Results 71 to 80 of about 20,865 (294)

Significance relations for the benchmarking of meta-heuristic algorithms [PDF]

open access: yes2013 13th International Conference on Intellient Systems Design and Applications, 2013
The experimental analysis of meta-heuristic algorithm performance is usually based on comparing average performance metric values over a set of algorithm instances. When algorithms getting tight in performance gains, the additional consideration of significance of a metric improvement comes into play.
Mario Köppen, Kei Ohnishi
openaire   +2 more sources

Retinal Vessel Segmentation: A Comprehensive Review From Classical Methods to Deep Learning Advances (1982–2025)

open access: yesAdvanced Intelligent Systems, EarlyView.
Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal   +6 more
wiley   +1 more source

Artificial Intelligence in Autonomous Mobile Robot Navigation: From Classical Approaches to Intelligent Adaptation

open access: yesAdvanced Intelligent Systems, EarlyView.
Artificial intelligence (AI) is reshaping autonomous mobile robot navigation beyond classical pipelines. This review analyzes how AI techniques are integrated into core navigation tasks, including path planning and control, localization and mapping, perception, and context‐aware decision‐making. Learning‐based, probabilistic, and soft‐computing methods
Giovanna Guaragnella   +5 more
wiley   +1 more source

Bio-Inspired Meta-Heuristics for Emergency Transportation Problems

open access: yesAlgorithms, 2014
Emergency transportation plays a vital role in the success of disaster rescue and relief operations, but its planning and scheduling often involve complex objectives and search spaces. In this paper, we conduct a survey of recent advances in bio-inspired
Min-Xia Zhang, Bei Zhang, Yu-Jun Zheng
doaj   +1 more source

Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models

open access: yes, 2016
The emergence and development of cancer is a consequence of the accumulation over time of genomic mutations involving a specific set of genes, which provides the cancer clones with a functional selective advantage.
Antoniotti, Marco   +4 more
core   +1 more source

OntoLogX: Ontology‐Guided Knowledge Graph Extraction From Cybersecurity Logs With Large Language Models

open access: yesAdvanced Intelligent Systems, EarlyView.
OntoLogX is an autonomous AI agent that uses large language models to transform unstructured cyber security logs into ontology grounded knowledge graphs. By integrating retrieval augmented generation, iterative correction, and a light‐weight log ontology, OntoLogX produces semantically consistent intelligence that links raw log events to MITRE ATT & CK
Luca Cotti   +4 more
wiley   +1 more source

Exploiting heterogeneity for cost efficient 5G base station deployment using meta‐heuristics

open access: yesIET Networks, 2020
A key concern in the design of 5G is the radio access network, which is expected to be significantly denser and more advanced, with considerably higher infrastructure and power consumption cost than that of conventional mobile network standards.
David Aondoakaa   +2 more
doaj   +1 more source

Generalized Task‐Driven Design of Soft Robots via Reduced‐Order Finite Element Method‐Based Surrogate Modeling

open access: yesAdvanced Intelligent Systems, EarlyView.
A unified, reusable modeling pipeline enables task‐driven design of soft robots across actuator families and task scenarios. High‐fidelity simulations are compressed into compact pseudo‐rigid‐body joint surrogates, while a design‐conditioned meta‐model generates new surrogates from geometry parameters without rerunning finite element method.
Yao Yao, David Howard, Perla Maiolino
wiley   +1 more source

Dynamic programming with meta-reinforcement learning: a novel approach for multi-objective optimization

open access: yesComplex & Intelligent Systems
Multi-objective optimization (MOO) endeavors to identify optimal solutions from a finite array of possibilities. In recent years, deep reinforcement learning (RL) has exhibited promise through its well-crafted heuristics in tackling NP-hard combinatorial
Qi Wang, Chengwei Zhang, Bin Hu
doaj   +1 more source

Nurse Scheduling with Opposition-Based Parallel Harmony Search Algorithm

open access: yesJournal of Intelligent Systems, 2019
One of the advances made in the management of human resources for the effective implementation of service delivery is the creation of personnel schedules.
Cetin Yagmur Ece, Sarucan Ahmet
doaj   +1 more source

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