syedzayyan/paper_mill_benchmark huggingface dataset:
Manta Ray Foraging Optimization with Vector Quantization Based Microarray Image Compression Technique

Dna microarray technologies enable the analysis of the expression of numerous genes in an individual experiment and become an important approach in the field of medicine and biology for investing genetic function, regulation, and interaction.Microarray images can be investigated well for obtaining the contained genetic data.But is it undesirable to retain the genetic data and avoid the microarray images?Due to considerable attention to dna microarray and several experiments being performed under distinct conditions, a massive quantity of data gets produced over the globe.In order to store and share the microarray images, effective storage and communication models are needed in a natural way.Vector quantization (vq) is a commonly utilized tool for compressing images, which mainly aims to produce effective codebooks comprising a collection of codewords.Therefore, this paper presents a manta ray foraging optimization (mrfo) with linde–buzo–gray (lbg) based microarray image compression (mrfolbg-mic) technique.The lbg model is commonly utilized to design local optimal codebooks to compress images.The construction of codebooks can be defined as a nondeterministic polynomial time (np) hard problem and can be resolved by the mrfo algorithm.The codebooks produced from lbg-vq are optimized using the mrfo algorithm to attain optimum optimal codebooks.When the codebooks are produced by the mrfolbg-mic algorithm, deflate model can be applied to compress the index tables.The design of the mrfo algorithm with lbg and deflate based index table compression demonstrate the novelty of the work.For demonstrating the enhanced compression efficacy of the mrfolbg-mic model, a wide-ranging experimental validation process is performed using a benchmark dataset.The experimental outcomes inferred that the mrfolbg-mic model accomplished superior outcomes over the other existing models.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Visualization Method of Key Knowledge Points of Nursing Teaching Management System Based on SOM Algorithm and Biomedical Diagnosis

The traditional nursing teaching knowledge point recommendation algorithm based on collaborative filtering is difficult to deal with the problem of data sparsity, while the traditional recommendation algorithm based on matrix decomposition has poor scalability in dealing with high-dimensional data, and their recommendation results are only determined according to the prediction score, resulting in low recommendation accuracy.In view of this, a nursing teaching knowledge point recommendation method based on a som neural network and ranking factor decomposition machine is proposed.Firstly, the som neural network is used to cluster users based on users’ academic background information, then the partial order relationship of nursing teaching knowledge points is constructed by using users’ explicit and implicit web access behavior, and finally, the factor decomposition machine is used as the ranking function to classify users’ academic background web access behavior, borrowing nursing teaching introduction text, and other characteristic information were modeled, and the peer-to-peer ranking learning algorithm was used to accurately recommend nursing teaching knowledge points.Experimental results show that the proposed method can effectively alleviate the problem of data sparsity and improve the accuracy and efficiency of recommendations.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
C-Reactive Protein as a Prognostic Biomarker for Gynecologic Cancers: A Meta-Analysis

The prognostic role of crp (c-reactive protein) in gynecological tumors has been previously reported in individual studies, but whether crp can be used as a separate potential prognostic factor has not been systematically reviewed.The purpose of this research is to determine if there is a link between crp levels and the prognosis of gynecological cancer patients.Methods.A systematic search was carried out to find the literature evaluating the predictive role of crp in the prognosis of gynecological cancer patients.For the purpose of determining the relationship between crp and clinicopathological characteristics, the pooled odds ratio (or) was calculated.A hazard ratio (hr) with a 95% confidence interval (ci) was used to determine differences in overall survival (os), disease-free survival (dfs), or progression-free survival (pfs) between patients with low and high crp levels.Results.A total of 19 studies, including 4062 patients, were analyzed retrospectively.The figo stage was related to the crp level (or = 0.43, 95% ci: 0.19–1.00).Age, lymph node metastasis, and histological grade were not associated with crp level (or = 0.93, 95% ci: 0.69–1.25; or = 0.91, 95% ci: 0.65–1.28; or = 0.74, 95% ci: 0.52–1.05).Worse os (hr = 1.40, 95% ci: 1.23–1.57), dfs (hr = 1.20, 95% ci: 1.12–1.28), and pfs (hr = 1.57, 95%ci: 1.23–1.91) were associated with elevated crp levels, as shown by the pooled results.Subgroup analysis was performed according to cancer type (endometrial cancer: hr = 1.15, 95% ci: 1.02–1.28; ovarian cancer: hr = 1.67, 95% ci: 1.03–2.31; cervical cancer: hr = 1.42, 95% ci: 1.19–1.64), multivariate value (hr = 1.22, 95% ci: 1.10–1.33), and age (hr = 1.50, 95% ci: 1.28–1.72).Significant correlations were observed between crp and os.Conclusions.Crp may be utilized as a prognostic indicator for a variety of gynecologic malignancies, including cervical cancer, ovarian cancer, endometrial cancer, and vulvar cancer.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Meta-Analysis;
Tripartite Evolutionary Game of Agricultural Service Scale Management and Small Farmers’ Interests under Government Preferential Policies

