Preface Communications in Computer and Information Science, 2026
Slime Mould Metaheuristic for optimization and robot path planning Enol García, José R. Villar, Javier Sedano, Camelia Chira, Enrique de la Cal, Luciano Sánchez Neurocomputing, 2025 Function optimization represents a remarkable challenge in industry and society, aiming to find reasonable solutions –even if they are suboptimal– for everyday problems. Metaheuristics drive the optimization search towards the goals using a specific algorithm inspired by different concepts: from industrial processes to the behaviour of living beings in nature, from mathematical ideas to physics notions. This research proposes a new metaheuristic inspired by the Slime Mould and its foraging behaviours. On the one hand, an exploitation stage mimics the greedy amoeba’s conduct when food is plenty. On the other hand, an exploration stage copies the fruity aggregation of the cells and the subsequent spore dissemination. This study compares the most cited metaheuristics and the Slime Mould Optimization in two different experimentation stages: on the one hand, the optimization of standard benchmarking functions; on the other hand, solving the robot path planning problem. Moreover, a hybridization of the SMO and the WOA is presented, which keeps the SMO’s convergence speed and the WOA’s good performance in finding the best solutions.
Towards an interpretable breast cancer detection and diagnosis system Cristiana Moroz-Dubenco, Adél Bajcsi, Anca Andreica, Camelia Chira Computers in Biology and Medicine, 2025 According to the World Health Organization, breast cancer becomes fatal only if it spreads throughout the body. Therefore, regular screening is essential. Whilst mammography is the most frequently used technique, its interpretation can be challenging and time-consuming. For this reason, computer-aided detection and diagnosis systems are increasingly being used for second opinion. However, in order for doctors to trust such systems, they need to understand their decisions. We propose an automated and interpretable system for the detection and diagnosis of breast cancer, encompassing five steps. After a robust pre-processing and an unsupervised segmentation, we analyze four feature extraction techniques, both textural and shape-based, and three methods for feature selection. To facilitate interpretation, we employ the Decision Tree algorithm for benign/malignant classification and experiment with different methods to avoid overfitting: pre-pruning, post-pruning, and ensemble-based (Random Forest classifier). Our system reaches a maximum accuracy of 95% and 100% precision and specificity when tested on images from the mini-MIAS dataset, while also offering its users the possibility to analyze each of the steps. • Breast cancer becomes fatal only if it spreads throughout the body. • Mammography is the most frequently used screening technique. • Computer-aided detection and diagnosis systems are being used for a second opinion. • An interpretable system offers its users the possibility to analyze each step. • An interpretable system can be split into steps which can be individually displayed.
Evaluating Deep Learning Models for Cross-Platform UI Component Detection: A Study Across Web, Desktop, and Mobile Interfaces Mădălina Dicu, Camelia Chira Procedia Computer Science, 2025 User interfaces look different across web, desktop, and mobile platforms — not just in layout, but in how buttons, icons, and text appear. This makes it hard for deep learning models trained on one platform to accurately detect UI components on another. In this paper, we evaluate the cross-domain generalization of three modern object detectors — YOLOv8, YOLOv9, and Faster R-CNN — trained on one or more GUI platforms using three datasets: GENGUI (web), UICVD (desktop), and VINS (mobile). We focus on three common UI classes: Text, Button, and Icon, and compare model performance across four scenarios: in-domain training, domain adaptation, fine-tuning, and combined training. Our results show that YOLOv9 consistently delivers the best cross-domain performance, especially when fine-tuned — achieving up to 95.5% mAP when adapted from desktop to web interfaces. We also fnd that Text is the most transferable class, while Button and Icon require adaptation to new visual styles. Fine-tuning emerges as the most effective strategy for improving generalization with limited data.
Evaluating ResNet-Based Self-Explanatory Models for Breast Lesion Classification Adél Bajcsi, Camelia Chira, Annamária Szenkovits International Conference on Agents and Artificial Intelligence, 2025 : Breast cancer is one of the leading causes of mortality among women diagnosed with cancer. In recent years, numerous computer-aided diagnosis (CAD) systems have been proposed for the classification of breast lesions. This study investigates self-explanatory deep learning models, namely BagNet and ProtoPNet, for the classification of breast abnormalities. Our aim is to train models to distinguish between benign and malignant lesions in breast tissue using publicly available datasets, namely MIAS and DDSM. The study provides a comprehensive numerical comparison of the two self-explanatory models and their respective backbones, as well as a visual evaluation of model performance. The results indicate that, while the backbone (black-box model) exhibits slightly better performance, it does so at the expense of interpretability. Conversely, BagNet, despite being a simpler model, achieves results comparable to those of ProtoPNet. In addition, transfer learning and data augmentation techniques are employed to enhance the performance of the CAD system.
