Information Extraction Research Articles

Extraction of molecular information from experimental data on liquids using manifold learning

This study proposes that microscopic information can be extracted from experimental data on liquid substances using a manifold learning technique. Here, 98 liquid substances could be classified based on their functional groups by applying the isomap method, a manifold learning technique, to nine thermal properties of the substances. Moreover, information related to their intermolecular interactions, which could be divided into the contributions of van der Waals and polar interactions, molecular weight, and number of carbon atoms could be extracted by the proposed method. This study could guide future research on the utilization of manifold learning techniques in liquid-substance characterization and development.

Enhancing the performance of a resonance-based sensor network for soft robots using binary excitation.

Embedded, flexible, multi-sensor sensing networks have shown the potential to provide soft robots with reliable feedback while navigating unstructured environments. Time delay associated with extracting information from these sensing networks and the complexity of constructing them are significant obstacles to their development. This paper presents a novel enhancement to an existing class of embedded sensor network with the potential to overcome these challenges. In its original version, this sensor network extracts information from multiple reactive sensors on a two-wire electrical circuit simultaneously. This paper proposes to change the excitation signal applied to this sensor network to a binary signal. This change offers two key advantages: it provides the ability to employ small, inexpensive microcontrollers and results in a faster data extraction process. The potential of this enhanced system is demonstrated here with a proof of concept implementation. The self-inductance of all inductance-based sensors within this proof of concept sensor network can be measured at a rate of over 5000 times per second with an average measurement error of less than 2%.

Cardio-pulmonary exercise testing in adult congenital heart patients: a new data-focused approach to predict mortality

Abstract Background/Introduction The cardio-pulmonary exercise test (CPET) has been a pivotal tool for functional and prognostic evaluation in adults with congenital heart disease (ACHD). During a routine CPET test, a myriad of variables are collected reflecting the cardiovascular, pulmonary, and skeletal muscle systems. However, clinicians typically use only a few easily and accessible variables to establish prognosis, which can come at an accuracy cost. Machine learning algorithms have undergone significant development in recent years and have found applications in the medical field. One of their main advantages is the ability to handle a large number of variables and extract information from their various combinations, while excluding redundant data, thus offering a higher level of prediction accuracy. Purpose Our objective was to develop a machine learning model to predict death during follow-up in a large population of ACHD patients who have undergone routine CPET in a large-volume single specialist centre. Methods All available CPET studies for adult ACHD patients (age > 15 years) performed from December 1999 to December 2021 at our centre were included, and all standard available variables were extracted. The primary outcome was all-cause mortality since the CPET, collected up to November 2023. Data exploration, data cleaning, and feature engineering steps were conducted using Python (v3.8). Continuous variables were standardised. The supervised machine learning algorithm XGBoost was used for classification (all-cause mortality), with the best hyperparameters selected through cross-validation. For the final model, a feature importance analysis based on permutation techniques was used to explore the variables with more impact on model accuracy. Results A total of 6361 studies were included, with median age of 31 years (IQR 23-43), 56% male. During follow-up, a total of 491 deaths were recorded. Of available demographic, clinical and exercise parameters (n=129 variables), 21 were deemed sufficient in terms of completeness, absence of significant correlation and clinically relevant for the subsequent analysis. These included demographic variables (age and sex) and CPET variables (peak VO2, FEV1, peak heart rate, exercise time, etc.) and were used to train the model. The accuracy of the model in predicting death after CPET in the test set had an accuracy of 93.5% and an area under the ROC curve (AUC) of 85.0% (Figure1). The parameters with the highest relative feature importance were FEV1, body mass index and percent predicted peak VO2 (Figure 2). Conclusions Machine learning models can be effectively employed in specialised cardiac populations, such as ACHD, potentially providing a high level of prediction accuracy by efficiently harnessing the potential of available data. However, its applicability to mortality prediction for ACHD patients requires further external validation.ROC curve AUC for the model predictionsFeature importance using permutation

