High-quality Datasets Research Articles

A novel full-resolution convolutional neural network for urban-fringe-rural identification: A case study of urban agglomeration region

Monitoring urbanization processes is important because they are often accompanied by intensive landscape pattern transitions and pluralistic socioeconomic changes. To effectively monitor urban expansion and support regional planning, it is essential to develop a fast, accurate and universal urban–rural classification model, especially identifying the dynamic spatial patterns of urban, urban–rural fringe and rural areas. Although deep learning can effectively detect land cover changes, its applications in urban–rural identification have received little attention due to a lack of high-quality training datasets. In this study, we develop a novel transferable full-resolution convolutional neural network (FR-Net) to identify urban-fringe-rural areas. A large-scale training dataset was constructed using field surveys and aerial photography, and a data cube was stacked by multiple typical socio-natural indicators. We took the Beijing-Tianjin-Hebei (BTH) urban agglomeration region in China as a case study and identified spatiotemporal evolutions of urban-fringe-rural areas from 2000 to 2020. The results indicated that over the past two decades, the urban–rural fringe expanded outward with urban areas, and both areas gradually increased, with an inverted U-shaped growth rate. Accurate identification of these fringes can benefit regional urban–rural planning and social governance. Based on the identification results, complex socio-ecological impacts of urbanization could be further explored. Testing demonstrated that the developed FR-Net model has high accuracy and robustness. Our developed open-source FR-Net model exhibits transferability and can be applied to multi-scale urbanized areas.

Hybrid machine learning model based predictions for properties of poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose

Supermacroporous composite cryogels with enhanced adjustable functionality have received extensive interest in bioseparation, tissue engineering, and drug delivery. However, the variations in their components significantly impactfinal properties. This study presents a two-step hybrid machine learning approach for predicting the properties of innovative poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose (pHEMA-PVA-BC) based on their compositions. By considering the ratios of HEMA (1.0–22.0 wt%), PVA (0.2–4.0 wt%), poly(ethylene glycol) diacrylate (1.0–4.5 wt%), BC (0.1–1.5 wt%), and water (68.0–96.0 wt%) as investigational variables, overlay sampling uniform design (OSUD) was employed to construct a high-quality dataset for model development. The random forest (RF) model was used to classify the preparation conditions. Then four models of artificial neural network, RF, gradient boosted regression trees (GBRT), and XGBoost were developed to predict the basic properties of the composite cryogels. The results showed that the RF model achieved an accurate three-class classification of preparation conditions. Among the four models, the GBRT model exhibited the best predictive performance of the basic properties, with the mean absolute percentage error of 16.04 %, 0.85 %, and 2.44 % for permeability, effective porosity, and height of theoretical plate (1.0 cm/min), respectively. Characterization results of the representative pHEMA-PVA-BC composite cryogel showed an effective porosity of 81.01 %, a permeability of 1.20 × 10−12 m2, and a range of height of theoretical plate between 0.40–0.49 cm at flow velocities of 0.5–3.0 cm/min. These indicate that the pHEMA-PVA-BC cryogel was an excellent material with supermacropores, low flow resistance and high mass transfer efficiency. Furthermore, the model output demonstrates that the alteration of the proportions of PVA (0.2–3.5 wt%) and BC (0.1–1.5 wt%) components in composite cryogels resulted in significant changes in the material basic properties. This work represents an attempt to efficiently design and prepare target composite cryogels using machine learning and providing valuable insights for the efficient development of polymers.

New Tools for Data Harmonization and Their Potential Applications in Organ Transplantation.

