Network Learning Research Articles

Enhanced NSCLC subtyping and staging through attention-augmented multi-task deep learning: A novel diagnostic tool

ObjectivesThe objective of this study is to develop a novel multi-task learning approach with attention encoders for classifying histologic subtypes and clinical stages of non-small cell lung cancer (NSCLC), with superior performance compared to currently popular deep-learning models. Material and methodsData were collected from six publicly available datasets in The Cancer Imaging Archive (TCIA). Following the inclusion and exclusion criteria, a total of 4548 CT slices from 758 cases were allocated. We evaluated multiple multi-task learning models that integrate attention mechanisms to resolve challenges in NSCLC subtype classification and clinical staging. These models utilized convolution-based modules in their shared layers for feature extraction, while the task layers were dedicated to histological subtype classification and staging. Each branch sequentially processed features through convolution-based and attention-based modules prior to classification. ResultsOur study evaluated 758 NSCLC patients (mean age, 66.2 years ± 10.3; 473 men), spanning ADC and SCC cases. In the classification of histological subtypes and clinical staging of NSCLC, the MobileNet-based multi-task learning model enhanced with attention mechanisms (MN-MTL-A) demonstrated superior performance, achieving Area Under the Curve (AUC) scores of 0.963 (95 % CI: 0.943, 0.981) and 0.966 (95 % CI: 0.945, 0.982) for each task, respectively. The model significantly surpassed its counterparts lacking attention mechanisms and those configured for single-task learning, as evidenced by P-values of 0.01 or less for both tasks, according to DeLong’s test. ConclusionsThe integration of attention encoder blocks into our multi-task learning network significantly enhanced the accuracy of NSCLC histological subtyping and clinical staging. Given the reduced reliance on precise radiologist annotation, our proposed model shows promising potential for clinical application.

An Unsupervised Learning Network for Large-Scale LiDAR Point Clouds Registration

An Unsupervised Learning Network for Large-Scale LiDAR Point Clouds Registration

Deep Learning Based Detection of Silicosis from Computed Tomography Images

Artificial intelligence has increasingly been used in interpreting medical images to support the timely treatment of diseases by providing early and accurate diagnosis. Pneumoconiosis is a tissue reaction that develops as a result of the accumulation of inorganic dust in the lungs. The most common types of pneumoconiosis include diseases such as coal worker's pneumoconiosis, silicosis, asbestosis, and siderosis. Silicosis, which has maintained its importance since the 1900s and has seen over 182,000 articles published in the last 10 years, is a global health problem. The automated detection and recognition of silicosis in lung computed tomography (CT) images can be considered the backbone of assisting the silicosis diagnosis process. Automated medical assistance systems developed using artificial intelligence can simplify the medical examination process and reduce the time required to start accurate treatment. Although the literature contains various studies that benefit silicosis diagnosis using chest X-ray images or pneumoconiosis diagnosis using CT images, there is not enough classification study that can particularly aid the diagnosis of silicosis in CT images.The method of early detection of silicosis from chest radiographs and CT images has been a challenging task due to the high variability among pneumoconiosis readers. Based on the success of deep learning in the classification and segmentation of medical images, this study has shown that deep learning networks and transfer learning algorithms can detect silicosis with high accuracy by classifying CT images. The performance of the six algorithms examined in the study is compared, and the algorithm with the best performance is recommended. Performance criteria such as accuracy, precision, specificity, and F1-score of the algorithms used in the study were calculated. The accuracy rates of the models were obtained as 92.62%, 93.03%, 92.76%, 95.38%, 97.29%, and 95.17% for AlexNet, VGG16, ResNet50, InceptionV3, Xception, and DenseNet121, respectively. These results show that Xception outperformed the other algorithms and was the most successful algorithm in the automatic detection of silicosis with an accuracy rate of 97.29%.Additionally, a new dataset consisting of tomography images from silicosis patients is presented in this study. Experimental results have shown that transfer learning algorithms can significantly benefit the diagnosis of silicosis by successfully classifying CT images. The findings of the study highlight the clinical importance of artificial intelligence methods in medical image analysis and early disease diagnosis.

