• Propose SCS-SupCon, a supervised contrastive learning framework for image tasks. • Introduce sigmoid-based contrastive loss with learnable adaptive boundaries. • Emphasize hard negatives to reduce negative-sample dilution effectively. • Add style-distance constraint for better disentangling of style and content. • Achieve state-of-the-art accuracy on fine-grained benchmark datasets. Image classification can be inherently challenging due to subtle inter-class differences and substantial intra-class variations, limiting the effectiveness of existing contrastive learning approaches. In particular, supervised contrastive methods based on the InfoNCE loss often suffer from the negative-sample dilution issue and lack explicit mechanisms for adaptive decision-boundary control, significantly weakening their discriminative capability on fine-grained image classification tasks. To address these challenges, we propose a novel supervised contrastive learning framework, termed Sigmoid-based Common and Style Supervised Contrastive Learning (SCS-SupCon). In this framework, we introduce a sigmoid-based pairwise contrastive loss with adaptive decision boundaries, explicitly parameterized by learnable temperature and bias terms. This design places greater emphasis on critical discriminative information from hard negatives, thereby alleviating the problem of negative-sample dilution while fully leveraging supervision signals in contrastive learning. Furthermore, we incorporate an explicit style-distance constraint to disentangle style and content representations, leading to more robust and discriminative feature learning. Comprehensive experiments on six benchmark datasets, including prominent fine-grained datasets such as CUB200-2011 and Stanford Dogs, consistently demonstrate that our proposed SCS-SupCon achieves superior performance over the most closely related InfoNCE-based supervised contrastive baselines (SupCon, SelfCon, CS-SupCon and its overlapping variant) across diverse CNN and Transformer backbones. In particular, on CIFAR-100 with a ResNet-50 encoder, SCS-SupCon improves the mean top-1 accuracy over SupCon by about 3.9 percentage points and over CS-SupCon by about 1.7 percentage points under a five-fold cross-validation protocol. On challenging fine-grained datasets such as CUB200-2011 and Stanford Dogs with both CNN and Transformer architectures, our method achieves absolute improvements of approximately 0.4–3.0 percentage points over CS-SupCon. Extensive ablation studies and paired statistical tests further confirm the robustness and effectiveness of our framework, and a Friedman test with Nemenyi post-hoc analysis shows that SCS-SupCon attains the best average rank among the evaluated methods, even though pairwise differences with other strong competitors are not always statistically significant at the 0.05 level.

The advent of graph convolutional network (GCN)-based multi-view learning provides a powerful framework for integrating structural information from heterogeneous views, enabling effective modeling of complex multi-view data. However, existing methods often fail to fully exploit the complementary information across views, leading to suboptimal feature representations and limited performance. To address this, we propose MV-SupGCN, a semi-supervised GCN model that integrates several complementary components with clear motivations and mutual reinforcement. First, to better capture discriminative features and improve model generalization, we design a joint loss function that combines Cross-Entropy loss with Supervised Contrastive loss, encouraging the model to simultaneously minimize intra-class variance and maximize inter-class separability in the latent space. Second, recognizing the instability and incompleteness of single graph construction methods, we combine both KNN-based and semi-supervised graph construction approaches on each view, thereby enhancing the robustness of the data structure representation and reducing generalization error. Third, to effectively utilize abundant unlabeled data and enhance semantic alignment across multiple views, we propose a unified framework that integrates contrastive learning in order to enforce consistency among multi-view embeddings and capture meaningful inter-view relationships, together with pseudo-labeling, which provides additional supervision applied to both the cross-entropy and contrastive loss functions to enhance model generalization. Extensive experiments demonstrate that MV-SupGCN consistently surpasses state-of-the-art methods across multiple benchmarks, validating the effectiveness of our integrated approach. The source code is available at https://github.com/HuaiyuanXiao/MVSupGCN

