Competitive Results Research Articles

Evolving multispectral sensor configurations using genetic programming for estuary health monitoring

ABSTRACT Assessing ecosystem health on a large scale is crucial for a wide range of management and regulatory decisions. Technologies such as hyperspectral imaging allow noninvasive and rapid estimation of key attributes based on observed reflectance. However, these images are high-dimensional and real-world applications require models based on fewer wavelengths. This paper proposes a new wavelength selection and feature extraction method for hyperspectral image analysis based on genetic programming to automatically select key wavelength regions and informative image features. A dataset of hyperspectral images of sediment in the field was collected and paired with ground-truth measurements of the sediment porosity and organic matter content. Two new program structures were proposed to construct feature extraction trees from either the mean reflectance spectra (spectra-based) or full hyperspectral images (image-based). SVR models were constructed to predict attributes based on the extracted features. Various regression models were used to predict the porosity and organic matter content. Full-wavelength models were constructed to reliably predict the organic matter content. The proposed spectra-based genetic programming solutions show competitive results compared to common wavelength selection methods, such as SPA, CARS, and RC. Finally, the best-evolved solution was applied to predict sediment organic matter content across all collected images.

PolySegNet: improving polyp segmentation through swin transformer and vision transformer fusion.

Colorectal cancer ranks as the second most prevalent cancer worldwide, with a high mortality rate. Colonoscopy stands as the preferred procedure for diagnosing colorectal cancer. Detecting polyps at an early stage is critical for effective prevention and diagnosis. However, challenges in colonoscopic procedures often lead medical practitioners to seek support from alternative techniques for timely polyp identification. Polyp segmentation emerges as a promising approach to identify polyps in colonoscopy images. In this paper, we propose an advanced method, PolySegNet, that leverages both Vision Transformer and Swin Transformer, coupled with a Convolutional Neural Network (CNN) decoder. The fusion of these models facilitates a comprehensive analysis of various modules in our proposed architecture.To assess the performance of PolySegNet, we evaluate it on three colonoscopy datasets, a combined dataset, and their augmented versions. The experimental results demonstrate that PolySegNet achieves competitive results in terms of polyp segmentation accuracy and efficacy, achieving a mean Dice score of 0.92 and a mean Intersection over Union (IoU) of 0.86. These metrics highlight the superior performance of PolySegNet in accurately delineating polyp boundaries compared to existing methods. PolySegNet has shown great promise in accurately and efficiently segmenting polyps in medical images. The proposed method could be the foundation for a new class of transformer-based segmentation models in medical image analysis.

PointUR-RL: Unified Self-Supervised Learning Method Based on Variable Masked Autoencoder for Point Cloud Reconstruction and Representation Learning

Self-supervised learning has made significant progress in point cloud processing. Currently, the primary tasks of self-supervised learning, which include point cloud reconstruction and representation learning, are trained separately due to their structural differences. This separation inevitably leads to increased training costs and neglects the potential for mutual assistance between tasks. In this paper, a self-supervised method named PointUR-RL is introduced, which integrates point cloud reconstruction and representation learning. The method features two key components: a variable masked autoencoder (VMAE) and contrastive learning (CL). The VMAE is capable of processing input point cloud blocks with varying masking ratios, ensuring seamless adaptation to both tasks. Furthermore, CL is utilized to enhance the representation learning capabilities and improve the separability of the learned representations. Experimental results confirm the effectiveness of the method in training and its strong generalization ability for downstream tasks. Notably, high-accuracy classification and high-quality reconstruction have been achieved with the public datasets ModelNet and ShapeNet, with competitive results also obtained with the ScanObjectNN real-world dataset.