Machine Learning powered Patient Records Analysis for Injury Monitoring in Children and Adolescents

Abstract Background Injuries to children and adolescents are common and are associated with individual suffering and costs. Switzerland lacks data of injuries in children and adolescents. This study investigates the feasibility of utilizing machine learning (ML) methodology to automatically extract relevant data from electronic pediatric patient records needed for monitoring and prevention purposes. Methods The feasibility study follows three steps. 1. Utility evaluation of text data from electronic pediatric patient records regarding ML, injury monitoring and prevention requirements in a test-sample. 2. The development of an ML-based approach to extract relevant information based on electronic pediatric patient records (N = 30’884) of children treated for injuries at the University Children’s Hospital Zurich emergency department between 2018-2022. 3. The performance of the ML methodology is evaluated. Results Qualitative and ML expert assessment of the sample showed that the desired data on injuries is contained in the test-sample and necessary requirements are met. Data records differ in detail with severe injuries providing especially rich information records. The international IDB code-tree was proved to be too elaborate and adult-orientated and was adapted. Preliminary data analyses indicate that the number of accidents is highest in the age group from 0 to 4 years (36%). More male (58%) children were treated. Majority of accidents were classified as less severe (73%), few (<1%) as very severe. Further lessons learned, along with more detailed prevalence data on injuries in children and adolescents, will be available by August 2024. Conclusions Extraction of injury information from electronic patient record by means of ML may provide valuable information for injury monitoring and prevention, so far missing in Switzerland. Prerequisites for the successful application of ML are digital record access, usable coding-tree, and detailed text input by the emergency departments. Key messages • An ML-based approach has the potential to improve the data basis by extracting valuable accidents information from existing texts of patient records. • To successfully apply this method, digital record access and detailed text data are needed.

Study on the Method of Vineyard Information Extraction Based on Spectral and Texture Features of GF-6 Satellite Imagery

Accurately identifying the distribution of vineyard cultivation is of great significance for the development of the grape industry and the optimization of planting structures. Traditional remote sensing techniques for vineyard identification primarily depend on machine learning algorithms based on spectral features. However, the spectral reflectance similarities between grapevines and other orchard vegetation lead to persistent misclassification and omission errors across various machine learning algorithms. As a perennial vine plant, grapes are cultivated using trellis systems, displaying regular row spacing and distinctive strip-like texture patterns in high-resolution satellite imagery. This study selected the main oasis area of Turpan City in Xinjiang, China, as the research area. First, this study extracted both spectral and texture features based on GF-6 satellite imagery, subsequently employing the Boruta algorithm to discern the relative significance of these remote sensing features. Then, this study constructed vineyard information extraction models by integrating spectral and texture features, using machine learning algorithms including Naive Bayes (NB), Support Vector Machines (SVMs), and Random Forests (RFs). The efficacy of various machine learning algorithms and remote sensing features in extracting vineyard information was subsequently evaluated and compared. The results indicate that three spectral features and five texture features under a 7 × 7 window have significant sensitivity to vineyard recognition. These spectral features include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Water Index (NDWI), while texture features include contrast statistics in the near-infrared band (B4_CO) and the variance statistic, contrast statistic, heterogeneity statistic, and correlation statistic derived from NDVI images (NDVI_VA, NDVI_CO, NDVI_DI, and NDVI_COR). The RF algorithm significantly outperforms both the NB and SVM models in extracting vineyard information, boasting an impressive accuracy of 93.89% and a Kappa coefficient of 0.89. This marks a 12.25% increase in accuracy and a 0.11 increment in the Kappa coefficient over the NB model, as well as an 8.02% enhancement in accuracy and a 0.06 rise in the Kappa coefficient compared to the SVM model. Moreover, the RF model, which amalgamates spectral and texture features, exhibits a notable 13.59% increase in accuracy versus the spectral-only model and a 14.92% improvement over the texture-only model. This underscores the efficacy of the RF model in harnessing the spectral and textural attributes of GF-6 imagery for the precise extraction of vineyard data, offering valuable theoretical and methodological insights for future vineyard identification and information retrieval efforts.

