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In the era of informatization and digitization, the rapid increase in the number of scientific papers has given rise to various challenges, such as lengthy articles, difficulty in information extraction and high time costs associated with reading. Literature reading challenges for researchers are increasingly tedious and time-consuming. By utilizing the language models, the assited reading system of scientific papers has been designed to address these challenges. By adopting machine reading comprehension technology as the core, the system parses scientific texts and offers some common questions to achieve automated response capabilities. By fully utilizing the pre-trained language model PERT, the system enhances its capabilities in semantic understanding and information extraction, effectively resolving various challenges in reading scientific papers and helping readers improve the efficiency of scientific literature review. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease — COVID-19 is a historic catastrophe that has caused many devastating impacts on human life and wellness. Researchers in academia and industry strive to understand the causes of this pandemic disease and find new therapeutics combating it. Consequently, the number of COVID-19 related publications increases rapidly, and it is too difficult for medical researchers and practitioners to keep up with the latest research and development. Literature filtering and categorization, and knowledge discovery can use text mining as a powerful tool. In this paper, we propose a text mining method to explore the categories of COVID-19 related themes and identify the standard methodologies that have been used. We discuss the potential limitations of this preliminary study and present future perspectives related to COVID-19 research. This paper provides an quantitative and qualitative mixed analysis example of using some research papers by data mining method to dig out several hidden information and set up a foundation for data scientists to develop more effective algorithms to deal with COVID-19 related problems. [ABSTRACT FROM AUTHOR]

This paper summarizes librarian research on information visualization as well as general trends in the broader field, highlighting the most recent trends, important journals, and which subject disciplines are most involved with information visualization. By comparing librarian research to the broader field, the paper identifies opportunities for libraries to improve their information visualization support services. [ABSTRACT FROM AUTHOR]

This paper proposes a data-based mining and hesitant fuzzy C-canopy-K mean clustering degree algorithm and uses it in the parameter assessment model of teaching quality indicators. Simulation and training are carried out through data class mining, and information input, followed by combining the hesitant fuzzy K-mean classification assessment method, which involves a hesitant fuzzy type evaluation system, a neural network identification and prediction system, and an application system for module identity verification. The simulation results show that the results of the six simulation conditions are consistent with the actual results, with only slight differences in some amplitudes, and a high degree of consistency in the overall trend, the change rule, and the average peak value. Through the prediction model processing in this paper, the teaching quality index parameter assessment has high accuracy and can reach more than 95.0%, in addition, the development of the law also fits very well. a, b, c, d four kinds of teaching quality parameter assessment of the average calculation of the assessment speed increased by 52.5%. In addition, the assessment test after the integrated design of the module shows that the system can effectively identify the four clustering identification processes that can be seen as excellent, good, medium, and poor; at the same time, the test data show that the system class effectively for teaching quality indicator parameter assessment. [ABSTRACT FROM AUTHOR]

This paper comprehends the global research scenario about incorporating cultural components in contemporary urban planning practice. Various urban cultural elements have been analysed under the umbrella of 'Urban Cultural Dynamics' (UCD). This study aims to identify hot spots and explore influential sources through a bibliometric investigation of the evolutionary patterns of cultural dynamics in urban planning research. Data was collected from the Web of Science core collection (1965-2021). The data mining tools VOSviewer, Biblioshiny, KNIME, and WordStat were used to analyse hot topics, keywords, influential journals, core papers, top authors, and cooperation networks of countries and institutions. The Analytical Hierarchy Process (AHP) is employed in journal analysis. The analysis showed that publications on UCD increased exponentially between 2016 and 2022. Italy (32), China (26), and Spain (18) are the top countries in total publications. Technical universities of Dresden and Munich have the most significant influence on UCD's research, with 103 and 101 total citations, respectively. Landscape and Urban Planning, Journal of Cultural Heritage, and Tourism Geographies are the top three consistently influential journals. The top ten core papers were identified. The keywords and topic analysis charted out the evolution of multidisciplinary UCD research topics. The hotspot analysis revealed the significance of cultural dynamics in sustainable urban development. The results highlighted the need for intercontinental cooperation between developed countries and the third world. The future scope of research on UCD topics is discussed in the conclusion. [ABSTRACT FROM AUTHOR]

Reservoir dispatching regulations are a crucial basis for reservoir operation, and using information extraction technology to extract entities and relationships from heterogeneous texts to form triples can provide structured knowledge support for professionals in making dispatch decisions and intelligent recommendations. Current information extraction technologies require manual data labeling, consuming a significant amount of time. As the number of dispatch rules increases, this method cannot meet the need for timely generation of dispatch plans during emergency flood control periods. Furthermore, utilizing natural language prompts to guide large language models in completing reservoir dispatch extraction tasks also presents challenges of cognitive load and instability in model output. Therefore, this paper proposes an entity and relationship extraction method for reservoir dispatch based on structured prompt language. Initially, a variety of labels are refined according to the extraction tasks, then organized and defined using the Backus–Naur Form (BNF) to create a structured format, thus better guiding large language models in the extraction work. Moreover, an AI agent based on this method has been developed to facilitate operation by dispatch professionals, allowing for the quick acquisition of structured data. Experimental verification has shown that, in the task of extracting entities and relationships for reservoir dispatch, this AI agent not only effectively reduces cognitive burden and the impact of instability in model output but also demonstrates high extraction performance (with F1 scores for extracting entities and relationships both above 80%), offering a new solution approach for knowledge extraction tasks in other water resource fields. [ABSTRACT FROM AUTHOR]

This study aims to analyze India's publications on organic chemistry and related fields at the micro, meso, and macro levels. The study attempts to map and visualize the publications in organic chemistry from 2016 to 2020 to identify the country co-authorship, author co-authorship, bibliographic coupling of authors, keyword co-occurrence, etc. Performance analysis techniques incorporating publication-related metrics, citation-related metrics, and citation and publication-related metrics (number of cited publications, citations per publication, h-index) were utilized to analyze the publications. Further, science mapping techniques are used to investigate citation analysis, bibliographic coupling, and co-authorship analysis. Moreover, enriched bibliometric techniques meant for computational evaluation like network analysis, including cluster analysis and visualization, were used in later phases using the software VOSviewer and Biblioshiny. VOSviewer, a bibliometric mapping tool, is extensively used in scientometric studies. For analysis of performance and visualization of research hotspots in Organic Chemistry, Biblioshiny web interface through R-studio is used, which is performing intensive data mining. A total of 1804 journals are encountered in the period taken for study which obeys a non-exponential growth with the highest number of papers (370) in the year 2017 and the least (344) in the year 2020 and as per the forecasting, the highest number of publications are expected in the year 2021 (349). Analysis of prolific authors reveals that A Kumar is the most productive author in the period with 47 publications and an h-index of 12 and KR Prabhu of IISc. Bangalore is the most impactful author with 387 citations and 27.64 CPP. Journal of Organic Chemistry is the most productive journal. Trending topics of research like azo dyes and metal-organic frameworks indicate the industrial and commercial importance of organic compounds. No similar studies have been found with matching objectives with this study. [ABSTRACT FROM AUTHOR]