A method for extracting buildings from remote sensing images based on 3DJA-UNet3+

Building extraction aims to extract building pixels from remote sensing imagery, which plays a significant role in urban planning, dynamic urban monitoring, and many other applications. UNet3+ is widely applied in building extraction from remote sensing images. However, it still faces issues such as low segmentation accuracy, imprecise boundary delineation, and the complexity of network models. Therefore, based on the UNet3+ model, this paper proposes a 3D Joint Attention (3DJA) module that effectively enhances the correlation between local and global features, obtaining more accurate object semantic information and enhancing feature representation. The 3DJA module models semantic interdependence in the vertical and horizontal dimensions to obtain feature map spatial encoding information, as well as in the channel dimensions to increase the correlation between dependent channel graphs. In addition, a bottleneck module is constructed to reduce the number of network parameters and improve model training efficiency. Many experiments are conducted on publicly accessible WHU,INRIA and Massachusetts building dataset, and the benchmarks, BOMSC-Net, CVNet, SCA-Net, SPCL-Net, ACMFNet, MFCF-Net models are selected for comparison with the 3DJA-UNet3+ model proposed in this paper. The experimental results show that 3DJA-UNet3+ achieves competitive results in three evaluation indicators: overall accuracy, mean intersection over union, and F1-score. The code will be available at https://github.com/EnjiLi/3DJA-UNet3Plus.

Computer-Aided Diagnosis of Diabetic Retinopathy Lesions Based on Knowledge Distillation in Fundus Images

At present, the early diagnosis of diabetic retinopathy (DR), a possible complication of diabetes due to elevated glucose concentrations in the blood, is usually performed by specialists using a manual inspection of high-resolution fundus images based on lesion screening, leading to problems such as high work-intensity and accessibility only in specialized health centers. To support the diagnosis of DR, we propose a deep learning-based (DL) DR lesion classification method through a knowledge distillation (KD) strategy. First, we use the pre-trained DL architecture, Inception-v3, as a teacher model to distill the dataset. Then, a student model, also using the Inception-v3 model, is trained on the distilled dataset to match the performance of the teacher model. In addition, a new combination of Kullback–Leibler (KL) divergence and categorical cross-entropy (CCE) loss is used to measure the difference between the teacher and student models. This combined metric encourages the student model to mimic the predictions of the teacher model. Finally, the trained student model is evaluated on a validation dataset to assess its performance and compare it with both the teacher model and another competitive DL model. Experiments are conducted on the two datasets, corresponding to an imbalanced and a balanced dataset. Two baseline models (Inception-v3 and YOLOv8) are evaluated for reference, obtaining a maximum training accuracy of 66.75% and 90.90%, respectively, and a maximum validation accuracy of 35.94% and 81.52%, both for the imbalanced dataset. On the other hand, the proposed DR classification model achieves an average training accuracy of 99.01% and an average validation accuracy of 97.30%, overcoming the baseline models and other state-of-the-art works. Experimental results show that the proposed model achieves competitive results in DR lesion detection and classification tasks, assisting in the early diagnosis of diabetic retinopathy.

An efficient dual-domain deep learning network for sparse-view CT reconstruction

Background and ObjectiveWe develop an efficient deep-learning based dual-domain reconstruction method for sparse-view CT reconstruction with small training parameters and comparable running time. We aim to investigate the model's capability and its clinical value by performing objective and subjective quality assessments using clinical CT projection data acquired on commercial scanners. MethodsWe designed two lightweight networks, namely Sino-Net and Img-Net, to restore the projection and image signal from the DD-Net reconstructed images in the projection and image domains, respectively. The proposed network has small training parameters and comparable running time among dual-domain based reconstruction networks and is easy to train (end-to-end). We prospectively collected clinical thoraco-abdominal CT projection data acquired on a Siemens Biograph 128 Edge CT scanner to train and validate the proposed network. Further, we quantitatively evaluated the CT Hounsfield unit (HU) values on 21 organs and anatomic structures, such as the liver, aorta, and ribcage. We also analyzed the noise properties and compared the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) of the reconstructed images. Besides, two radiologists conducted the subjective qualitative evaluation including the confidence and conspicuity of anatomic structures, and the overall image quality using a 1–5 likert scoring system. ResultsObjective and subjective evaluation showed that the proposed algorithm achieves competitive results in eliminating noise and artifacts, restoring fine structure details, and recovering edges and contours of anatomic structures using 384 views (1/6 sparse rate). The proposed method exhibited good computational cost performance on clinical projection data. ConclusionThis work presents an efficient dual-domain learning network for sparse-view CT reconstruction on raw projection data from a commercial scanner. The study also provides insights for designing an organ-based image quality assessment pipeline for sparse-view reconstruction tasks, potentially benefiting organ-specific dose reduction by sparse-view imaging.