The acoustic complexity index (ACI): theoretical foundations, applied perspectives and semantics

The acoustic complexity index (ACI) is a commonly used metric in ecoacoustics, demonstrating reliability across diverse environments and ecological conditions. However, this index requires specific procedures to be applied correctly. Based on the Canberra metric, the ACI is an unsupervised metric formulated to extract information from fast Fourier transform (FFT) sonic matrices. The ACI measures contiguous differences in acoustic energy of each frequency bin along temporal steps (ACItf) and a temporal interval along the frequency bins (ACIft). Aggregating data after an FFT with a clumping procedure allows for better scaling of sonic signals before computing the ACI. A filter must be applied to reduce the effects of nonenvironmental signals produced by microphone electrical noise . Due to the singularity of the index for values of 0, ACI requires ad hoc procedures to exclude element pairs for which one of the elements is equal to 0 from the comparisons. The spectral and temporal sonic signatures are vectors obtained from the sequence of ACItf and ACIft values, respectively. The comparison between sonic signatures using the chord distance index returns spectral and temporal sonic dissimilarities, allowing the evaluation of sonic patterns at different temporal and spatial resolutions. Sonic variability, sonic evenness, and the effective number of frequency bins are further derivative metrics that help interpret sonic heterogeneity by distinguishing the temporal and spatial heterogeneity of sonoscapes. Moreover, this paper proposes changing the terminology of ‘acoustic complexity index' to ‘sonic heterogeneity index.'

Disruption Dynamics and Charge Transfer of a Single Attoliter Emulsion Droplet Revealed by Combined Fast-Scan Sinusoidal Voltammetry and Short Time Fourier Transform Analysis.

Single-entity electrochemistry has gained significant attention for the analysis of individual cells, nanoparticles, and droplets, which is leveraged by robust electrochemical techniques such as chronoamperometry and cyclic voltammetry (CV) to extract information about single entities, including size, kinetics, mass transport, etc. For an in-depth understanding such as dynamic interaction between the electrode and a single entity, the unconventional fast electrochemical technique is essential for time-resolved analysis. This fast experimental technique is unfortunately hindered by a substantial nonfaradaic response. In this work, we introduce fast-scan sinusoidal voltammetry (FSSV) combined with a short-time Fourier transform (STFT) for analyzing single emulsion droplets. Utilizing ultramicroelectrode and fast potential sweeps up to apparent 200 V/s, we achieved high temporal resolution (8 ms per voltammogram) to capture the current signals during droplet collisions. STFT analysis reveals the amplitude and phase changes, allowing for the accurate detection of collision events even in the absence of redox species. By adopting an algorithm of drift-free baseline subtraction, a conventional CV shape was obtained in FSSV. The reacted charge from the single-entity voltammogram at every 8 ms was also plotted. This method effectively addresses limitations in traditional techniques, providing insights into emulsion dynamics such as droplet contact and droplet breakdown.

Linguistic Secret Sharing via Ambiguous Token Selection for IoT Security

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, including weak authentication, insufficient data protection, and firmware vulnerabilities. To address these issues, we propose a linguistic secret sharing scheme tailored for IoT applications. This scheme leverages neural networks to embed private data within texts transmitted by IoT devices, using an ambiguous token selection algorithm that maintains the textual integrity of the cover messages. Our approach eliminates the need to share additional information for accurate data extraction while also enhancing security through a secret sharing mechanism. Experimental results demonstrate that the proposed scheme achieves approximately 50% accuracy in detecting steganographic text across two steganalysis networks. Additionally, the generated steganographic text preserves the semantic information of the cover text, evidenced by a BERT score of 0.948. This indicates that the proposed scheme performs well in terms of security.