In high-voltage transmission lines, the tension tower needs to withstand the tension load of the overhead power line for a long time, which is prone to damage, and it is an important part of the inspection in the circuit inspection. In the modern circuit inspection process, operation and maintenance personnel mostly use unmanned aerial vehicles (UAVs) to photograph various parts of the tension tower, obtain inspection images, and manually classify and name the massive inspection images, which is low in accuracy and efficiency. Based on the above problems, this paper collects a large number of real-life UAV inspection images of various parts of a tension tower, and proposes the Stower-13 inspection image dataset, which is used to train the classification model to achieve automatic classification and naming of inspection images. Based on this dataset, this paper also proposes an improved MobileViT model, in which the Scale-Correlated Pyramid Convolution Block Attention Block (SCPCbam) module is introduced, which adds the Convolution Block Attention Module (CBAM) to the four branches of the original Scale-Correlated Pyramid Convolution (SCPC) module, so as to strengthen the ability of image multi-scale information extraction and improve the classification accuracy. This paper discusses a number of experiments on the model, and the experimental results show that the dataset proposed in this paper helps the model to understand the feature information. At the same time, the improved MobileViT model has a strong ability to extract image spatial feature information, the classification accuracy is higher than that of other models of the same type, so it is able to cope with a wide range of problems that arise in the course of practice, and it meets the practical needs of automatically naming transmission line inspection images. [ABSTRACT FROM AUTHOR]

This paper aims to help students better grasp the required professional knowledge and core concepts. This paper presents a design method for a multi-layer knowledge base based on XML. According to learners' identity characteristics and learning behaviour, using the mathematical statistics method, the feature expression for the learning system is constructed. Multivariable linear regression theory establishes convergence constraints for accurate and deep mining. The average detection results of the collected samples are used for high-quality deep mining of user portraits in the learning system. This project intends to study the method of solving accurate confidence intervals for user portrait data in the education system. Excel and Access are used to complete the data collection of the teaching object and the construction of the database. A multi-mode interactive editing and processing method of user portrait information for education systems is studied in cloud computing. Finally, a learning system based on mathematical loading mode is proposed, and an object-oriented learning recommendation system is designed. The developed teaching software can enable students to get more teaching guidance when they acquire the required knowledge to improve students' learning effect effectively. [ABSTRACT FROM AUTHOR]

This paper aims to structure and semantically describe the information within the industrial chain by constructing an Industry Chain Knowledge Graph (ICKG), enabling more efficient and intelligent information management and analysis. In more detail, this paper constructs a multi-domain industrial chain dataset and proposes a method that combines the top-down establishment of a semantic expression framework with the bottom-up establishment of a data layer to build an ICKG. In the data layer, a deep learning algorithm based on BERT-BiLSTM-CRF is used to extract industry chain entities from relevant literature and reports. The results indicate that the model can effectively identify industry chain entities. These entities and relationships populate a Neo4j graph database, creating a large-scale ICKG for visual display and aiding cross-domain applications. [ABSTRACT FROM AUTHOR]

Vacuum is an important parameter in cutter suction dredging operations because the equipment is underwater and can easily fail. It is necessary to analyze other parameters related to the vacuum to make real-time predictions about it, which can improve the construction efficiency of the dredger under abnormal working conditions. In this paper, a data-driven method for predicting the vacuum of the underwater pump of the cutter suction dredger (CSD) is proposed with the help of big data, machine learning, data mining, and other technologies, and based on the historical data of "Hua An Long" CSD. The method eliminates anomalous data, standardizes the data set, and then relies on theory and engineering experience to achieve feature extraction using the Spearman correlation coefficient. Then, six machine learning methods were employed in this study to train and predict the data set, namely, lasso regression (lasso), elastic network (Enet), gradient boosting decision tree (including traditional GBDT, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM)), and stacking. The comparison of the indicators obtained through multiple rounds of feature number iteration shows that the LightGBM model has high prediction accuracy, a good running time, and a generalization ability. Therefore, the methodological framework proposed in this paper can help to improve the efficiency of underwater pumps and issue timely warnings in abnormal working conditions. [ABSTRACT FROM AUTHOR]

The extraction of entities and relationships is a crucial task in the field of natural language processing (NLP). However, existing models for this task often rely heavily on a substantial amount of labeled data, which not only consumes time and labor but also hinders the development of downstream tasks. Therefore, with a focus on enhancing the model's ability to learn from small samples, this paper proposes an entity and relationship extraction method based on the Universal Information Extraction (UIE) model. The core of the approach is the design of a specialized prompt template and schema on cotton pests and diseases as one of the main inputs to the UIE, which, under its guided fine-tuning, enables the model to subdivide the entity and relationship in the corpus. As a result, the UIE-base model achieves an accuracy of 86.5% with only 40 labeled training samples, which really solves the problem of the existing models that require a large amount of manually labeled training data for knowledge extraction. To verify the generalization ability of the model in this paper, experiments are designed to compare the model with four classical models, such as the Bert-BiLSTM-CRF. The experimental results show that the F1 value on the self-built cotton data set is 1.4% higher than that of the Bert-BiLSTM-CRF model, and the F1 value on the public data set is 2.5% higher than that of the Bert-BiLSTM-CRF model. Furthermore, experiments are designed to verify that the UIE-base model has the best small-sample learning performance when the number of samples is 40. This paper provides an effective method for small-sample knowledge extraction. [ABSTRACT FROM AUTHOR]

This paper investigates the application of data mining based on a correlation-rule algorithm in business English performance in colleges and universities. The extracted correlation degree rules are screened by adopting three indexes of support degree confidence degree and lifting degree to measure the correlativity. Experimental validation is carried out on different sets of data sets, and the experimental results show the effectiveness of the Lift-Apriori-DP algorithm. Based on the improved Lift-Apriori-DP algorithm, it is applied to the analysis of students' performance. Taking the chapter test scores of students in business English courses in colleges and universities as an example, the student's achievements are extracted and analyzed, and the final appropriate parameter values are selected according to the evaluation of the extraction results under different privacy pre-estimation. The experimental results verify the effectiveness of the Lift-Apriori-DP algorithm in the analysis of student grades and evaluate the accuracy of the algorithm application. The results of this paper show that data mining based on the correlation degree rule algorithm has a wide range of applications in business English grades in colleges and universities, which can provide useful references for teaching and at the same time protect students' private information from being leaked. In addition, this paper also explores the evaluation class method of the mining results under different privacy pre-estimation, which provides a useful reference for the application of privacy-protecting relevance degree rule mining type algorithms. [ABSTRACT FROM AUTHOR]

