syedzayyan/paper_mill_benchmark huggingface dataset:
Application of Image Recognition Based on Wireless Sensors in Dance Teaching System

With the vigorous development of higher education in china, many universities have made great progress in various indicators in recent years.As the number of college students increases year by year, the effect of instruction in the classroom is especially important.The high quality of teaching directly affects the efficiency of students’ listening to lectures, and more and more universities are receiving attention.However, the traditional dance classroom education and the one-to-many education model cannot adapt to the development trend of higher art education under the changes of the times and cannot effectively guarantee the quality of classroom education.The development of wireless sensor networks provides practical and feasible technical solutions for the development of dance education systems.Compared with general detection methods, image sensors can provide more real-time and more intuitive on-site information and wirelessly send image information to user terminals.This article describes the classic feature extraction algorithm and proposes a new feature extraction algorithm based on chart filling.The effectiveness of each algorithm is verified through several data sets.Image recognition is carried out by computer, including from computer to image processing, through the computer to recognize objects and various different modes of the target technology.The identification process usually includes several steps.First, the preprocessing of the image is required, then the segmentation of the image is performed, and then the feature extraction and matching are performed.In layman’s terms, image recognition hopes to imitate the human heart to read photos.By applying the image recognition technology to the dance education system, changes in the methods and forms of dance education can be stimulated.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
MRI Imaging Omics and Risk Factors Analysis of PWMD in Premature Infants Based on Fuzzy Clustering Algorithm

The magnetic resonance imaging (mri) characteristics of periventricular white matter damage (pwmd) in premature infants using the fuzzy c‐means clustering algorithm (fcm) is explored, and the influencing factors are further clarified.A total of 100 premature infants admitted to the neonatal department of our hospital from february 2020 to february 2022 are selected for in‐depth investigation.According to the occurrence of pwmd, they are divided into the pwmd group and the simple premature delivery group, with 50 cases in each group.All preterm infants are examined by mri and the changes in image characteristics and apparent diffusion coefficient (adc) values are analyzed.Clinical information of the subjects is collected and the influencing factors of pwmd in preterm infants are analyzed by multivariate regression analysis.In the first magnetic resonance imaging (mri) examination, the cases of punctured, clustered, and linear lesions are 28 cases, 12 cases, and 10 cases, respectively.The experimental results showed that pwmd of preterm infants presented punctate, clustered, and high linear t1 signal mri manifestations, which caused a downward trend of adc value, and caused respiratory distress, low birth weight, premature rupture of membranes, respiratory tract infection, and other risk symptoms.

Journal: Contrast Media & Molecular Imaging (2022)
Publication types: Clinical Study;
Clinical Observation of the Effect of Nasal Breathing on Nonanalgesic and Sedative Gastroscopy

This study aimed to investigate the safety, tolerance, and comfort of the subjects in gastroscopy by observing and comparing the effect of gastroscopy under two different breathing modes: nasal breathing and nasal inspiration and oral expiration.A total of 60 subjects who underwent routine gastroscopy in the hospital from january 2021 to june 2021 were selected as the research subjects.According to the willingness of the subjects, they were divided into a nasal breathing group and a nasal inspiration and oral expiration group.The differences in vital signs, adverse reactions, and visual analog scale (vas) scores were compared between the two groups.There were no significant differences in mean arterial pressure, heart rate, and blood oxygen saturation between the two groups before, during, and after the examination ( p &gt; 0.05).The nasal breathing group had fewer adverse reactions such as nausea, cough, belching, and restlessness than the nasal inspiration and oral expiration group, and the difference was statistically significant ( p &lt; 0.01).Vas score of the nasal breathing group was lower than that of the nasal inspiration and oral expiration group, and the difference was statistically significant ( p &lt; 0.01).Subjects are more tolerant to nasal breathing mode, which causes fewer adverse reactions, less pain, and more comfort and is more worthy to be popularized in primary hospitals.

