Samir Haddad

Introduction: The rapid growth of e-commerce has increased pressure on postal logistics networks, especially in remote regions. Methods: This study proposes a fuzzy hyperheuristic genetic algorithm for optimizing a facilitated hub-and-spoke network with drone integration under uncertainty. Results: The proposed approach improves service robustness by 25–35% and expands drone coverage to 93.4% of remote demand, with only a modest cost increase (8–12%). Discussion: Results highlight the effectiveness of fuzzy optimization and adaptive hyperheuristics in designing resilient and cost-efficient postal logistics systems.

The Hub Location Problem (HLP) remains a cornerstone of strategic decision-making in logistics and transportation networks. However, emerging challenges driven by climate variability, energy consumption constraints, and carbon emission regulations are reshaping the complexity of these classical models. This paper introduces a novel Energy-Aware Dynamic Hub Location Problem (EA-DHLP) that integrates climate-driven demand variations, renewable energy availability, and carbon penalties into the hub network design. We formulate the core EA-DHLP as a mixed-integer linear program (MILP) by treating climate and energy series as exogenous parameters and isolating any nonlinear couplings as optional MINLP extensions. To solve large instances of this MILP-based and dynamic problem, we propose a Hybrid Reinforcement Learning and Thermodynamic Hyper-Heuristic (RL-THH) framework. The reinforcement learning agent adaptively selects from a pool of low-level heuristics based on real-time performance feedback, while a thermodynamic-inspired temperature control mechanism dynamically balances exploration and exploitation throughout the search process. We benchmark RL-THH against (i) exact MILP solving on small/medium instances and (ii) advanced metaheuristics/hyperheuristics (including Artificial Bee Colony, Grey Wolf Optimizer, and an RL-only HH variant) under matched computational budgets, with nonparametric significance testing (Wilcoxon with Holm–Bonferroni correction) and effect-size reporting (Cliff’s δ). Extensive computational experiments are conducted on both synthetic and real-world datasets, including the Australian Post and CAB datasets, incorporating climate and energy data. Results show statistically significant improvements in minimizing total costs, reducing energy consumption, and lowering carbon emissions when compared to traditional metaheuristics and the aforementioned state-of-the-art baselines. For small/medium instances, the exact MILP provides optimality certificates that contextualize RL-THH’s performance; for larger instances, RL-THH scales while preserving solution quality and stability. This study offers a scalable, adaptive, and environmentally conscious solution framework for modern hub location problems under sustainability and uncertainty considerations.

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.

Symmetric cryptographic schemes such as RSA and ECDSA (Elliptic Curve Digital Signature Algorithm), used for digital signatures in protocols like TLS, DTLS, and secure messaging, are computationally intensive. This makes them unsuitable for constrained environments, such as the Internet of Things (IoT) and the Internet of Vehicles (IoV). This study introduces a blockchain-based framework that utilizes the Ethereum network to store and verify public keys associated with digital certificates. By replacing signature decryption with blockchain-based public key verification, the solution significantly reduces cryptographic overhead and latency in V2V messages. It supports various certificate formats, including Public Key Infrastructure (PKI)/Certificate Authority (CA) certificates such as X.509 and L-ECQV, as well as self-signed certificates. Applications include secure communication protocols like Datagram Transport Layer Security (DTLS)/Transport Layer Security (TLS), V2V mutual authentication in V2X messaging, and lightweight certificate management within IoT ecosystems. Empirical results show that the DTLS handshake with this scheme is reduced from 12 s to less than 6 s. Additionally, it enables vehicles and IoT devices to perform one-time signature verification with minimal latency in V2V messaging, demonstrating significant performance improvements for high-density deployments involving mutual authentication between IoT devices and V2V communication.

This paper presents a novel enhancement in the Hybrid TESLA protocol, integrating the Generic Bootstrapping Architecture (GBA) to replace public-key cryptography in the synchronization phase. The proposed protocol securely establishes communication between IoT devices and application servers without transmitting sensitive credentials over the network by leveraging GBA’s symmetric encryption and session key generation. Utilizing the existing mobile network infrastructure, GBA ensures secure authentication, making the protocol highly suitable for resource-constrained IoT environments. A simulation analysis conducted using Java demonstrated successful synchronization between clients and servers, with previous GBA experimental results confirming the protocol’s efficiency in terms of authentication time and power consumption. The Hybrid TESLA protocol’s use of SHA-2 and SHA-3 hashing functions ensures minimal computational demands while maintaining robust cybersecurity. This integration expands Hybrid TESLA’s utility in metaverse environments, enabling secure, seamless interactions across mobile networks.

