Information Extraction Process Research Articles

SD-YOLOv8: An Accurate Seriola dumerili Detection Model Based on Improved YOLOv8.

Accurate identification of Seriola dumerili (SD) offers crucial technical support for aquaculture practices and behavioral research of this species. However, the task of discerning S. dumerili from complex underwater settings, fluctuating light conditions, and schools of fish presents a challenge. This paper proposes an intelligent recognition model based on the YOLOv8 network called SD-YOLOv8. By adding a small object detection layer and head, our model has a positive impact on the recognition capabilities for both close and distant instances of S. dumerili, significantly improving them. We construct a convenient S. dumerili dataset and introduce the deformable convolution network v2 (DCNv2) to enhance the information extraction process. Additionally, we employ the bottleneck attention module (BAM) and redesign the spatial pyramid pooling fusion (SPPF) for multidimensional feature extraction and fusion. The Inner-MPDIoU bounding box regression function adjusts the scale factor and evaluates geometric ratios to improve box positioning accuracy. The experimental results show that our SD-YOLOv8 model achieves higher accuracy and average precision, increasing from 89.2% to 93.2% and from 92.2% to 95.7%, respectively. Overall, our model enhances detection accuracy, providing a reliable foundation for the accurate detection of fishes.

AI Based Model for Prediction of Heavy/High Impact Rain Events

India is a farming country whose economy is heavily reliant on the development of rainforests. In order to analyse agricultural yields, rainfall estimates are essential and fundamental for all ranchers. The ability to predict the climate with the aid of science and innovation is unsurprising rainfall. To effectively use water resources, horticulture production, and water arranging, it is important to determine the amount of rainfall. Rainfall can be predicted using a variety of information mining techniques. The process of information extraction is used to evaluate rainfall. Probably the most well-known rainfall forecast calculations may be found in this article. Some of the computations compared with this record include Logistic Regression, K-Neighbors Classifier, Random Forest Classifier, and Certificate Tree. It is possible to decompose the process of precisely predicting rainfall from a relative standpoint. To do this, we gathered satellite data. We have taken a number of variables into account in order to predict (temperature, dew point, humidity, wind pressure, wind speed, wind direction, etc.). Key Words: Random Forest Classifier, KNeighbors Classifier, Logistic Regression, Oversampling, Under sampling, and Rainfall Prediction.

Damage Severity Assessment of Multi-Layer Complex Structures Based on a Damage Information Extraction Method with Ladder Feature Mining.

Multi-layer complex structures are widely used in large-scale engineering structures because of their diverse combinations of properties and excellent overall performance. However, multi-layer complex structures are prone to interlaminar debonding damage during use. Therefore, it is necessary to monitor debonding damage in engineering applications to determine structural integrity. In this paper, a damage information extraction method with ladder feature mining for Lamb waves is proposed. The method is able to optimize and screen effective damage information through ladder-type damage extraction. It is suitable for evaluating the severity of debonding damage in aluminum-foamed silicone rubber, a novel multi-layer complex structure. The proposed method contains ladder feature mining stages of damage information selection and damage feature fusion, realizing a multi-level damage information extraction process from coarse to fine. The results show that the accuracy of damage severity assessment by the damage information extraction method with ladder feature mining is improved by more than 5% compared to other methods. The effectiveness and accuracy of the method in assessing the damage severity of multi-layer complex structures are demonstrated, providing a new perspective and solution for damage monitoring of multi-layer complex structures.

Peanut origin traceability: A hybrid neural network combining an electronic nose system and a hyperspectral system

Peanuts, sourced from various regions, exhibit noticeable differences in quality owing to the impact of their natural environments. This study proposes a fast and nondestructive detection method to identify peanut quality by combining an electronic nose system with a hyperspectral system. First, the electronic nose and hyperspectral systems are used to gather gas and spectral information from peanuts. Second, a module for extracting gas and spectral information is designed, combining the lightweight multi-head transposed attention mechanism (LMTA) and convolutional computation. The fusion of gas and spectral information is achieved through matrix combination and lightweight convolution. A hybrid neural network, named UnitFormer, is designed based on the information extraction and fusion processes. UnitFormer demonstrates an accuracy of 99.06 %, a precision of 99.12 %, and a recall of 99.05 %. In conclusion, UnitFormer effectively distinguishes quality differences among peanuts from various regions, offering an effective technological solution for quality supervision in the food market.