Words of Wisdom: A Journey through the Realm of NLP for Learning Analytics - A Systematic Literature Review

Learning Analytics (LA) is one of the world’s most influential research fields related to educational technology. Among many themes that the LA community considers, the application of Natural Language Processing (NLP) algorithms has been largely adopted to extract information from textual data generated in learning environments (e.g., student essays and short answers, online discussion and chat). NLP can shed light on the learning process and student outcomes in different contexts. Based on the importance of NLP for education, this paper conducted a systematic literature review of the application of NLP to understand how the LA community has been applying this method. Our methodology includes automatic and manual methods to extract information about authors, relevant papers, and specific data related to educational applications and algorithms used in the field. This review selected 156 papers that reveal essential aspects of the topic, such as: (i) the majority of the works focused on the analysis of online discussions and essay assessment; (ii) in general, the authors did not apply the developed models in real settings; (iii) recent papers selected start to evaluate deep learning models (e.g., BERT) more frequently; (iv) the datasets used in the experimentation are usually small and containing English text; (v) the average models performance reaches 0.54 and 0.79 of Cohen’s Kappa and Accuracy, respectively. The results of this study and its practical implications are further discussed.

Enhancing Multimodal Emotional Information Extraction in Film and Television through Adaptive Feature Fusion with DenseNe, Transformer, and 3D CNN Models

ABSTRACT The extraction of multimodal emotional information enables a more nuanced representation of the emotional subtleties embedded in film and television works. However, conventional approaches that independently extract features from images and text fail to capture the rich semantic interplay between these modalities, impeding the feature learning process within each modality. To address this, this paper presents an innovative model for extracting emotional information from multimodal film and television content. The model utilizes DenseNe for image feature extraction, enhancing network depth via the MSC module. Text feature extraction is achieved through a Transformer encoder, while video feature extraction employs a 3D CNN model. Refinements are made to the number and placement of convolutional layers, planar convolution size, and 3D convolution depth. Moreover, a multi-head scaled dot-product attention mechanism is incorporated into the interaction module to compute the similarity between each image block within the sequence and every word in the text sequence. Experimental evaluations on the CMU-MOSEI and CMU-MOSI datasets showcase superior performance compared to the baseline model, achieving accuracy and F1 scores of 0.708 and 0.698, respectively. Noteworthy is the proposed adaptive feature fusion module, which enriches the expressiveness of pivotal emotional features while eliminating redundant data.

A Heuristic Attribute-Reduction Algorithm Based on Conditional Entropy for Incomplete Information Systems

With the continuous expansion of databases, the extraction of information has been an urgent research topic in many fields. As an effective method to remove redundant attributes, attribute reduction demonstrates extraordinary ability in simplifying information systems. This paper applies a novel form of conditional entropy to investigate the attribute reduction in incomplete information systems. Firstly, a novel definition of conditional entropy is introduced based on tolerance relation. Additionally, in order to reduce time complexity, we propose a binsearch heuristic attribute-reduction algorithm with conditional entropy as heuristic knowledge. Furthermore, two examples are used to illustrate the feasibility and validity of the reduction algorithm.

Identifying rice lodging based on semantic segmentation architecture optimization with UAV remote sensing imaging

Lodging, a prevalent issue during rice growth, detrimentally impacts both yield and quality. It also complicates the harvesting process, reducing the efficiency of mechanized collection. Existing monitoring methods, predominantly based on manual observation and satellite remote sensing, fall short in addressing the requirements of contemporary, efficient, and real-time agriculture. This research integrates image analysis techniques with advanced optimization algorithms to develop a semantic segmentation model specifically designed for detecting rice lodging in remote sensing images. The model, named MI-UConvNeXt, employs a ConvNeXt-based feature extraction network utilizing UNet architecture (UConvNeXt) and incorporates an improved multi-objective salp swarm algorithm with Latin hypercube sampling and an elite opposition-based learning strategy (ISSA-LE) to dynamically adjusting the number of UConvNeXt channels. MI-UConvNeXt achieves a balance between accuracy and complexity. Compared to seven other semantic segmentation models from the literature, MI-UConvNeXt exhibits enhanced performance, with a Pixel Accuracy (PA) of 95.59%, mean Pixel Accuracy (mPA) of 95.62%, and mean Intersection over Union (mIoU) of 91.91% on the validation set. This demonstrates the model’s superior accuracy, lower computational resource demands, and enhanced efficiency. By integrating deep learning with intelligent optimization algorithms, this study offers a novel and effective approach for monitoring crop lodging in agricultural production, providing robust technical support for the accurate extraction of crop phenotypic information.

