syedzayyan/paper_mill_benchmark huggingface dataset:
ECG-ViT: A Transformer-Based ECG Classifier for Energy-Constraint Wearable Devices

The advancement in deep learning techniques has helped researchers acquire and process multimodal data signals from different healthcare domains.Now, the focus has shifted towards providing end-to-end solutions, i.e., processing these data and developing models that can be directly implemented on edge devices.To achieve this, the researchers try to solve two problems: (i) reduce the complex feature dependencies and (ii) reduce the complexity of the deep learning model without compromising accuracy.In this paper, we focus on the later part of reducing the complexity of the model by using the knowledge distillation framework.We have introduced knowledge distillation on the vision transformer model to study the mit-bih arrhythmia database.A tenfold crossvalidation technique was used to validate the model, and we obtained a 99.7% f1 score and 99.3% accuracy.The model was further tested on the xilinx alveo u50 fpga accelerator, and it is found fit for any low-powered wearable device implementation.

Journal: Journal of Sensors (2022)
Publication types: Research Article;
Early Stroke Prediction Methods for Prevention of Strokes

The emergence of the latest technologies gives rise to the usage of noninvasive techniques for assisting health-care systems.Amongst the four major cardiovascular diseases, stroke is one of the most dangerous and life-threatening disease, but the life of a patient can be saved if the stroke is detected during early stage.The literature reveals that the patients always experience ministrokes which are also known as transient ischemic attacks (tia) before experiencing the actual attack of the stroke.Most of the literature work is based on the mri and ct scan images for classifying the cardiovascular diseases including a stroke which is an expensive approach for diagnosis of early strokes.In india where cases of strokes are rising, there is a need to explore noninvasive cheap methods for the diagnosis of early strokes.Hence, this problem has motivated us to conduct the study presented in this paper.A noninvasive approach for the early diagnosis of the strokes is proposed.The cascaded prediction algorithms are time-consuming in producing the results and cannot work on the raw data and without making use of the properties of eeg.Therefore, the objective of this paper is to devise mechanisms to forecast strokes on the basis of processed eeg data.This paper is proposing time series-based approaches such as lstm, bi lstm, gru, and ffnn that can handle time series-based predictions to make useful decisions.The experimental research outcome reveals that all the algorithms taken up for the research study perform well on the prediction problem of early stroke detection, but gru performs the best with 95.6% accuracy, whereas bi lstm gives 91% accuracy and lstm gives 87% accuracy and ffnn gives 83% accuracy.The experimental outcome is able to measure the brain waves to predict the signs of strokes.The findings can certainly assist the physicians to detect the stroke at early stages to save the lives of the patients.

Journal: Behavioural Neurology (2022)
Publication types: Research Article;
Synthesis, Crystal Structures, and Anti-cervical Cancer Activity Evaluation of Three Trinuclear Transition Metal(II) Complexes with N,O-Donor Schiff Base Ligand

A new bi‐nucleating schiff base ligand, 2‐(((3‐(dimethylamino)propyl)imino)methyl)‐6‐methoxyphenol (hl 1 ) was prepared by a one‐pot condensation reaction, which was further used in the construction of three trinuclear schiff base transition metal(ii) complexes [cu 3 (l1) 2 (oh) 2 (h 2 o) 2 ](no 3 ) 2 ( 1 ), [co 3 (l1) 2 (oh) 2 (h 2 o) 2 ](no 3 ) 2 ( 2 ), and [cu 3 (l1) 2 (n 3 ) 4 ] ( 3 ).Furthermore, a green hand grinding technique was implemented to reduce the particle size of the coordination complexes to generate the nanoscale compounds.The sem studies reveal the formation of square and spherical particles for nano 1 and 2 , and nanorod for nano 3 .In addition, the anti‐proliferation activity of nano 1 – 3 was detected on the human cervical cancer hela cells with cck‐8 assay.The cell viability curves and ic 50 values indicated that only nano 1 has anti‐proliferation activity on hela cells.To further investigate the mechanism of nano 1 induced hela cell death, the annexin v‐fitc/pi double staining assay, western blot assay, and ros level detection was conducted.