Exponential qubit reduction in optimization for financial transaction settlement

We extend the qubit-efficient encoding presented in (Tan et al. in Quantum 5:454, 2021) and apply it to instances of the financial transaction settlement problem constructed from data provided by a regulated financial exchange. Our methods are directly applicable to any QUBO problem with linear inequality constraints. Our extension of previously proposed methods consists of a simplification in varying the number of qubits used to encode correlations as well as a new class of variational circuits which incorporate symmetries thereby reducing sampling overhead, improving numerical stability and recovering the expression of the cost objective as a Hermitian observable. We also propose optimality-preserving methods to reduce variance in real-world data and substitute continuous slack variables. We benchmark our methods against standard QAOA for problems consisting of 16 transactions and obtain competitive results. Our newly proposed variational ansatz performs best overall. We demonstrate tackling problems with 128 transactions on real quantum hardware, exceeding previous results bounded by NISQ hardware by almost two orders of magnitude.

The influence of physiological and functional characteristics of the body of girls aged 17-18 years on playing sports

Achieving competitive results in women’s sports for athletes aged 17-18 requires structuring training processes around their physiological characteristics. This article analyzes recent scientific data on the impact of sexual dimorphism in girls aged 17-18 on sports participation and training. Relevant scientific works from the past five years were collected and analyzed. At this age, girls undergo the final stage of sexual maturation. The effectiveness of sports activities varies individually, influenced by the ovarian-menstrual cycle, hormonal background, and symptoms in each cycle phase. Physical activity declines during the menstrual, ovulatory, and premenstrual phases, while postmenstrual and postovulatory phases are more favorable for sports participation. Performance indicators depend on sexual dimorphism: female athletes show unique features in central nervous system activity, high psycho-emotional excitability, and earlier development of somatic and physical qualities compared to males. The skeletal structure in 17-18-year-old female athletes is smaller and shorter, with a wider pelvis and more developed shoulder girdle. These findings can be used by sports schools, rehabilitation specialists, coaches, and sports physicians to adapt and structure training processes, taking into account the physiological characteristics of 17-18-year-old female athletes, aiming for highly competitive results. Keywords: Sexual Dimorphism, Hormonal Changes, Women’s Sports, Physical Culture, Sports Performance.

Time series features and fuzzy memberships combination for time series classification

Time series classification is an increasingly attractive field with the appearance of new problems in an expanding digitalized world. Most of the proposals in the state-of-the-art have focused just on improving the results’ performance, leaving interpretability on a secondary level. The available interpretable proposals do not provide competitive results, which is an issue to be addressed. This paper introduces a new fuzzy feature-based time series classification method, which joins the ability of time series features to capture essential information about the time series with Fuzzy logic. This proposal allows the fuzzy-based approach to incorporate global information about the behavior of time series in the membership calculation with the aim of improving the performance and interpretability of the results by using an interpretable classifier. The proposed method has been evaluated over the 112 state-of-the-art time series classification datasets from the UCR repository, and the results obtained show a better performance. Furthermore, the combination of time series features and fuzzy memberships has also increased the interpretability of final models.

Performance of data-driven models based on seasonal-trend decomposition for streamflow forecasting in different climate regions of Türkiye