Political-RAG: using generative AI to extract political information from media content

ABSTRACT In the digital era, media content is crucial for political analysis, providing valuable insights through news articles, social media posts, speeches, and reports. Natural Language Processing (NLP) has transformed Political Information Extraction (IE), automating tasks such as event extraction and sentiment analysis. Traditional NLP methods, while effective, are often task-specific and require specialized expertise. In contrast, Large Language Models (LLMs) powered by Generative Artificial Intelligence (GenAI) offer a more integrated solution. However, domain-specific challenges persist, which led to the development of the Retrieval-Augmented Generation (RAG) framework. RAG enhances LLMs by incorporating external data retrieval, addressing issues related to data availability. To demonstrate RAG’s capabilities, we introduce the Political-RAG system, designed to extract political event information from media content, including Twitter data and news articles. Initially developed for event extraction, the Political-RAG system lays the foundation for developing various complex Political IE tasks. These include detecting hate speech, analyzing conflicts, assessing political bias, and evaluating social trends, sentiment, and opinions.

Extracting terrain elevation information in front of the vehicle based on vehicle-mounted LiDAR in dynamic environments

Abstract Point cloud maps constructed using 3D LiDAR, are widely used for robot navigation and localization. Few studies have utilized point cloud maps to extract terrain elevationinformation in front of a vehicle, which can be used as active suspension inputs to reduce vehicle bumps. In addition, the trajectories of dynamic objects in point cloud maps and global navigation satellite system (GNSS) data loss can affect the extraction of elevation information. To solve these problems, this paper proposes a framework for extracting terrain elevation information in front of the vehicle based on vehicle-mounted LiDAR in dynamic environments. The framework consists of two modules: point cloud map construction and vehicle front terrain elevation information extraction. In the point cloud map construction module, a system for simultaneous localization and mapping (SLAM) is proposed, which is capable of building point cloud maps without GNSS. Furthermore, a dynamic descriptor-based dynamic object filtering algorithm is proposed which is applied to SLAM. Therefore, the SLAM system overcomes the influence of dynamic objects on vehicle position and attitude estimation, and there are no trajectories of dynamic objects in the point cloud maps built by the system. In the vehicle front terrain elevation information extraction module, the unscented Kalman filter is utilized to predict the vehicle position at the next moment. Based on the geometric features of the tire-ground contact area, the terrain elevation information of the tire contact area at the predicted position on the point cloud map is extracted. Experiments show that the algorithm in this paper overcomes the effect of dynamic objects and builds a vehicle point cloud map without dynamic objects under GNSS data loss, which improves the accuracy of the extraction of terrain elevation information in front of the vehicle.

MCI net: mamba- convolutional lightweight self-attention medical image segmentation network.