Background: MicroRNAs (miRNAs) emerge in various organisms, ranging from viruses to humans, and play crucial regulatory roles within cells, participating in a variety of biological processes. In numerous prediction methods for miRNA-disease associations, the issue of over-dependence on both similarity measurement data and the association matrix still hasn't been improved. In this paper, a miRNA-Disease association prediction model (called TP-MDA) based on tree path global feature extraction and fully connected artificial neural network (FANN) with multi-head self-attention mechanism is proposed. The TP-MDA model utilizes an association tree structure to represent the data relationships, multi-head self-attention mechanism for extracting feature vectors, and fully connected artificial neural network with 5-fold cross-validation for model training. Results: The experimental results indicate that the TP-MDA model outperforms the other comparative models, AUC is 0.9714. In the case studies of miRNAs associated with colorectal cancer and lung cancer, among the top 15 miRNAs predicted by the model, 12 in colorectal cancer and 15 in lung cancer were validated respectively, the accuracy is as high as 0.9227. Conclusions: The model proposed in this paper can accurately predict the miRNA-disease association, and can serve as a valuable reference for data mining and association prediction in the fields of life sciences, biology, and disease genetics, among others. [ABSTRACT FROM AUTHOR]

Current low-light image enhancement techniques prioritize increasing image luminance but fail to address issues including loss of intricate distortion of colors and image details. In order to address these issues that has been overlooked by all parties, this paper suggests a multi-module optimization network for enhancing low-light images by integrating deep learning with Retinex theory. First, we create a decomposition network to separate the lighting components and reflections from the low-light image. We incorporated an enhanced global spatial attention (GSA) module into the decomposition network to boost its flexibility and adaptability. This module enhances the extraction of comprehensive information from the image and safeguards against information loss. To increase the illumination component's luminosity, we subsequently constructed an enhancement network. The Multiscale Guidance Block (MSGB) has been integrated into the improvement network, together with multilayer extended convolution to expand the sensing field and enhance the network's capability for feature extraction. Our proposed method out-performs existing ways in both objective measures and personal evaluations, emphasizing the virtues of the procedure outlined in this paper. [ABSTRACT FROM AUTHOR]

Target maneuver recognition is a prerequisite for air combat situation awareness, trajectory prediction, threat assessment and maneuver decision. To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data, and automatically and adaptively complete the task of extracting the target maneuver pattern, in this paper, an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder, G-G clustering algorithm and the selective ensemble clustering analysis algorithm. Firstly, the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension; Then, taking the time information into account, the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm, and a large number of maneuver primitives are extracted; Finally, the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm, which can prove that each class represents a maneuver action. The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3% of maneuver actions，indicating that the method is effective and satisfies the requirements for engineering accuracy. In addition, this method can provide data support for various target maneuvering recognition methods proposed in the literature, greatly reduce the workload and improve the recognition accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a new detection method of pin defects based on image segmentation and ResNe-50 is proposed, which realizes the defect detection of faulty pins in many aviation connectors. In this paper, a new dataset image segmentation method is used to segment many aviation connectors in a single image to generate a dataset, which reduces the tedious work of manually labeling the dataset. In the defect detection model, based on ResNet-50, a ResNet-B residual structure is introduced to reduce the loss of features during information extraction; a continuously differentiable CELU is used as the activation function to reduce the neuron death problem of ReLU; a new deformable convolution network (DCN v2) is introduced as the convolution kernel structure of the model to improve the recognition of aviation connectors with prominent geometric deformation pin recognition. The improved model achieved 97.2% and 94.4% accuracy for skewed and missing pins, respectively, in the experiments. The detection accuracy improved by 1.91% to 96.62% compared to the conventional ResNet-50. Compared with the traditional model, the improved model has better generalization ability. [ABSTRACT FROM AUTHOR]

Traumatic haemorrhage is a prevalent clinical condition, and effective and timely haemostasis is crucial for the preservation of patients' lives. In recent years, injectable hemostatic materials have gained significant attention due to their excellent hemostatic efficacy, biocompatibility, and biodegradability, making them widely applied in the treatment of incompressible traumatic haemorrhage. Systematic analysis of injectable hemostatic materials is crucial for research in this area. This article provides a comprehensive review of the development and research trends of injectable hemostatic materials over the past 20 years using visualization techniques. Analysis of collaboration and co‐citation networks revealed localized research collaboration networks, highlighting the need for enhanced international collaboration in the field of injectable hemostatic materials. Current research focuses primarily on hemostatic materials, hemostatic processes, and hemostatic mechanisms. Injectable hemostatic materials with excellent performance offer promising strategies for wound healing. This review provides a comprehensive and systematic summary of injectable hemostatic materials, offering valuable guidance for the development and clinical application of novel injectable hemostatic materials. Additionally, visualized methodology and mapping analysis are effective data mining methods that provide approaches and strategies for clear knowledge network analysis. These methods facilitate better understanding and interpretation of research dynamics in the field of injectable hemostatic materials, thereby guiding and inspiring future research. [ABSTRACT FROM AUTHOR]

Improving the sales of e-commerce platforms is the primary goal of this paper. This paper studies the data of e-commerce product recommendations from the perspective of user preference and association rules. The characteristics of positive and reverse association rules in data mining are analyzed. Then, a multi-dimension association rule calculation method is proposed. Create a data attribute unit set. By analyzing each attribute's weighted coefficient and similarity, the attribute confidence degree is obtained, and the data is preprocessed. An example is given to verify the effectiveness of the proposed method. The recommendation engine based on user preferences and association rules significantly improves the accuracy, recall rate and prediction coverage of e-commerce recommendation systems. [ABSTRACT FROM AUTHOR]

New discoveries and knowledge are summarized in thousands of published papers per year per scientific domain, making it incomprehensible for scientists to account for all available knowledge relevant for their studies. In this paper, we present ACCORDION (ACCelerating and Optimizing model RecommenDatIONs), a novel methodology and an expert system that retrieves and selects relevant knowledge from literature and databases to recommend models with correct structure and accurate behavior, enabling mechanistic explanations and predictions, and advancing understanding. ACCORDION introduces an approach that integrates knowledge retrieval, graph algorithms, clustering, simulation, and formal analysis. Here, we focus on biological systems, although the proposed methodology is applicable in other domains. We used ACCORDION in nine benchmark case studies and compared its performance with other previously published tools. We show that ACCORDION is: comprehensive, retrieving relevant knowledge from a range of literature sources through machine reading engines; very effective, reducing the error of the initial baseline model by more than 80%, recommending models that closely recapitulate desired behavior, and outperforming previously published tools; selective, recommending only the most relevant, contextspecific, and useful subset (15%-20%) of candidate knowledge in literature; diverse, accounting for several distinct criteria to recommend more than one solution, thus enabling alternative explanations or intervention directions. [ABSTRACT FROM AUTHOR]

