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- IV International Conference on Optimization, Learning Algorithms and Applications: Book of AbstractsPublication . Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Lima, José; Oneto, Luca; Pacheco, Maria F.; Lopes, Rui PedroWelcome to OL2A 2025 - International Conference on Optimization, Learning Algorithms and Applications. OL2A offers a forum for the research community on optimization and learning to get together and share the latest developments and techniques, as well as to develop new paths and collaborations. OL2A provides a broad scope of presentations, covering many areas of optimization and learning and state-of the-art applications to multi-objective optimization, optimization for machine learning, machine learning for optimization, optimization and learning under uncertainty and the fourth industrial revolution. It is with great pleasure that the Organizing Committee welcomes you all to OL2A 2025!
- Design, modeling, and control of an autonomous legged-wheeled hybrid robotic vehicle with non-rigid jointsPublication . Pinto, Vítor H.; Soares, Inês N.; Rocha, Marco; Lima, José; Gonçalves, José; Costa, Paulo Gomes daThis paper presents a legged-wheeled hybrid robotic vehicle that uses a combination of rigid and non-rigid joints, allowing it to be more impact-tolerant. The robot has four legs, each one with three degrees of freedom. Each leg has two non-rigid rotational joints with completely passive components for damping and accumulation of kinetic energy, one rigid rotational joint, and a driving wheel. Each leg uses three independent DC motors—one for each joint, as well as a fourth one for driving the wheel. The four legs have the same position configuration, except for the upper hip joint. The vehicle was designed to be modular, low-cost, and its parts to be interchangeable. Beyond this, the vehicle has multiple operation modes, including a low-power mode. Across this article, the design, modeling, and control stages are presented, as well as the communication strategy. A prototype platform was built to serve as a test bed, which is described throughout the article. The mechanical design and applied hardware for each leg have been improved, and these changes are described. The mechanical and hardware structure of the complete robot is also presented, as well as the software and communication approaches. Moreover, a realistic simulation is introduced, along with the obtained results.
- XAI Framework for Fall Detection in an AAL SystemPublication . Messaoudi, Chaima; Kalbermatter, Rebeca B.; Lima, José; Pereira, Ana I.; Guessoum, Zahia; Kalbermatter, Rebeca B.The Ambient Assisted Living (AAL) systems are humancentered and designed to prioritize the needs of elderly individuals, providing them with assistance in case of emergencies or unexpected situations. These systems involve caregivers or selected individuals who can be alerted and provide the necessary help when needed. To ensure effective assistance, it is crucial for caregivers to understand the reasons behind alarm triggers and the nature of the danger. This is where an explainability module comes into play. In this paper, we introduce an explainability module that offers visual explanations for the fall detection module. Our framework involves generating anchor boxes using the K-means algorithm to optimize object detection and using YOLOv8 for image inference. Additionally, we employ two well-known XAI (Explainable Artificial Intelligence) algorithms, LIME (Local Interpretable Model) and Grad-CAM (Gradient-weighted Class Activation Mapping), to provide visual explanations.
- Optimization, Learning Algorithms and Applications (OL2A 2024) Part IPublication . Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Lima, José; Pacheco, Maria F.; Lopes, Rui Pedro; Álvarez, Santiago T.This volume, CCIS 2280, contains the refereed proceedings of the 4th International Conference on Optimization, Learning Algorithms and Applications (OL2A 2024), a hybrid event held on July 24–26. OL2A provided a space for the research community on optimization and learning to get together and share the latest developments, trends and techniques as well as develop new paths and collaborations. OL2A had the participation of more than three hundred participants in an online and face-to-face environment throughout three days, discussing topics associated with areas such as optimization and learning and state-ofthe- art applications related to multi-objective optimization, optimization for machine learning, robotics, health informatics, data analysis, optimization and learning under uncertainty, and the 4th industrial revolution. Three special sessions were organized on the topics Learning Algorithms in Engineering Education, Optimization in the SDG context, and Optimization in Control Systems Design. The event accepted 41 papers. All papers were carefully reviewed and selected from 105 submissions. All the reviews were carefully carried out by a scientific committee of 115 researchers from twenty-six countries.
- Optimization, Learning Algorithms and Applications (OL2A 2024) Part IIPublication . Pereira, Ana I.; Coelho, João Paulo; Teixeira, João Paulo; Lima, José; Pacheco, Maria F.; Lopes, Rui Pedro; Álvarez, Santiago T.; Fernandes, Florbela P.This volume, CCIS 2280, contains the refereed proceedings of the 4th International Conference on Optimization, Learning Algorithms and Applications (OL2A 2024), a hybrid event held on July 24–26. OL2A provided a space for the research community on optimization and learning to get together and share the latest developments, trends and techniques as well as develop new paths and collaborations. OL2A had the participation of more than three hundred participants in an online and face-to-face environment throughout three days, discussing topics associated with areas such as optimization and learning and state-of-the- art applications related to multi-objective optimization, optimization for machine learning, robotics, health informatics, data analysis, optimization and learning under uncertainty, and the 4th industrial revolution. Three special sessions were organized on the topics Learning Algorithms in Engineering Education, Optimization in the SDG context, and Optimization in Control Systems Design. The event accepted 41 papers. All papers were carefully reviewed and selected from 105 submissions. All the reviews were carefully carried out by a scientific committee of 115 researchers from twenty-six countries.