With the development of deep learning in the field of medical image segmentation, various network segmentation models have been developed. Currently, the most common network models in medical image segmentation can be roughly categorized into pure convolutional networks, Transformer-based networks, and networks combining convolution and Transformer architectures. However, when dealing with complex variations and irregular shapes in medical images, existing networks face issues such as incomplete information extraction, large model parameter sizes, high computational complexity, and long processing times. In contrast, models with lower parameter counts and complexity can efficiently, quickly, and accurately identify lesion areas, significantly reducing diagnosis time and providing valuable time for subsequent treatments. Therefore, this paper proposes a lightweight network named MCI-Net, with only 5.48M parameters, a computational complexity of 4.41, and a time complexity of just 0.263. By performing linear modeling on sequences, MCI-Net permanently marks effective features and filters out irrelevant information. It efficiently captures local-global information with a small number of channels, reduces the number of parameters, and utilizes attention calculations with exchange value mapping. This achieves model lightweighting and enables thorough interaction of local-global information within the computation, establishing an overall semantic relationship of local-global information. To verify the effectiveness of the MCI-Net network, we conducted comparative experiments with other advanced representative networks on five public datasets: X-ray, Lung, ISIC-2016, ISIC-2018, and capsule endoscopy and gastrointestinal segmentation. We also performed ablation experiments on the first four datasets. The experimental results outperformed the other compared networks, confirming the effectiveness of MCI-Net. This research provides a valuable reference for achieving lightweight, accurate, and high-performance medical image segmentation network models.

Accurate identification of moving vehicle loads on beam-like bridge structures integrating novel PCA-based dictionary with grouping and weighting strategy

Most moving load identification (MLI) methods incorporate the dictionary theory as a regularization technique to address the ill-posedness of the problem. However, the selection of atoms in the dictionary often inadequately represents the actual vehicle loads in existing methods, and the prior information from these atoms is disregarded in the subsequent MLI computations. Therefore, a novel MLI framework is proposed for beam-like bridge structures based on a novel dictionary derived from principal component analysis (PCA) and newly grouping and weighting strategy in this study. At first, a vehicle-bridge coupling system (VBCS) is established to obtain the interaction forces between the moving vehicles and bridge deck. The system matrices between the interaction forces and structural responses are derived in the time domain. The PCA technique is then employed to extract information from these interaction forces and to subsequently construct a PCA-based dictionary. Based on the eigenvalues of each principal component in the dictionary, a weighted group sparse model, incorporating the newly grouping and weighting strategy, is defined to obtain the coefficients of each atom. The solution to this model is obtained using the alternating direction method (ADM). Finally, the proposed method is validated in numerical simulations comparing with some existing methods. The effects of noise levels, road surface roughness, number of training data, response combinations and the tolerances in ADM were also studied. Similarity law for the VBCS is conducted in experimental verifications to construct the PCA-based dictionary, allowing for a reasonable identification of gross vehicle weights. The results indicate that the MLI accuracy has been enhanced by the proposed method, and the similarity law employed in experiments is reasonable. Settingthe tolerance in ADM as ε = 1 × 10−5 can lead to a higher accuracy and reduce the computation cost in both numerical simulations and experimental verifications.

Extraction of Canal Distribution Information Based on UAV Remote Sensing System and Object-Oriented Method

At present, the extraction of irrigation canal network distribution information is of great significance for developing a digital twin irrigation district. However, due to the low resolution of remote sensing images, it is difficult to effectively identify the canal networks, especially for channels with a width of less than 1 m, where recognition is insufficient. Therefore, the purpose of this study is to extract canal networks of different widths in an irrigation district in Shaanxi Province as the research area. A rule-based object-oriented classification method was employed, utilizing image data collected by the DJI Mavic 3 multispectral UAV (Unmanned Aerial Vehicle) to explore the accuracy of this method in extracting canal distribution information. Based on UAV multispectral remote sensing imagery, the segmentation parameters for the remote sensing imagery were determined using ENVI 5.6 software, with the segmentation threshold set at 60 and the merging threshold set at 80. By combining the spectral and spatial differences between the canals and other ground objects, rules for extracting canal network distribution information were established, and the information on the distribution of channels in this irrigation area was finally obtained. The experimental results showed a maximum recall rate of 91.88% and a maximum precision rate of 57.59%. The overall recall precision rates for the irrigation district were 85.74% and 55.08%, respectively. This method provides a new solution for identifying and extracting canal systems in irrigation districts, offering valuable insights for acquiring canal distribution information and providing a scientific basis for precision irrigation.