Machine vision-based detection of surface defects in cylindrical battery cases

Automotive 21700 series lithium batteries are prone to surface defects during production and transportation, thus affecting their performance, so we propose a full-surface defect detection method for battery cases based on the synthesis of traditional image processing and deep learning to address this problem. First, the mechanism of surface defects on a battery case is analysed, and the types of surface defects are summarized. A suitable platform for image acquisition is designed for the severely reflective surface. Since there is no publicly available defect dataset for cylindrical battery cases, a defect dataset is established, and the dataset is augmented and expanded via the traditional method and the ACGAN model. For the defects on the top of the battery case, a traditional image processing algorithm is used to combine the roundness and the area of the area pixels to make a comprehensive judgement. For the defects on the bottom and side of the battery case, after comparing and analysing a variety of deep learning networks, the YOLOv7 model is selected to address the data characteristics of the large experimental inputs and the small targeted defects. To improve the accuracy of the model to meet the needs of real-time detection in industry, the CA attention mechanism, the DYHEAD dynamic detector head, and the slicing-assisted super inference (SAHI) method are used, with a final map accuracy reaching 98.1 %, which is 2.7 % higher than that of the initial model. Compared with mainstream target detection algorithms, the algorithm in this paper has good detection performance for cylindrical battery case defect detection and can be better applied to real-time detection in industry.

Cardiac motion correction with a deep learning network for perfusion defect assessment in SPECT myocardial perfusion imaging

BackgroundIn myocardial perfusion imaging (MPI) with SPECT ungated studies are used for evaluation of perfusion defects despite motion blur. We investigate the potential benefit of motion correction using a deep-learning (DL) network for evaluating perfusion defects. MethodsWe employed a DL network for cardiac motion correction in ECG-gated SPECT-MPI images, wherein the image data from different cardiac phases are combined with respect to a reference gate to reduce motion blur. For training the DL network 197 cases were used. Given the variability of gated images during the cardiac cycle, we investigated the detectability of perfusion defects in two distinct reference gates. To assess perfusion defect detection, we performed receiver-operating-characteristic (ROC) analyses on the motion-corrected images using a separate test dataset of clinical 194 subjects, in which studies were created from actual patient data with inserted simulated-lesions as ground truth. The reconstructed images were assessed by the quantitative-perfusion SPECT (QPS) software. We also evaluated the performance on reduced-count studies (by two and four folds). ResultsThe quantitative results, measured by area-under-the-ROC curve (AUC), demonstrated that DL motion correction improves the detectability of perfusion defects significantly on both standard- and reduced-count studies, and that the detectability can vary with reference cardiac phases. A joint assessment from two reference-phases achieved AUC=0.841 on the quarter-count data, higher than with ungated full-count data (AUC=0.795, p-value=0.0054). ConclusionsDL motion correction can benefit assessment of perfusion defects in standard- and reduced-count SPECT-MPI studies. It can also be beneficial to evaluate perfusion images over multiple cardiac phases.

Image restoration through few-mode fiber using a new comprehensive attention model

Few-mode fibers (FMF) has unique advantages in single fiber imaging (SFI) in special applications. However, the limited number of transmission modes restricts the information carriers, hence the high-quality image restoration is a major challenge. A deep learning network with new comprehensive attention, called few-mode fiber Swin Transformer (FFST), is developed based on Swin Transformer V2 to realize successful image restoration with a FMF on handwriting letters and numbers. This network is proved outperforming nine widely used convolutional neural networks or Transformer-based models with four evaluation criteria. In addition, single-fiber-multi-location (SFML) experiment is conducted to investigate the effect of different view on imaging feasibility, the result of which shows that SFML can improve the restoration quality despite one object corresponds to multiple different speckle images, indicating that multi-mapping relationships has constructive effect in single FMF imaging. This paper provides a method for imaging in very narrow cavities.

