Accurate Identification Research Articles

Rapid identification of an effective bauxite gas reservoir by principal component analysis.

In recent years, industrial gas flow has been obtained from the bauxite gas reservoir in the southwestern Ordos Basin, which has made the identification of aluminium-bearing rock reservoirs a popular topic. To accelerate the exploration and development of this type of gas reservoir, major element testing, rock thin section identification and principal component analysis (PCA) were conducted, and a method for rapid and accurate identification of bauxite reservoirs via conventional logging was established. The test results clearly revealed the vertical stratification of major elements and three lithologies in the aluminium (Al)-bearing rock series in the study area. The log response characteristics of effective gas reservoirs were summarized, providing a basis for subsequent research on identifying effective bauxite reservoirs via mathematical dimensionality reduction of logging curves. The porosity comparison of strata with different lithologies suggests that dissolution pores are more developed in Al-rich layers, providing insight for identifying effective reservoirs by Al2O3 content. On the basis of the above findings, a lithological identification chart of Al-bearing rock series was established via principal component analysis (PCA), and an effective bauxite reservoir logging identification model based on Al2O3 content prediction was developed. The results show that using the dimensionality reduction method for principal component analysis of logging curves with overlapping information can avoid model distortion caused by multicollinearity. The research results can be used to identify bauxite reservoirs quickly and accurately without other test data.

Rapid and accurate identification of multiple metal ions using a bispyrene‐based fluorescent sensor array with aggregation‐induced enhanced emission property

AbstractMultiple metal ions are traditionally detected using inductively coupled plasma mass spectrometry (ICP‐MS) and atomic fluorescence spectrometry. Although these methods are sensitive and accurate, they depend on complex instruments and require highly trained operators, making low costly rapid detection challenging. It is urgent to develop a convenient, rapid and sensitive method to detect multiple metal ions. Herein, we designed a bispyrene derivative (BP) with aggregation‐induced enhanced emission (AIEE) property to construct a high fluorescent sensor array to realize the effective identification of four metal ions (Fe3+, Cu2+, Co2+ and Cd2+). The differential coordination capability between metal ions and BP with the aid of acetate ions resulted the possibility of array‐based sensing. The four heavy metal ions could be immediately classified in the concentration of 100 nM. The limit of detection (LOD) of Fe3+, Cu2+, Co2+, and Cd2+ were as low as 16.2, 21.8, 51.4, and 25.9 nM, respectively. Furthermore, the sensor array was applied for identification multiple heavy metal ions in environmental samples and iron ion in rat serums with identified of 100%. The sample consumption as low as 2 µL for each detection and the results could be extracted by smartphones under ultraviolet lamps. It provided a rapid, sensitive, low‐cost, and on‐site multiple metal ions detection method.

Solid–liquid separation state control system for shale shaker based on computer vision

Shale shakers are crucial in drilling operations, separating solid–liquid mixtures, protecting equipment, improving efficiency, and supporting environmental protection and resource recovery. Typically, the solid–liquid separation status on shaker screens is assessed through manual inspection and experiential judgment, with the screen tilt angle adjusted manually. This method has inherent delays, failing to promptly detect and address screen surface abnormalities. To overcome this, a computer vision-driven intelligent control system has been proposed, enhancing drilling sieving operations’ efficiency. The system uses computer vision and servo motors to monitor and adjust the sieving state. Twin industrial cameras capture images, which undergo preprocessing like perspective correction, grayscale conversion, noise reduction, and illumination equalization. Local threshold segmentation and morphological analysis distinguish between liquid and solid states. The segmented images’ contours and area analysis precisely determine the separation state. Results are transmitted to motors via an industrial computer to adjust the sieve net angle. Servo motors on both sides manipulate the sliding block lifting mechanism for precise tilt adjustment. Experiments demonstrate that the method achieves an average accuracy of 93.635% in identifying mud edges. The system’s high efficiency and reliability ensure accurate identification and adjustment of the solid–liquid separation state, thereby optimizing drilling sieving workflows.