Journal: Contrast Media & Molecular Imaging (2022)
Publication types: Research Article;
Exploration of the Effects of TGF-β Pathway-Based Pituitary Tumor of Rats on GH3 Cell Line after Intervention with Different Concentrations of TGZ

The effect of the tgf‐ β pathway‐based pituitary tumor of rats on the gh3 cell line after intervention with different concentrations of troglitazone (tgz) is explored.The ch3 cell line of 24 clean male sd rats with pituitary adenoma is selected.The cells are divided into a blank contrast set and an experimental set.The experimental set is divided into different tgz concentration sets, including 1 × 10 −3 tgz set, 1 × 10 −4 tgz set, and 1 × 10 −5 tgz set.The cell proliferation is detected by the cck‐8 method, the protein expressions of cd147, tgf‐ β 1, and mmp‐9 are detected by the western blot method, and the relative m rna expressions of cd147, tgf‐ β 1, and mmp‐9 are detected by the q rt‐pcr method.The expression levels of cd147, tgf‐ β 1, and mmp‐9 in ch3 cells of pituitary adenoma rats are notoriously lower, while the expression of cd147, tgf‐31, and mmp‐9 could be reduced by tgz acting on the gh3 cell line.The specific mechanism of action of this effect on the invasive ability of gh3 cell lines is multifaceted, suggesting that peroxisome proliferator activator‐receptor (ppar‐ γ ) agonists have good clinical application prospects in tumor therapy and can provide new targets and approaches for tumor drug therapy.

Journal: Contrast Media & Molecular Imaging (2022)
Publication types: Research Article;
Nursing Teaching Curriculum Setting by Introducing Postcompetency Model under the Vision of Internet Informatization

The nursing curriculum is to be modernized to improve the student’s skills in meeting the recent trends in medical and healthcare fields.The curriculum improvements are based on expert recommendations, authors, and informative data from different web sources.The challenging task is to improve the readability and understandability of the curriculum to real‐time standards.Considering the above facts, this article introduces a refined curriculum with internet information analysis (rc‐iia) method.The proposed method incorporates the distributed internet, journal, and previous curriculum information within the active nursing syllabus.This prevents repetitions and less‐informative content within the active curriculum.Besides, classification learning for knowledge‐based representations is used within the curriculum to improve competency.Based on the refined information, a recommendation‐based curriculum is preferred for varying information across different standards.The proposed analysis method relies on existing and distributed information across multiple curriculum providers for leveraging the visibility and prolonging the stealth of the nursing curriculum.

Journal: Contrast Media & Molecular Imaging (2022)
Publication types: Research Article;
Fuzzy Logic-Based Machine Learning Algorithm for Cultural and Creative Product Design

In order to effectively assist industrial designers in the color scheme design of cultural and creative products and output color schemes that meet users’ image preferences, an interactive color scheme design method for cultural and creative products based on triangular fuzzy number is proposed.Through the group consistency decision based on triangular fuzzy number, an interactive genetic evolution of the color scheme population of cultural and creative products is performed to generate color schemes that satisfy the group consistency imagery preference and satisfaction, and the final selection scheme is calculated by color beauty.This paper verifies that the method can effectively integrate the imagery preferences of group users and help industrial designers to better design color schemes for cultural and creative products through the results of multiuser decision consistency.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
The Operation Mode of Agricultural Supply Chain Finance Using Blockchain

With the rapid development of agriculture, modern agriculture has achieved remarkable results.Agriculture is the foundation of china’s national economy, and the party central committee has always insisted on solving the “three rural issues” as the top priority of the work of the whole party.However, china’s agricultural foundation is weak, and the problem of unbalanced supply and demand structure of agricultural products still exists in agricultural development.With the development of blockchain, the application basis in the agricultural value chain has been established.At present, the research on the application of agricultural value chain financing is still in progress, but it is still in the initial stage.The integration of agricultural finance blockchain is low, and there are a series of problems.Extending blockchain technology to the financial area of agricultural value chains can help overcome the information barriers to traditional agricultural value chain financing and improve access to information resources for traditional agricultural value chains.The high cost of these value chains and inadequate financial management mechanisms remove bottlenecks in financing agricultural development.In this paper, we study the operation model and revenue distribution model of agricultural value chains using blockchain, analyze examples, and finally identify the basic elements of agricultural value chain financing based on sectoral chain technology.It provides theoretical support for the financing decision and production decision of each member of the agricultural supply chain, and it is hoped that the content and conclusions of the study can provide methodological reference and theoretical guidance for agricultural supply chain enterprises.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Optimal Allocation of Enterprise Resources Based on the Intelligent Algorithm from the Perspective of Multidimensional Dynamic Innovation