MuGIL: A Multi-Graph Interaction Learning Network for Multi-Task Traffic Prediction

MuGIL: A Multi-Graph Interaction Learning Network for Multi-Task Traffic Prediction

Identification of apple watercore based on ConvNeXt and Vis/NIR spectra

This paper proposes a method for discriminating between normal apples and watercore apples using visible/near-infrared spectroscopy technology, which combines Gramian Angular Fields (GAF) encoding technology and the ConvNeXt deep learning network. The existing feature extraction methods for visible/near-infrared spectroscopy data do not perform deeper information mining on the extracted features, which results in the quality of the established model being entirely determined by the extracted features. Additionally, the process of building a visible/near-infrared spectroscopy data classification model is complex and time-consuming, and the accuracy of the established model is not high. To address these issues, the experimental visible/near-infrared spectroscopy data of apples was first transformed into two-dimensional images using Gramian Angular Summation Fields (GASF) and Gramian Angular Difference Fields (GADF) with sizes of 64, 128, 256, and 512. These images were then input into the ConvNeXt network, and the performance of different encoding methods and sizes was compared. The results showed that, under the conditions provided in this paper, the GADF encoding method with a size of 256 achieved the highest classification accuracy of 98.48%. Next, ResNet, EfficientNet, and RegNet deep learning networks were selected to classify the encoded images under the same conditions. The results above indicate that the apple variety discrimination method based on GAF encoding technology and ConvNeXt network combined with visible/near-infrared spectroscopy technology can achieve deep information mining of visible/near-infrared spectroscopy data and provide a relatively simple method for establishing qualitative classification models of visible/near-infrared spectroscopy. This method has a relatively excellent discrimination effect between normal apples and watercore apples.

Low-Complexity Wireless Technique Classification With Multifeature Fusion Broad Learning Network

Low-Complexity Wireless Technique Classification With Multifeature Fusion Broad Learning Network

Deep Multilayer Sparse Regularization Time- Varying Transfer Learning Networks With Dynamic Kullback–Leibler Divergence Weights for Mechanical Fault Diagnosis

Deep Multilayer Sparse Regularization Time- Varying Transfer Learning Networks With Dynamic Kullback–Leibler Divergence Weights for Mechanical Fault Diagnosis

A soft scanning electron microscopy for efficient segmentation of alloy microstructures based on a new self-supervised pre-training deep learning network

To provide an on-site metallographic segmentation using only optical microscopy images, sSEM-Net, a soft scanning electron microscopy network, is developed based on a self-supervised pre-training deep learning framework. During model training, only a sparse collection of SEM images is necessary for annotation assistance. By integrating CNN and Transformer, sSEM-Net efficiently utilizes global context information while mitigating data dependency and computational resource constraints. Using only readily available optical microscopy images as input, sSEM-Net achieves metallographic segmentation comparable to SEM images, catering to rapid and cost-effective industrial needs. This methodology leverages non-destructive inspection attributes, catering to rapid and cost-sensitive industrial requirements. The efficacy of the proposed sSEM-Net is demonstrated through metallographic structure analysis of TC4 titanium alloy, with potential extensions to other alloy types.

Fluorescence spectroscopy combined with multilayer perceptron deep learning to identify the authenticity of monofloral honey—Rape honey

Honey authenticity is critical to honey quality. The development of a quick, easy, and non-destructive technique for determining the authenticity of honey encourages an improvement in honey quality. Here, the authenticity of monofloral honey—rape honey was determined using fluorescence spectroscopy combined with multilayer perceptron (MLP) deep learning, without the need for any prior feature extraction or pre-processing. A total of 91 raw fluorescence intensity data of the real and adulterated honey samples at a fixed excitation wavelength of 280 nm were first matrixed, and all data were then categorized into a training set, a validation set, and a test set with numbers of 64, 16, and 11, respectively. The connection with dropout was selected to build and link the MLP internal network. The activation function, learning rate, optimizer, and number of epochs were among the hyperparameters of the MLP neural network that were tuned. A good MLP deep learning network model for determining the authenticity of monofloral honey, rape honey, was developed after constant validation and debugging. According to the accuracy curve of the MLP model, the accuracy of the training set increased with the number of epochs and eventually converged to 100 %, while the accuracy of the validation set could be well stabilized at about 100 % after 5000 epochs. Finally, the accuracy of the MLP model on the test set was close to 100 %. According to our findings, the MLP neural network and fluorescence intensity have great potential applications in identifying the authenticity of honey.