This study examines the ability of different methods such as machine learning, ensemble models, and metaheuristic algorithms to predict streamflow. For this purpose, five different methods were used: Artificial Neural Networks (ANN), Support Vector Machines (SVM), Adaptive Boosting, Particle Swarm Optimization (PSO), and BSPSO hybridized with Band Similarity (BS), a relatively new method. Additionally, the impact of seasonality and trend components obtained through Seasonal-Trend decomposition using LOESS (locally weighted regression and scatterplot smoothing) (STL) data decomposition technique on prediction success was investigated. Models were developed in three basins with three different climate characteristics: continental, temperate, and arid. The results showed higher prediction success in input structures including seasonality and trend components. While higher prediction successes were achieved at Karasu in the continental climate class and Körkün in the temperate climate class, model performances were lower at Küçük Muhsine in the arid climate class. While the most successful modeling for Küçük Muhsine (NSE = 0.696) and Karasu stations (NSE = 0.811) was obtained with the BSPSO method, the SVM method produced the best results for Körkün station (NSE = 0.818). Moreover, BSPSO models outperformed the prediction successes obtained by using PSO alone for each scenario at all three stations. The percentages of the BSPSO method improving the prediction success according to the NSE metric ranged from 3.53% to 17.40% at Küçük Muhsine, 0.49%–3.72% at Karasu, and 1.24%–7.24% at Körkün. The competitive results achieved by the BSPSO approach compared to ANN and SVM in flow prediction constitute the innovative aspect of this study.

Multiscale-integrated deep learning approaches for short-term load forecasting

Accurate short-term load forecasting (STLF) is crucial for the power system. Traditional methods generally used signal decomposition techniques for feature extraction. However, these methods are limited in extrapolation performance, and the parameter of decomposition modes needs to be preset. To end this, this paper develops a novel STLF algorithm based on multi-scale perspective decomposition. The proposed algorithm adopts the multi-scale deep neural network (MscaleDNN) to decompose load series into low- and high-frequency components. Considering outliers of load series, this paper introduces the adaptive rescaled lncosh (ARlncosh) loss to fit the distribution of load data and improve the robustness. Furthermore, the attention mechanism (ATTN) extracts the correlations between different moments. In two power load data sets from Portugal and Australia, the proposed model generates competitive forecasting results.

MAS-Net: Multi-Attention Hybrid Network for Superpixel Segmentation

Superpixels, as essential mid-level image representations, have been widely used in computer vision due to their computational efficiency and redundant compression. Compared with traditional superpixel methods, superpixel algorithms based on deep learning frameworks demonstrate significant advantages in segmentation accuracy. However, existing deep learning-based superpixel algorithms suffer from a loss of details due to convolution and upsampling operations in their encoder–decoder structure, which weakens their semantic detection capabilities. To overcome these limitations, we propose a novel superpixel segmentation network based on a multi-attention hybrid network (MAS-Net). MAS-Net is still based on an efficient symmetric encoder–decoder architecture. First, utilizing residual structure based on a parameter-free attention module at the feature encoding stage enhanced the capture of fine-grained features. Second, adoption of a global semantic fusion self-attention module was used at the feature selection stage to reconstruct the feature map. Finally, fusing the channel with the spatial attention mechanism at the feature-decoding stage was undertaken to obtain superpixel segmentation results with enhanced boundary adherence. Experimental results on real-world image datasets demonstrated that the proposed method achieved competitive results in terms of visual quality and metrics, such as ASA and BR-BP, compared with the state-of-the-art approaches.

Progressive expansion for semi-supervised bi-modal salient object detection

Existing bi-modal salient object detection (SOD) methods primarily rely on fully supervised training strategies that require extensive manual annotation. Undoubtedly, extensive manual annotation is time-consuming and laborious, and the fully supervised strategy is also prone to overfitting on the training set. Therefore, we introduce a semi-supervised learning architecture (SSLA) to alleviate these problems while ensuring detection performance. Considering that the inherent training mode and concise architecture of basic SSLA will limit its ability to effectively explore the learning potential of the model, we further propose two optimization strategies, dynamic adjustment and active expansion. Specifically, we dynamically adjust the supervision scheme for unlabeled samples during training so that the model can continuously utilize the model’s gains (pseudo labels) to supervise and guide the model to further explore the unlabeled samples. Furthermore, the active expansion strategy enables the model to acquire more beneficial supervised information and focuses its attention on difficult-to-segment samples. In summary, an effective progressive expansion network (PENet) architecture for semi-supervised bi-modal SOD is proposed. Extensive experiments indicate that our PENet architecture, while effectively alleviating 90% of annotation burdens, has achieved highly competitive results in RGB-T and RGB-D tasks compared to fully supervised methods. The performance is even more pronounced during cross-dataset testing.