In agricultural production with small farmers as the main body, the service scale operation is one of the ways to obtain scale operation benefits.This paper constructs a tripartite evolutionary game model of agricultural scale service providers (integrators), demand subjects (farmers), and agriculture-related government departments and analyzes the influencing factors, influencing relations, and evolutionary stable equilibrium results of each participant’s strategy choice.The results show that improving rewards and punishments and strengthening supervision by agriculture-related government departments will help integrators provide high-quality services and encourage farmers to actively produce and reduce speculation; when the cost difference between high-quality service and low-quality service is large, the probability of high-quality service provided by integrators will be reduced, and the evolution result of low pareto efficiency may appear in the system; when the speculative cost of farmers is not high, the lobbying efforts of integrators will increase the speculative behavior of farmers.Finally, matlab2018a is used to simulate and analyze the validity of the model results, which provides a theoretical reference for the scale operation of agricultural services.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Analysis of Structured Data in Biomedicine Using Soft Computing Techniques and Computational Analysis

In the field of biomedicine, enormous data are generated in a structured and unstructured form every day.Soft computing techniques play a major role in the interpretation and classification of the data to make appropriate decisions for making policies.The field of medical science and biomedicine needs efficient soft computing-based methods which can process all kind of data such as structured data, categorical data, and unstructured data to generate meaningful outcome for decision-making.The soft-computing methods allow clustering of similar data, classification of data, predictions from big-data analysis, and decision-making on the basis of analysis of data.A novel method is proposed in the paper using soft-computing methods where clustering mechanisms and classification mechanisms are used to process the biomedicine data for productive outcomes.Fuzzy logic and c-means clustering are devised as a collaborative approach to analyze the biomedicine data by reducing the time and space complexity of the clustering solutions.This research work is considering categorical data, numeric data, and structured data for the interpretation of data to make further decisions.Timely decisions are very important especially in the field of biomedicine because human health and human lives are involved in this field and delays in decision-making may cause threats to human lives.The covid-19 situation was a recent example where timely diagnosis and interpretations played significant roles in saving the lives of people.Therefore, this research work has attempted to use soft computing techniques for the successful clustering of similar medical data and for quicker interpretation of data to support the decision-making processes related to medical fields.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model

Esophageal cancer (ec) is a commonly occurring malignant tumor that significantly affects human health.Earlier recognition and classification of ec or premalignant lesions can result in highly effective targeted intervention.Accurate detection and classification of distinct stages of ec provide effective precision therapy planning and improve the 5-year survival rate.Automated recognition of ec can aid physicians in improving diagnostic performance and accuracy.However, the classification of ec is challenging due to identical endoscopic features, like mucosal erosion, hyperemia, and roughness.The recent developments of deep learning (dl) and computer-aided diagnosis (cad) models have been useful for designing accurate ec classification models.In this aspect, this study develops an atom search optimization with a deep transfer learning-driven ec classification (asodtl-ecc) model.The presented asodtl-ecc model mainly examines the medical images for the existence of ec in a timely and accurate manner.To do so, the presented asodtl-ecc model employs gaussian filtering (gf) as a preprocessing stage to enhance image quality.In addition, the deep convolution neural network- (dcnn-) based residual network (res net) model is applied as a feature extraction approach.Besides, aso with an extreme learning machine (elm) model is utilized for identifying the presence of ec, showing the novelty of the work.The performance of the asodtl-ecc model is assessed and compared with existing models under several medical images.The experimental results pointed out the improved performance of the asodtl-ecc model over recent approaches.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
On the Design of Secured and Reliable Dynamic Access Control Scheme of Patient E-Healthcare Records in Cloud Environment