Faced with the upsurge of digital technology, many small and medium-sized startups are aware of the opportunities and challenges it brings and are actively responding to digital transformation.Digital transformation is a type of strategic change with certain risks.On the one hand, this requires business leaders to have a sense of change, and on the other hand, it also requires certain resources to be invested.As a result, in the real world, not all small and medium-sized entrepreneurial firms have the ability or inclination to implement digital transformation.Smes’ digital transformation has become a key factor in promoting the national economy’s transition from new and old kinetic energy and providing new prospects for development of small and medium firms.As a starting point, this study devises an index system for assessing the digital maturity of small and medium-sized businesses.Based on the ahp method in the multicriteria framework, a hierarchical structure is established for each index, a three-layer basic framework is established, and the weight of the evaluation index is calculated.Second, this work establishes a bp network model for digital transformation maturity assessment of startups based on ahp and establishes a three-layer bp network structure.This work aims to optimize bp network using the improved isfla algorithm.It improves the adaptive step size update formula in the sfla algorithm by using the mutation operator and improves the evolution method of the worst individual of the frog to the simultaneous evolution of multiple poor individuals.The constructed isfla-bp algorithm is then used to evaluate the digital transformation maturity of small and medium-sized startups.Finally, systematic and comprehensive experiments are carried out to verify superiority as well as feasibility of the method.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
3D Real Scene Data Collection of Cultural Relics and Historical Sites Based on Digital Image Processing

Traditional digital geometry mainly focuses on local geometric features such as curvature and normal of 3d models.Although the curvature can describe the geometric curvature of the model surface, these local geometric properties cannot describe the global functional structure and associated properties of the 3d model.The purpose of this paper was to study the accurate splicing of cultural relic fragments based on the intrinsic structural features of the 3d model, such as geometry, texture, and function, whose method is the key to realizing the virtual reconstruction of damaged cultural relics.In this paper, a method for analyzing the intrinsic structure of 3d point cloud data is proposed, the feature representation method of 3d discrete curves and surfaces is studied, and a method for identifying geometric features of 3d point clouds based on the similarity measure of the principal curvature is proposed, which realizes the effective extraction of geometric features of the 3d point cloud model of cultural relics.By calculating the visual curvature distribution of the model under multiscale constraints, the effective extraction of the structural primitives of cultural relics with rich surface noise is realized.The experimental results in this paper show that the initial matching time is 1.416 seconds, the final matching time is 1.555 seconds, the average number of iterations is 13, the average stitching error is 1.7233 mm, and the standard deviation is 1.0265 mm.The experimental data show that the algorithm proposed in this chapter has good convergence characteristics and effectively avoids the divergence phenomenon that is easy to occur in the existing stitching algorithms.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Cultivation of College Basketball Referee Ability Using Infrared Thermal Imaging Target Recognition and Tracking System

With the development of economy and culture and the improvement of infrastructure, basketball is becoming more and more popular.At the same time, whether in school or society, basketball events are increasing, and the demand for basketball referees is also rising.As the base and cradle of training basketball referees, colleges and universities are important places for incubating basketball referees.However, the basketball referee ability cultivated in colleges and universities is still lacking, which cannot meet the increasingly standardized basketball events and basketball training.Infrared thermal imaging has the characteristics of passive imaging, high recognition accuracy, and strong anti-interference.It is especially suitable for tracking and testing basketball players, and this is expected to further improve the comprehensive ability of basketball referees.Based on this, a target recognition and tracking system based on infrared thermal imaging is proposed in this study.Its purpose is to use it to carry out comprehensive ability training for college basketball referees.Experiments show that target recognition and tracking based on infrared thermal imaging can improve the comprehensive ability of basketball referees by 40.32%, and the awareness of basketball referees to fouls has also reached 88.6%.This fully shows that the target recognition and tracking system based on infrared thermal imaging can effectively improve the comprehensive ability of basketball referees and make basketball more regular and standardized.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Online Labor Education Optimization Method Based on Computer Intelligent Algorithm