Combination Pattern Method Using Deep Learning for Pill Classification

The accurate identification of pills is essential for their safe administration in the medical field. Despite technological advancements, pill classification encounters hurdles such as ambiguous images, pattern similarities, mixed pills, and variations in pill shapes. A significant factor is the inability of 2D imaging to capture a pill’s 3D structure efficiently. Additionally, the scarcity of diverse datasets reflecting various pill shapes and colors hampers accurate prediction. Our experimental investigation shows that while color-based classification obtains a 95% accuracy rate, shape-based classification only reaches 66%, underscoring the inherent difficulty distinguishing between pills with similar patterns. In response to these challenges, we propose a novel system integrating Multi Combination Pattern Labeling (MCPL), a new method designed to accurately extract feature points and pill patterns. MCPL extracts feature points invariant to rotation and scale and effectively identifies unique edges, thereby emphasizing pills’ contour and structural features. This innovative approach enables the robust extraction of information regarding various shapes, sizes, and complex pill patterns, considering even the 3D structure of the pills. Experimental results show that the proposed method improves the existing recognition performance by about 1.2 times. By improving the accuracy and reliability of pill classification and recognition, MCPL can significantly enhance patient safety and medical efficiency. By overcoming the limitations inherent in existing classification methods, MCPL provides high-accuracy pill classification, even with constrained datasets. It substantially enhances the reliability of pill classification and recognition, contributing to improved patient safety and medical efficiency.

A Novel Method for Identifying Landslide Surface Deformation via the Integrated YOLOX and Mask R-CNN Model

The detection of landslide areas and surface characteristics is the prerequisite and basis of landslide hazard risk assessment. The traditional method relies mainly on manual field identification, and discrimination is based on the lack of unified quantitative standards. Thus, the use of neural networks for the quantitative identification and prediction of landslide surface deformation is explored. By constructing an integrated model based on YOLO X-CNN and Mask R-CNN, a deep learning-based feature detection method for landslide surface images is proposed. First, the method superimposes Unmanned Aerial Vehicle (UAV) oblique photography data (UOPD) and Internet heterosource image data (IHID) to construct a landslide surface image dataset and landslide surface deformation database. Second, an integrated model suitable for small- and medium-scale target detection and large-scale target edge extraction is constructed to automatically identify and extract landslide surface features and to achieve rapid detection of landslide surface features and accurate segmentation and deformation recognition of landslide areas. The results show that the detection accuracy for small rock targets is greater than 80% and that the speed is 57.04 FPS. The classification and mask segmentation accuracies of large slope targets are approximately 90%. A speed of 7.89 FPS can meet the needs of disaster emergency response; this provides a reference method for the accurate identification of landslide surface features.

A novel approach for rapidly determining the reproductive status of walleye pollock (Gadus chalcogrammus) using Raman spectroscopy

Knowledge of the reproductive biology of fishes is essential for effective fisheries management. Information derived from an understanding of fish reproduction, such as size and age at maturity, is used in models to assess fish stocks and can affect estimates of important ecological processes such as recruitment, abundance, and trophic interactions. Common practices for determining the reproductive status of teleost fishes include macroscopic evaluation of gonads as well as histological analysis. However, macroscopic evaluation can be biased and histological analysis is time-consuming, resulting in limitations to spatial and temporal data availability. Here, we explore Raman spectroscopy of ovaries as a novel approach to rapidly determine the reproductive status of walleye pollock (Gadus chalcogrammus), a commercially and ecologically important species in the North Pacific. We used a two-stage partial least-squares (PLS) regression analysis followed by a linear discriminant analysis (LDA) to classify walleye pollock ovary samples as physiologically mature or immature and to subsequently predict their histologically-determined reproductive stage based on the Raman spectra. Biologically mature samples with visible yolk differentiated from mature and immature samples (non-yolked; 99% accuracy). Non-yolked ovaries that were physiologically mature (either mature non-developing or previously spawned) were further differentiated from physiologically immature ovaries (93% accuracy). In addition, detailed, histologically-determined reproductive stages of yolked samples also differentiated via Raman spectroscopy, but with reduced accuracy (79% - 86% accuracy). Our results indicate that accurate identification of maturity status and the reproductive staging of oocytes of walleye pollock based on spectral data from ovaries is possible. This can provide a fast and efficient way to increase the availability of a key component of reproductive data to inform fisheries research and management.