Traditional healthcare services have changed into modern ones in which doctors can diagnose patients from a distance.All stakeholders, including patients, ward boy, life insurance agents, physicians, and others, have easy access to patients’ medical records due to cloud computing.The cloud’s services are very cost-effective and scalable, and provide various mobile access options for a patient’s electronic health records (ehrs).Ehr privacy and security are critical concerns despite the many benefits of the cloud.Patient health information is extremely sensitive and important, and sending it over an unencrypted wireless media raises a number of security hazards.This study suggests an innovative and secure access system for cloud-based electronic healthcare services storing patient health records in a third-party cloud service provider.The research considers the remote healthcare requirements for maintaining patient information integrity, confidentiality, and security.There will be fewer attacks on e-healthcare records now that stakeholders will have a safe interface and data on the cloud will not be accessible to them.End-to-end encryption is ensured by using multiple keys generated by the key conclusion function (kcf), and access to cloud services is granted based on a person’s identity and the relationship between the parties involved, which protects their personal information that is the methodology used in the proposed scheme.The proposed scheme is best suited for cloud-based e-healthcare services because of its simplicity and robustness.Using different amazon ec2 hosting options, we examine how well our cloud-based web application service works when the number of requests linearly increases.The performance of our web application service that runs in the cloud is based on how many requests it can handle per second while keeping its response time constant.The proposed secure access scheme for cloud-based web applications was compared to the ethereum blockchain platform, which uses internet of things (io t) devices in terms of execution time, throughput, and latency.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
The Impact of Education Based on New Internet Media Technology on College Students' Mental Health and Biomedical Diagnosis

There has been an upsurge in signs of gloom, tension, dietary problems, and other dysfunctional behaviors in undergrad populaces lately.At the same time, the need for advisory services is constantly increasing.Some have interpreted these patterns as mental health emergencies that require immediate investigation and the development of possible treatments to meet the needs of students.Later, other studies have linked the observed increase in side effects to shape individual shape enhancement, especially the widespread use of web-based entertainment, and the time spent on such development is clearly a decrease in psychological well-being.Showed to be related while the use of personalized computing innovations has drastically changed the scene in which undergrads interact with one another and appears to have a significant impact on emotional wellness.Similar advances also offer various opportunities for psychological well-being improvement and dysfunctional behaviour treatment.In this segment, we examine the hardships and open doors for undergrad psychological wellness that pc gadgets give.We accentuate potential for extra examination in this field, as well as ways for people and associations to draw in more benefits with these advances in valuable and health-advancing ways.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
The Computer-Aided Design Algorithm of Dyeing and Weaving Graphics from the Perspective of Public Art

With the acceleration of economic development, people put forward higher requirements for clothing style.In this context, the application of traditional patterns has good artistic effects, can show a unique style, and can also express the artistic beauty of clothing through deformation, color distribution, and other forms, and occupies a relatively large position in the design process of dyeing and weaving art.Aiming at the problem of inaccurate extraction of image information from known visual scenes in the original art-aided design, resulting in unclear output images, this paper proposes a computer-aided design algorithm for dyeing and weaving graphics in the field of public art by color segmentation of the known visual scene images according to the set threshold, morphological processing of the segmented images, reducing noise and fractures affecting the acquired connected areas, and formulating extraction rules to screen candidate areas.Furthermore, dense sampling form is used to extract more scale invariant feature transform (sift) target features in the candidate area, match the feature points, integrate the coordinate system of known image information into a unified coordinate system, output the design image, and complete the auxiliary design of dyeing and weaving graphics.The results of simulation experiments show that the computer-aided design algorithm of dyeing and weaving graphics in the public art field is more accurate than the original method in extracting information from known images, which helps to solve the problem of clear output dyeing and weaving images, and improves the quality of dyeing and weaving images.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Application of Machine Learning and Information Coverage Centralized Genetic Method in Safety Management of Football Training

In recent years, china’s competitive sports have developed rapidly.Among them, football is a sport with high energy consumption, high intensity, strong antagonism, and high speed.As a result, injuries are common in football practice.It is impossible to do adequate football training in order to avoid such incidents.At the moment, football injury incidents are common, severely limiting the full growth of the sport in china.This work investigates the safety management of football training using machine learning and an information coverage-centralized genetic technique.To begin, this article describes in detail the machine learning and information coverage-centralized genetic algorithm, summarizes the classification of machine learning models, and introduces the verification and evaluation process of machine learning models and trusted information coverage models as an important theoretical basis for football training safety management.Then, the genetic algorithm based on information coverage concentration is used in football training to analyze the safety risk of football training and the analysis of training speed type.The results show that the human factor accounts for the highest proportion in football training safety accidents, accounting for 28.75%.In the analysis of football training speed, the average passing time of medium strength accounts for the highest proportion of 39.75%.In football training, in order to ensure the safety of training, the combination of medium strength and high strength can be adopted to avoid training injury.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Evaluation of Salt Tolerance Mechanism and Study on Salt Tolerance Relationship of Different Salt-Tolerant Wheat Varieties