Information extraction from automotive reports for ontology population

In this paper, we showcase our research on the use of ontologies and information extraction for the purpose of modeling damages incurred on car bodies. With the increasing use of technology in the automotive industry, it is important to have a standardized and efficient way of documenting and analyzing car damage reports. Most existing reports are unstructured, and there is a lack of standardization in describing the damage. To address this issue, we have developed a domain ontology for car damage modeling (OCD),11 industryportal.enit.fr/ontologies/OCD,22 github.com/OntologyCarDamage/OCD and proposed an end-to-end system to extract information from French automotive reports. The information extraction process involves using named entity recognition (NER) and relationship extraction (RE) techniques to identify and extract relevant information from the reports. Then, the extracted information is used to populate the OCD ontology, allowing a structured and standardized representation of the damage information. The proposed system was tested on a real dataset of automotive reports and showed promising results.

A Lightweight Hybrid Model with Location-Preserving ViT for Efficient Food Recognition.

Food-image recognition plays a pivotal role in intelligent nutrition management, and lightweight recognition methods based on deep learning are crucial for enabling mobile deployment. This capability empowers individuals to effectively manage their daily diet and nutrition using devices such as smartphones. In this study, we propose an Efficient Hybrid Food Recognition Net (EHFR-Net), a novel neural network that integrates Convolutional Neural Networks (CNN) and Vision Transformer (ViT). We find that in the context of food-image recognition tasks, while ViT demonstrates superiority in extracting global information, its approach of disregarding the initial spatial information hampers its efficacy. Therefore, we designed a ViT method termed Location-Preserving Vision Transformer (LP-ViT), which retains positional information during the global information extraction process. To ensure the lightweight nature of the model, we employ an inverted residual block on the CNN side to extract local features. Global and local features are seamlessly integrated by directly summing and concatenating the outputs from the convolutional and ViT structures, resulting in the creation of a unified Hybrid Block (HBlock) in a coherent manner. Moreover, we optimize the hierarchical layout of EHFR-Net to accommodate the unique characteristics of HBlock, effectively reducing the model size. Our extensive experiments on three well-known food image-recognition datasets demonstrate the superiority of our approach. For instance, on the ETHZ Food-101 dataset, our method achieves an outstanding recognition accuracy of 90.7%, which is 3.5% higher than the state-of-the-art ViT-based lightweight network MobileViTv2 (87.2%), which has an equivalent number of parameters and calculations.

Optimal Information Hiding: Advanced Bitstream‐Based Watermarking for Secure and Efficient Integration of Image Data in Digital Videos

Video watermarking is a technique used to embed a visible or invisible mark onto a video file in order to claim ownership, protect intellectual property rights, and detect unauthorized users. The issue of video piracy arises due to the advancement of Internet services and the utilization of various storage technologies. This problem becomes particularly significant with the widespread propagation of media through online channels. The constant progress in sharing of data, videos, images, and applications necessitates the implementation of measures focused on security and the protection of intellectual ownership rights. To address this issue, we proposed a novel Bitstream video watermarking technique that integrates cutting‐edge methodologies to seamlessly embed image data within digital videos. This technique stands apart from less sophisticated watermarking methods by capitalizing on spatial and temporal domain concepts in video processing. An experiment is conducted to analyze the time required for the insertion and extraction processes to gain insights into the performance of bitstream watermarking in the video. While enhancing the existing approach, we systematically explored diverse strategies aimed at augmenting watermark capacity, bolstering robustness, and heightening visual imperceptibility. These strategies encompassed the intricate infusion of watermark fragments into the frame area through chaotic mechanisms, the integration of error‐correcting codes to ensure data fidelity, and the selective omission of dynamic video segments during information extraction processes. Rigorous experimentations are undertaken by the constraints of lossy compression scenarios, maintaining equivalent or reduced bitrate parameters. The findings substantiate the favorable outcomes stemming from the implemented enhancement, affirming the tangible suitability of the phase watermarking technique for the protection of video copyrights. In order to assess the quality of the bitstream video watermarking technique, the peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), and normalized correlation coefficient (NCC) are computed and examined by the testing data. The suggested model achieved a PSNR value up to 32.52 dB, SSIM value up to 0.97, and NCC value is 1. The dynamic range (DR) and spatial resolution (SR) are measured in the evaluation process. Security is enhanced significantly after implementing the multifaceted approach. The watermarking scheme is more robust and can effectively resist the potential attacks. In summary, this study advances the field of video watermarking by proposing a sophisticated bitstream technique and rigorously evaluating its performance. The outcomes underscore its practical viability for copyright protection, contributing to a more secure digital media landscape.