Optimizing Touchless Fingerprint Identification: A Machine Learning Approach to Modelling and Performance Evaluation

This paper explored the modelling and performance analysis of a smartphone-based fingerprint identification system using Convolutional Neural Networks (CNN). The research developed a theoretical framework to validate picture-based fingerprint identification as a feasible alternative to traditional touch-based methods. A modified Automated Fingerprint Identification System (AFIS) model served as the study's foundation. To enhance the model's capabilities, data from two databases, IIT India and SOCOFing, were utilized. The evaluation of the CNN architecture focused on mobile device fingerprint recognition. It emphasized key processes such as data pre-processing, model training, and the optimization of the CNN through a Siamese-CNN approach to boost accuracy and efficiency. Python scripts developed for this purpose were converted to Android code using TensorFlow for deployment on Android devices. Performance metrics, including identification accuracy, processing speed, and resource utilization, were analysed to determine the system's feasibility. The results demonstrated that CNN-based fingerprint identification systems hold significant promise for delivering robust and reliable biometric authentication on smartphones, highlighting both their practical applications and limitations. decrease medical as well as financial burden, hence improving the management of cirrhotic patients. These predictors, however, need further work to validate reliability.

Intelligent Lost Circulation Monitoring Method Based on Data Augmentation and Temporal Models

Deep and offshore drilling operations face complex geological formations, uncertain formation pressures, and narrow safety density windows, making them susceptible to lost circulation risks. To address these challenges, this paper introduces an innovative, intelligent lost circulation monitoring model that incorporates geological lithology information. This model not only utilizes real-time drilling parameters, but also encodes geological information such as rock type as inputs to the model. By combining these key lithological features, the model can comprehensively assess wellbore stability and reduce the lost circulation risks. In this paper, the Conditional Tabular Generative adversarial network (CTGAN) model is used to enhance the data of small-sample risk data, which can effectively expand the data distribution space and improve the performance of the model. This paper conducts a comparative analysis of intelligent monitoring results using artificial neural networks (ANNs), long short-term memory (LSTM), and temporal convolutional networks (TCNs). The results show that the TCN achieves an identification accuracy of 93.7%. Furthermore, the analysis reveals that the inclusion of lithology information significantly enhances the model’s performance, resulting in a 7.1% increase in accuracy. The false alarm rate of the model can be reduced by 10.2%, considering the fluctuation of the logging curve caused by the on/off condition of the pump. This indicates that the introduction of lithology information and the condition of the pump on−off provide advantages in monitoring and identifying lost circulation risks, enabling a more precise assessment of wellbore stability and a reduction in lost circulation incidents. The method of lost circulation monitoring proposed in this paper provides an important safety guarantee for the oil drilling industry.

First experience using a plate for fixing histological specimens of parotid salivary gland tumors

Relevance. Accurately assessing all resection margins during the removal of parotid salivary gland tumors is crucial due to the risk of recurrence and the potential for benign processes to become malignant over time. Macroscopic specimens obtained during these surgeries are sent to the pathology laboratory in containers filled with fixative solution. During fixation, the soft tissue fragments, typically round in shape, can alter their configuration because of the tissue's heterogeneity. This can create challenges for the pathologist in properly orienting the surgical specimen, which in turn complicates the precise evaluation of the resection margins.Materials and methods. To address this issue, the "Plate for fixing histological specimens of parotid salivary gland tumors" (patented) was developed. Twenty cases of its clinical use were analyzed: the surgical material (parotid gland tissue containing a tumor) was sent to the pathology department, fixed on this plate to preserve its original anatomical orientation. A macroscopic and histological evaluation of the neoplasms was then carried out, with special attention given to the accurate identification of all resection margins. In all 20 clinical cases, the use of this invention improved the convenience for the pathologist in handling the excised tissue block by standardizing the method and clearly identifying the resection margins.Conclusion. The application of this invention enhances the accuracy of marking and evaluating the resection margins in parotid gland tumors.

Identification methodology for MIMO Hammerstein nonlinear model with process noise

In this paper, we present a methodology for identifying the multi-input–multi-output (MIMO) Hammerstein nonlinear model under colored noise. The Hammerstein model presented comprises neural fuzzy models (NFMs) as its static nonlinear block and rational transfer functions (RTF) model as its dynamic linear block. The hybrid signals consisting of separable signals and random signals are utilized to deal with the MIMO Hammerstein model identification issue, and the separable signals to implement separation identification of MIMO Hammerstein model is introduced, that is, the two blocks are separately identified. First, parameters of the linear block are estimated applying correlation function-based least squares method in the presence of measurable input–output of Gaussian signals, which can efficiently weaken the process noise interference. Second, estimate of noise parameters vector is introduced to solve the unknown noise vector in the information matrix, then a recursive extended least squares method is developed for identifying parameters of nonlinear block and colored noise model based on available input–output of random signals. The validity and precision of the presented methodology are demonstrated applying a numerical simulation and a nonlinear process, and it is known from the research results that compared with existing identification techniques, the methodology utilized achieved higher identification accuracy.