In organ transplantation, accurate analysis of clinical outcomes requires large, high-quality data sets. Not only are outcomes influenced by a multitude of factors such as donor, recipient, and transplant characteristics and posttransplant events but they may also change over time. Although large data sets already exist and are continually expanding in transplant registries and health institutions, these data are rarely combined for analysis because of a lack of harmonization. Promoted by the digitalization of the healthcare sector, effective data harmonization tools became available, with potential applications also for organ transplantation. We discuss herein the present problems in the harmonization of organ transplant data and offer solutions to enhance its accuracy through the use of emerging new tools. To overcome the problem of inadequate representation of transplantation-specific terms, ontologies and common data models particular to this field could be created and supported by a consortium of related stakeholders to ensure their broad acceptance. Adopting clear data-sharing policies can diminish administrative barriers that impede collaboration between organizations. Secure multiparty computation frameworks and the artificial intelligence (AI) approach federated learning can facilitate decentralized and harmonized analysis of data sets, without sharing sensitive data and compromising patient privacy. A common image data model built upon a standardized format would be beneficial to AI-based analysis of pathology images. Implementation of these promising new tools and measures, ideally with the involvement and support of transplant societies, is expected to produce improved integration and harmonization of transplant data and greater accuracy in clinical decision-making, enabling improved patient outcomes.

Multi-view fusion for instruction mining of large language model

Large Language Models (LLMs) obtain their instruction-following ability through instruction tuning. While the quality of instruction data is considered critical for a successful LLM, the selection of high-quality datasets for finetuning still lacks clear guidelines and quantitative analyses. In this work, we introduce three analytical views for instruction mining: diversity, complexity, and accuracy, which can aid in selecting an optimal subset of instruction data for fine-tuning. Based on these views, we propose a multi-view fusion framework for efficient instruction selection, including diversity sampling based on LoRA representation distribution, complexity scoring based on uncertainty quantification, and accuracy scoring based on reward modeling. We perform the framework on various open-sourced instruction datasets, and achieved enhanced performance of LLMs with a carefully curated subset, underscoring the effectiveness of our proposed framework.

Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot.

The goal of machine learning (ML) is to create informative signals and useful tasks by leveraging large datasets to derive computational algorithms. ML has the potential to revolutionize the healthcare industry by boosting productivity, enhancing safe and effective patient care, and lightening the load on clinicians. In addition to gaining mechanistic insights into cancer-associated thrombosis (CAT), ML can be used to improve patient outcomes, streamline healthcare delivery, and spur innovation. Our review paper delves into the present and potential applications of this cutting-edge technology, encompassing three areas: i) computer vision-assisted diagnosis of thromboembolism from radiology data; ii) case detection from electronic health records using natural language processing; iii) algorithms for CAT prediction and risk stratification. The availability of large, well-annotated, high-quality datasets, overfitting, limited generalizability, the risk of propagating inherent bias, and a lack of transparency among patients and clinicians are among the challenges that must be overcome in order to effectively develop ML in the health sector. To guarantee that this powerful instrument can be utilized to maximize innovation in CAT, clinicians can collaborate with stakeholders such as computer scientists, regulatory bodies, and patient groups.

Jellyfish detection algorithm based on multi-gradient flow feature fusion

Jellyfish detection is a challenge in the field of underwater biological identification. Although early works have solved some of the problems of jellyfish detection, there are still shortcomings. Earlier jellyfish datasets were not as extensive, necessitating heavy reliance on data augmentation. Moreover, the detection capability is limited, especially in scenarios involving jellyfish gatherings and occlusions. To overcome these limitations, we establish a high-quality dataset with more jellyfish species and propose a more robust real-time detection algorithm. Our algorithm primarily consists of a multi-gradient flow backbone and a feature fusion module GFPN. Additionally, we have designed a receptive field expansion module SPPFCSPC_G. The entire network employs the FReLU activation function, while the bounding box regression utilizes the WIOU loss function. Our methods demonstrate accuracy and run-time performance in comparison with the state-of-the-art yolo series algorithms. Results show that our algorithm achieves the highest Precision, Recall and mAP50, exceeding the baseline yolov5 by 1.1%, 4.1%, and 4.5%, and outperforming the latest yolov8 by 0.9%, 1.3%, and 2.5%. Importantly, our method effectively addresses aggregation, occlusion, and deformation issues commonly encountered in jellyfish detection.

Artificial intelligence based data curation: enabling a patient-centric European health data space.