Journal: ZAAC: Zeitschrift für anorganische und allgemeine Chemie (Journal of Inorganic and General Chemistry) (2019)
Publication types: Research Article;
A InIII-MOF with Imidazole Decorated Pores as 5-Fu Delivery System to Inhibit Colon Cancer Cells Proliferation and Induce Cell Apoptosis in vitro and in vivo

A new nontoxic porous in iii ‐based metal‐organic framework [in(hpbic)(pbic)](dmf) 2 ( 1 ) (dmf = n , n ‐dimethylacetamide) was successfully prepared with 2‐(pyridin‐4‐yl)‐1h‐benzo[d]imidazole‐5‐carboxylic acid (h 2 pbic) as organic linker via a solvothermal process.Further, the nanostructure 1 could be obtained via a green grinding method.Nitrogen adsorption measurements revealed the presence of micropores as well as moderate high bet surface areas in the activated nanostructure 1 ( 1a ).The drug loading experiment shows that 5‐fluorouracil (5‐fu) is preferentially captured into the pore of the nanostructure 1a with a loading capacity of 32.6 %.Meanwhile, the controlled release of 5‐fu in a simulated human body with liquid phosphate‐buffered saline solution was realized.In addition, the cell counting kit‐8 (cck‐8) assay was conducted to determine the inhibitory effect of 5‐fu@ 1a on human colon cancer cell sw60 viability and proliferation, the results indicated the excellent anti‐cancer activity of 5‐fu@ 1a in vitro.To reveal the related mechanism, the annexin v‐fitc/pi assay and reactive oxygen species (ros) level detection was carried out after 5‐fu@ 1a treatment.Finally, the in vivo xenograft model was constructed in mice, the tumor volume, and mice body weight were recording at indicated time.The in vivo results suggested the significant inhibitory activity of 5‐fu@ 1a in mice.

Journal: ZAAC: Zeitschrift für anorganische und allgemeine Chemie (Journal of Inorganic and General Chemistry) (2019)
Publication types: Research Article;
Diagnosis and Exercise Rehabilitation of Knee Joint Anterior Cruciate Ligament Injury Based on 3D-CT Reconstruction

The joint capsule of the knee joint is attached to the edges of various articular surfaces and is thin and loose.Therefore, ligament reinforcement is needed to protect the knee joint and increase the stability of the joint.It plays a vital role in human activities.In this paper, a 3d-ct three-dimensional reconstruction method is used to reconstruct the acl natural femoral imprint and double-bone tract.The relative positional relationship between the two center points is compared, and the law is summarized to guide the improvement of acl anatomic double-beam reconstruction under arthroscopy.The 3d reconstruction results suggest that the bone layer in the anterior medial portion is the thickest, forming a peak, and the thickness of the bone layer in the posterior medial portion gradually decreases in a stepwise manner.The entire bone tissue in the anterior medial portion and posterior medial portion is integrated into one body.The tissues are connected as a whole, and the thickness is relatively uniform.The two parts of the bone tissues are not connected.The cf tissue was inserted into the bone tissue in a zigzag pattern.The changes of cf tissues in the anterior medial and posteromedial cf tissues were similar, and they were distributed stepwise from the inside to the outside.According to the bone and cf spatial structure and changing rules, acl is divided into medial and lateral beams.According to this study, it can be summarized that (1) 3d reconstruction can clearly reconstruct the natural footprint of acl femoral stops and postoperative osseous position and (2) 3d reconstruction can be used to evaluate the position of osseous postoperative acl anatomic double-beam reconstruction.Arthroscopy double-beam reconstruction of acl is instructive.