SERS liquid biopsy in breast cancer. What can we learn from SERS on serum and urine? Stefania D. Iancu, Ramona G. Cozan, Andrei Stefancu, Maria David, Tudor Moisoiu, Cristiana Moroz-Dubenco, Adel Bajcsi, Camelia Chira, Anca Andreica, Loredana F. Leopold, Daniela Eniu, Adelina Staicu, Iulian Goidescu, Carmen Socaciu, Dan T. Eniu, Laura Diosan, Nicolae Leopold Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy, 2022
A hybrid evolutionary and multiagent reinforcement learning approach to accelerate the computation of traffic assignment Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas, 2015
Error-correcting output codes for multi-label text categorization Ceur Workshop Proceedings, 2012
Preface Carlos Alberto Ochoa Ortiz Zezzatti, Camelia Chira, Arturo Hernandez, Miguel Basurto Logistics Management and Optimization Through Hybrid Artificial Intelligence Systems, 2012
Merge method for shape-based clustering in time series microarray analysis Irene Barbero, Camelia Chira, Javier Sedano, Carlos Prieto, José R. Villar, Emilio Corchado Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012
Intelligent system for measuring stress: StressTIC Dyna Spain, 2012
Collaborative community detection in complex networks Camelia Chira, Anca Gog Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2011
Learning sensitive stigmergic agents for solving complex problems Computing and Informatics, 2010
Solving the linear ordering problem using ant models Camelia Chira, Camelia M. Pintea, Gloria C. Crisan, D. Dumitrescu Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference Gecco 2009, 2009
Hybrid multi-population collaborative asynchronous search Anca Gog, Camelia Chira, D. Dumitrescu Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2008
An agent-based approach to combinatorial optimization International Journal of Computers Communications and Control, 2008
A hybrid ant-based system for gate assignment problem Camelia-M. Pintea, Petrica C. Pop, Camelia Chira, D. Dumitrescu Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2008
Exploring population geometry and multi-agent systems: A new approach to developing evolutionary techniques Gecco 08 Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008, 2008
An agent-based collaborative evolutionary model for multimodal optimization Gecco 08 Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008, 2008
Sensitive Ant Model for combinatorial optimization Mendel 2008 14th International Conference on Soft Computing Evolutionary Computation Genetic Programming Fuzzy Logic Rough Sets Neural Networks Fractals Bayesian Methods, 2008
Multi-population agent search: Stigmergy and heterogeneity Camelia Chira, Camelia-M. Pintea, D. Dumitrescu Proceedings of the 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Synasc 2008, 2008
A sensitive metaheuristic for solving a large optimization problem Camelia-M. Pintea, Camelia Chira, D. Dumitrescu, Petrica C. Pop Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2008
Stigmergic collaborative agents Journal of Universal Computer Science, 2007
The generalized traveling salesman problem solved with ant algorithms Journal of Universal Computer Science, 2007
Agent-based management and optimization system for distributed computing Proceedings of the 3rd IASTED International Conference on Computational Intelligence Ci 2007, 2007
Multi-agent systems and ontologies for distributed collaboration Wseas Transactions on Information Science and Applications, 2006
Combining meta-heuristics to solve the rook problem Camelia-m. Pintea, Camelia Chira, D. Dumitrescu Proceedings of the 8th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Synasc 2006, 2006
Multi-agent support for distributed engineering design Camelia Chira, Ovidiu Chira, Thomas Roche Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2005
An agent-based approach to knowledge management in distributed design Proceedings of the 10th Ispe International Conference on Concurrent Engineering Research and Application Enhanced Interoperable Systems, 2003