With a significant number of mechanical products approaching the retirement phase, the batch recycling of discarded mechanical parts necessitates a preliminary assessment of their surface condition. However, the presence of surface rust poses a challenge to defect identification. Therefore, this paper proposes a method for detecting heavily rusted surface defects based on an improved YOLOv8n network. In the Backbone, the C2f-DBB module of re-parameterized deep feature extraction was introduced, and the attention module was designed to improve the accuracy of information extraction. In the Neck part, a Bi-Afpn multiscale feature fusion strategy is designed to facilitate information exchange between features at different scales. Finally, Focal-CIoU is employed as the bounding box loss function to enhance the network's localization performance and accuracy for defects. Experimentally, it is proved that the improved network in this paper improves the Recall, Precision, and mAP0.5 by 1.2%, 2.1%, and 1.9%, respectively, on the original basis, which is better than other network models. [ABSTRACT FROM AUTHOR]

The real-time operational safety of in-service bridges has received wide attention in recent years. By fully utilizing the health monitoring data of bridges, a structural abnormal pattern detection method based on data mining can be established to effectively ensure the safety of in-service bridges. This paper takes a large-span arch bridge as the research object, analyzes the time-based variation of the main monitoring data of the structure, establishes Lasso regression models for load characteristic indicators and vertical bending fundamental frequency of the structure under different time scales, and uses the residuals of the Lasso model to indicate the structural state and identify abnormal patterns. Firstly, the monitoring data of bridge structural temperature, girder end displacement, and girder acceleration were analyzed, and the interrelationships were studied to extract characteristic parameters of structural load characteristics and structural frequency. Then, the time-varying patterns of structural response were analyzed, and Lasso regression models and their regression variables were discussed based on monitoring data under two different time scales: daily cycle and annual cycle. The abnormal pattern detection method for bridge structures was developed. Finally, the effectiveness of this method was verified by taking the bridge deck pavement replacement as the abnormal pattern. The research results indicate that the proposed bridge structure abnormal pattern detection method based on Lasso regression can effectively monitor changes in the state of the bridge, and the residual dispersion of the model established on the annual cycle scale is relatively smaller than that on the daily cycle scale, resulting in better abnormal detection performance. [ABSTRACT FROM AUTHOR]

With the exponential growth of data, extracting actionable insights becomes resource-intensive. In many organizations, normalized relational databases store a significant portion of this data, where tables are interconnected through some relations. This paper explores relational learning, which involves joining and merging database tables, often normalized in the third normal form. The subsequent processing includes extracting features and utilizing them in machine learning (ML) models. In this paper, we experiment with the propositionalization algorithm (i.e., Wordification) for feature engineering. Next, we compare the algorithms PropDRM and PropStar, which are designed explicitly for multi-relational data mining, to traditional machine learning algorithms. Based on the performed experiments, we concluded that Gradient Boost, compared to PropDRM, achieves similar performance (F1 score, accuracy, and AUC) on multiple datasets. PropStar consistently underperformed on some datasets while being comparable to the other algorithms on others. In summary, the propositionalization algorithm for feature extraction makes it feasible to apply traditional ML algorithms for relational learning directly. In contrast, approaches tailored specifically for relational learning still face challenges in scalability, interpretability, and efficiency. These findings have a practical impact that can help speed up the adoption of machine learning in business contexts where data is stored in relational format without requiring domain-specific feature extraction. [ABSTRACT FROM AUTHOR]

Purpose: The expansion of research across various disciplines has led to a substantial increase in published papers and journals, highlighting the necessity for reliable text mining platforms for database construction and knowledge acquisition. This abstract introduces GPDMiner(Gene, Protein, and Disease Miner), a platform designed for the biomedical domain, addressing the challenges posed by the growing volume of academic papers. Methods: GPDMiner is a text mining platform that utilizes advanced information retrieval techniques. It operates by searching PubMed for specific queries, extracting and analyzing information relevant to the biomedical field. This system is designed to discern and illustrate relationships between biomedical entities obtained from automated information extraction. Results: The implementation of GPDMiner demonstrates its efficacy in navigating the extensive corpus of biomedical literature. It efficiently retrieves, extracts, and analyzes information, highlighting significant connections between genes, proteins, and diseases. The platform also allows users to save their analytical outcomes in various formats, including Excel and images. Conclusion: GPDMiner offers a notable additional functionality among the array of text mining tools available for the biomedical field. This tool presents an effective solution for researchers to navigate and extract relevant information from the vast unstructured texts found in biomedical literature, thereby providing distinctive capabilities that set it apart from existing methodologies. Its application is expected to greatly benefit researchers in this domain, enhancing their capacity for knowledge discovery and data management. [ABSTRACT FROM AUTHOR]

Rapid mapping demands efficient methods for a fast extraction of information from satellite data while minimizing data requirements. This paper explores the potential of deep learning for the generation of high-resolution urban elevation data from Synthetic Aperture Radar (SAR) imagery. In order to mitigate occlusion effects caused by the side-looking nature of SAR remote sensing, two SAR images from opposing aspects are leveraged and processed in an end-to-end deep neural network. The presented approach is the first of its kind to implicitly handle the transition from the SAR-specific slant range geometry to a ground-based mapping geometry within the model architecture. Comparative experiments demonstrate the superiority of the dual-aspect fusion over single-image methods in terms of reconstruction quality and geolocation accuracy. Notably, the model exhibits robust performance across diverse acquisition modes and geometries, showcasing its generalizability and suitability for height mapping applications. The study's findings underscore the potential of deep learning-driven SAR techniques in generating high-quality urban surface models efficiently and economically. [ABSTRACT FROM AUTHOR]

This paper uses data mining technology to dynamically monitor tobacco Industrial Enterprise' information systems. This paper builds an Internet security situation awareness system under a big data environment. The weight clustering method is used to classify users' network behavior. The spacing of weights is optimized to ensure the maximum difference in classification. Then, NAWL-ILSTM technology establishes a security situational awareness model for the Internet environment. In this project, the extended and short-memory Nadam optimal algorithm (NAWL) is used to realize data deep learning. Finally, the tobacco industry network security situation assessment method is designed to complete the dynamic monitoring of tobacco industry network security based on data mining. Simulation results show that the proposed method can effectively improve the safety evaluation performance of the system and reduce evaluation errors. [ABSTRACT FROM AUTHOR]

Emergent news is characterized by few labels, and news detection methods that rely on a large number of labels are difficult to apply to learned features for emerging events and are ineffective in coping with less labeled emergent news detection. To address the challenge of limited labeled data, this study first establishes a scenario for detecting breaking news, ensuring that the domain of detecting events is distinct from the domain of historical events. Secondly, we propose the Clip-GCN multimodal fake news detection model. The model utilizes the Clip pre-training model to perform joint semantic feature extraction of image-text information, with text information as the supervisory signal, which solves the problem of semantic interaction between modalities. Meanwhile, considering the domain attributes of news, the model is trained to extract inter-domain invariant features through Adversarial Neural Network ideation, and intra-domain knowledge information is utilized through graph convolutional networks (GCN) to detect emergent news. Through an extensive number of experiments on Chinese and English datasets from two major social media platforms, Weibo and Twitter, it is demonstrated that the model proposed in this paper can accurately screen multimodal emergent news on social media with an average accuracy of 88.7%. The contribution of this study lies not only in the improvement of model performance but also in the proposal of a solution for the challenges posed by limited labels and multimodal breaking news. This provides robust support for research in related fields. [ABSTRACT FROM AUTHOR]