The physiological and biochemical indexes of different salt-tolerant wheat cultivars under salt stress are affected to different degrees.The changes in physiological and biochemical indexes in salt-tolerant wheat varieties are moderate, while salt-sensitive wheat varieties have bigger changes.In this article, through comprehensive utilization of germination and seedling indoor test morphological indexes, physiological and biochemical index, and output index, combined with the evaluation mechanism of wheat’s salt resistance, different salt tolerance types in the middle and lower reaches of the yangtze river in shandong province were studied; a collection of 100 wheat varieties and 11 wheat varieties planted over a large area were classified into three groups: strong salt resistance, medium salt resistance, and weak salt resistance.Comparative analysis of different wheat varieties’ salt resistance evaluation mechanism was performed, as well as the analysis of the germination rate of wheat varieties, coleoptile growth situation, emergence rate, protect wheat seeding rate, tillering rate, seedling height, root length, seedling dry weight, wet weight, number of leaves, plant growth situation, agronomic characters of the na+/k+ ratio, and other physiological and biochemical indexes such as salt resistance index.The evaluation mechanism of salt tolerance and the relationship of salt tolerance of different wheat varieties were obtained.The results showed that there were significant differences in salt tolerance among the 100 wheat varieties at the germination stage.Among them, three varieties, ym (yangmai) 25, ym (yangmai) 24, and em (emai) 25, had the strongest salt tolerance at the germination stage, reaching the level of the salt tolerance variety dk 961, and the salt tolerance was in the first level, while nm 17, nm 23, and other 21 varieties reached the level of the salt tolerance.These 24 varieties with strong salt tolerance at the germination stage can be used to screen the salt tolerance of wheat planted in the saline soil of jiangsu coastal beach.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
NOD1 and NOD2 Are Potential Therapeutic Targets for Cancer Immunotherapy

The nucleotide oligomerization domain (nod)-like receptors (nlrs) are a group of intracellular proteins that are essential for controlling the host's innate immune response.The cytosolic nucleotide binding oligomerization domains 1 and 2 receptors (nod1 and nod2) are the most widely investigated nlrs.As pattern recognition receptors (prrs), nod1 and nod2 may recognize and bind endogenous damage associated molecular patterns (damps) and external pathogenic associated molecular patterns (pamps), directing the activation of inflammatory caspases through engaging the adaptor protein rip2, which further activates the nf-κb and mitogen-activated protein kinase (mapk) signaling pathways, thereby mediating host innate immunity and regulating the adaptive immunity.Previous research has identified nod1 and nod2 as key players in inflammatory disease and host-microbial defense.Despite numerous studies claiming that nod1 and nod2 are linked to tumorigenesis and tumor development, it is still unclear whether nod1 and nod2 act as cancer's friends or foes.In this review, we focus on concluding the current research progress on the role of nod1 and nod2 in a variety of cancers and discussing the potential reasons for the contradicting role of nod1 and nod2 in cancers.This review may help better understand the role of nod1 and nod2 in cancer and shed light on nod1 and nod2 as potential therapeutic targets for tumor immunotherapy.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Review Article;
Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms

With the rapid development of mobile medical care, medical institutions also have the hidden danger of privacy leakage while sharing personal medical data.Based on the k-anonymity and l-diversity supervised models, it is proposed to use the classified personalized entropy l-diversity privacy protection model to protect user privacy in a fine-grained manner.By distinguishing solid and weak sensitive attribute values, the constraints on sensitive attributes are improved, and the sensitive information is reduced for the leakage probability of vital information to achieve the safety of medical data sharing.This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features.Data analysis and experimental results show that this method can minimize execution time while improving data accuracy and service quality, which is more effective than existing solutions.The limits of solid and weak on sensitive qualities are enhanced, sensitive data are reduced, and the chance of crucial data leakage is lowered, all of which contribute to the security of healthcare data exchange.This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features.The scope of this research is that this paper enhances data accuracy while minimizing the algorithm’s execution time.

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