syedzayyan/paper_mill_benchmark huggingface dataset:
Sparse Bayesian Model and Artificial Intelligence in Enterprise Goodwill Evaluation and Dynamic Management

With the rapid development of mobile internet information technology, automated search text has occupied a leading position in many industries.This article not only makes a detailed case study on the basic working principles of text feature extraction and classification methods but also makes in-depth case analysis on the extraction algorithm and its basic concepts as well as some problems that may be encountered in text feature classification and explained their advantages and disadvantages in detail.Aiming at the shortcomings of various algorithms, a sparse bayesian probability model is proposed, so that it can better meet the requirements of database and text classification and further improve related technologies.Nowadays, the evaluation of china’s goodwill value, whether in theory or in practice, usually simply adopts traditional fixed asset evaluation methods.However, traditional methods have the disadvantages of ignoring comparisons with the same industry and failing to take into account different factors that affect corporate goodwill.This article adopts a new method that combines traditional methods to evaluate goodwill and tries to improve the results obtained by this traditional method to make the evaluation results more accurate.Then, by studying the adaptability of traditional chinese risk assessment and forecasting models, a comprehensive comparison is made.Aiming at the embarrassing situation that the current methods of corporate excess asset return risk assessment difficult to predict in practice, the new gray factors evaluation models are creatively studied.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Study on 3D Clothing Color Application Based on Deep Learning-Enabled Macro-Micro Adversarial Network and Human Body Modeling

In real life, people’s life gradually tends to be simple, so the convenience of online shopping makes more and more research begin to explore the convenience optimization of shopping, in which the fitting system is the research product.However, due to the immaturity of the virtual fitting system, there are a lot of problems, such as the expression of clothing color is not clear or deviation.In view of this, this paper proposes a 3d clothing color display model based on deep learning to support human modeling‐driven.Firstly, the macro‐micro adversarial network (mman) based on deep learning is used to analyze the original image, and then, the results are preprocessed.Finally, the 3d model with the original image color is constructed by using uv mapping.The experimental results show that the accuracy of the mman algorithm reaches 0.972, the established three‐dimensional model is emotional enough, the expression of the clothing color is clear, and the difference between the color difference and the original image is within 0.01, and the subjective evaluation of volunteers is more than 90 points.The above results show that it is effective to use deep learning to build a 3d model with the original picture clothing color, which has great guiding significance for the research of character model modeling and simulation.

Journal: Computational Intelligence and Neuroscience (2021)
Publication types: Research Article;
Hypoxia-Induced Nestin Regulates Viability and Metabolism of Lung Cancer by Targeting Transcriptional Factor Nrf2, STAT3, and SOX2

To investigate hypoxia-induced nestin regulates lung cancer viability and metabolism by targeting transcription factors nrf2, stat3, and sox2.Methods.Eighty-four cases of nonsmall cell lung cancer (nonsmall cell lung cancer, nsclc), which had been treated from june 2020 to february 2021, were randomly selected from our clinicopathology database.Immunohistochemical staining of collected tissue cells was performed to assess the expression patterns of nestin, stat3, nrf2, and sox2.Data were quantified and statistically analyzed using one-way and two-way anova tests with p &lt; 0.05 .Results.Clinicopathological findings showed significant differences in lymph node metastasis, tissue differentiation, and histology on induction of nestin expression; nestin expression correlated with stat3, nrf2, and sox2 expression.nestin/stat3/sox2/nrf2 are involved in angiogenesis and lung cancer development.Conclusion.Hypoxia-induced nestin promotes the progression of nonsmall lung cancer cells by targeting the downstream transcription factors stat3, nrf2, and sox2.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors

Skin disease is the major health problem around the world.The diagnosis of skin disease remains a challenge to dermatologist profession particularly in the detection, evaluation, and management.Health data are very large and complex due to this processing of data using traditional data processing techniques is very difficult.In this paper, to ease the complexity while processing the inputs, we use multilayered perceptron with backpropagation neural networks (mlp-bpnn).The image is collected from the devices that contain nanotechnology sensors, which is the state-of-art in the proposed model.The nanotechnology sensors sense the skin for its chemical, physical, and biological conditions with better detection specificity, sensitivity, and multiplexing ability to acquire the image for optimal classification.The mlp-bpnn technique is used to envisage the future result of disease type effectively.By using the above mlp-bpnn technique, it is easy to predict the skin diseases such as melanoma, nevus, psoriasis, and seborrheic keratosis.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
An Optical Fiber Sensing Method for Measuring the Surface Flatness of an Object