A multi-agent architecture for distributed design Ovidiu Chira, Camelia Chira, David Tormey, Attracta Brennan, Thomas Roche Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2003
Development of engineering design methodologies and software tools to support the creative process of design in a distributed environment Proceedings of the International Conference on Engineering Design Iced, 2003
The DFE Workbench software tool case study T. Roche, E. Man, C. Chira, J. Browne Proceedings 2nd International Symposium on Environmentally Conscious Design and Inverse Manufacturing, 2001
RECENT SCHOLAR PUBLICATIONS
Feature combination from different mammogram perspectives to improve lesion classification A Bajcsi, C Chira Logic Journal of the IGPL 34 (1), jzaf041 , 2026 2026
Polarity Related Influence Maximization through Multi-Agent Reinforcement Learning A Kopacz, C Chira 2026
Pairwise 3D Fragment Matching Classification with Graph Neural Networks RD Chiş, C Chira International Conference on Innovative Perspectives on Computational … , 2025 2025
Analyzing the Impact of Data Augmentation on Tumor Detection and Classification in Mammograms M Dicu, EG González, C Chira, JR Villar International Conference on Hybrid Artificial Intelligence Systems, 79-90 , 2025 2025
REGULAR ISSUE: Electronics, Energy and Computing J Ceron, C Tinipuclla, P Shiguiharau, OA Mejia-Rosado, ... IEEE Latin America Transactions 23 (10), 837 , 2025 2025
Slime Mould Metaheuristic for optimization and robot path planning E García, JR Villar, J Sedano, C Chira, E de la Cal, L Sánchez Neurocomputing 647, 130551 , 2025 2025 Citations: 3
Hybrid Adaptive Greedy Algorithm Addressing the Multi-Robot Path Planning Problem A Kopacz, EG González, C Chira, JRV Flecha IEEE Latin America Transactions 23 (10), 856-864 , 2025 2025 Citations: 2
fMRI Analysis for Alzheimer’s Disease Detection: Traditional vs. Deep Learning Models A Bajcsi, C Chira International Conference on Artificial Intelligence in Medicine, 11-21 , 2025 2025
A Hybrid Granular Ball-Ant Colony Optimization for the Multi-Depot Half-Open Time-Dependent Electric Vehicle Routing Problem Y Xu, A Kopacz, C Chira 2025 IEEE Congress on Evolutionary Computation (CEC), 1-8 , 2025 2025
Towards an interpretable breast cancer detection and diagnosis system C Moroz-Dubenco, A Bajcsi, A Andreica, C Chira Computers in Biology and Medicine 185, 109520 , 2025 2025 Citations: 10
Blood Biomarkers and Machine Learning in Prognosis of Nasopharyngeal Carcinoma A Bajcsi, LD Cernău, C Chira, L Dioșan, TM Ursu 2025
GenGUI: A Dataset for Automatic Generation of Web User Interfaces Using ChatGPT. M Dicu, EG González, C Chira, JR Villar ICAART (3), 707-714 , 2025 2025 Citations: 2
Evaluating ResNet-Based Self-Explanatory Models for Breast Lesion Classification. A Bajcsi, C Chira, A Szenkovits ICAART (3), 288-295 , 2025 2025
Automatic Classification of Signal and Noise in Functional Magnetic Resonance Imaging Scans Using Convolutional Neural Networks G Anghelescu, C Chira, KNT Månsson International Conference on Intelligent Data Engineering and Automated … , 2024 2024
Machine learning model predicts postoperative outcomes in chronic rhinosinusitis with nasal polyps A Gata, L Raduly, L Budișan, A Bajcsi, TM Ursu, C Chira, L Dioșan, ... Clinical Otolaryngology 49 (6), 776-784 , 2024 2024 Citations: 11
The impact of data annotations on the performance of object detection models in icon detection for gui images M Dicu, EG González, C Chira, JR Villar International Conference on Hybrid Artificial Intelligence Systems, 251-262 , 2024 2024 Citations: 2
Benchmarking analysis for biological-based metaheuristics E García-González, JR Villar-Flecha, J Sedano, C Chira Dyna 99 (3) , 2024 2024 Citations: 2
The Impact of Augmentation Techniques on Icon Detection Using Machine Learning Techniques M Dicu, C Chira IFIP International Conference on Artificial Intelligence Applications and … , 2024 2024 Citations: 1
