Abstract The dynamics of Saturn’s satellite system offer a rich framework for studying orbital stability and resonance interactions. Traditional methods for analysing such systems, including Fourier analysis and stability metrics, struggle with the scale and complexity of modern datasets. This study introduces a machine learning-based pipeline for clustering $$\sim 22,300$$ simulated satellite orbits, addressing these challenges with advanced feature extraction and dimensionality reduction techniques. The key to this approach is using MiniRocket, which efficiently transforms 400 timesteps into a 9,996-dimensional feature space, capturing intricate temporal patterns. Additional automated feature extraction and dimensionality reduction techniques refine the data, enabling robust clustering analysis. This pipeline reveals stability regions, resonance structures, and other key behaviours in Saturn’s satellite system, providing new insights into their long-term dynamical evolution. By integrating computational tools with traditional celestial mechanics techniques, this study offers a scalable and interpretable methodology for analysing large-scale orbital datasets and advancing the exploration of planetary dynamics.

This paper investigates video game identification through single screenshots, utilizing ten convolutional neural network (CNN) architectures (VGG16, ResNet50, ResNet152, MobileNet, DenseNet169, DenseNet201, EfficientNetB0, EfficientNetB2, EfficientNetB3, and EfficientNetV2S) and three transformers architectures (ViT-B16, ViT-L32, and SwinT) across 22 home console systems, spanning from Atari 2600 to PlayStation 5, totalling 8,796 games and 170,881 screenshots. Except for VGG16, all CNNs outperformed the transformers in this task. Using ImageNet pre-trained weights as initial weights, EfficientNetV2S achieves the highest average accuracy (77.44%) and the highest accuracy in 16 of the 22 systems. DenseNet201 is the best in four systems and EfficientNetB3 is the best in the remaining two systems. Employing alternative initial weights fine-tuned in an arcade screenshots dataset boosts accuracy for EfficientNet architectures, with the EfficientNetV2S reaching a peak accuracy of 77.63% and demonstrating reduced convergence epochs from 26.9 to 24.5 on average. Overall, the combination of optimal architecture and weights attains 78.79% accuracy, primarily led by EfficientNetV2S in 15 systems. These findings underscore the efficacy of CNNs in video game identification through screenshots.

Restricted Boltzmann Machines (RBM) are stochastic neural networks mainly used for image reconstruction and unsupervised feature learning. An enhanced version, the temperature-based RBM (T-RBM), considers a new temperature parameter during the learning process that influences the neurons' activation. Nevertheless, the major vulnerability of such models concerns selecting an adequate system's temperature, which might lead them to inadequate training or even overfitting when wrongly set, thus limiting the network from predicting or working effectively over unseen data. This paper addresses the problem of selecting a suitable system's temperature through a meta-heuristic optimization process. Meta-heuristic-driven techniques, such as Particle Swarm Optimization, Bat Algorithm, and Artificial Bee Colony are employed to find proper values for the temperature parameter. Additionally, for comparison purposes, three standard temperature values and a random search are used as baselines. The results revealed that optimizing T-RBM is suitable for training purposes, primarily due to their complex fitness landscape, which makes fine-tuning temperatures a nontrivial task.

Navigation and mobility are some of the major problems faced by visually impaired people in their daily lives. Advances in computer vision led to the proposal of some navigation systems. However, most of them require expensive and/or heavy hardware. In this paper we propose the use of convolutional neural networks (CNN), transfer learning, and semi-supervised learning (SSL) to build a framework aimed at the visually impaired aid. It has low computational costs and, therefore, may be implemented on current smartphones, without relying on any additional equipment. The smartphone camera can be used to automatically take pictures of the path ahead. Then, they will be immediately classified, providing almost instantaneous feedback to the user. We also propose a dataset to train the classifiers, including indoor and outdoor situations with different types of light, floor, and obstacles. Many different CNN architectures are evaluated as feature extractors and classifiers, by fine-tuning weights pre-trained on a much larger dataset. The graph-based SSL method, known as particle competition and cooperation, is also used for classification, allowing feedback from the user to be incorporated without retraining the underlying network. 92% and 80% classification accuracy is achieved in the proposed dataset in the best supervised and SSL scenarios, respectively.

Due to the growing availability of unlabeled data and the difficulties in obtaining labeled data, the use of semi-supervised learning approaches becomes even more promising. The capacity of taking into account the dataset structure is of crucial relevance for effectively considering the unlabeled data. In this paper, a novel classifier is proposed through a manifold learning approach. The graph is constructed based on a new hybrid similarity measure which encodes both supervised and unsupervised information. Next, strongly connected components are computed and used to analyze the dataset manifold. The classification is performed through a voting scheme based on primary (labeled) and secondary (unlabeled) voters. An experimental evaluation is conducted, considering various datasets, diverse situations of training/test dataset sizes and comparison with baselines. The proposed method achieved positive results in most of situations.