The emerging European Health Data Space (EHDS) Regulation opens new prospects for large-scale sharing and re-use of health data. Yet, the proposed regulation suffers from two important limitations: it is designed to benefit the whole population with limited consideration for individuals, and the generation of secondary datasets from heterogeneous, unlinked patient data will remain burdensome. AIDAVA, a Horizon Europe project that started in September 2022, proposes to address both shortcomings by providing patients with an AI-based virtual assistant that maximises automation in the integration and transformation of their health data into an interoperable, longitudinal health record. This personal record can then be used to inform patient-related decisions at the point of care, whether this is the usual point of care or a possible cross-border point of care. The personal record can also be used to generate population datasets for research and policymaking. The proposed solution will enable a much-needed paradigm shift in health data management, implementing a 'curate once at patient level, use many times' approach, primarily for the benefit of patients and their care providers, but also for more efficient generation of high-quality secondary datasets. After 15 months, the project shows promising preliminary results in achieving automation in the integration and transformation of heterogeneous data of each individual patient, once the content of the data sources managed by the data holders has been formally described. Additionally, the conceptualization phase of the project identified a set of recommendations for the development of a patient-centric EHDS, significantly facilitating the generation of data for secondary use.

AI-based image classification algorithms for Leukaemia diagnostics and hematologic cytomorphology: From single cells to molecular features

Due to the progress of image analysis and classification systems in recent years, algorithms have been developed that support morphologic examination of both single cells and tissue samples. These algorithms are typically developed using data-driven strategies, which require comprehensive, large-scale datasets. In the diagnostic workup of hematopoietic malignancies, cytomorphologic examination and differentiation represents a key first step. In recent years, the availability of large-scale, high-quality datasets of single leukocytes from peripheral blood and bone marrow has led to the development of diagnostic support algorithms for this modality. These methods not only allow a faster and more consistent classification of diagnostically relevant cell types, but also pave the way for integrated analysis of cytomorphologic and molecular findings.

Identifying minibonds issued level’s determinants to empower companies’ funding managerial strategies

PurposeThis paper aims to explore the determinants of the level of minibonds issued by companies. In doing so, it discusses the importance of minibonds in providing a market-based funding source. In the empirical analysis, special attention is paid to the study of the recovery from the COVID-19 crisis.Design/methodology/approachThe analysis is carried out through an econometric approach, on the basis of a high-quality empirical dataset related to the Italian small- and medium-sized enterprises (SMEs). The reference period covers the recent pandemic. From a theoretical point of view, a regression model is implemented, including a multicollinearity analysis and an outlier detection procedure.FindingsThe results of the study indicate that factors such as leverage, cash flow, firm collaterals and seniority can explain the amount of minibonds issued. These findings provide valuable insights into the drivers of minibond issuance and highlight the potential benefits of minibonds as a funding option for Italian SMEs.Practical implicationsImportantly, results highlight relevant managerial implications at two levels. On one side, we carry on a managerial discussion about the worthiness of accessing the minibonds market; on the other side, we give insights on the managerial implications related to the features of the companies issuing minibonds.Originality/valueThe paper investigates an innovative financial instrument that has been introduced recently and has not yet been studied in depth. To the best of our knowledge, this is the first contribution assessing the main drivers for minibonds issuance level, which is a timely and relevant managerial research topic. In addition, this study also takes into account the impact of the COVID-19 pandemic on minibond issuance, making the analysis appropriate for explaining the current economic context.

Cross-modality transfer learning with knowledge infusion for diabetic retinopathy grading.