The intelligent appearance quality classification method for Auricularia auricula is of great significance to promote this industry. This paper proposes an appearance quality classification method for Auricularia auricula based on the improved Faster Region-based Convolutional Neural Networks (improved Faster RCNN) framework. The original Faster RCNN is improved by establishing a multiscale feature fusion detection model to improve the accuracy and real-time performance of the model. The multiscale feature fusion detection model makes full use of shallow feature information to complete target detection. It fuses shallow features with rich detailed information with deep features rich in strong semantic information. Since the fusion algorithm directly uses the existing information of the feature extraction network, there is no additional calculation. The fused features contain more original detailed feature information. Therefore, the improved Faster RCNN can improve the final detection rate without sacrificing speed. By comparing with the original Faster RCNN model, the mean average precision (mAP) of the improved Faster RCNN is increased by 2.13%. The average precision (AP) of the first-level Auricularia auricula is almost unchanged at a high level. The AP of the second-level Auricularia auricula is increased by nearly 5%. And the third-level Auricularia auricula AP is increased by 1%. The improved Faster RCNN improves the frames per second from 6.81 of the original Faster RCNN to 13.5. Meanwhile, the influence of complex environment and image resolution on the Auricularia auricula detection is explored. [ABSTRACT FROM AUTHOR]

The geoscience knowledge graph (GeoKG) has gained worldwide attention due to its ability in the formal representation of spatiotemporal features and relationships of geoscience knowledge. Currently, a quantitative review of the state and trends in GeoKG is still scarce. Thus, a bibliometric analysis was performed in this study to fill the gap. Specifically, based on 294 research articles published from 2012 to 2023, we conducted analyses in terms of the (1) trends in publications and citations; (2) identification of the major papers, sources, researchers, institutions, and countries; (3) scientific collaboration analysis; and (4) detection of major research topics and tendencies. The results revealed that the interest in GeoKG research has rapidly increased after 2019 and is continually expanding. China is the most productive country in this field. Co-authorship analysis shows that inter-national and inter-institutional collaboration should be reinforced. Keyword analysis indicated that geoscience knowledge representation, information extraction, GeoKG construction, and GeoKG-based multi-source data integration were current hotspots. In addition, several important but currently neglected issues, such as the integration of Large Language Models, are highlighted. The findings of this review provide a systematic overview of the development of GeoKG and provide a valuable reference for future research. [ABSTRACT FROM AUTHOR]

The suddenness of landslide disasters often causes significant loss of life and property. Accurate assessment of landslide disaster susceptibility is of great significance in enhancing the ability of accurate disaster prevention. To address the problems of strong subjectivity in the selection of assessment indicators and low efficiency of the assessment process caused by the insufficient application of a priori knowledge in landslide susceptibility assessment, in this paper, we propose a novel landslide susceptibility assessment framework by combing domain knowledge graph and machine learning algorithms. Firstly, we combine unstructured data, extract priori knowledge based on the Unified Structure Generation for Universal Information Extraction Pre-trained model (UIE) fine-tuned with a small amount of labeled data to construct a landslide susceptibility knowledge graph. We use Paired Relation Vectors (PairRE) to characterize the knowledge graph, then construct a target area characterization factor recommendation model by calculating spatial correlation, attribute similarity, Term Frequency–Inverse Document Frequency (TF-IDF) metrics. We select the optimal model and optimal feature combination among six typical machine learning (ML) models to construct interpretable landslide disaster susceptibility assessment mapping. Experimental validation and analysis are carried out on the three gorges area (TGA), and the results show the effectiveness of the feature factors recommended by the knowledge graph characterization learning, with the overall accuracy of the model after adding associated disaster factors reaching 87.2%. The methodology proposed in this research is a better contribution to the knowledge and data-driven assessment of landslide disaster susceptibility. [ABSTRACT FROM AUTHOR]

The safety condition of concrete gravity dams is influenced by multiple factors, and assessing their safety solely based on a single factor is difficult to comprehensively evaluate. Therefore, this paper proposes a comprehensive modeling and analysis approach to assess dam safety by considering long-term temperature, stress, and strain monitoring data of actual concrete gravity dams. Firstly, the K-means clustering algorithm is utilized to classify the data. Then, the study area of the dam is meshed and three indicator evaluation values for all the elements are calculated. The other elements' evaluation values can be obtained by the Inverse Distance Weighting (IDW) method. Finally, the analytic hierarchy process extended by the D numbers preference relation (D-AHP) method is applied to compute the weights of temperature, stress, and strain and evaluate the dam's safety comprehensively. The effectiveness of this method is validated through application to specific engineering cases. The results demonstrate that compared to assessing methods considering only single factors, the comprehensive evaluation method proposed in this paper can more comprehensively and accurately reflect the actual safety condition of concrete gravity dams, providing important references for engineering decision-making. [ABSTRACT FROM AUTHOR]

To reveal complex causes of aircraft events, this paper aims to mine association rules between the trigger probability and relative strength via a modified Apriori algorithm. Clustering is adopted for data preprocessing and TF–IDF value calculation. Causative item sets of aircraft events are obtained based on the accident causation 2–4 model and are coded to establish code indicators. By avoiding the use of statistical methodologies to resolve not-a-number (NaN) values for altering the interrelations among causes, an enhancement in the Apriori algorithm is proposed by considering frequent items. By extracting frequent patterns, in this paper, all the association rules that satisfy three perspectives (support, confidence and lift) are determined by constantly generating and pruning candidate item sets. A network graph is used to visualize the association rules between different unsafe events and all types of causes. Finally, 9835 representative pieces of data, including general unsafe events, general incidents and serious incidents from the Southwest Air Traffic Management Bureau, are selected for analysis. The results show that improper energy allocation, poor conflict resolution ability, inadequate onsite management duties, adoption of a luck mentality, and occurrence of controller oversight are highly correlated with general unsafe events, and failure to rectify incorrect recitation is notably correlated with general incidents, while inadequate manual promotion, lack of conflict judgement and insufficient safety management are strongly correlated with serious incidents. This study quantitatively reveals the potential patterns and characteristics of mutual interactions among various types of historical aircraft events and highlights directions for controllable prevention and prediction of aircraft events. [ABSTRACT FROM AUTHOR]