On-Surface Synthesis of Two-dimensional Carbon-based Networks via Hierarchical Ullmann Coupling Reactions.

The recent developed bottom-up on-surface synthesis offers unprecedent opportunities for the fabrication of two-dimensional (2D) carbon-based networks with atomic precision. Hierarchical coupling approach has been proposed as an efficient strategy for improving the corresponding reaction selectivity and quality of target structures. Herein, we report the synthesis of a nitrogen-doped carbon-based network on Ag(100) utilizing a hierarchical Ullmann coupling strategy. The accurate identification of reaction intermediates and products by scanning tunneling microscopy allows us to unravel the reaction mechanism. The synthetic process of 2D carbon-based networks is kinetics-driven, relying on the competition between dechlorination and C-C coupling. We expect that our discussion on the mechanism of hierarchical coupling may shed light on the rational design and precise synthesis of 2D carbon-based networks on surfaces.

The Complete Chloroplast Genome of Meconopsis simplicifolia and Its Genetic Comparison to Other Meconopsis Species.

Background: Chloroplasts, due to their high conservation and lack of recombination, serve as important genetic resources for the classification and evolutionary analysis of closely related species that are difficult to distinguish based on their morphological features. Meconopsis simplicifolia (M. simplicifolia), an endangered herb within the Meconopsis genus, has demonstrated therapeutic potential in treating various diseases. However, the highly polymorphic morphology of this species poses a challenge for accurate identification. Methods: In this study, the complete chloroplast genome of M. simplicifolia was sequenced and assembled using Illumina sequencing technology. Simple sequence repeats (SSRs) and repetitive sequences were characterized. In addition, a comparative analysis was conducted with the chloroplast genomes of six other Meconopsis species. Results: The chloroplast genome of M. simplicifolia has a quadripartite circular structure with a total length of 152,772 bp. It consists of a large single-copy region of 83,824 bp and a small single-copy region of 17,646 bp, separated by a pair of inverted repeat sequences (IRa and IRb, 25,651 bp). The genome contains 131 genes, 33 SSRs, and 27 long repetitive sequences. Comparative analysis with six other chloroplast genomes of Meconopsis revealed that M. simplicifolia is closely related to M. betonicifolia and that the rpl2 (ribosomal protein L2) gene in the IRb region has been deleted. This deletion is of significant importance for future taxonomic studies of M. simplicifolia. Conclusions: This study provides a valuable reference for the identification of M. simplicifolia and contributes to a deeper understanding of the phylogeny and evolution of the Meconopsis genus.

Cancelable palmprint: intelligent framework toward secure and privacy-aware recognition system

Cancelable template protection techniques are indispensable to provide essential security and privacy privileges in biometric systems. This paper introduces an efficient cancelable palmprint recognition technique based on multi-level transformations. Gabor filtering with feature remapping is introduced for extracting highly discriminative features. Histogram remapping is applied to nonlinearly transform the downsampled Gabor features to be normally distributed. This feature-remapping step is proposed to enhance the discriminatory power and alleviate the effect of feature variability and image artifacts. Comb filtering is applied to the mapped features as a first protection layer. To provide security guarantees against linkability attacks, index-based locality-sensitive hashing (LSH) is introduced as a second protection layer to transform the comb-filtered mapped real-valued features into maximum-ranked indices. Recognition is performed in the secured domain to accelerate matching and to preserve the user’s privacy. Results show that the proposed scheme provides a large re-issuance ability, protects templates from being inverted, and demonstrates strong unlinkability across different databases. In addition, favorable verification/identification accuracy is obtained and the system satisfies the needs for real-time applications. A global measure D↔sys\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${D}_{\\leftrightarrow }^{sys}$$\\end{document} value of 0.01 is obtained, and thus, correlation attacks are mitigated. The recognition results for the legitimate scenario are 100% identification accuracy and 0% EER. For the worst case (same token scenario), the corresponding results are 99.752% and 0.31%, respectively.

SMOTE-Based Automated PCOS Prediction Using Lightweight Deep Learning Models.