Ultra-wide-field (UWF) fundus photography represents an emerging retinal imaging technique offering a broader field of view, thus enhancing its utility in screening and diagnosing various eye diseases, notably diabetic retinopathy (DR). However, the application of computer-aided diagnosis for DR using UWF images confronts two major challenges. The first challenge arises from the limited availability of labeled UWF data, making it daunting to train diagnostic models due to the high cost associated with manual annotation of medical images. Secondly, existing models' performance requires enhancement due to the absence of prior knowledge to guide the learning process. By leveraging extensively annotated datasets within the field, which encompass large-scale, high-quality color fundus image datasets annotated at either image-level or pixel-level, our objective is to transfer knowledge from these datasets to our target domain through unsupervised domain adaptation. Our approach presents a robust model for assessing the severity of diabetic retinopathy (DR) by leveraging unsupervised lesion-aware domain adaptation in ultra-wide-field (UWF) images. Furthermore, to harness the wealth of detailed annotations in publicly available color fundus image datasets, we integrate an adversarial lesion map generator. This generator supplements the grading model by incorporating auxiliary lesion information, drawing inspiration from the clinical methodology of evaluating DR severity by identifying and quantifying associated lesions. We conducted both quantitative and qualitative evaluations of our proposed method. In particular, among the six representative DR grading methods, our approach achieved an accuracy (ACC) of 68.18% and a precision (pre) of 67.43%. Additionally, we conducted extensive experiments in ablation studies to validate the effectiveness of each component of our proposed method. In conclusion, our method not only improves the accuracy of DR grading, but also enhances the interpretability of the results, providing clinicians with a reliable DR grading scheme.

Impact of imperfect annotations on CNN training and performance for instance segmentation and classification in digital pathology

Segmentation and classification of large numbers of instances, such as cell nuclei, are crucial tasks in digital pathology for accurate diagnosis. However, the availability of high-quality datasets for deep learning methods is often limited due to the complexity of the annotation process. In this work, we investigate the impact of noisy annotations on the training and performance of a state-of-the-art CNN model for the combined task of detecting, segmenting and classifying nuclei in histopathology images. In this context, we investigate the conditions for determining an appropriate number of training epochs to prevent overfitting to annotation noise during training. Our results indicate that the utilisation of a small, correctly annotated validation set is instrumental in avoiding overfitting and maintaining model performance to a large extent. Additionally, our findings underscore the beneficial role of pre-training.

Seeing through the Tactile

Humans spend about one-third of their lives resting. Reconstructing human dynamics in in-bed scenarios is of considerable significance in sleep studies, bedsore monitoring, and biomedical factor extractions. However, the mainstream human pose and shape estimation methods mainly focus on visual cues, facing serious issues in non-line-of-sight environments. Since in-bed scenarios contain complicated human-environment contact, pressure-sensing bedsheets provide a non-invasive and privacy-preserving approach to capture the pressure distribution on the contact surface, and have shown prospects in many downstream tasks. However, few studies focus on in-bed human mesh recovery. To explore the potential of reconstructing human meshes from the sensed pressure distribution, we first build a high-quality temporal human in-bed pose dataset, TIP, with 152K multi-modality synchronized images. We then propose a label generation pipeline for in-bed scenarios to generate reliable 3D mesh labels with a SMPLify-based optimizer. Finally, we present PIMesh, a simple yet effective temporal human shape estimator to directly generate human meshes from pressure image sequences. We conduct various experiments to evaluate PIMesh's performance, showing that PIMesh archives 79.17mm joint position errors on our TIP dataset. The results demonstrate that the pressure-sensing bedsheet could be a promising alternative for long-term in-bed human shape estimation.

TFSemantic: A Time–Frequency Semantic GAN Framework for Imbalanced Classification Using Radio Signals

Recently, wireless sensing techniques have been widely used for Internet of Things (IoT) applications. Unlike traditional device-based sensing, wireless sensing is contactless, pervasive, low cost, and non-invasive, making it highly suitable for relevant IoT applications. However, most existing methods are highly dependent on high-quality datasets, and the minority class will not achieve a satisfactory performance when suffering from a class imbalance problem. In this article, we propose a time–frequency semantic generative adversarial network framework (i.e., TFSemantic) to address the imbalanced classification problem in human activity recognition using radio frequency (RF) signals. Specifically, the TFSemantic framework can learn semantic features from the minority classes and then generate high-quality signals to restore data balance. It includes a data pre-processing module, a semantic extraction module, a semantic distribution module, and a data augmenter module. In the data pre-processing module, we process four different RF datasets (i.e., WiFi, RFID, UWB, and mmWave). We also develop Fourier semantic feature convolution and attention semantic feature embedding methods for the semantic extraction module. A discrete wavelet transform is utilized for reconstructed RF samples in the semantic distribution module. In data augmenter module, we design an associated loss function to achieve effective adversarial training. Finally, we validate the effectiveness of the proposed TFSemantic framework using different RF datasets, which outperforms several state-of-the-art methods.