The evolution of knowledge acquisition and representation in manufacturing technologies is vital for translating complex manufacturing data into actionable insights and advancing a comprehensive knowledge framework. This framework is pivotal in driving innovation and efficiency in intelligent manufacturing. This review aggregates recent research on knowledge acquisition and representation within the manufacturing process, addressing existing challenges and mapping potential future developments. It includes an analysis of 123 papers that focus on harnessing advanced intelligent analytics to extract operationally relevant knowledge from the extensive datasets typical in manufacturing environments. The narrative then examines the methodologies for constructing models of knowledge in manufacturing processes and explores their applications in manufacturing principles, design, management, and decision-making. This paper highlights the limitations of current technologies and projects emerging research avenues in the acquisition and representation of process knowledge within intelligent manufacturing systems, with the objective of informing future technological breakthroughs. [ABSTRACT FROM AUTHOR]

Neighborhood rough sets, as an effective tool for processing numerical data, is widely used in many fields, such as data mining, machine learning and decision-making system. However, most of the existing neighborhood rough set-based attribute reduction algorithms have low efficiency. To address the limitation, this paper has proposed an efficient positive region search algorithm based on multiple hash buckets and multiple granularity mechanisms. This algorithm achieves a more accurate neighborhood extent by superimposing the effects of multiple hash buckets, and accelerates positive region searching through the idea of multiple granularity. In addition, on the foundation the positive region search algorithm, we improved the existing algorithm and proposed an attribute reduction algorithm based on multi-hash bucket and multi-granularity. To further remove the redundant attributes, the two algorithms mentioned above are applied into a novel attribute reduction approach based on random walk. Experiments conducted on UCI datasets show that our attribute reduction algorithm has high efficiency. Moreover, attribute reduction approach we proposed can further compress the reduced attribute set, and the results maintain similar or even better classification accuracy. [ABSTRACT FROM AUTHOR]

In recent years, the field of bionics has attracted the attention of numerous scholars. Some models combined with biological vision have achieved excellent performance in computer vision and image processing tasks. In this paper, we propose a new bio-inspired lightweight contour detection network (BLCDNet) by combining parallel processing mechanisms of bio-visual information with convolutional neural networks. The backbone network of BLCDNet simulates the parallel pathways of ganglion cell–lateral geniculate nucleus and primary visual cortex (V1) area, realizing parallel processing and step-by-step extraction of input information, effectively extracting local features and detailed features in images, and thus improving the overall performance of the model. In addition, we design a depth feature extraction module combining depth separable convolution and residual connection in the decoding network to integrate the output of the backbone network, which further improves the performance of the model. We conducted a large number of experiments on BSDS500 and NYUD datasets, and the experimental results show that the BLCDNet proposed in this paper achieves the best performance compared with traditional methods and previous biologically inspired contour detection methods. In addition, BLCDNet still outperforms some VGG-based contour detection methods without pre-training and with fewer parameters, and it is competitive among all of them. The research in this paper also provides a new idea for the combination of biological vision and convolutional neural networks. [ABSTRACT FROM AUTHOR]

To achieve real‐time and effective prediction of industrial site risks, this paper proposes an industrial risk prediction framework for multimodal data based on edge computing. First, the authors gather and annotate industrial risk multimodal data that consist of text descriptions, images, and videos. Then, the authors transfer the data to the edge server, and apply deep learning models such as Bidirectional Encoder Representations from Transformers (BERT), ResNet etc., to extract features and learn representations for text, image, and video data respectively. The authors input the fused feature data into an enhanced long short term memory (LSTM) model and train it on the dataset. Finally, the authors perform the risk prediction based on the collected multimodal data. The experimental results demonstrate that the method proposed in this paper exhibits superior performance, achieving a 1.4% enhancement in predictive accuracy. [ABSTRACT FROM AUTHOR]

Efficient document recognition and sharing remain challenges in the healthcare, insurance, and finance sectors. One solution to this problem has been the use of deep learning techniques to automatically extract structured information from paper documents. Specifically, the structured extraction of a medical examination report (MER) can enhance medical efficiency, data analysis, and scientific research. While current methods focus on reconstructing table bodies, they often overlook table headers, leading to incomplete information extraction. This paper proposes MSIE-Net (multi-stage-structured information extraction network), a novel structured information extraction method, leveraging refined attention transformers and associated entity detection to enhance comprehensive MER information retrieval. MSIE-Net includes three stages. First, the RVI-LayoutXLM (refined visual-feature independent LayoutXLM) targets key information extraction. In this stage, the refined attention accentuates the interaction between different modalities by adjusting the attention score at the current position using previous position information. This design enables the RVI-LayoutXLM to learn more specific contextual information to improve extraction performance. Next, the associated entity detection module, RIFD-Net (relevant intra-layer fine-tuned detection network), identifies each test item's location within the MER table body. Significantly, the backbone of RIFD-Net incorporates the intra-layer feature adjustment module (IFAM) to extract global features while homing in on local areas, proving especially sensitive for inspection tasks with dense and long bins. Finally, structured post-processing based on coordinate aggregation links the outputs from the prior stages. For the evaluation, we constructed the Chinese medical examination report dataset (CMERD), based on real medical scenarios. MSIE-Net demonstrated competitive performance in tasks involving key information extraction and associated entity detection. Experimental results validate MSIE-Net's capability to successfully detect key entities in MER and table images with various complex layouts, perform entity relation extraction, and generate structured labels, laying the groundwork for intelligent medical documentation. [ABSTRACT FROM AUTHOR]

With the vigorous development of data mining field, more and more algorithms have been proposed or improved. How to quickly select a data mining algorithm that is suitable for data sets in medical field is a challenge for some medical workers. The purpose of this paper is to study the comparative characteristics of the general medical data set and the general data sets in other fields, and find the applicability rules of the data mining algorithm suitable for the characteristics of the current research data set. The study quantified characteristics of the research data set with 26 indicators, including simple indicators, statistical indicators and information theory indicators. Eight machine learning algorithms with high maturity, low user involvement and strong family representation were selected as the base algorithms. The algorithm performances were evaluated by three aspects: prediction accuracy, running speed and memory consumption. By constructing decision tree and stepwise regression model to learn the above metadata, the algorithm applicability knowledge of medical data set is obtained. Through cross-verification, the accuracy of all the algorithm applicability prediction models is above 75%, which proves the validity and feasibility of the applicability knowledge. [ABSTRACT FROM AUTHOR]

During a heavy traffic flow featuring a substantial number of vehicles, the data reflecting the strain response of asphalt pavement under the vehicle load exhibit notable fluctuations with abnormal values, which can be attributed to the complex operating environment. Thus, there is a need to create a real-time anomalous-data diagnosis system which could effectively extract dynamic strain features, such as peak values and peak separation from the large amount of data. This paper presents a dynamic response signal data analysis method that utilizes the DBSCAN clustering algorithm and the findpeaks function. This method is designed to analyze data collected by sensors installed within the pavement. The first step involves denoising the data using low-pass filters and other techniques. Subsequently, the DBSCAN algorithm, which has been improved using the K-Dist method, is used to diagnose abnormal data after denoising. The refined findpeaks function is further implemented to carry out the adaptive feature extraction of the denoised data which is free from anomalies. The enhanced DBSCAN algorithm is tested via simulation and illustrates its effectiveness while detecting abnormal data in the road dynamic response signal. The findpeaks function enables the relatively accurate identification of peak values, thus leading to the identification of strain signal peaks of complex multi-axle lorries. This study is valuable for efficient data processing and effective information utilization in pavement monitoring. [ABSTRACT FROM AUTHOR]