Journal: Complexity (2020)
Publication types: Research Article;
Development of Urban Waste Recycling Industry from the Perspective of Ecology

Urban garbage has become an important field affecting the healthy development of urban ecology, and urban ecological environment protection has naturally become a crucial part in the process of modern urban development.Deepening the relevant concepts of ecological and environmental protection also makes modern cities pay attention to the recycling and utilization of waste resources.This work is combined with the development status of urban waste recycling industry.Taking urban construction waste and domestic waste as examples, this paper focuses on the operation model and development path of urban waste recycling industry.For example, in the aspect of domestic waste treatment, the resource industrialization path of incineration fly ash is proposed, focusing on the water washing and dechlorination of incineration fly ash, the production of unburned ceramsite from water washed fly ash, and the separation and recovery of inorganic salts in fly ash water washing solution.The results show that the recoveries of na cl, kcl, and ca cl2 are 96.21%, 95.85%, and 94.72%.Through the analysis of social, environmental, and economic benefits, the government, scientific research institutions, and enterprises are encouraged to make joint efforts to develop the construction waste recycling industry, so as to fundamentally solve the current situation of “mountains of garbage and cities surrounded by garbage” in china.

Journal: International Journal of Antennas and Propagation (2022)
Publication types: Research Article;
Design and Research of Building Indoor Environment Monitoring System Relying on 3D Modeling and Image Processing

In order to improve the monitoring effect of building indoor environment, this paper carries out indoor environment simulation through 3d modeling, uses image processing technology to collect environmental monitoring data, and proposes a routing algorithm applied in this environment.Moreover, this paper introduces the energy consumption model of sensor nodes and the multi-attribute decision-making method and uses the multi-attribute decision-making to propose an adaptive energy balance routing algorithm based on the indoor environment of the building.In addition, this paper establishes a simulation analysis, compares it with several other routing algorithms, and verifies that the proposed algorithm has better balance in network energy consumption and longer network life cycle.Finally, this paper combines three-dimensional modeling and image processing technology to construct the building indoor environment monitoring system.It can be seen from the simulation experiments that the proposed building indoor environment monitoring system based on 3d modeling and image processing can meet the actual needs of intelligent building indoor monitoring.

Journal: Journal of Electrical and Computer Engineering (2022)
Publication types: Research Article;
Deep-Learning-Guided Point Cloud Modeling with Applications in Intelligent Manufacturing

Machining feature recognition is a key technology to realize cad/capp/cam system integration.Aiming at high robustness of traditional processing feature recognition in image reasoning, an automatic processing shape recognition method based on fuzzy learning of processing surrounding point black data is proposed.The cloud rnn in the point net stage strongly demonstrates that the framework originates from convolutional neural spider webs.Protector shape for detailed discoloration data on constructed prominence surfaces for automatic rifle recognition is conducted.Spot staining data sample library is also constructed.The prosecuting feature recognizer gained advantages through sample training, which realized robot-style notification of 36 processing shapes.This is conducted with a recognition accuracy rate of over 90%.The method is simple and efficient, although it is not suitable for point cloud data with backlash and defects.It is sensible and still has usable robustness and confirmation performance against mischief around shape peripheries due to shape intersections.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Deep Learning Model for Automatic Classification and Prediction of Brain Tumor

A brain tumor (bt) is an unexpected growth or fleshy mass of abnormal cells.Depending upon their cell structure they could either be benign (noncancerous) or malign (cancerous).This causes the pressure inside the cranium to increase that may lead to brain injury or death.This causes excessive exhaustion, hinders cognitive abilities, headaches become more frequent and severe, and develops seizures, nausea, and vomiting.Therefore, in order to diagnose bt computerized tomography (ct), magnetic resonance imaging (mri), positron emission tomography (pet), and blood and urine tests are implemented.However, these techniques are time consuming and sometimes yield inaccurate results.Therefore, to avoid such lengthy and time-consuming techniques, deep learning models are implemented that are less time consuming, require less sophisticated equipment, yield results with greater accuracy, and are easy to implement.This paper proposes a transfer learning-based model with the help of pretrained vgg19 model.This model has been modified by utilizing a modified convolutional neural network (cnn) architecture with preprocessing techniques of normalization and data augmentation.The proposed model achieved the accuracy of 98% and sensitivity of 94.73%.It is concluded from the results that proposed model performs better as compared to other state-of-art models.For training purpose, the dataset has been taken from the kaggle having 257 images with 157 with brain tumor (bt) images and 100 no tumor (nt) images.With such results, these models could be utilized for developing clinically useful solutions that are able to detect bt in ct images.