The effect of the re-segmentation method on improving the performance of rectal cancer image segmentation models.

Rapid and accurate segmentation of tumor regions from rectal cancer images can better understand the patientâs lesions and surrounding tissues, providing more effective auxiliary diagnostic information. However, cutting rectal tumors with deep learning still cannot be compared with manual segmentation, and a major obstacle to cutting rectal tumors with deep learning is the lack of high-quality data sets. We propose to use our Re-segmentation Method to manually correct the model segmentation area and put it into training and training ideas. The data set has been made publicly available. Methods: A total of 354 rectal cancer CT images and 308 rectal region images labeled by experts from Jiangxi Cancer Hospital were included in the data set. Six network architectures are used to train the data set, and the region predicted by the model is manually revised and then put into training to improve the ability of model segmentation and then perform performance measurement. In this study, we use the Resegmentation Method for various popular network architectures. By comparing the evaluation indicators before and after using the Re-segmentation Method, we prove that our proposed Re-segmentation Method can further improve the performance of the rectal cancer image segmentation model.

Supervised Contrast Learning Text Classification Model Based on Data Quality Augmentation

Token-level data augmentation generates text samples by modifying the words of the sentences. However, data that are not easily classified can negatively affect the model. In particular, not considering the role of keywords when performing random augmentation operations on samples may lead to the generation of low-quality supplementary samples. Therefore, we propose a supervised contrast learning text classification model based on data quality augmentation. First, dynamic training is used to screen high-quality datasets containing beneficial information for model training. The selected data is then augmented with data based on important words with tag information. To obtain a better text representation to serve the downstream classification task, we employ a standard supervised contrast loss to train the model. Finally, we conduct experiments on five text classification datasets to validate the effectiveness of our model. In addition, ablation experiments are conducted to verify the impact of each module on classification.

Comparing Deep Learning and MCWST Approaches for Individual Tree Crown Segmentation

Abstract. Accurate segmentation of individual tree crowns (ITC) segmentation is essential for investigating tree-level based growth trends and assessing tree vitality. ITC segmentation using remote sensing data faces challenges due to crown heterogeneity, overlapping crowns and data quality. Currently, both classical and deep learning methods have been employed for crown detection and segmentation. However, the effectiveness of deep learning based approaches is limited by the need for high-quality annotated datasets. Benefiting from the BaKIM project, a high-quality annotated dataset can be provided and tested with a Mask Region-based Convolutional Neural Network (Mask R-CNN). In addition, we have used the deep learning based approach to detect the tree locations thus refining the previous Marker controlled Watershed Transformation (MCWST) segmentation approach. The experimental results show that the Mask R-CNN model exhibits better model performance and less time cost compared to the MCWST algorithm for ITC segmentation. In summary, the proposed framework can achieve robust and fast ITC segmentation, which has the potential to support various forest applications such as tree vitality estimation.

Predicting Indian GDP with Machine Learning: A Comparison of Regression Models

In recent years, the application of machine learning algorithms in economic forecasting has gained significant traction. This study focuses on predicting the Gross Domestic Product (GDP) of India by harnessing advanced machine learning techniques. We assembled a comprehensive dataset incorporating time series analysis and inflation rates from diverse sources. Our investigation involved a comparative analysis employing both linear and polynomial regression methods to discern the most accurate predictive model. Our findings underscore the superiority of the polynomial regression model, which excelled in capturing non-linear relationships between independent variables and GDP. Specifically, the polynomial regression model achieved an impressive prediction accuracy rate of surpassing the accuracy achieved by the linear regression model. This study underscores the critical role of advanced machine learning algorithms in economic forecasting. By emphasizing the significance of high-quality datasets and the application of techniques like polynomial regression, we illustrate how these factors can substantially enhance the precision of economic forecasts. These insights hold substantial implications for policy makers and businesses alike, aiding informed decision-making and effective economic policy development. This study serves as a valuable reference for future research, highlighting the importance of advanced machine learning methods and robust data sources in economic forecasting.