This study investigates the application of machine learning (ML) algorithms to predict the received signal strength indicator (RSSI) in free space optical (FSO) communication systems operating in forest areas. FSO technology offers high data transmission rates and bandwidths, but faces challenges due to atmospheric conditions, especially in dynamic environments such as forests. Conventional empirical models often fail to accurately estimate RSSI due to the complex, non-linear nature of these environments. To address this problem, we collected a comprehensive dataset over a 12-month period, covering various atmospheric parameters such as temperature, humidity and wind speed. We evaluated several ML models - K-Nearest Neighbors (KNN), Decision Trees (DT), Random Forest (RF), Gradient Boosting Regression (GBR) and Artificial Neural Networks (ANN). In addition, we have developed a new ML model that combines RF and GBR methods and provides the best results, called “Enhanced Stacked Ensemble Regressor” (ESER) - to determine its effectiveness in predicting RSSI. The results show that the ESER model has the lowest root mean square error (RMSE) and a high coefficient of determination (R2), demonstrating its robustness and accuracy. This study highlights the potential of ML techniques to improve the reliability of FSO links in challenging environments. It highlights directions for future work, including real-time prediction and adaptive FSO systems.

The rapid recovery of reliable communication networks in disaster areas is crucial for effective emergency response. Uncrewed aerial vehicles (UAVs) equipped with free-space optical (FSO) communication systems offer a flexible and rapid solution to restore connectivity when traditional infrastructure is compromised. However, the performance of FSO systems is highly susceptible to environmental factors such as fog, rain, and turbulence, which can degrade signal quality. This paper proposes a machine learning-based system to improve the deployment and operation of UAV-based FSO networks in disaster scenarios. The method uses predictive modeling to anticipate atmospheric conditions, optimizes UAV positioning to maximize communication reliability, and integrates anomaly detection to maintain network resilience. The proposed ML-FSO model simultaneously addresses regression (signal prediction using RMSE and $\mathbf{R}^{\mathbf{2}}$) and classification (signal quality assessment using Precision, Recall, F1-score, and Accuracy). Despite the relatively small dataset (100 samples), extensive cross-validation, simulations, and proposed real-world tests demonstrate the system’s ability to improve communication reliability and resilience in dynamic and challenging environments. The results show that the ensemble model (MLFSO) significantly outperforms conventional approaches, making it a promising tool for enhancing communication networks in disaster situations. To our knowledge, this is the first integrated approach combining predictive ML, trajectory optimization, and anomaly detection for UAV-FSO disaster communication.

A Wireless Sensor Network (WSN) may be viewed as a set of sensor devices where each sensor can monitor a specific area and relay detected events to a central data sink for analysis and processing. However, due to factors such as sensor failures or imperfect initial deployment, uncovered areas, known as “coverage holes”, may emerge, leading to gaps in monitoring. To address this issue, mobile sensor nodes can be deployed to dynamically cover these blind regions and restore network coverage. Efficient path planning for these mobile nodes is crucial to ensure maximum coverage while minimizing travel time and energy consumption. In this study, we propose a two-phase optimization-based approach for guiding mobile sensor nodes to efficiently cover uncovered zones in a sensing area. The first phase employs a Binary Integer Linear Programming (BILP) model to determine the optimal number of critical sensing area positions that the mobile sensor must visit to achieve dynamic total area coverage. The second phase identifies the optimal position locations and optimizes the movement trajectory to minimize travel distance. The performance of the two-phase method is evaluated through simulations using various network sizes and node sensing ranges. Comparisons with existing approaches in the literature clearly demonstrate that the proposed approach can achieve superior results in a reasonable time frame.