- A Practical Approach to Teaching Differential Robot Control Using a Goal-to-Goal InterfacePublication . Amorim, Johann S.J. C. C.; Moraes, Camile A.; Neto, Accacio F. dos; Amorim, Josef G. J. C. C.; Santos, Patricia S.; Haddad, Diego; Pinto, Milena F.; Lima, JoséThis paper presents an interface to assist students in performing robot controllers. This application is designed to stabilize the movement of a goal-to-goal robot. The primary objective is to enhance the learning experience by offering an easy application that showcases the robot’s behavior based on its kinematic model. The paper outlines the various stages involved in developing and testing this simple interface, including a study of the kinematics of differential robots. It discusses the discrete- time equations for goal-to-goal behavior, the data required to obtain the controller, the application of the controller in a discrete microcontroller, and the development of code to simulate the robot’s behavior. The results show the developed interface and the possible outcomes through the use of this application in control classes.
- Optimizing Olive Disease Classification Through Hybrid Machine Learning and Deep Learning TechniquesPublication . Mendes, João; Moso, Juliet; Berger, Guido; Lima, José; Costa, Lino; Guessoum, Zahia; Pereira, Ana I.Olive trees play a crucial role in the global agricultural landscape, serving as a primary source of olive oil production. However, olive trees are susceptible to several diseases, which can significantly impact yield and quality. This study addresses the challenge of improving the diagnosis of diseases in olive trees, specifically focusing on aculus olearius and Olive Peacock Spot diseases. Using a novel hybrid approach that combines deep learning and machine learning methodologies, the authors aimed to optimize disease classification accuracy by analyzing images of olive leaves. The presented methodology integrates Local Binary Patterns (LBP) and an adapted ResNet50 model for feature extraction, followed by classification through optimized machine learning models, including Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), and Random Forest (RF). The results demonstrated that the hybrid model achieved a groundbreaking accuracy of 99.11%, outperforming existing models. This advancement underscores the potential of integrated technological approaches in agricultural disease management and sets a new benchmark for the early and accurate detection of foliar diseases.
- Predicting Retail Store Transaction Patterns: A Comparison of ARIMA and Machine Learning ModelsPublication . Vaz, Clara B.; Sena, Inês; Braga, Ana Cristina; Novais, Paulo; Lima, José; Pereira, Ana I.Retail transactions represent sales of consumer goods, or final goods, by consumer companies. This sector faces security challenges due to the hustle and bustle of sales, affecting employees’ workload. In this context, it is essential to estimate the number of customers who will appear in the store daily so that companies can dynamically adjust employee schedules, aligning workforce capacity with expected demand. This can be achieved by forecasting transactions using past observations and forecasting algorithms. This study aims to compare the ARIMA time series algorithm with several Machine Learning algorithms to predict the number of daily transactions in different store patterns, considering data variability. The study identifies four typical store patterns based on these criteria using daily transaction data between 2019 and 2023 from all retail stores of the leading company in Portugal. Due to data variability and the results obtained, the algorithm that presents the most minor errors in predicting daily transactions is selected for each store. This study’s ultimate goal is to fill the gap in forecasting daily customer transactions and present a suitable forecasting model to mitigate risks associated with transactions in retail stores.
- Optimization, Learning Algorithms and Applications: Third International Conference, OL2A 2023Publication . Pereira, Ana I. (Ed.); Mendes, Armando (Ed.); Fernandes, Florbela P. (Ed.); Pacheco, Maria F. (Ed.); Coelho, João Paulo (Ed.); Lima, José (Ed.)The volumes CCIS 1981 and 1982 contains the refereed proceedings of the III International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023), a hybrid event held on September 27–29. OL2A provided a space for the research community in optimization and learning to get together and share the latest developments, trends and techniques as well as develop new paths and collaborations. OL2A had the participation of more than four hundred participants in an online and face-to-face environment throughout three days, discussing topics associated with areas such as optimization and learning and state-ofthe- art applications related to multi-objective optimization, optimization for machine learning, robotics, health informatics, data analysis, optimization and learning under uncertainty and 4th industrial revolution. Six special sessions were organized under the topics Learning Algorithms in Engineering Education, Optimization in the SDG context, Optimization in Control Systems Design, Computer Vision Based on Learning Algorithms, Machine Learning and AI in Robotics and Machine Learning and Data Analysis in Internet of Things. The event had 66 accepted papers. All papers were carefully reviewed and selected from 172 submissions. All the reviews were carefully carried out by a scientific committee of 115 PhD researchers from 23 countries.
- Application of machine learning in dementia diagnosis: a systematic literature reviewPublication . Kantayeva, Gauhar; Lima, José; Pereira, Ana I.According to the World Health Organization forecast, over 55 million people worldwide have dementia, and about 10 million new cases are detected yearly. Early diagnosis is essential for patients to plan for the future and deal with the disease. Machine Learning algorithms allow us to solve the problems associated with early disease detection. This work attempts to identify the current relevance of the application of machine learning in dementia prediction in the scientific world and suggests open fields for future research. The literature review was conducted by combining bibliometric and content analysis of articles originating in a period of 20 years in the Scopus database. Twenty-seven thousand five hundred twenty papers were identified firstly, of which a limited number focused on machine learning in dementia diagnosis. After the exclusion process, 202 were selected, and 25 were chosen for analysis. The recent increasing interest in the past five years in the theme of machine learning in dementia shows that it is a relevant field for research with still open questions. The methods used to identify dementia or what features are used to identify or predict this disease are explored in this study. The literature review revealed that most studies used magnetic resonance imaging (MRI) and its types as the main feature, accompanied by demographic data such as age, gender, and the mini-mental state examination score (MMSE). Data are usually acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Classification of Alzheimer’s disease is more prevalent than prediction of Mild Cognitive Impairment (MCI) or their combination. The authors preferred machine learning algorithms such as SVM, Ensemble methods, and CNN because of their excellent performance and results in previous studies. However, most use not one machine-learning technique but a combination of techniques. Despite achieving good results in the studies considered, there are new concepts for future investigation declared by the authors and suggestions for improvements by employing promising methods with potentially significant results.