Sensing of data or object is a mature research domain where intelligent devices, may possibly be embedded with the artificial intelligence-based techniques, are placed in the closed vicinity of the object or phenomenon.This sensing activity becomes more complex if it is related to the measurements of an object which is required to be accurate and precise.In this paper, the reflection interference spectrum method is used to measure the flatness of the surface of the object.The thickness of the tested object is between 0.4 and 16 μm.The optical fiber sensor is moved on the surface of the object, and the reflection spectrum of each point on the surface of the object is analyzed.The thickness of each point, and through the movement of the stepping motor, the thickness of different points on the object is continuously measured, so as to obtain the surface topography of the object.This method has no destructive effect on the surface of the object, has no lateral test range limitation, and has a simple test system structure, high test accuracy, and reliable test results.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Analysis of Main Movement Characteristics of Hip Hop Dance Based on Deep Learning of Dance Movements

In order to explore the main action characteristics of hip hop dance, a deep learning recognition system based on dance action is proposed.The network is based on convolution, pooling, and full connection calculation in a convolutional neural network (cnn).On the one hand, the pixel information in the video frame is extracted as the network input feature in the spatial domain.On the other hand, in the time domain, in order to better represent the change characteristics of video actions, optical flow information is introduced, and the optical flow vector change of pixels in dt time is calculated by the pyramid algorithm (lk) as the time domain convolution feature.In order to evaluate the performance of the network, this article takes the recognition of dance movements as an example to test the application of the algorithm.The test dataset contains 101 fully identified dance movements.The test results show that the proposed algorithm is 10.90% higher than f1 of inception v3, and the recognition accuracy is 10.85% and 5.27% higher than that of inception v3 and 3d-cnn networks, respectively.For the problems and difficulties brought by single-mode video action recognition, a multimodal action recognition method is introduced to achieve better results based on a large number of training data.Different depth networks have different characteristics.Cnn network pays more attention to the relationship between local information, so it is suitable for image recognition and detection tasks.The rnn network is expanded in the time dimension, so it is suitable for the modal information related to similar videos.Therefore, based on multimodal information and a depth neural network, a depth feature extraction and fusion method for multimodal information is designed.Different methods of feature extraction and fusion are tried in the experiment, and the experimental results are analyzed.It proves that the deep learning and recognition of dance movement can effectively explore the main movement characteristics of hip hop dance.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Effect of Tetracycline Antibiotics on Anaerobic Digestion of Chicken Manure Based on Data Mining

Although anaerobic bioengineering treatment of chicken manure has the advantages of low energy consumption, less pollution, and recyclable biomass energy, antibiotics are usually added in the process of modern intensive farming.And antibiotics still exist in feces and pose a threat to human health.Therefore, this study aimed to deeply understand the role of tc in the anaerobic digestion of chicken manure and to analyze the effect of tetracycline antibiotics on the anaerobic digestion of chicken manure based on data mining.In this study, chicken manure was used as raw material for anaerobic fermentation, and the effects of tetracycline (tc) on anaerobic and anaerobic fermentation of chicken manure were compared through batch and sequence experiments.Also, this study analyzes the fermentative transformation to elucidate the effect of tc on anaerobic manure conversion in chicken manure, which further studies the effect of tc on the anaerobic fermentation of chicken manure.The experimental results in this study show that when the tc concentration is 50–150 mg/l, the content of tryptophan and tyrosine proteins in the treatment group is also higher than that in the control group.It shows that at low concentrations (10, 25 mg/l), tc mainly promotes the degradation of leps by promoting the dissolution of intracellular substances in the sludge.With the increase of tc concentration (50–300 mg/l), tc mainly promotes the breakdown of the sludge cell membrane by promoting the rupture of the sludge cell membrane, thereby promoting the degradation of leps.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Construction Means of Soil Microbial Synusiologic Network Based on ANN

With the construction of synusiologic civilization and synusiologic environmental protection entering a new era driven by data, the breadth and depth of application of the dm technique in the domain of synusiologic environmental protection are constantly strengthened.If reasonable planning is not carried out in the process of social construction, it will cause unpredictable damage to the synusiologic environment.However, traditional synusiologic planning means too much human interference, and there are still some shortcomings in accuracy and operability, which means they cannot guide synusiologic construction well.In order to analyze the contribution of the soil nutrient data to soil fertility and dig out the knowledge describing soil fertility, this paper studies the construction means of soil microbial synusiologic network by ann.By simulating the learning, memorizing, and processing problems of human brain neurons, the artificial network establishes a parallel distributed processing system computing dmg model with a large number of connections, which can quickly acquire knowledge from the outside world and store and process it and respond to the changes in the external environment in time.According to the research in this paper, the network performance of this algorithm is 18% better than that of the traditional algorithm, and it is suitable to be widely put into practice.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Inhibition of inflammation using diacerein markedly improved renal function in endotoxemic acute kidney injured mice

