Research Papers
Permanent URI for this collectionhttps://localhost:4000/handle/123456789/54
Browse
3 results
Search Results
Item LBP-CA: A Short-term Scheduler with Criticality Arithmetic(University of Hertfordshire, 2022) Fadlelseed, Sajid; Kirner, Raimund; Menon, CatherineIn safety-critical systems a smooth degradation of services is a way to deal with resource shortages. Criticality arithmetic is a technique to implement services of higher criticality by several tasks of lower criticality. In this paper, we present LBP-CA, a mixed-criticality scheduling protocol with smooth degradation based on criticality arithmetic. In the experiments we show that LPB-CA can schedule more tasks than related scheduling protocols (BP and LBP) in case of resource shortage, minimising the negative effect on low-criticality services. This is achieved by considering information about criticality arithmetic of services.Item Artificial Ecosystem‑Based Optimization with Dwarf Mongoose Optimization for Feature Selection and Global Optimization Problems(International Journal of Computational Intelligence Systems, 2023) Al‑Shourbaji, Ibrahim; Kachare, Pramod; Fadlelseed, Sajid; Jabbari, Abdoh; Hussien, Abdelazim G.; Al‑Saqqar, Faisal; Abualigah, Laith; Alameen, AbdallaMeta-Heuristic (MH) algorithms have recently proven successful in a broad range of applications because of their strong capabilities in picking the optimal features and removing redundant and irrelevant features. Artificial Ecosystem-based Opti mization (AEO) shows extraordinary ability in the exploration stage and poor exploitation because of its stochastic nature. Dwarf Mongoose Optimization Algorithm (DMOA) is a recent MH algorithm showing a high exploitation capability. This paper proposes AEO-DMOA Feature Selection (FS) by integrating AEO and DMOA to develop an efficient FS algorithm with a better equilibrium between exploration and exploitation. The performance of the AEO-DMOA is investigated on seven datasets from different domains and a collection of twenty-eight global optimization functions, eighteen CEC2017, and ten CEC2019 benchmark functions. Comparative study and statistical analysis demonstrate that AEO-DMOA gives competi tive results and is statistically significant compared to other popular MH approaches. The benchmark function results also indicate enhanced performance in high-dimensional search space.Item Recent Advances in Demand Responsive Transport: Opportunities With Autonomous Bus Service—A System-of-Systems Overview(IEEE Access, 2025) Fadlelseed Sajid; Pouria SarhadiThis study explores recent advancements in emerging technologies for Demand Responsive Transport (DRT), focusing on the potential integration of autonomous bus services. We review the historical development andcurrent state of research, extending the discussion into the era of Connected and Automated Mobility (CAM), particularly in mass transit. Key components of modern DRT systems are categorised frommultiple perspectives, including vehicle types, infrastructure, devices, simulation approaches, decision making algorithms, and optimisation factors. Each element is discussed in detail, supported by comparativea tables to help readers interpret the results effectively. Our findings highlight the growing interest and ongoing research in this promising domain. We identify common practices across studies and areas needing improvement.Tosupportthis, weproposeaSystem-of-Systems(SoS)approachtoevaluatethetechnological maturity of DRT solutions, in line with our ongoing project. A System Readiness Level (SRL) analysis is performed by identifying constituent systems and assessing their Technology Readiness Levels (TRLs) and Integration Readiness Levels (IRLs). The study also explores the deployment of DRT in a specific use case: the Maylands Business Park in Hertfordshire, UK. It aims to serve as a comprehensive reference for addressing various dimensions of DRT in mass transit, particularly in the context of automation. It offers insights into current progress and outlines opportunities for deploying DRT as a cost-effective, scalable solution to improve sustainability in Intelligent Transportation Systems (ITS).