Age estimation from facial images is a complex and multifaceted challenge in computer vision. In this study, we present a novel hybrid architecture that combines ConvNeXt, a state-of-the-art advancement of convolutional neural networks (CNNs), with Vision Transformers (ViT). While each model independently delivers excellent performance on a variety of tasks, their integration leverages the complementary strengths of the CNNs’ localized feature extraction capabilities and the Transformers’ global attention mechanisms. Our proposed ConvNeXt-ViT hybrid solution was thoroughly evaluated on benchmark age estimation datasets, including MORPH II, CACD, and AFAD, and achieved superior performance in terms of mean absolute error (MAE). To address computational constraints, we leverage pre-trained models and systematically explore different configurations, using linear layers and advanced regularization techniques to optimize the architecture. Comprehensive ablation studies highlight the critical role of individual components and training strategies, and in particular emphasize the importance of adapted attention mechanisms within the CNN framework to improve the model’s focus on age-relevant facial features. The results show that the ConvNeXt-ViT hybrid not only outperforms traditional methods, but also provides a robust foundation for future advances in age estimation and related visual tasks. This work underscores the transformative potential of hybrid architectures and represents a promising direction for the seamless integration of CNNs and transformers to address complex computer vision challenges. • We developed a hybrid deep learning model for age estimation from facial images . • It integrates ViT with ConvNeXt , retaining both global and local features. • This fusion captures both fine-grained facial details and holistic structural cues . • It was evaluated on four public datasets : MORPH II, CACD, AFAD, and IMDB-Clean . • It achieved state-of-the-art performance across all datasets, demonstrating its robustness.

Multi-view data significantly improves the accuracy of machine learning algorithms by providing a holistic representation of object features. However, previous research on the use of Graph Convolutional Networks (GCNs) for processing node connectivity and data features is still limited. Current methods mainly focus on weighted summation of graph matrices, while only a few integrate the features into graphs. To address these challenges, this paper presents an integrated deep learning architecture: the Feature Fusion and Multi-Graph Fusion Learning Framework (CGCN-FMF). The framework consists of two key modules: (1) a Feature Fusion Network designed to extract essential features from multiple views, and (2) a Multi-Graph Fusion Network that constructs multiple graphs for each view and optimizes both the graph weights and the GCN model. Experimental results from multiple multi-view datasets show that CGCN-FMF outperforms state-of-the-art methods on semi-supervised multi-view classification tasks. • We address deep graph-based semi-supervised classification for multi-view data. • Data structures are set using both KNN graphs and semi-supervised graph schemes. • Autoencoders and 1D convolution layers are leveraged to learn unified features. • A learnable attention mechanism and staged training approach are employed. • Extensive experiments are conducted on six publicly available multi-view datasets.

In the realm of graph-based semi-supervised learning (GSSL), traditional methodologies often struggle to effectively handle labeled samples and scale to accommodate large datasets. To increase supervision information in semi-supervised learning, the self-training paradigm is often used, mainly in datasets with moderate sizes. On the other hand, the use of anchors was adopted with large datasets. In this research endeavor, we propose a novel framework for GSSL that leverages a novel self-training principle tailored for very large datasets, and introduces an advanced method for automatic graph construction using anchors. Our approach focuses on utilizing generated labels of random batches of unlabeled samples, subsequently incorporating these predictions into the training set to enhance the model’s accuracy. Pseudo-labeling, a specific instance of self-training, assigns pseudo-labels to the most confidently predicted unlabeled examples, treating them as ground truth during the training phase. By constructing anchor-to-anchor affinity graphs that incorporate both feature and label information, our method facilitates robust learning on large-scale datasets. Through comprehensive experimentation across diverse large datasets, our approach demonstrates its efficacy in achieving scalable and reliable semi-supervised learning outcomes. These findings represent a significant advancement in the field of GSSL, with wide-ranging implications for various applications across different domains. Our method not only addresses the scalability issue but also ensures the effective integration of both labeled and pseudo labeled data, thereby enhancing the overall learning process.