The reproductive age of women is particularly vulnerable to the effects of polycystic ovarian syndrome (PCOS). High levels of testosterone and other male hormones are frequent contributors to PCOS. It is believed that miscarriages and ovulation problems are majorly caused by PCOS. A recent study found that 31.3% of Asian women have been afflicted with PCOS. Healing women with life-threatening disorders associated with PCOS requires more research. In prior research, methods have involved autonomously classified PCOS using a number of different machine learning techniques. ML-based approaches involve hand-crafted feature extraction and suffer from low performance issues, which cannot be ignored for the accurate prediction and identification of PCOS. Hence, predicting PCOS using cutting-edge deep learning methods for automated feature engineering with better performance is the prime focus of this study. The proposed method suggests three lightweight (LSTM-based, CNN-based, and CNN-LSTM-based) deep learning models, incorporating SMOTE for dataset balancing to obtain a valid performance. The proposed three models tend to offer an accuracy of 92.04%, 96.59%, and 94.31%, an ROC-AUC of 92.0%, 96.6%, and 94.3%, the number of parameters of 6689, 297, and 13285, and a training time of 67.27 s, 10.02 s, and 18.51 s, respectively. In addition, the DeLong test is also performed to compare AUCs to assess the statistical significance of all three models. Among all three models, the SMOTE + CNN models performs better in terms of accuracy, precision, recall, AUC, number of parameters, training time, DeLong's p-value over the other. Moreover, a performance comparison is also carried out with other state-of-the-art PCOS detection studies and methods, which validates the better performance of the proposed model. Thus, the proposed model provides the greatest performance, which can lead to a reduction in the number of failed pregnancies and help in finding PCOS in the early stages.

Incremental learning model for sustainable agricultural land assessment using multimodal satellite data

ABSTRACT The identification of agricultural lands is crucial for sustainable development in rural areas. In this paper, an augmented model that utilizes multimodal satellite-based data samples for identifying agricultural lands via incremental learning. The Whale Optimization Algorithm (WOA) is used for augmenting collected images and data samples to enhance the accuracy of the model under real-time conditions. To identify agricultural lands, Deep Convolutional Neural Networks (CNNs) are trained on the augmented data samples. Additionally, the incorporation of Q-Learning for continuous optimization of the model to enhance its efficiency and effectiveness in identifying agricultural lands. The proposed model offers many edges over existing methods. Firstly, the use of multimodal satellite-based data samples allows for a comprehensive and accurate analysis of agricultural lands. Secondly, the incorporation of the Whale Optimization Algorithm enables the augmentation of collected data samples, leading to improved accuracy and reliability of the model. Thirdly, Deep CNNs allows the extraction of complex features from the data, leading to more accurate identification of agricultural lands. Finally, the use of Q-Learning ensures that the model is continuously optimized to improve its efficiency and effectiveness. The need for this work arises from the limitations of existing methods in accurately identifying agricultural lands. Traditional methods based on manual surveys and visual interpretation are time-consuming, expensive, and prone to errors. Moreover, existing automated methods often lack the ability to analyse multimodal satellite-based data samples and fail to provide accurate results. Based on these observations, the proposed augmented model offers a promising solution for identifying agricultural lands for sustainable development. The use of multimodal satellite-based data samples, WOA, Deep CNNs, and Q-Learning allows for an accurate and efficient analysis of agricultural lands, which can aid in sustainable development planning and decision-making operations.

Near-infrared spectroscopy combined with support vector machine for the identification of Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) adulteration using wavelength selection algorithms

The frequent occurrence of adulterating Tartary buckwheat powder with crop flours in the market necessitates an urgent need for a simple analysis method to ensure the quality of Tartary buckwheat. This study employed near-infrared spectroscopy (NIRS) for the collection of spectral data from Tartary buckwheat samples adulterated with whole wheat, oat, soybean, barley, and sorghum flours. The competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were deployed to identify informative wavelengths. By integrating support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA), we constructed qualitative models to discern Tartary buckwheat adulteration. The PLS-DA model exhibited prediction accuracies between 89.78 % and 94.22 %, while the mean-centering (MC)-PLS-DA model showcased impressive predictive accuracy of 93.33 %. Notably, the feature-based Autoscales-CARS-CV-SVM model achieved more excellent identification accuracy. These findings exhibit the excellent potential of chemometrics as a powerful tool for detecting food product adulteration.