Background inflammation is an important pathogenic component of endotoxemia-induced acute kidney injury (aki), finally resulting in renal failure.Diacerein is an interleukin-1β (il-1β) inhibitor used for osteoarthritis treatment by exerting anti-inflammatory effects.This study aims to investigate the effects of diacerein on endotoxemia-induced aki.Methods male c57bl/6 mice were intraperitoneally injected with lipopolysaccharide (lps, 10 mg/kg) for 24 h prior to diacerein treatment (15 mg/kg/day) for another 48 h.Mice were examined by histological, molecular and biochemical approaches.Results lps administration showed a time-dependent increase of il-1β expression and secretion in kidney tissues.Diacerein treatment normalized urine volume and osmolarity, reduced blood urea nitrogen (bun), fractional excretion of sodium (fena), serum creatinine and osmolarity, and protected renal function in an endotoxemic aki mice model.In the histopathologic study, diacerein also improved renal tubular damage such as necrosis of the tubular segment.Moreover, diacerein inhibited lps-induced increase of inflammatory cytokines, such as il-1β, tumor necrosis factor-α, monocyte chemoattractant protein-1 and nitric oxide synthase 2.In addition, lps administration markedly decreased aquaporin 1 (aqp1), aqp2, aqp3, na,k-atpase α1, apical type 3 na/h exchanger and na-k-2cl cotransporter expression in the kidney, which was reversed by diacerein treatment.We also found that diacerein or il-1β inhibition prevented the secretion of inflammatory cytokines and the decrease of aqp and sodium transporter expression induced by lps in hk-2 cells.Conclusion our study demonstrates for the first time that diacerein improves renal function efficiently in endotoxemic aki mice by suppressing inflammation and altering tubular water and sodium handing.These results suggest that diacerein may be a novel therapeutic agent for the treatment of endotoxemic aki.

Journal: Cellular & Molecular Biology Letters (2018)
Publication types: Research Article;
SLCO4A1-AS1 mediates pancreatic cancer development via miR-4673/KIF21B axis

In this study, we intended to figure out the biological significance of long non-coding rnas (lnc rnas) solute carrier organic anion transporter family member 4a1 antisense rna 1 (slco4a1-as1) in pancreatic cancer (pc).Cell counting kit-8, colony formation, wound healing, transwell, and flow cytometry experiments were performed to reveal how slco4a1-as1 influences pc cell proliferation, migration, invasion, and apoptosis.Thereafter, bioinformatics analysis, rna immunoprecipitation assay, luciferase reporter assay, and rna pull-down assay were applied for determining the binding sites and binding capacities between slco4a1-as1 and mi r-4673 or kinesin family member 21b (kif21b) and mi r-4673.The results depicted that slco4a1-as1 was upregulated in pc, and slco4a1-as1 knockdown suppressed pc cell growth, migration, invasion, and induced cell apoptosis.Furthermore, slco4a1-as1 was verified to modulate the expression of kif21b by binding with mi r-4673.Slco4a1-as1 exerted an oncogenic function in pc.The overexpression of slco4a1-as1 aggravated the malignant behaviors of pc via the upregulation of kif21b by sponging mi r-4673.Our findings revealed a novel molecular mechanism mediated by slco4a1-as1, which might play a significant role in modulating the biological processes of pc.

Journal: Open Medicine (2022)
Publication types: Research Article;
Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High-Order Simulated Annealing Neural Network Algorithm

With the development of medical informatization, the data related to medical field are growing at an amazing speed, and medical big data appears.The mining and analysis of these data plays an important role in the prediction, monitoring, diagnosis, and treatment of tumor diseases.Therefore, this paper proposes a clustering algorithm of the high‐order simulated annealing neural network algorithm and uses this algorithm to extract tumor disease‐related big data, constructs training set according to the relevant information mined, designs a kind of dimension reduction model, aiming at the problem of excessive and wrong diagnosis and treatment in the diagnosis and treatment module of tumor disease monitoring mode, and establishes the corresponding control mechanism, so as to optimize the tumor disease monitoring mode.The results show that the clustering accuracy of the high‐order simulated annealing neural network algorithm on different data sets (iris, wine, and pima india diabetes) is 97.33%, 82.11%, and 70.56% and the execution time is 0.75 s, 0.562 s, and 1.092 s, which are better than those of the fast k ‐medoids algorithm and improved k ‐medoids clustering algorithm.To sum up, the high‐order simulated annealing neural network algorithm can achieve good clustering effect in medical big data mining.The establishment of model m1 can reduce the probability of excessive and wrong medical treatment and improve the effectiveness of diagnosis and treatment module monitoring in tumor disease monitoring mode.