Journal: Journal of Sensors (2022)
Publication types: Research Article;
Decision Tree Algorithm-Based Model and Computer Simulation for Evaluating the Effectiveness of Physical Education in Universities

In this paper, the forest algorithm and the decision tree algorithm are mainly used to analyze students’ physical education information, course exam results, and student learning data and relevant feature attributes from the online teaching platform.We aim to generate decision trees using the decision tree algorithm for the purpose of generating classification rules, based on which we can find factors that are important to students’ physical education performance and form data basis for improving teaching quality to help teaching management and teachers improve teaching methods and adjust teaching strategies.We specifically achieved this objective by constructing a model for assessing the effectiveness of student teaching, the steps of which include data collection and preparation, data preprocessing (data cleaning, conversion, integration), model construction (algorithm training), and algorithm optimization, as well as realizing the simulation results of the model.At the same time, the importance of the relevant attributes of the model is analyzed, and some measures are proposed to improve the universities: the standard of physical education teaching and the corresponding strategies for improving teaching methods.The mainstream development environment is chosen to ensure the complete operation of the project system that integrates learning, operation, and evaluation.The sports virtual simulation experimental teaching system realized in this paper has good functionality, stability, and application benefits in operation and use.

Journal: Complexity (2020)
Publication types: Research Article;
Decision Optimization of Low-Carbon Dual-Channel Supply Chain of Auto Parts Based on Smart City Architecture

Affected by the internet, computer, information technology, etc., building a smart city has become a key task of socialist construction work.The smart city has always regarded green and low-carbon development as one of the goals, and the carbon emissions of the auto parts industry cannot be ignored, so we should carry out energy conservation and emission reduction.With the rapid development of the domestic auto parts industry, the number of car ownership has increased dramatically, producing more and more co 2 and waste.Facing the pressure of resources, energy, and environment, the effective and circular operation of the auto parts supply chain under the low-carbon transformation is not only a great challenge, but also a development opportunity.Under the background of carbon emission, this paper establishes a decision-making optimization model of the low-carbon supply chain of auto parts based on carbon emission responsibility sharing and resource sharing.This paper analyzes the optimal decision-making behavior and interaction of suppliers, producers, physical retailers, online retailers, demand markets, and recyclers in the auto parts industry, constructs the economic and environmental objective functions of low-carbon supply chain management, applies variational inequality to analyze the optimal conditions of the whole low-carbon supply chain system, and finally carries out simulation calculation.The research shows that the upstream and downstream auto parts enterprises based on low-carbon competition and cooperation can effectively manage the carbon footprint of the whole supply chain through the sharing of responsibilities and resources among enterprises, so as to reduce the overall carbon emissions of the supply chain system.

Journal: Complexity (2020)
Publication types: Research Article;
Data Research on Tobacco Leaf Image Collection Based on Computer Vision Sensor