People’s lives are undergoing tremendous changes with the development of the times.Compared with the past, people’s pursuit of spiritual and cultural life also makes our education field usher in a huge development to adapt to the changes in the context of the times.But, at the same time, the development of labor education is gradually being downplayed by people, resulting in a series of problems such as people preferring comfort and not working.Aiming at this common problem, this paper will use the ant colony algorithm and particle swarm optimization algorithm in the computer intelligent algorithm to optimize the way of labor education.It includes the principle and basic process of the ant colony algorithm, the establishment of the mathematical model of the original ant colony algorithm, and the improved algorithm of the ant colony algorithm.The research results of the optimization method of labor education showed the following: when the number of ant colonies reaches 51, the number of iterations of the algorithm will be the least, and the corresponding shortest path is also the best solution; when the combination of pheromone intensity and volatility factor is 3, the optimal solution can be quickly found, and the algorithm inflection point of mmas is 44.82.From the research results, it can be seen that the computer intelligent algorithm has a good choice for the optimization of labor education and can achieve a major breakthrough in the traditional model of labor education.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection

Human-centric biomedical diagnosis (hcbd) becomes a hot research topic in the healthcare sector, which assists physicians in the disease diagnosis and decision-making process.Leukemia is a pathology that affects younger people and adults, instigating early death and a number of other symptoms.Computer-aided detection models are found to be useful for reducing the probability of recommending unsuitable treatments and helping physicians in the disease detection process.Besides, the rapid development of deep learning (dl) models assists in the detection and classification of medical-imaging-related problems.Since the training of dl models necessitates massive datasets, transfer learning models can be employed for image feature extraction.In this view, this study develops an optimal deep transfer learning-based human-centric biomedical diagnosis model for acute lymphoblastic detection (odlhbd-alld).The presented odlhbd-alld model mainly intends to detect and classify acute lymphoblastic leukemia using blood smear images.To accomplish this, the odlhbd-alld model involves the gabor filtering (gf) technique as a noise removal step.In addition, it makes use of a modified fuzzy c-means (mfcm) based segmentation approach for segmenting the images.Besides, the competitive swarm optimization (cso) algorithm with the efficient net b0 model is utilized as a feature extractor.Lastly, the attention-based long-short term memory (abi lstm) model is employed for the proper identification of class labels.For investigating the enhanced performance of the odlhbd-alld approach, a wide range of simulations were executed on open access dataset.The comparative analysis reported the betterment of the odlhbd-alld model over the other existing approaches.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Automated COVID-19 Classification Using Heap-Based Optimization with the Deep Transfer Learning Model

The outbreak of the covid-19 pandemic necessitates prompt identification of affected persons to restrict the spread of the covid-19 epidemic.Radiological imaging such as computed tomography (ct) and chest x-rays (cxr) is considered an effective way to diagnose covid-19.However, it needs an expert’s knowledge and consumes more time.At the same time, artificial intelligence (ai) and medical images are discovered to be helpful in effectively assessing and providing treatment for covid-19 infected patients.In particular, deep learning (dl) models act as a vital part of a high-performance classification model for covid-19 recognition on cxr images.This study develops a heap-based optimization with the deep transfer learning model for detection and classification (hbodtl-dc) of covid-19.The proposed hbodtl-dc system majorly focuses on the identification of covid-19 on cxr images.To do so, the presented hbodtl-dc model initially exploits the gabor filtering (gf) technique to enhance the image quality.In addition, the hbo algorithm with a neural architecture search network (nas net) large model is employed for the extraction of feature vectors.Finally, elman neural network (enn) model gets the feature vectors as input and categorizes the cxr images into distinct classes.The experimental validation of the hbodtl-dc model takes place on the benchmark cxr image dataset from the kaggle repository, and the outcomes are checked in numerous dimensions.The experimental outcomes stated the supremacy of the hbodtl-dc model over recent approaches with a maximum accuracy of 0.9992.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Mobility and Quality of Life in Chinese Elderly and Geriatric Patients and Biomedical Diagnosis

Mobilization hugely affects the personal satisfaction of old people.We found that preparing old and geriatric patients expands their personal satisfaction using statistical comparisons of observed groups.As a technique of evaluation, we employed the nonparametric mann–whitney test.The pearson correlation coefficient was employed to determine relationships between variables.In both the bref questionnaire (item 15) and the adl test, geriatric individuals had similar mobility rates.Physical activity is a crucial feature of mobility in seniors, according to findings from other studies.In hospitalized geriatric patients, reduced mobility is the most significant impediment.Seniors’ confidence in their own talents grew as a result of the favourable overall influence of physical activity.Geriatric patients must be mobilized on a regular basis in nursing, depending on their level of dependency.Physical activity had a positive overall effect on older people’s confidence in their own abilities, and this was shown in how they felt about themselves.Nursing staff must constantly move geriatric patients based on how much assistance they require.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;