CNN Intelligent diagnosis method for bearing incipient faint faults based on adaptive stochastic resonance-wave peak cross correlation sliding sampling

As a representative of deep learning networks, convolutional neural networks (CNN) have been widely used in bearing fault diagnosis with good results. However, the signal length and segmentation of the input CNN can have a significant impact on diagnostic accuracy. In addition, the signal-to-noise ratio of early bearing faults is usually very low, which makes it difficult for traditional CNNs to accurately identify and classify these faults. To solve this problem, this paper proposes an adaptive stochastic resonance wave peak cross-correlation sliding sampling method. Firstly, the adaptive stochastic resonance is used to reduce the noise of the original signal, and then the data is divided from the position of the signal wave peak, the correlation coefficient between the divided signals is calculated, and the maximum value is found to determine the size of the division window. Finally, it is converted into a 2D image by Gramian Angular Field and input into CNN for diagnostic classification. The design methodology was validated using the Case Western Reserve University bearing dataset. Subsequently, three validation strategies were established on a self-built platform, including mixed diagnosis of 10 different bearing states, variable speed diagnosis, and low sampling data diagnosis. The proposed method outperforms the conventional CNN by 10% in the Case Western Reserve University dataset test set. The variable speed test set is 24.67% and 31.17% higher, respectively. It is 30% higher in low sampling data diagnosis.

TAGL: Temporal-Guided Adaptive Graph Learning Network for Coordinated Movement Classification

TAGL: Temporal-Guided Adaptive Graph Learning Network for Coordinated Movement Classification

Neural Networks Learn Specified Information for Imbalanced Data Classification

Neural Networks Learn Specified Information for Imbalanced Data Classification

Fluid Grey 2: How Well Does Generative Adversarial Networks Learn Deeper Topology Structure in Architecture That Matches Images?

Taking into account the regional characteristics of ‘intrinsic’ and ‘extrinsic’ properties of space is an essential issue in architectural design and urban renewal, which is often achieved step by step using image and graph-based GANs. However, each model nesting and data conversion may cause information loss, and it is necessary to streamline the tools to facilitate architects and users to participate in the design. Therefore, this study hopes to prove that I2I GAN also has the potential to recognize topological relationships autonomously. Therefore, this research proposes a method for quickly detecting the ability of pix2pix to learn topological relationships, which is achieved by adding two Grasshopper-based detection modules before and after GAN. At the same time, quantitative data is provided and its learning process is visualized, and changes in different input modes such as greyscale and RGB affect its learning efficiency. There are two innovations in this paper: 1) It proves that pix2pix can automatically learn spatial topological relationships and apply them to architectural design. 2) It fills the gap in detecting the performance of Image-based Generation GAN from a topological perspective. Moreover, the detection method proposed in this study takes a short time and is simple to operate. The two detection modules can be widely used for customizing image datasets with the same topological structure and for batch detection of topological relationships of images. In the future, this paper may provide a theoretical foundation and data support for the application of architectural design and urban renewal that use GAN to preserve spatial topological characteristics.

Crafting innovative paths in non-linear professional learning for bilingual education: the role of connectivism in the age of AI

ABSTRACT This study introduces a novel Connectivist model for professional development (PD) aimed at enhancing AI literacy among bilingual/ESL teachers in K-12 education. As AI technologies rapidly reshape educational landscapes, effective PD programs are essential to equip educators with the skills to integrate AI responsibly and equitably. Grounded in Connectivism, which emphasizes non-linear learning and dynamic knowledge networks, this research explores how PD can transform educators’ perceptions of AI from apprehension to empowerment. The study involved a two-phase PD program with 30 elementary teachers and administrators, incorporating in-person AI training sessions, asynchronous tutorials, and collaborative activities. Data were collected through a phenomenological approach to capture educators’ lived experiences, with triangulation methods using surveys, interviews, observations, and Q&A sessions. The analysis revealed significant improvements in participants’ attitudes toward AI and their openness to integrating it into their teaching practices. The Connectivist model developed in this study fosters ongoing collaborative learning networks and equips educators to leverage AI in addressing resource gaps in bilingual education. This model offers a transformative, sustainable, and adaptive framework for integrating AI into K-12 education, providing a forward-looking approach to professional development in the digital age.