Two-wheeled non-motorized vehicles (TNVs) have become the primary mode of transportation for short-distance travel among residents in many underdeveloped cities in China due to their convenience and low cost. However, this trend also brings corresponding risks of traffic accidents. Therefore, it is necessary to analyze the driving behavior characteristics of TNVs through their trajectory data in order to provide guidance for traffic safety. Nevertheless, the compact size, agile steering, and high maneuverability of these TNVs pose substantial challenges in acquiring high-precision trajectories. These characteristics complicate the tracking and analysis processes essential for understanding their movement patterns. To tackle this challenge, we propose an enhanced You Only Look Once Version X (YOLOx) model, which incorporates a median pooling-Convolutional Block Attention Mechanism (M-CBAM). This model is specifically designed for the detection of TNVs, and aims to improve accuracy and efficiency in trajectory tracking. Furthermore, based on this enhanced YOLOx model, we have developed a micro-trajectory data mining framework specifically for TNVs. Initially, the paper establishes an aerial dataset dedicated to the detection of TNVs, which then serves as a foundational resource for training the detection model. Subsequently, an augmentation of the Convolutional Block Attention Mechanism (CBAM) is introduced, integrating median pooling to amplify the model's feature extraction capabilities. Subsequently, additional detection heads are integrated into the YOLOx model to elevate the detection rate of small-scale targets, particularly focusing on TNVs. Concurrently, the Deep Sort algorithm is utilized for the precise tracking of vehicle targets. The process culminates with the reconstruction of trajectories, which is achieved through a combination of video stabilization, coordinate mapping, and filtering denoising techniques. The experimental results derived from our self-constructed dataset reveal that the enhanced YOLOx model demonstrates superior detection performance in comparison to other analogous methods. The comprehensive framework accomplishes an average trajectory recall rate of 85% across three test videos. This significant achievement provides a reliable method for data acquisition, which is essential for investigating the micro-level operational mechanisms of TNVs. The results of this study can further contribute to the understanding and improvement of traffic safety on mixed-use roads. [ABSTRACT FROM AUTHOR]

Mangroves are ecosystems that grow in the intertidal areas of coastal zones, playing crucial ecological roles and possessing unique economic and social values. They have garnered significant attention and research interest. Semantic segmentation of mangroves is a fundamental step for further investigations. However, mangrove remote sensing images often have large dimensions, with a substantial portion of the image containing mangrove features. Deep learning convolutional kernels may lead to inadequate receptive fields for accurate mangrove recognition. In mangrove remote sensing images, various challenges arise, including the presence of small and intricate details aside from the mangrove regions, which intensify the segmentation complexity. To address these issues, this paper primarily focuses on two key aspects: first, the exploration of methods to achieve a large receptive field, and second, the fusion of multi-scale information. To this end, we propose the Multi-Scale Fusion Attention Network (MSFANet), which incorporates a multi-scale network structure with a large receptive field for feature fusion. We emphasize preserving spatial information by integrating spatial data across different scales, employing separable convolutions to reduce computational complexity. Additionally, we introduce an Attention Fusion Module (AFM). This module helps mitigate the influence of irrelevant information and enhances segmentation quality. To retain more semantic information, this paper introduces a dual channel approach for information extraction through the deep structure of ResNet. We fuse features using the Feature Fusion Module (FFM) to combine both semantic and spatial information for the final output, further enhancing segmentation accuracy. In this study, a total of 230 images with dimensions of 768 pixels in width and height were selected for this experiment, with 184 images used for training and 46 images for validation. Experimental results demonstrate that our proposed method achieves excellent segmentation results on a small sample dataset of remote-sensing images, with significant practical value. This paper primarily focuses on three key aspects: the generation of mangrove datasets, the preprocessing of mangrove data, and the design and training of models. The primary contribution of this paper lies in the development of an effective approach for multi-scale information fusion and advanced feature preservation, providing a novel solution for mangrove remote sensing image segmentation tasks. The best Mean Intersection over Union (MIoU) achieved on the mangrove dataset is 86%, surpassing other existing models by a significant margin. [ABSTRACT FROM AUTHOR]

The precise detection of small lesions on grape leaves is beneficial for early detection of diseases. In response to the high missed detection rate of small target diseases on grape leaves, this paper adds a new prediction branch and combines an improved channel attention mechanism and an improved E-ELAN (Extended-Efficient Long-range Attention Network) to propose an improved algorithm for the YOLOv7 (You Only Look Once version 7) model. Firstly, to address the issue of low resolution for small targets, a new detection head is added to detect smaller targets. Secondly, in order to increase the feature extraction ability of E-ELAN components in YOLOv7 for small targets, the asymmetric convolution is introduced into E-ELAN to replace the original 3 × 3 convolution in E-ELAN network to achieve multi-scale feature extraction. Then, to address the issue of insufficient extraction of information from small targets in YOLOv7, a channel attention mechanism was introduced and improved to enhance the network's sensitivity to small-scale targets. Finally, the CIoU (Complete Intersection over Union) in the original YOLOv7 network model was replaced with SIoU (Structured Intersection over Union) to optimize the loss function and enhance the network's localization ability. In order to verify the effectiveness of the improved YOLOv7 algorithm, three common grape leaf diseases were selected as detection objects to create a dataset for experiments. The results show that the average accuracy of the algorithm proposed in this paper is 2.7% higher than the original YOLOv7 algorithm, reaching 93.5%. [ABSTRACT FROM AUTHOR]

The customers of a sports club are among the important pillars of its survival. In this paper, with the help of data mining and machine learning methods, a framework is presented to predict the level of effectiveness of salespersons' performance to encourage customers of clubs to choose an appropriate shopping strategy. This framework uses a set of data on the topics of idealized influence behavior, inspirational motivation behavior, intellectual stimulation behavior, individualized consideration behavior, and smart selling behavior, as its inputs. In the proposed framework, first, the data is refined using the Pearson criterion, and invaluable questions/features are removed from the data set. There are five levels of effectiveness in our questionnaire, and each of them has a different number of records in the data set. So, in the second step, the data set is balanced using repetition, SMOTE, and Int-SMOTE methods. The Int-SMOTE balancing method is introduced in this paper for the first time. It is a SMOTE method with integer outputs. Finally, using different classifiers, we predict the level of effectiveness of salesperson's behaviors in encouraging customers. Evaluating the models indicates that the different models have been able to correctly identify the level of effectiveness of salesperson's behaviors between 76.16% to 96.82%. Also, we confirm our findings about the effects of different salesperson's behavior to encourage customers using several other published papers. [ABSTRACT FROM AUTHOR]