1D CNNs and face-based random walks: A powerful combination to enhance mesh understanding and 3D semantic segmentation

In this paper, we present a novel face-based random walk method aimed at addressing the 3D semantic segmentation issue. Our method utilizes a one-dimensional convolutional neural network for detailed feature extraction from sequences of triangular faces and employs a stacked gated recurrent unit to gather information along the sequence during training. This approach allows us to effectively handle irregular meshes and utilize the inherent feature extraction potential present in mesh geometry. Our study's results show that the proposed method achieves competitive results compared to the state-of-the-art methods in mesh segmentation. Importantly, it requires fewer training iterations and demonstrates versatility by applying to a wide range of objects without the need for the mesh to adhere to manifold or watertight topology requirements.

Disease gene prioritization with quantum walks.

Disease gene prioritization methods assign scores to genes or proteins according to their likely relevance for a given disease based on a provided set of seed genes. This scoring can be used to find new biologically relevant genes or proteins for many diseases. Although methods based on classical random walks have proven to yield competitive results, quantum walk methods have not been explored to this end. We propose a new algorithm for disease gene prioritization based on continuous-time quantum walks using the adjacency matrix of a protein-protein interaction (PPI) network. We demonstrate the success of our proposed quantum walk method by comparing it to several well-known gene prioritization methods on three disease sets, across seven different PPI networks. In order to compare these methods, we use cross-validation and examine the mean reciprocal ranks of recall and average precision values. We further validate our method by performing an enrichment analysis of the predicted genes for coronary artery disease. The data and code for the methods can be accessed at https://github.com/markgolds/qdgp.

Leveraging GANs data augmentation for imbalanced medical image classification

Machine learning algorithms have been widely applied and researched in the field of medical image classification. Most of the current algorithms are designed based on the assumption of data balance. However, in practical applications, medical data often face the problem of imbalanced data. The traditional data augmentation approaches can balance the original data, but they are unable to generate more effective features. Generative Adversarial Networks (GANs) can effectively perform sample augmentation. However, GAN often encounter issues such as intra-class mode collapse and generating noisy samples when faced with imbalanced data. This study proposes a GAN model called IBGAN that focuses on generating intra-class sparse samples and class boundary samples. Specifically, IBGAN addresses the issue of imbalanced data through two stages. In the first stage, the Isolation Forest (iForest) algorithm and boundary sample detection algorithm are designed to identify sparse region samples and boundary samples within the intra-class data. This enables the GAN model to effectively focus on generating such samples during training. The second stage involves the refinement process of the samples. We propose a sample evaluation method based on Support Vector Data Description (SVDD) to filter out noisy data in the generated samples, ensuring the quality of the generated data. We conducted extensive and in-depth experiments on five real-world datasets. The experimental results demonstrate that IBGAN can generate high-quality and diverse augmented samples, which contribute to the improvement of classifier performance. We compared our proposed method with seven baseline methods, including traditional approaches and classic GAN models. The experimental results, including the visualization of the generated data and the evaluation of classification performance, consistently show that our proposed method achieves more competitive results.

Convolutional Spiking Neural Networks targeting learning and inference in highly imbalanced datasets

Spiking Neural Networks (SNNs) are regarded as the next frontier in AI, as they can be implemented on neuromorphic hardware, paving the way for advancements in real-world applications in the field. SNNs provide a biologically inspired solution that is event-driven, energy-efficient and sparse. While showing promising results, there are challenges that need to be addressed. For example, the design-build-evaluate process for integrating the architecture, learning, hyperparameter optimization and inference need to be tailored to a specific problem. This is particularly important in critical high-stakes industries such as finance services. In this paper, we present SpikeConv, a novel deep Convolutional Spiking Neural Network (CSNN), and investigate this process in the context of a highly imbalanced online bank account opening fraud problem. Our approach is compared with Deep Spiking Neural Networks (DSNNs) and Gradient Boosting Decision Trees (GBDT) showing competitive results.

Multi-head CRF classifier for biomedical multi-class named entity recognition on Spanish clinical notes.