Multi-Criteria Decision-Making (MCDM) techniques play a critical role in solving complex decision problems involving multiple conflicting criteria across various domains. The Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) is a widely adopted MCDM method known for its structured and transparent ranking process. However, conventional PROMETHEE implementations often rely on manually assigned criteria weights, introducing subjectivity and inconsistency in decision-making. This study proposes a novel hybrid approach that integrates Machine Learning (ML) techniques with PROMETHEE to enhance decision-making, particularly in blockchain security risk assessment. The proposed ML-PROMETHEE model leverages ML algorithms for feature selection, weight assignment, and preference function optimization, ensuring an automated and data-driven ranking process that minimizes human bias and enhances decision reliability. A real-world case study in blockchain security risk assessment is conducted to validate the proposed methodology. This study employs ML models such as Random Forest (RF) for feature selection and SHapley Additive exPlanations (SHAP) analysis to determine the most influential security risk factors. Additionally, Artificial Neural Networks (ANNs) and eXtreme Gradient Boosting (XGBoost) are used to optimize weight assignment, refining the decision-making process before integrating it into the PROMETHEE framework. Experimental results demonstrate that the ML-enhanced PROMETHEE model achieves a 17.3% improvement in decision accuracy, a 43.5% reduction in execution time, and a 40.2% reduction in subjective bias compared to traditional PROMETHEE-based decision-making. These findings highlight the potential of ML-enhanced MCDM models in strengthening the robustness, transparency, and scalability of decision frameworks, particularly in rapidly evolving domains such as blockchain security. Future research will explore the integration of deep learning and reinforcement learning techniques to further enhance automated decision-making frameworks.

Cardiovascular disease (CVD) is a leading cause of global mortality, necessitating predictive models that are both accurate and interpretable. This study introduces the Hybrid Polynomial Ensemble Model (HPEM), a novel ensemble learning framework that combines Random Forest (RF), Gradient Boosting (GB), and Support Vector Machines (SVM), with Extreme Gradient Boosting (XGBoost) as a meta-learner. The model integrates advanced feature engineering, including second-degree polynomial expansion and mutual information-based selection, to enhance learning from heterogeneous patient data. HPEM was evaluated on three publicly available datasets, achieving an accuracy of 91%, 88%, and 89% respectively. It also attained F1-scores of 0.91, 0.88, and 0.89, and Area Under the Curve (AUC) values up to 0.96, outperforming baseline models across all metrics.To ensure transparency, explainability tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were applied, identifying key clinical predictors including cholesterol levels, exercise-induced angina, and age. The model also achieved Cohen’s Kappa scores above 0.79 and Matthews Correlation Coefficient (MCC) values above 0.80, demonstrating reliability and robustness. These results underscore the potential of HPEM as a clinically relevant tool for early detection and risk stratification of cardiovascular disease.

The Internet of Things (IoT) is expanding rapidly, but the security of IoT devices remains a noteworthy concern due to resource limitations and existing security conventions. This research investigates and proposes the use of a Light certificate with the Constrained Application Protocol (CoAP) instead of the X509 certificate based on traditional PKI/CA. We start by analyzing the impediments of current CoAP security over DTLS with the certificate mode based on CA root in the constrained IoT device and suggest the implementation of LightCert4IoT for CoAP over DTLS. The paper also describes a new modified handshake protocol in DTLS applied for IoT devices and Application server certificate authentication verification by relying on a blockchain without the complication of the signed certificate and certificate chain. This approach streamlines the DTLS handshake process and reduces cryptographic overhead, making it particularly suitable for resource-constrained environments. Our proposed solution leverages blockchain to reinforce IoT gadget security through immutable device characters, secure device registration, and data integrity. The LightCert4IoT is smaller in size and requires less power consumption. Continuous research and advancement are pivotal to balancing security and effectiveness. This paper examines security challenges and demonstrates the effectiveness of giving potential solutions, guaranteeing the security of IoT networks by applying LightCert4IoT and using the CoAP over DTLS with a new security mode based on blockchain.