Opinion Mining in Arabic Extremism Texts: A Systematic Literature Review

In this paper, a systematic literature review was provided that investigated the present evidence regarding extremist words in Arabic opinion mining methods. This study aimed to perform a Systematic Literature Review (SLR) in order to detect, evaluate, and synthesize the existing evidence regarding opinion mining techniques for extremist Arabic text. From the SLR, it is evident that opinion-mining techniques have several opportunities for detecting extremism in the Arabic text. Over the past few years, multimedia sentiment analysis has gained traction as visual content is becoming more incorporated into social media networking. Opinion mining is the process of identifying, extracting, and categorizing views about anything. It is a sort of Natural Language Processing (NLP) used to track public sentiment about a certain law, policy, or marketing, for example. It entails the creation of a method for collecting and analyzing comments and opinions concerning legislation, regulations, policies, and so on that are posted on social media. The process of information extraction is critical since it is both a beneficial tool and a difficult undertaking. In this article, we have examined the recent and advanced methodologies to extract sentiment from a web-wide item, opinion-mining methods must be automated. Also, we have analyzed the novel Artificial Intelligence and lexical-based algorithms for sentiment analysis. These methodologies find better applications in the customer feedback analysis of any organization.

An image deblurring method using improved U-Net model based on multilayer fusion and attention mechanism

The investigation of image deblurring techniques in dynamic scenes represents a prominent area of research. Recently, deep learning technology has gained extensive traction within the field of image deblurring methodologies. However, such methods often suffer from limited inherent interconnections across various hierarchical levels, resulting in inadequate receptive fields and suboptimal deblurring outcomes. In U-Net, a more adaptable approach is employed, integrating diverse levels of features effectively. Such design not only significantly reduces the number of parameters but also maintains an acceptable accuracy range. Based on such advantages, an improved U-Net model for enhancing the image deblurring effect was proposed in the present study. Firstly, the model structure was designed, incorporating two key components: the MLFF (multilayer feature fusion) module and the DMRFAB (dense multi-receptive field attention block). The aim of these modules is to improve the feature extraction ability. The MLFF module facilitates the integration of feature information across various layers, while the DMRFAB module, enriched with an attention mechanism, extracts crucial and intricate image details, thereby enhancing the overall information extraction process. Finally, in combination with fast Fourier transform, the FRLF (Frequency Reconstruction Loss Function) was proposed. The FRLF obtains the frequency value of the image by reducing the frequency difference. The present experiment results reveal that the proposed method exhibited higher-quality visual effects. Specifically, for the GoPro dataset, the PSNR (peak signal-to-noise ratio) reached 31.53, while the SSIM (structural similarity index) attained a value of 0.948. Additionally, for the Real Blur dataset, the PSNR achieved 31.32, accompanied by an SSIM score of 0.934.

Clustering algorithms for analysing electronic medical record: A mapping study

<p>Electronic Medical Records (EMRs) contain patients’ history related to their medication, vaccine, test results and insurance information. EMRs need to be stored to facilitate the application of clinical treatment and prevention protocols. Clustering algorithms automate the process of information extraction and support health data management. Hence, in this mapping study, we systematically examine the literature on clustering algorithms used for analysing EMRs. We focus on studies published in 2016-2021 to present an overview of clustering techniques used in these studies to analyse medical data. We found 27 studies on clustering techniques, clustering technique problems and the evaluation parameters for analysing EMRs. However, although several studies have focused on this topic, only a few have taken the significant step of examining the clustering techniques used for analysing medical data particularly electronic medical record. Our results highlight that three clustering techniques have been used to analyse medical data, namely, the partitioning, the hierarchical and the density-based algorithms. We identified several clustering technique problems and 10 different evaluation parameters. The results suggest that researchers should focus on analysing medical data that will drive data-driven decision-making by management and promote a data-driven culture to ensure health care quality.</p>

A Distributed Microservice Architecture Pattern for the Automated Generation of Information Extraction Pipelines

Companies often build their businesses based on product information and therefore try to automate the process of information extraction (IE). Since the information source is usually heterogeneous and non-standardized, classic extract, transform, load techniques reach their limits. Hence, companies must implement the newest findings from research to tackle the challenges of process automation. They require a flexible and robust system that is extendable and ensures the optimal processing of the different document types. This paper provides a distributed microservice architecture pattern that enables the automated generation of IE pipelines. Since their optimal design is individual for each input document, the system ensures the ad-hoc generation of pipelines depending on specific document characteristics at runtime. Furthermore, it introduces the automated quality determination of each available pipeline and controls the integration of new microservices based on their impact on the business value. The introduced system enables fast prototyping of the newest approaches from research and supports companies in automating their IE processes. Based on the automated quality determination, it ensures that the generated pipelines always meet defined business requirements when they come into productive use.