Wireless Network Security Prediction Using Machine Learning and Graph Neural Networks

Abstract: We study the application of graph neural networks and machine learning in the prediction of risks to wireless networks. Advanced predictive methods are necessary, since mere security measures cannot thwart the rising complexity of cyber-attacks. In the present work, we will use a hybrid model that combines several machine learning methods to reduce false negatives and positives and improve accuracy in the prediction of risks to wireless networks. Models are trained and validated with large amounts of data that include the performance indicators for accuracy, precision, recall, F1-score, and AUC-ROC. From this, it is also seen that the hybrid model performs much better than the standard models in real-time threat detection. The impact of the size of the dataset on the performance of the model was also studied, and it has come out that larger datasets improve predictive powers significantly. The result demonstrated that the latest advancements in machine learning techniques can lead to important improvements in the security of wireless networks.

Research on Pavement Defect Detection Algorithm Based on SEM-YOLOv8n

Automated detection and identification of pavement distresses is essential for timely pavement repair. Subtle pavement defects and multiple defects detection is a challenging task under complex background. With the deepening of the deep learning network, some subtle features tend to disappear and are more difficult to detect under the influence of the complex background. To solve the above problems, this paper proposes the SEM-YOLOv8n pavement defect detection algorithm. Firstly, SPD-Conv is used to replace the traditional convolution, which is conducive to retaining more defect detail information in the image and improving the detection ability of subtle defects; then an efficient multi-scale attention mechanism is added to the fusion network, so that the network suppresses the background information and focuses more on the defect information. Finally, MPDIoU is introduced as a loss function, which optimizes the minimum perpendicular distance between the predicted bounding box and the real bounding box and improves the localization ability, thus improving the accuracy of the network. Finally, the effectiveness of the proposed network is verified on the IRRDD dataset, and the results show that the method achieves 91.9% (Precision), 91.3% (Recall), and 71.3% (mAP) for the classification and detection of road multi-scale minor defects, which meets the demand of real-time road defect detection.

DEVELOPMENT OF A SYSTEMATIC APPROACH TO ASSESSING THE COMPLIANCE OF UNIVERSITY EDUCATIONAL PROGRAMS WITH THE UN SUSTAINABLE DEVELOPMENT GOALS

The purpose of the article is to develop and test in practice a systematic approach to assessing the compliance of educational programs of universities with the goals of sustainable development of the United Nations. The systematic approach includes the development of a sequence of development and approval of a questionnaire to determine the degree of compliance of educational programs of universities with the goals of sustainable development. Methodology. The survey was based on a comparison of the data of three groups of respondents (developers of educational programs and their curators at three levels of higher education). Approbation of the approach took place on the basis of the National Transport University (Kyiv, Ukraine). In total, 64 curators and developers of educational programs took part in the survey (this is 60% of educational programs of NTU). The results of the survey showed that educational program developers understand the importance of including the tasks of the United Nations Development Program in separate educational components or modules of disciplines in order to form relevant competencies in students. However, respondents noted the need to review educational programs and their components, as well as the educational process in the context of compliance with the goals of sustainable development and its ecologization. Practical implications. The results of this research will be used during the regular review of educational programs and their components at the National Transport University, and will also serve as a basis for further improvement of the concept of sustainable development of the NTU. The research data can be distributed to other universities. Also, the research results will be used in the project the Transformational Learning Network for Resilience – Enabling Ukrainian higher education to ensure a sustainable and robust reconstruction of (post-war) Ukraine (TransLearnN). The results of this study will contribute to the development of the program of sustainable development of universities in Ukraine. Value / Originality. The obtained results will make it possible to determine prospective directions for improving the educational process and educational programs of universities, taking into account the goals of sustainable development and trends in the ecologization of education.