Transformative Impact of Deep Learning in Stock Market Decision-Making: A Comparative Study of Convolutional Neural Networks

This research delves into the transformative impact of deep learning, specifically Convolutional Neural Networks (CNNs) such as VGG16, ResNet50, and InceptionV3, on organizational management and business intelligence. The study follows a comprehensive methodology, emphasizing the importance of high-quality datasets in leveraging deep learning for enhanced decision-making. Results demonstrate the superior performance of CNN models over traditional algorithms, with CNN (VGG19) achieving an accuracy rate of 89.45%. The findings underscore the potential of deep learning in extracting meaningful insights from complex data, offering a paradigm shift in optimizing various organizational processes. The article concludes by emphasizing the significance of investing in infrastructure and expertise for successful CNN integration, ensuring ethical considerations, and addressing data privacy concerns. This research contributes to the growing discourse on the application of deep learning in organizational management, providing a valuable resource for businesses navigating the dynamic landscape of the global market.

A comprehensive laser image dataset for real-time measurement of wheelset geometric parameters

Railway transportation has experienced significant growth worldwide, offering numerous benefits to society. Most railway accidents are caused by wheelset faults so it’s significant to monitor wheelset conditions. Therefore, we need to collect wheelset images, repaint them, extract laser stripe centerlines, construct 3D contour, and measure their geometric parameters to judge the wheelset’s conditions. Deep learning can fulfill the tasks satisfyingly because it’s adaptable, robust, and generalize compared with traditional methods. To train the deep learning models effectively, we need rich and high-quality wheelset datasets. So far, there are no applicable public train wheelset datasets available, which greatly hinders the research on train wheelsets. Thus we construct a publicly available Wheelset Laser Image Dataset (WLI-Set). WLI-Set consists of four sub-datasets, Original, Inpainting, Segmentation, and Centerline. The dataset contains abundant annotated multiline laser stripe images that can facilitate the research on train wheelsets effectively.

Learning spectral-indices-fused deep models for time-series land use and land cover mapping in cloud-prone areas: The case of Pearl River Delta

Mapping of highly dynamic changes in land use and land cover (LULC) can be hindered by various cloudy conditions with optical satellite images. These conditions result in discontinuities in high-temporal-density LULC mapping. In this paper, we developed an integrated time series mapping method to enhance the LULC mapping accuracy and frequency in cloud-prone areas by incorporating spectral-indices-fused deep models and time series reconstruction techniques. The proposed method first reconstructed cloud-contaminated pixels through time series filtering, during which the cloud masks initialized by a deep model were refined and updated during the reconstruction process. Then, the reconstructed time series images were fed into a spectral-indices-fused deep model trained on samples collected worldwide for classification. Finally, post-classification processing, including spatio-temporal majority filtering and time series refinement considering land–water interactions, was conducted to enhance the LULC mapping accuracy and consistency. We applied the proposed method to the cloud- and rain-prone Pearl River Delta (i.e., Guangdong–Hong Kong–Macao Greater Bay Area, GBA) and used time series Sentinel-2 images as the experimental data. The proposed method enabled seamless LULC mapping at a temporal frequency of 2–5 days, and the production of 10 m resolution annual LULC products in the GBA. The assessment yielded a mean overall accuracy of 87.01% for annual mapping in the four consecutive years of 2019–2022 and outperformed existing mainstream LULC products, including ESA WorldCover (83.98%), Esri Land Cover (85.26%), and Google Dynamic World (85.06%). Our assessment also reveals significant variations in LULC mapping accuracies with different cloud masks, thus underscoring their critical role in time series LULC mapping. The proposed method has the potential to generate seamless and near real-time maps for other regions in the world by using deep models trained on datasets collected globally. This method can provide high-quality LULC data sets at different time intervals for various land and water dynamics in cloud- and rain-prone regions. Notwithstanding the difficulties of obtaining high-quality LULC maps in cloud-prone areas, this paper provides a novel approach for the mapping of LULC dynamics and the provision of reliable annual LULC products.