Significance of Training Images and Feature Extraction in Lesion Classification. A Bajcsi, A Andreica, C Chira ICAART (3), 117-124 , 2024 2024 Citations: 4
UICVD: A Computer Vision UI Dataset for Training RPA Agents. M Dicu, A Sterca, C Chira, R Orghidan ENASE, 414-421 , 2024 2024 Citations: 3
MOST CITED SCHOLAR PUBLICATIONS
Improving fall detection using an on-wrist wearable accelerometer SB Khojasteh, JR Villar, C Chira, VM González, E De la Cal Sensors 18 (5), 1350 , 2018 2018 Citations: 189
Improving human activity recognition and its application in early stroke diagnosis JR Villar, S González, J Sedano, C Chira, JM Trejo-Gabriel-Galan International journal of neural systems 25 (04), 1450036 , 2015 2015 Citations: 100
Heuristic algorithms for solving the generalized vehicle routing problem PC Pop, I Zelina, V LupÅŸe, CP Sitar, C Chira International Journal of Computers Communications & Control 6 (1), 158-165 , 2011 2011 Citations: 98
The generalized traveling salesman problem solved with ant algorithms CM Pintea, PC Pop, C Chira Complex Adaptive Systems Modeling 5 (1), 8 , 2017 2017 Citations: 83
Classifiers with a reject option for early time-series classification N Hatami, C Chira 2013 IEEE symposium on computational intelligence and ensemble learning … , 2013 2013 Citations: 82
An agent-based approach to knowledge management in distributed design O Chira, C Chira, T Roche, D Tormey, A Brennan Journal of Intelligent Manufacturing 17 (6), 737-750 , 2006 2006 Citations: 78
An efficient multi-robot path planning solution using A* and coevolutionary algorithms E García, JR Villar, Q Tan, J Sedano, C Chira Integrated Computer-Aided Engineering 30 (1), 41-52 , 2023 2023 Citations: 52
SERS liquid biopsy in breast cancer. What can we learn from SERS on serum and urine? SD Iancu, RG Cozan, A Stefancu, M David, T Moisoiu, C Moroz-Dubenco, ... Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 273, 120992 , 2022 2022 Citations: 50
A genetic algorithm for solving the generalized vehicle routing problem PC Pop, O Matei, CP Sitar, C Chira International Conference on Hybrid Artificial Intelligence Systems, 119-126 , 2010 2010 Citations: 46
S4MPLE—sampler for multiple protein-ligand entities: methodology and rigid-site docking benchmarking L Hoffer, C Chira, G Marcou, A Varnek, D Horvath Molecules 20 (5), 8997-9028 , 2015 2015 Citations: 44
An improved immigration memetic algorithm for solving the heterogeneous fixed fleet vehicle routing problem O Matei, PC Pop, JL Sas, C Chira Neurocomputing 150, 58-66 , 2015 2015 Citations: 44
Network analysis based on important node selection and community detection A Mester, A Pop, BEM Mursa, H Greblă, L Dioşan, C Chira Mathematics 9 (18), 2294 , 2021 2021 Citations: 38
Best-order crossover for permutation-based evolutionary algorithms A Andreica, C Chira Applied Intelligence 42 (4), 751-776 , 2015 2015 Citations: 35
A hybrid ant-based system for gate assignment problem CM Pintea, PC Pop, C Chira, D Dumitrescu International Workshop on Hybrid Artificial Intelligence Systems, 273-280 , 2008 2008 Citations: 34
Sensitive ants in solving the generalized vehicle routing problem CM Pintea, C Chira, D Dumitrescu, PC Pop arXiv preprint arXiv:1208.5341 , 2012 2012 Citations: 33
Heterogeneous sensitive ant model for combinatorial optimization C Chira, D Dumitrescu, CM Pintea Proceedings of the 10th annual conference on Genetic and evolutionary … , 2008 2008 Citations: 33
AutoPPI : An Ensemble of Deep Autoencoders for Protein–Protein Interaction Prediction G Czibula, AI Albu, MI Bocicor, C Chira Entropy 23 (6), 643 , 2021 2021 Citations: 30
Game theory and extremal optimization for community detection in complex dynamic networks RI Lung, C Chira, A Andreica PloS one 9 (2), e86891 , 2014 2014 Citations: 30
An intelligent route management system for electric vehicle charging J Sedano, C Chira, JR Villar, EM Ambel Integrated Computer-Aided Engineering 20 (4), 321-333 , 2013 2013 Citations: 28
Error-Correcting Output Codes for Multi-Label Text Categorization. G Armano, C Chira, N Hatami IIR, 26-37 , 2012 2012 Citations: 26