A novel approach is presented in this paper to address the limitations of virtual machine technology, active kernel technology, heuristic killing technology, and behaviour killing technology in computer network virus defence. The proposed method provides data mining technology, specifically Object-Oriented Analysis (OOA) mining, to detect deformed and unknown viruses by analyzing the sequence of Win API calls in PE files. Experimental results showcase the Data Mining-based Antivirus (DMAV) system's superiority over existing virus scanning software in multiple aspects: higher accuracy in deformed virus detection, enhanced active defence capabilities against unknown viruses (with a recognition rate of 92%), improved efficiency, and a reduced false alarm rate for non-virus file detection. Furthermore, the paper introduces an OOA rule generator to optimize feature extraction, enhancing the system's intelligence and robustness. This research provides a promising solution to support virus detection accuracy, active defence mechanisms, and overall efficiency while minimizing false positives in virus scanning, thus contributing significantly to the advancement of computer network security. [ABSTRACT FROM AUTHOR]

Background: A huge amount of research is carried out nowadays in Artificial Intelligence to propose automated ways to analyse medical data with the aim to support doctors in delivering medical diagnoses. However, a main issue of these approaches is the lack of transparency and interpretability of the achieved results, making it hard to employ such methods for educational purposes. It is therefore necessary to develop new frameworks to enhance explainability in these solutions. Results: In this paper, we present a novel full pipeline to generate automatically natural language explanations for medical diagnoses. The proposed solution starts from a clinical case description associated with a list of correct and incorrect diagnoses and, through the extraction of the relevant symptoms and findings, enriches the information contained in the description with verified medical knowledge from an ontology. Finally, the system returns a pattern-based explanation in natural language which elucidates why the correct (incorrect) diagnosis is the correct (incorrect) one. The main contribution of the paper is twofold: first, we propose two novel linguistic resources for the medical domain (i.e, a dataset of 314 clinical cases annotated with the medical entities from UMLS, and a database of biological boundaries for common findings), and second, a full Information Extraction pipeline to extract symptoms and findings from the clinical cases and match them with the terms in a medical ontology and to the biological boundaries. An extensive evaluation of the proposed approach shows the our method outperforms comparable approaches. Conclusions: Our goal is to offer AI-assisted educational support framework to form clinical residents to formulate sound and exhaustive explanations for their diagnoses to patients. [ABSTRACT FROM AUTHOR]

The paper reports findings from a study that examining how cross-platform social media analysis can help to map the digital discourse on sustainable mobility and sustainable transport, and enhance the understanding of sociotechnical low-carbon transport transitions. Using the hashtag search queries #sustainabletransport and #sustainablemobility, 33,121 Tweets (2013–2021) and 8,089 Instagram images including captions (2017/2018–2021) were scraped using the Python modules Twint and Instaloader. Quantitative text and sentiment analyses were applied to the Tweets and image captions. Additionally, an automated machine learning-based image analysis of the Instagram images was conducted using object detection via OpenCV. Synthesized results formed the base for a cross-platform analysis inspired by Rogers' method comprising hot topics/key themes, user mentions, sentiment polarity, and co-hashtags. Notably, electromobility emerged as a prominent theme, particularly on Instagram, while #sustainabletransport was closely associated with active travel, notably bicycling, and #sustainablemobility showcased a dominance of electromobility discourse. The study demonstrates the investigative potentials of cross-platform social media analysis studies to enhance the understanding of sociotechnical low-carbon transport transitions. Drawing on key results, the paper suggests an adapted version of the Geelsean Multi-Level Perspective on Sociotechnical Transitions. [ABSTRACT FROM AUTHOR]

Background: Emotion is a complex phenomenon that greatly affects human behavior and thinking in daily life. Electroencephalography (EEG), one of the human physiological signals, has been emphasized by most researchers in emotion recognition as its specific properties are closely associated with human emotion. However, the number of human emotion recognition studies using computer games as stimuli is still insufficient as there were no relevant publicly available datasets provided in the past decades. Most of the recent studies using the Gameemo public dataset have not clarified the relationship between the EEG signal's changes and the emotion elicited using computer games. Thus, this paper is proposed to introduce the use of data mining techniques in investigating the relationships between the frequency changes of EEG signals and the human emotion elicited when playing different kinds of computer games. The data acquisition stage, data pre-processing, data annotation and feature extraction stage were designed and conducted in this paper to obtain and extract the EEG features from the Gameemo dataset. The cross-subject and subject-based experiments were conducted to evaluate the classifiers' performance. The top 10 association rules generated by the RCAR classifier will be examined to determine the possible relationship between the EEG signal's frequency changes and game-induced emotions. Results: The RCAR classifier constructed for cross-subject experiment achieved highest accuracy, precision, recall and F1-score evaluated with over 90% in classifying the HAPV, HANV and LANV game-induced emotions. The 20 experiment cases' results from subject-based experiments supported that the SVM classifier could accurately classify the 4 emotion states with a kappa value over 0.62, demonstrating the SVM-based algorithm's capabilities in precisely determining the emotion label for each participant's EEG features' instance. Conclusion: The findings in this study fill the existing gap of game-induced emotion recognition field by providing an in-depth evaluation on the ruleset algorithm's performance and feasibility of applying the generated rules on the game-induced EEG data for justifying the emotional state prediction result. [ABSTRACT FROM AUTHOR]

In view of the problems in planning and recommending tour routes, this paper constructs a feature text mining (FTM) method and spatial accessibility model (SAM) as the key factors for scenic spot recommendation (SSR) and tour route recommendation (TRR). The scenic spot clustering algorithm (SSCA) based on FTM was constructed by tourists' text evaluation data mining. Considering the spatial attributes of scenic spots, the scenic spot topology tree algorithm (SSTTA) based on dynamic buffer spatial accessibility (DBSA) was constructed. The optimal scenic spots were recommended based on interest matching and spatial accessibility optimization. As to the recommended scenic spots, this paper proposes an optimal tour route recommendation algorithm (TRRA) based on SSTTA, which aims to determine the optimal adjacent section path structure tree (ASPST) with the lowest cost under travel constraints and transportation modes. The experiment verifies that the proposed algorithm can recommend scenic spots that match tourists' interests and have optimal spatial accessibility, and the optimal tour routes with the lowest costs under certain travel constraints. Compared with the searched sub-optimal tour routes, the optimal tour route recommended by the proposed algorithm produces the lowest travel costs, and all the scenic spots in the tour route meet the tourists' interests. Compared with the commonly used BDMA and GDMA methods, the proposed algorithm can determine the optimal routes with lower travel costs. [ABSTRACT FROM AUTHOR]