Journal: Computational Intelligence and Neuroscience (2021)
Publication types: Research Article;
Decision-Making Model of Product Modeling Big Data Design Scheme Based on Neural Network Optimized by Genetic Algorithm

At present, machine learning artificial neural network technology, as one of the core technologies of enterprises, has received unprecedented attention.This technology is widely used in automatic driving, pattern recognition, teaching aid, product modeling, and other fields.According to the development of product design, this paper analyzes the factors that affect the decision‐making of product design.The neural network optimized by genetic algorithm is studied, and the technical analysis of neural network algorithm before and after optimization is mainly carried out.The basic process of product modeling design model based on image processing under the background of big data is introduced.The multidirectional group decision‐making model of product modeling design scheme in big data cloud environment is constructed.The final decision model can improve the overall design efficiency, shorten the manufacturing period, and provide a new idea for product modeling design.

Journal: Computational Intelligence and Neuroscience (2021)
Publication types: Research Article;
Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions

One of the leading causes of deaths around the globe is heart disease.Heart is an organ that is responsible for the supply of blood to each part of the body.Coronary artery disease (cad) and chronic heart failure (chf) often lead to heart attack.Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns.Therefore, researchers have developed various automated diagnostic systems based on machine learning (ml) and data mining techniques.Ml-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection.Various ml, data mining methods, and data modalities have been utilized in the past.Many previous review papers have presented systematic reviews based on one type of data modality.This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ecg.Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods.Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Review Article;
Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis

Pneumonitis is an infectious disease that causes the inflammation of the air sac.It can be life-threatening to the very young and elderly.Detection of pneumonitis from x-ray images is a significant challenge.Early detection and assistance with diagnosis can be crucial.Recent developments in the field of deep learning have significantly improved their performance in medical image analysis.The superior predictive performance of the deep learning methods makes them ideal for pneumonitis classification from chest x-ray images.However, training deep learning models can be cumbersome and resource-intensive.Reusing knowledge representations of public models trained on large-scale datasets through transfer learning can help alleviate these challenges.In this paper, we compare various image classification models based on transfer learning with well-known deep learning architectures.The kaggle chest x-ray dataset was used to evaluate and compare our models.We apply basic data augmentation and fine-tune our feed-forward classification head on the models pretrained on the image net dataset.We observed that the dense net201 model outperforms other models with an auroc score of 0.966 and a recall score of 0.99.We also visualize the class activation maps from the dense net201 model to interpret the patterns recognized by the model for prediction.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Research Article;
Evaluation Algorithm for the Effectiveness of Stroke Rehabilitation Treatment Using Cross-Modal Deep Learning

It is important to study the evaluation algorithm for the stroke rehabilitation treatment effect to make accurate evaluation and optimize the stroke disease treatment plan according to the evaluation results.To address the problems of poor restoration effect of positron emission tomography (pet) image and recognition restoration effect of evaluation data and so on.In the paper, we propose a stroke rehabilitation treatment effect evaluation algorithm based on cross-modal deep learning.Magnetic resonance images (mri) and pet of stroke patients were collected as evaluation data to construct a multimodal evaluation dataset, and the data were divided into positive samples and negative samples.According to the mapping relationship between mri and pet, three-dimensional cyclic adversarial is used to generate the neural network model to recover the missing pet data.Using the cross-modal depth learning network model, the rgb image, depth image, gray image, and normal images of mri and pet are taken as the feature images and the multifeature fusion method is used to fuse the feature images, output the recognition results of mri and pet, and evaluate the effect of stroke rehabilitation treatment according to the recognition results.The results show that the proposed algorithm can accurately restore pet images, the evaluation data recognition effect is good, and the evaluation data recognition accuracy is higher than 95%.The evaluation accuracy of stroke rehabilitation treatment effect is high, the evaluation time varies between 0.56 s and 0.91 s, and the practical application effect is good.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;