The identification of medical concepts from clinical narratives has a large interest in the biomedical scientific community due to its importance in treatment improvements or drug development research. Biomedical named entity recognition (NER) in clinical texts is crucial for automated information extraction, facilitating patient record analysis, drug development, and medical research. Traditional approaches often focus on single-class NER tasks, yet recent advancements emphasize the necessity of addressing multi-class scenarios, particularly in complex biomedical domains. This paper proposes a strategy to integrate a multi-head conditional random field (CRF) classifier for multi-class NER in Spanish clinical documents. Our methodology overcomes overlapping entity instances of different types, a common challenge in traditional NER methodologies, by using a multi-head CRF model. This architecture enhances computational efficiency and ensures scalability for multi-class NER tasks, maintaining high performance. By combining four diverse datasets, SympTEMIST, MedProcNER, DisTEMIST, and PharmaCoNER, we expand the scope of NER to encompass five classes: symptoms, procedures, diseases, chemicals, and proteins. To the best of our knowledge, these datasets combined create the largest Spanish multi-class dataset focusing on biomedical entity recognition and linking for clinical notes, which is important to train a biomedical model in Spanish. We also provide entity linking to the multi-lingual Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) vocabulary, with the eventual goal of performing biomedical relation extraction. Through experimentation and evaluation of Spanish clinical documents, our strategy provides competitive results against single-class NER models. For NER, our system achieves a combined micro-averaged F1-score of 78.73, with clinical mentions normalized to SNOMED CT with an end-to-end F1-score of 54.51. The code to run our system is publicly available at https://github.com/ieeta-pt/Multi-Head-CRF. Database URL: https://github.com/ieeta-pt/Multi-Head-CRF.

Active Reinforcement Learning for the Semantic Segmentation of Urban Images

Image segmentation using supervised learning algorithms usually requires large amounts of annotated training data, while urban datasets frequently contain unbalanced classes leading to poor detection of under-represented classes. We investigate the use of a reinforced active learning method to address the limitations of semantic segmentation on complex urban scenes. In this method, an agent learns to select small informative regions of the image to be labeled from a pool of unlabeled data. The agent is represented by a deep Q-Network, where a Markov Decision Process (MDP) is used to formulate the Active Learning problem. We introduced the Frequency Weighted Average IoU (FWA IoU) as the image region selection performance metric to reduce the amount of training data while achieving competitive results. Using the Cityscapes and GTAv urban datasets, three baseline image segmentation networks (FPN, DeepLabV3, DeepLabV3+) trained with image regions selected by the proposed FWA IoU metric performed better compared to baseline region selection by active learning methods such as the Random selection, Entropy-based selection, and Bayesian Active Learning by Disagreement. Training performance equivalent to 98% of the fully supervised segmentation network was achieved by labeling only 8% of the dataset.

A Study on the Percentage of Pacing Strategies in Elite Asian Rowers: Analysis of Crew, Boat Type, and Gender

Background: For optimal performance, rowers should maintain a consistent rowing cadence over the entire distances. However, the rowing cadence of each category can be influenced by several factors. Understanding the rowing strategy related to these factors may help improve rowing performance. Objective: This analytical research aimed to examine the pacing strategies used by elite Asian rowers in different categories: crew, boat type, and gender during competitions at the 2023 Asian Games. Methods: The official Final A competition results of 14 events, comprising 42 male and 37 female rowers, totaling 79 datasets, were used for statistical analysis. Pacing techniques for each category were analyzed in the 500 m intervals and expressed as a percentage. Pearson correlation coefficient was used to assess rowing interval correlation. Results: The 2- and 2X showed the most percentage change (9.56%), while the 8+ showed the lowest (6.83%). After 8.33% and 8.37% adjustments, sweeps and sculls were essentially identical. Male rowers changed 7.08%, while female rowers changed 9.49%. The 500 m and 1000 m interval had a moderate positive correlation (r = 0.462), while the 2000 m distance had a significant negative correlation (r = -0.750) and the 1000 m had a strong negative correlation (r = -0.818) (p0.05). Conclusion: Rower’s size and gender are the major factors influencing pacing percentage. Smaller boats are more affected than larger ones. Males are less different per interval than females. Crew, boat type, and gender may impact rowing performance during certain intervals. Coaches and athletes need specialized pacing strategies for competition success.