In the process of tobacco silk making, how to better improve the quality of stem and leaf separation has become an issue of concern.His research mainly discusses the data collected from tobacco leaf images based on computer vision sensors.In lm (levenberg‐marquarelt) as a training function, the algorithm uses threshing effect samples for training and learning.This paper is aimed at extracting the shape characteristic parameters of tobacco leaves and obtains the shape parameters of the length, width, area, circumference, and roundness of the tobacco leaves.In this paper, boundary tracking is used to obtain the coordinate and direction information of the tobacco leaf boundary pixels in the image, which provides a basis for obtaining the subsequent extraction of tobacco leaf characteristic parameters.In the tobacco leaf grading system, the tobacco leaf feature parameter extraction module displays the geometric characteristics of tobacco leaf, such as length, width, area, aspect ratio, rectangularity, and color characteristic, hue h , saturation s , a channel, and b channel in detail through the computer vision sensor.Finally, the subjective and objective combination weighting method is used to combine and weight the indicators of the threshing effect of the first‐level threshing machine, which not only considers the quantity of information provided by the indicators but also takes into account the subjective view of the experts, which increases the weight of the indicators, accuracy, and scientificity.The approximation accuracy of the training samples of the threshing effect prediction model based on the bp neural network lm algorithm is 99.495%, the approximation accuracy of the validation set is 96.535%, and the approximation accuracy of the test set is 98.392%.This research will greatly improve production efficiency and meet the enterprise’s requirements for high efficiency and low cost.

Journal: Journal of Sensors (2021)
Publication types: Research Article;
Data Mining and Analysis of the Compatibility Law of Traditional Chinese Medicines Based on FP-Growth Algorithm

The compatibility law of prescriptions is the core link of tcm theory of “theory, method, prescription and medicine,” which is of great significance for guiding clinical practice, new drug development and revealing the scientific connotation of tcm theory, and is also one of the hot spots and difficulties of tcm modernization research.How to efficiently analyze the frequency of drug use, core combination, and association rules between drugs in prescription is a basic core problem in the study of prescription compatibility law.In this paper, a systematic study was made on the compatibility rules of traditional chinese antiviral classical prescriptions and the mechanism of traditional chinese medicine molecules.Fp-growth algorithm was used to analyze association rules of 961 classical prescriptions collected and to explore the compatibility rules of traditional chinese antiviral classical prescriptions.In terms of compatibility law of traditional chinese antiviral prescriptions, this paper studied the compatibility law of traditional chinese antiviral prescriptions based on the fp-growth algorithm and made exploratory research on the compatibility law information of 961 traditional classical antiviral prescriptions.Firstly, fp tree was constructed based on the classic recipe data set.Then, frequent item set rules were established, and association rules contained in fp tree were extracted.Finally, the frequency and association rules of antiviral tcm prescriptions were analyzed according to dosage forms (decoction, pill, paste, and ingot).The results show that the fp-growth algorithm adopted in this paper has excellent algorithm performance and strong generalization and robustness in the screening and mining of large-scale prescription data sets, which can provide important processing tools and technical methods for the study of the compatibility rule of traditional chinese medicine prescriptions.

Journal: Journal of Mathematics (2021)
Publication types: Research Article;
Evaluation Algorithm of Labor Legal Effectiveness for Affirmative Action against Gender Discrimination

Ing at the problems of large evaluation error and low accuracy of determining the key degree of evaluation indicators in the existing evaluation of labor legal effectiveness, this paper designs a labor legal effectiveness evaluation algorithm for affirmative action against gender discrimination.Firstly, using hits degree, the degree of gender discrimination, and social influence, enterprise practice and government supervision and management are determined as the evaluation indexes of labor legal effectiveness in this paper, and on this basis, the labor legal effectiveness evaluation system against gender discrimination is designed.Then, the judgment matrix of the evaluation index of labor legal effectiveness against gender discrimination is constructed.After normalization, the weight of the evaluation index is calculated by entropy method, which lays a foundation for subsequent research.Finally, the tree enhanced bayesian network is used to classify the labor legal effectiveness evaluation indicators, and the correlation between the indicators is determined through the spearman rank correlation coefficient.Finally, the labor legal effectiveness evaluation model against gender discrimination is designed through the clustering algorithm, and the labor legal effectiveness evaluation indicators against gender discrimination are input to complete the effective evaluation.The experimental results show that the error of the evaluation algorithm is small, and the accuracy of determining the key degree of the evaluation index is high.

Journal: Journal of Mathematics (2022)
Publication types: Research Article;