Improved User Identification through Calibrated Monte-Carlo Dropout

This paper presents an enhanced approach to user identification using smartphone and wearable sensor data. Our methodology involves segmenting input data and independently analyzing subsequences with CNNs. During testing, we apply calibrated Monte-Carlo Dropout to measure prediction uncertainty. By leveraging the weights obtained from uncertainty quantification, we integrate the results through weighted averaging, thereby improving overall identification accuracy. The main motivation behind this paper is the need to calibrate the CNN for improved weighted averaging. It has been observed that incorrect predictions often receive high confidence, while correct predictions are assigned lower confidence. To tackle this issue, we have implemented the Ensemble of Near Isotonic Regression (ENIR) as an advanced calibration technique. This ensures that certainty scores more accurately reflect the true likelihood of correctness. Furthermore, our experiment shows that calibrating CNN reduces the need for Monte Carlo samples in uncertainty quantification, thereby reducing computational costs. Our thorough evaluation and comparison of different calibration methods have shown improved accuracy in user identification across multiple datasets. Our results showed notable performance improvements when compared to the latest models available. In particular, our approach achieved better results than DB2 by 1.12% and HAR by 0.3% in accuracy.

RRNA operon improves species-level classification of bacteria and microbial community analysis compared to 16S rRNA.

We quantitatively demonstrated that the rRNA operon outperformed the 16S rRNA and its V3-V4 regions in accuracy for both individual species identification and species-level microbial community analysis. Our findings can provide guidelines for selecting appropriate markers in the field of microbial research.

Complete chloroplast genome sequence of Artemisia littoricola (Asteraceae) from Dokdo Island Korea: genome structure, phylogenetic analysis, and biogeography study.

The Asteraceae family, particularly the Artemisia genus, presents taxonomic challenges due to limited morphological characteristics and frequent natural hybridization. Molecular tools, such as chloroplast genome analysis, offer solutions for accurate species identification. In this study, we sequenced and annotated the chloroplast genome of Artemisia littoricola sourced from Dokdo Island, employing comparative analyses across six diverse Artemisia species. Our findings reveal conserved genome structures with variations in repeat sequences and junction boundaries. Notably, the chloroplast genome of A. littoricola spans 150,985bp, consistent with other Artemisia species, and comprises 131 genes, including 86 protein-coding, 37 tRNA, and 8 rRNA genes. Among these genes, 16 possess a single intron, while clpP and ycf3 exhibit two introns each. Furthermore, 18 genes display duplicated copies within the IR regions. Moreover, the genome possesses 42 Simple Sequence Repeats (SSRs), predominantly abundant in A/T content and located within intergenic spacer regions. The analysis of codon usage revealed that the codons for leucine were the most frequent, with a preference for ending with A/U. While the chloroplast genome exhibited conservation overall, non-coding regions showed lower conservation compared to coding regions, with the Inverted Repeat (IR) region displaying higher conservation than single-copy regions. Phylogenetic analyses position A. littoricola within subgenus Dracunculus, indicating a close relationship with A. scoparia and A. desertorum. Additionally, biogeographic reconstructions suggest ancestral origins in East Asia, emphasizing Mongolia, China (North East and North Central and South Central China), and Korea. This study underscores the importance of chloroplast genomics in understanding Artemisia diversity and evolution, offering valuable insights into taxonomy, evolutionary patterns, and biogeographic history. These findings not only enhance our understanding of Artemisia's intricate biology but also contribute to conservation efforts and facilitate the development of molecular markers for further research and applications in medicine and agriculture.

Emotional dialogue generation model of electronic commerce intelligent customer service based on topic expansion

Current electronic commerce (e-commerce) customer service systems often rely on retrieval-based methods, resulting in impersonal and emotionally flat responses. These systems struggle to handle diverse expressions and emotional nuances in user queries, leading to poor user experience. In this paper, we applied artificial intelligence techniques, notably the Gated Recurrent Unit model, to generate emotionally resonant dialogues in e-commerce customer service systems. We integrate intent recognition, emotion recognition, and topic expansion for accurate intent identification and nuanced responses. The model is trained on a customer service dataset from Jing Dong, a leading Chinese e-commerce platform. Through extensive experiments, we conducted comparative analyses against benchmark model Gated Recurrent Unit neural network. The results demonstrate that our model significantly outperforms existing approaches, achieving fluency, rationality, and emotionality scores of 62%, 58% and 61% respectively. These findings indicate that our approach generates responses with greater consistency to user intent and richer emotional content. The significance of these results lies in their potential to enhance user experience in e-commerce customer service interactions by providing more personalized and emotionally resonant automated responses.