Many interactive image processing approaches are based on semi-supervised learning, which employ both labeled and unlabeled data in its training process. In the interactive image segmentation problem, a human specialist labels some pixels of an object while the semi-supervised algorithm labels the remaining pixels of the segment. The particle competition and cooperation model is a recent graph-based semi-supervised learning approach. It employs particles walking in a graph to classify the data items corresponding to graph nodes. Each particle group aims to dominate most unlabeled nodes, spreading their label, and preventing enemy particles invasion. In this paper, the particle competition and cooperation model is extended to perform interactive image segmentation. Each image pixel is converted into a graph node, which is connected to its nearest neighbors according to their visual features and location in the original image. Labeled pixel generates particles that propagate their label to the unlabeled pixels. The particle model also takes the contributions from the adjacent pixels to classify less confident labeled pixels. Computer simulations are performed on real-world images, including images from the Microsoft GrabCut dataset, which allows a straightly comparison with other techniques. The segmentation results show the effectiveness of the proposed approach.

Both Active Learning and Semi-Supervised Learning are important techniques when labeled data are scarce and unlabeled data are abundant. In this paper, these two machine learning techniques are combined into a new nature-inspired method, which employs particles walking in networks generated from the data. It uses combined competitive and cooperative behavior in order to possess nodes of the network, and thus labeling the corresponding data items. Particles represent labeled nodes, and new particles can be added on the fly to the network as the result of queries (new labels). This built-in mechanism saves a lot of execution time comparing to active learning frameworks, since only nodes affected by the new particles are updated, i.e., the algorithm does not have to be executed again for each new query (or new set of queries). The algorithm naturally adapts itself to new scenarios, i.e., more particles and more labeled nodes. Experimental results on some real-world data sets are presented and the proposed active semi-supervised learning method shows better classification accuracy than its only semi-supervised learning counterpart when the same amount of labeled data is used. Some criteria for selecting the rule to be used to choose data items to be queried are also identified.

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually “forgetting” older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a “divide-and-conquer” effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

In machine learning study, semi-supervised learning has received increasing interests in the last years. It is applied to classification problems where only a small portion of the data points is labeled. In these situations, the reliability of these labels is extremely important because it is common to have mislabeled samples in a data set and these may propagate their wrong labels to a large portion of the data set, resulting in major classification errors. In spite of its importance, wrong label propagation in semi-supervised learning has received little attention from researchers. In this paper we propose a particle walk semi-supervised learning method with both competitive and cooperative mechanisms. Then we study error propagation by applying the proposed model in modular networks. We show that the model is robust against mislabeled samples and it can produce good classification results even in the presence of considerable proportion of mislabeled data. Moreover, our numerical analysis uncover a critical point of mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. These studies have practical importance to design secure and robust machine learning techniques.

Semi-Supervised Learning (SSL) is a machine learning research area aiming the development of techniques which are able to take advantage from both labeled and unlabeled samples. Additionally, most of the times where SSL techniques can be deployed, only a small portion of samples in the data set is labeled. To deal with such situations in a straightforward fashion, in this paper we introduce a semi-supervised learning approach based on neuronal synchrony in a network of coupled integrate-and-fire neurons. For that, we represent the input data set as a graph and model each of its nodes by an integrate-and-fire neuron. Thereafter, we propagate the class labels from the seed samples to unlabeled samples through the graph by means of the emerging synchronization dynamics. Experimentations on synthetic and real data show that the introduced technique achieves good classification results regardless the feature space distribution or geometrical shape.

Chaotic phase synchronization among coupled oscillators is a phenomenon of interest in many physical and engineering systems. It has also been observed in biological systems, where groups of different functional units interact with each other in order to produce coherent behaviors in higher levels. While biological systems have facility to capture salient object(s) in a given scene, visual selection is still a challenging task to artificial visual systems. In this paper, a visual selection mechanism based on chaotic phase synchronization is proposed. Oscillators representing the salient object in a given scene are phase synchronized, while no synchronization is observed for background objects. In this way, the salient object is highlighted. Due to the modeling by phase synchronization instead of complete synchronization, the proposed model is robust, biologically inspired and good simulation results were achieved.

In this paper, a Visual Selection Mechanism based on a lattice of coupled chaotic Wilson-Cowan oscillators is proposed. The oscillators representing each object in a given visual scene are synchronized to produce a chaotic trajectory. Cooperation and competition mechanisms are also introduced to accelerate oscillating frequency of the salient object as well as to slow down other objects in the same scene. The model can not only discriminate each object among others in a given visual scene, but also deliver the focus of attention to the salient object. In comparison to other visual selection approaches, this model presents at least two new features. First, it is able to highlight objects in complex forms, including those that are non-linear separable. Second, oscillators representing the salient object will jump from chaotic phase to periodic phase. This behavior matches well to biological experiments on pattern recognition of rabbit. Computer simulations are performed and the results show that the proposed model is promising as a Selection Mechanism embedded in a Visual Attention System.