Health IoT Threats: Survey of Risks and Vulnerabilities

The secure and efficient collection of patients’ vital information is a challenge faced by the healthcare industry. Through the adoption and application of Internet of Things (IoT), the healthcare industry has seen an improvement in the quality of delivered services and patient safety. However, IoT utilization in healthcare is challenging due to the sensitive nature of patients’ clinical information and communicating this across heterogeneous networks and among IoT devices. We conducted a semi-systematic literature review to provide an overview of IoT security and privacy challenges in the healthcare sector over time. We collected 279 studies from 5 scientific databases, of which 69 articles met the requirements for inclusion. We performed thematic and qualitative content analysis to extract trends and information. According to our analysis, the vulnerabilities in IoT in healthcare are classified into three main layers: perception, network, and application. We comprehensively reviewed IoT privacy and security threats on each layer. Different technological advancements were suggested to address the identified vulnerabilities in healthcare. This review has practical implications, emphasizing that healthcare organizations, software developers, and device manufacturers must prioritize healthcare IoT security and privacy. A comprehensive, multilayered security approach, security-by-design principles, and training for staff and end-users must be adopted. Regulators and policy makers must also establish and enforce standards and regulations that promote the security and privacy of healthcare IoT. Overall, this study underscores the importance of ensuring the security and privacy of healthcare IoT, with stakeholders’ coordinated efforts to address the complex and evolving security and privacy threats in this field. This can enhance healthcare IoT trust and reliability, reduce the risks of security and privacy issues and attacks, and ultimately improve healthcare delivery quality and safety.

Analysis and Comprehensive Evaluation of Urban Green Space Information Based on Gaofen 7: Considering Beijing’s Fifth Ring Area as an Example

Urban green spaces constitute a vital component of the ecosystem. This study focused on urban green spaces located within the Fifth Ring Road of Beijing, using Gaofen 7 (GF-7) as the primary data source for analysis. The main objective was to develop a system for extracting and classifying urban green spaces in Beijing by applying deep learning and machine learning algorithms, and further, the results were validated with ground survey samples. This study provides detailed extraction and classification of urban green space coverage by creating a comprehensive evaluation system. The primary findings indicate that the deep learning algorithm enhances the precision of green space information extraction by 10.68% compared to conventional machine learning techniques, effectively suppresses “pretzel noise”, and eventually aids in extracting green space information with complete edges. The thorough assessment of green spaces within the study area indicated favorable outcomes showing the high service capacity of park green spaces. The overall classification accuracy of the final extraction results was 94.31%. Nonetheless, challenges, such as unequal distribution of green zones and a significant fragmentation level throughout the study area, were still encountered. Consequently, the use of GF-7 high-resolution imagery, in conjunction with the collaborative application of deep learning and machine learning techniques, enabled the acquisition of highly accurate information regarding urban green zone coverage. According to the established grading standards of evaluation indices, the landscape pattern of urban green spaces within the study area was comprehensively assessed. This evaluation offers essential data support for monitoring urban green spaces and planning landscape patterns, thereby contributing to the achievement of sustainable development objectives related to urban greening and ecological conservation.

Secure authentication and encryption via diffraction imaging-based encoding and vector decomposition

Abstract Traditional optical encryption systems have security risks due to their linearity and usually encounter problems such as the heavy burden of key transmission and storage. This paper proposes a novel security-enhanced optical image authentication and encryption framework that combines diffractive imaging-based encryption with the vector decomposition algorithm (VDA). Chaotic random phase masks (CRPMs) are used to encrypt data for authentication via VDA, and a pair of complementary binary matrix keys are utilized to extract information from the encrypted data to generate ciphertext. During the authentication and decryption processes, a sparse reference image is reconstructed from the ciphertext for verification. If the authentication is successful, image decryption can be executed using a key-assisted phase retrieval algorithm. The employment of nonlinear VDA, an additional layer of authentication, and the use of CRPMs and binary matrix keys enhance security and address key burden concerns. Simulation results demonstrate the feasibility, effectiveness, and security of the scheme.