Context learning from a ship trajectory cluster for anomaly detection

This paper presents a context information extraction process over Automatic Identification System (AIS) real-world ship data, building a system with the capability to extract representative points of a trajectory cluster. With the trajectory cluster, the study proposes the use of trajectory segmentation algorithms to extract representative points of each trajectory and then use the k-means algorithm to obtain a series of centroids over all the representative points. These centroids, combined, form a new representative trajectory of the cluster. This new representative trajectory of the input cluster represents new contextual information extracted from the original set of trajectories, being possible to apply anomaly detection approaches over the new obtained context. The results show a suitable approach with several compression algorithms that are compared with a metric based on the Perpendicular Euclidean Distance.

Sparse mixed attention aggregation network for multimodal images fusion tracking

Recent years have witnessed the exciting performance of trackers based on Transformer. However, they usually separate the process of information extraction and integration, weakening the information interaction between the target and search region. In addition, they depend on traditional Transformer to model the long range dependency, which leads to a lack of focus on the primary information needed by high-accuracy trackers. In this paper, a sparse mixed attention aggregation model is proposed for robust tracking based on visible and thermal infrared images. To be specific, a backbone network composed of sparse mixed attention is designed to achieve information extraction and integration. This is helpful to obtain specific discriminative feature information and enhance their communication. To give full play to the complementary visible and thermal information, a confidence aware aggregation network is designed, which can learn the reliable confidence of visible and thermal branches. Finally, a corner-based localization head is introduced to estimate the target state. Extensive experiments on three large-scale multimodal tracking benchmarks demonstrate the superior tracking ability of the proposed tracker over other advanced trackers.

Sentiment Analysis Using PSEUDO Nearest Neighbor and TF-IDF TEXT Vectorizer

Twitter is one of the social media that is often used by researchers as an object of research to conduct sentiment analysis. Twitter is also a good indicator in influencing research, problems that often arise in research in the field of sentiment analysis are the many factors such as the use of colloquial or informal language and other factors that can affect sentiment results. To improve the results of sentiment classification, it is necessary to carry out a good information extraction process. One of the word weighting methods resulting from the information extraction process is the TF-IDF Vectorizer. This study examines the effect of the TF-IDF Vectorizer weighting results in sentiment analysis using the Pseudo Nearest Neighbor method. The results of the f-measure classification of sentiment using the TF-IDF Vectorizer at parameters k-2 = 89%, k-3 = 89%, k-4 = 71% and k-5 = 75% while without using the TF-IDF Vectorizer on the parameters k-2 = 90%, k-3 = 92%, k-4 = 84% and k-5 = 89%. From the results of the classification of sentiment analysis that does not use the TF-IDF Vectorizer, the f-measure value is slightly better than using it.

DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications

The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai.

A KNOWLEDGE GRAPH AND RULE BASED REASONING METHOD FOR EXTRACTING SAPPHIRE INFORMATION FROM TEXT

AbstractRepresentation of design information using causal ontologies is very effective for creative ideation in product design. Hence researchers created databases with models of engineering and biological systems using causal ontologies. Manually building many models using technical documents requires significant effort by specialists. Researchers worked on the automatic extraction of design information leveraging the computational techniques of Machine Learning. But these methods are data intensive, have manual touch points and have not yet reported the end-to-end performance of the process. In this paper, we present the results of a new method inspired by the cognitive process followed by specialists. This method uses the Knowledge Graph with Rule based reasoning for information extraction for the SAPPhIRE causality model from natural language texts. Unlike the supervised learning methods, this new method does not require data intensive modelling. We report the performance of the end-to-end information extraction process, which is found to be a promising alternative.

A novel wind speed forecasting combined model using variational mode decomposition, sparse auto-encoder and optimized fuzzy cognitive mapping network

The nonlinear, random and fluctuating characteristics of wind speed bring great challenges to its accurate forecast, so no model that can adapt to all situations. In order to solve the problem of unbalanced forecast accuracy and stability in the current wind speed forecast model, a novel and advanced wind speed combined forecast model (CFM) is proposed in this study. The CFM adopts a two-phase data processing strategy composed of variable mode decomposition-sparse autoencoder (VMD-SAE) to extract the original wind speed features, high-order fuzzy cognitive mapping (HFCM) neural network modeling and batch gradient descent optimization algorithm to make up for its shortcomings. The two-phase data processing strategy performs smoothing and feature information extraction processing on the original data. The forecast module adopts the SAE-HFCM combination strategy, and utilizes their respective advantages to achieve accurate and stable result output. The results show that this CFM has the best forecast accuracy and generalization performance compared with 7 benchmark models in datasets from three different sites. Compared with the benchmark models, the performance of CFM in point forecasting and interval forecasting, the average improvement percentage of IP_RMSE, IP_MAE and IP_MAPE minimum values are 40.9%, 40.1% and 40.6%, respectively. The performance of interval indicators is also the best, and its application prospects are broad.

