels for accurate imaging. Extended multiplicative signal correction (EMSC) has recently emerged as an effective method to neutralize the paraffin signal in recorded Raman spectral images. This article introduces a novel methodology combining EMSC with multivariate analysis techniques to separate paraffin and tissue pixels. Blind source separation techniques such as independent component analysis (ICA), non‐negative matrix factorization (NMF), principal component analysis (PCA), and singular value decomposition (SVD) are applied to Raman images acquired solely on paraffin to model and extract the pure paraffin component accurately. To validate our approach, we employ k‐means clustering on the corrected spectra obtained through the proposed method and compare the results with those from the traditional EMSC method that does not utilize pure paraffin components modeled by blind source separation techniques. The deparaffinized spectra are then used to construct Raman images of human tissues, which are compared with hematoxylin‐eosin (H&E) stained tissues for verification. This study demonstrates the potential of Raman spectroscopy, combined with EMSC and blind source separation techniques, as a digital dewaxing tool for analyzing paraffin‐embedded tissues.
Carbon-Aware Scheduling in Cloud Computing Operations: A Multi-Objective Optimisation Approach Kassem Danach, Kassem Hamze, Hassan Harb, Hassan Kanj Iet Smart Grid, 2026 The rapid expansion of cloud computing has intensified the environmental impact of large‐scale data centres, which now represent a significant portion of global electricity consumption. Traditional scheduling strategies typically optimise performance or cost, disregarding the fluctuating carbon intensity of regional power grids. This study proposes a dynamic carbon‐aware scheduling framework that integrates real‐time carbon intensity forecasting with multi‐objective optimisation and adaptive rolling‐horizon control. The proposed model simultaneously minimises operational cost and greenhouse gas emissions by intelligently shifting computational workloads across time and geography in response to renewable energy availability. The framework combines an ensemble forecasting module, using long short‐term memory (LSTM) and gradient boosting regression, with a mixed‐integer linear programming (MILP) model solved via the ‐constraint method. It adaptively updates scheduling decisions based on updated carbon forecasts and workload arrivals. Experimental validation on real datasets from the UK National Grid and Google Cloud workload traces demonstrates an average reduction in emissions, a improvement in cost efficiency and less than performance degradation compared to conventional schedulers. Pareto front analysis further reveals actionable trade‐offs between economic efficiency and environmental sustainability. The results confirm that integrating operational research with carbon intelligence enables cloud infrastructures to become both cost‐effective and climate‐aligned.
Adaptive Hyper-Heuristics for Smart Logistics Optimization Kassem Danach, Hasan Fayyad-Kazan, Wissam Khalil, Samir Haddad, Jinane Sayah Lecture Notes in Electrical Engineering, 2026 The complexity of logistics combinatorial optimization problems including vehicle routing, warehouse scheduling, and dynamic delivery has increased because of rising demand and evolving constraints. Metaheuristics show effectiveness but need problem-specific tuning and demonstrate limited general applicability. This research presents a learning-based hyper-heuristic framework which operates at high abstraction levels to select or generate low-level heuristics through dynamic decision-making based on problem features and real-time performance feedback. The proposed system uses reinforcement learning to select heuristics while pursuing adaptability, scalability and domain independence. Additionally, the framework demonstrates its effectiveness through benchmark dataset experiments, which show better solution quality and improved computational efficiency and robustness compared to traditional metaheuristics. Moreover, the framework shows its capability to perform automated decision-making while minimizing human involvement and demonstrating effective adaptation to changing logistics environments. Finally, this research presents an adaptable intelligent optimization system which enhances operational efficiency and resilience in smart supply chain systems.
Hyper-heuristic driven smart contracts for DeFi: a framework for dynamic rule optimization and adaptive executions Kassem Danach, Hassan Rkein, Ahmad Farroukh, Ziad E. L. Balaa, Samir Haddad Frontiers in Blockchain, 2026 The static and hard-coded logic of smart contracts in Decentralized Finance (DeFi) platforms significantly limits their adaptability in dynamic and volatile market environments. To address this challenge, we propose a novel hyper-heuristic driven framework that enables real-time rule optimization within smart contracts, thereby enhancing responsiveness, gas efficiency, and operational robustness. The framework features a two-layer architecture: a reinforcement learning-based high-level controller selects appropriate low-level rule heuristics from a domain-specific library based on evolving transaction contexts and on-chain data. Implemented and evaluated on Uniswap v2 and Aave v3 protocols, the system dynamically optimizes parameters such as slippage tolerance, gas usage thresholds, and loan-to-value ratios. Experimental results on real-world datasets show significant performance improvements, including a 45.6% increase in transaction success rate, 28.3% reduction in average gas consumption, and 38.4% drop in liquidation events under market stress scenarios. This research demonstrates the feasibility and advantages of embedding intelligent, adaptive decision-making mechanisms within DeFi smart contracts, opening new pathways toward autonomous, resilient, and regulation-aligned blockchain systems.