Semi-supervised learning with a graph-based approach has become increasingly popular in machine learning, particularly when dealing with situations where labeling data is a costly process. Graph Convolution Networks (GCNs) have been widely employed in semi-supervised learning, primarily on graph-structured data like citations and social networks. However, there exists a significant gap in applying these methods to non-graph multi-view data, such as collections of images. To bridge this gap, we introduce a novel deep semi-supervised multi-view classification model tailored specifically for non-graph data. This model independently reconstructs individual graphs using a powerful semi-supervised approach and subsequently merges them adaptively into a unified consensus graph. The consensus graph feeds into a unified GCN framework incorporating a label smoothing constraint. To assess the efficacy of the proposed model, experiments were conducted across seven multi-view image datasets. Results demonstrate that this model excels in both the graph generation and semi-supervised classification phases, consistently outperforming classical GCNs and other existing semi-supervised multi-view classification approaches. 1 1 Source code: https://github.com/BiJingjun/SCFG .

Abstract The goal of multi-view clustering is to partition unlabeled objects into disjoint clusters or groups using consistent and complementary information provided by the different features of the same object. Most existing methods perform this clustering task sequentially: Computation of the individual or consistent graph matrices, spectral embedding, and clustering. In this work, we present an approach that can overcome some of the limitations of previous multiview clustering methods. We introduce a single objective function whose minimization can jointly determine the consistent graph matrix for all views, the unified spectral data representation, the soft cluster indices, and the view weights. We present a constraint term that relates the cluster indices to the convolution of the consistent spectral data representations over the consistent graph. The method we present has two interesting features that are not simultaneously present in recent work. First, the cluster indices can be estimated directly without the need for an additional clustering step, which depends heavily on initialization. Second, the soft cluster indices are directly linked to the kernel representation of the features of the views. Moreover, our proposed method automatically determines the weights of each view, thus requiring fewer hyperparameters. A series of experiments have been conducted on real datasets. These demonstrate the efficiency of the proposed method, which compares favorably to many multi-view clustering methods.

Automatic classification methods widely used for diagnosing and analyzing COVID-19 cases. These methods assume known labels and rely on a single view of the dataset. Given the prevalence of COVID-19 cases and the extensive volume of patient records lacking labels, this communication underscores our unique approach—conducting the first study on COVID-19 case diagnosis in an unsupervised manner. Our work operates under the assumption of prior knowledge regarding the number of classes, such as COVID-19, pneumonia, and normal, in a case study. By adopting an unsupervised learning paradigm, we leverage the wealth of unlabeled data, reducing dependence on human experts for annotating numerous images. This paper introduces an enhanced version of a recent direct method where non-negative cluster indices and spectral embeddings are jointly estimated. Beyond the inherent advantages of this method, our proposed model introduces improvements through two additional types of constraints: (i) ensuring consistent smoothing of cluster labels across all views and (ii) imposing an orthogonality constraint on the matrix of cluster assignments. The efficacy of the proposed method is demonstrated using the public COVIDx dataset with three classes, showcasing promising results in categorizing radiographs. The proposed approach is tested on other public image datasets to assess its effectiveness.

Regional dropout strategies have demonstrated to be very effective in improving both the performance and the generalization capability of deep learning models. However, when such strategies are performed in a totally random manner, the background noise and label mismatch problems arise. To tackle such problems, existing approaches typically focus on regions with the highest distinctiveness. Yet, there are two main drawbacks of existing approaches: (I) Many existing region-based augmentation methods can only use rectangular regions, resulting in the loss of object contour information; (II) Deterministic selection of the most discriminative regions leads to poor diversification in data augmentation. In fact, a trade-off is needed between diversification and concentration, which can decrease the undesirable noise. In this paper, we propose a novel object-centric contour-aware CutMix data augmentation strategy with arbitrary- shape and size superpixel supports, which is hereafter referred to as OcCaMix for short. It not only captures the most discriminative regions, but also effectively preserves the contour details of the objects. Moreover, it enables the search of natural object parts of different sizes. Extensive experiments on a large number of benchmark datasets show that OcCaMix significantly outperforms state-of-the-art CutMix based data augmentation methods in classification tasks. The source codes and trained models are available at https://github.com/DanielaPlusPlus/OcCaMix.