PipCKG-BS: A Method to Build Cybersecurity Knowledge Graph for Blockchain Systems via the Pipeline Approach

The increasing sophistication of cyberattacks on blockchain systems has significantly disrupted security experts from gaining immediate insight into the security situation. The Cybersecurity Knowledge Graph (CKG) currently provides a novel technical solution for blockchain system situational awareness by integrating massive fragmented Cyber Threat Intelligence (CTI) about blockchain technology. However, the existing literature does not provide a solution for building CKG appropriate for blockchain systems. Therefore, designing a method to construct a CKG for blockchain systems by efficiently extracting information from the CTI is mandatory. This paper proposes PipCKG-BS, a pipeline-based approach that builds CKG for blockchain systems. The PipCKG-BS incorporates contextual features and Pre-trained Language Models (PLMs) to improve the performance of the information extraction process. Precisely, we develop the Named Entity Recognition (NER) and Relation Extraction (RE) models for cybersecurity text in PipCKG-BS. In the NER model, we apply the prompt-based learning paradigm to cybersecurity text by constructing prompt templates. In the RE model, we employ external features and prior knowledge of sentences to improve entity relationship extraction accuracy. Several experimental results demonstrate that PipCKG-BS is better than advanced methods in extracting CTI information and is an appealing solution to build high-quality CKG for blockchain systems.

Harvesting spatiotemporal correlation from sky image sequence to improve ultra-short-term solar irradiance forecasting

With photovoltaic power being increasingly integrated into power grid, accurately forecasting solar irradiance is of critical for ensuring stable and economical operation of power systems. The forecast accuracy in ultra-short-term horizons can be greatly improved by employing ground-based sky images. Although wide range of computer vision methods have been used for modelling, effectively extracting spatiotemporal features from sky image sequence is still a tough task. In this study, a sparse spatiotemporal feature descriptor is introduced to enhance the process of dynamic spatiotemporal information extraction from continuous grayscale sky images, while spatial pyramid pooling is used for feature refinement. Parallelly, dense convolutional network is used to extract static features from the nearest single-frame RGB sky images. Both dynamic and static spatiotemporal features were adequately extracted and subsequently fused for the multi-step prediction of global horizontal irradiance. In addition, various competitive models in object detection are adopted as benchmarks for comparison. The experimental results revealed that the proposed method outperformed baseline models, with up to 5.51% reduction on normalized root mean square error (NRMSE) and 9.38% improvement on ramp event forecast. The proposed method can be widely applied to photovoltaic stations equipped with all-sky-imagers.

Root cause prediction for failures in semiconductor industry, a genetic algorithm–machine learning approach

Failure analysis has become an important part of guaranteeing good quality in the electronic component manufacturing process. The conclusions of a failure analysis can be used to identify a component’s flaws and to better understand the mechanisms and causes of failure, allowing for the implementation of remedial steps to improve the product’s quality and reliability. A failure reporting, analysis, and corrective action system is a method for organizations to report, classify, and evaluate failures, as well as plan corrective actions. These text feature datasets must first be preprocessed by Natural Language Processing techniques and converted to numeric by vectorization methods before starting the process of information extraction and building predictive models to predict failure conclusions of a given failure description. However, not all-textual information is useful for building predictive models suitable for failure analysis. Feature selection has been approached by several variable selection methods. Some of them have not been adapted for use in large data sets or are difficult to tune and others are not applicable to textual data. This article aims to develop a predictive model able to predict the failure conclusions using the discriminating features of the failure descriptions. For this, we propose to combine a Genetic Algorithm with supervised learning methods for an optimal prediction of the conclusions of failure in terms of the discriminant features of failure descriptions. Since we have an unbalanced dataset, we propose to apply an F1 score as a fitness function of supervised classification methods such as Decision Tree Classifier and Support Vector Machine. The suggested algorithms are called GA-DT and GA-SVM. Experiments on failure analysis textual datasets demonstrate the effectiveness of the proposed GA-DT method in creating a better predictive model of failure conclusion compared to using the information of the entire textual features or limited features selected by a genetic algorithm based on a SVM. Quantitative performances such as BLEU score and cosine similarity are used to compare the prediction performance of the different approaches.