syedzayyan/paper_mill_benchmark huggingface dataset:
Environmental Impact and Carbon Footprint Assessment of Sustainable Buildings: An Experimental Investigation

Carbon emission has been considerably higher in india in the last few decades.The greenhouse gases increased to an imaginary volume, a major contributor to global warming.Chennai is one of india’s large cosmopolitan cities, contributing more gross domestic product (g.d.p.) and carbon to the atmosphere.The infrastructure sector is always a booming sector in and around chennai, which requires more construction materials.In turn, the construction of new buildings expands the city with a large area of urban and suburban chennai, where i.t.Division, automobile division, and industrial estates are available.Hence, this study deals with the carbon emission of a residential building constructed with conventional materials in and around chennai.So, one can estimate the emission of carbon by the conventional building, which leads to global warming and climate change.

Journal: Adsorption Science & Technology (2022)
Publication types: Research Article;
Intelligent CO2 Monitoring for Diagnosis of Sleep Apnea Using Neural Cryptography Techniques

In biomass wastage, carbon is one of the adsorbent materials.Biomass wastage contains complex materials, and pressure, various temperatures, and presence of various chemical components which are subjected to the adsorption of carbon are a tedious task, and it is used in the sustainable waste management system.While screening the biomass wastage management system, prediction of activated carbon’s quality and understanding of the mechanism of adsorption of [formula: see text] are a complicated task.Many research works have been developed; the main issues are inaccurate and inefficient prediction of carbon available in the various feedstock of biomass wastage.To overcome these issues, this paper proposed gene expression programming (gep) with [formula: see text]-nearest neighbour (gep-knn).The key advantage of the proposed work shows excellent performance in the prediction of adsorbing carbon and accuracy.The accuracy of the gep-knn algorithm with different [formula: see text] values produced the highest accuracy at [formula: see text] and [formula: see text] of 95.12% and 95.67%; the lowest accuracy is [formula: see text] of 65.34%.

Journal: Adsorption Science & Technology (2022)
Publication types: Research Article;
Nonlinear Autoregressive Neural Network for Antimicrobial Waste Water Treatment

Antibiotics become an emerging contaminant and receive more interests due to its ecotoxicological and strong stability in water ecosystems.Antibiotic adsorption onto carbon materials are biochars among the wastewater mechanisms.This research used machine learning (ml) techniques to generate general adsorption forecasting model for sulfamethoxazole (smx) and tetracycline (tc) on cbm.Dirichlet design parameters and a combined combination of neumann and dirichlet boundary situation are applied to the system of differential equations.In addition, the proposed method use the learning under supervision technique of a nonlinear autoregressive for estimating the co 2 concentration and flows in units of rate of a reaction characteristics, an exogenous (narx) neural network model with two activation functions was used (log-sigmoid and hyperbolic tangent) and for both the findings of a tc and smx absorption simulations showed the random forest performed support vector tree and nonlinear autoregressive exogenous neural networks and machine learning methods.Their relevance and complete dependency graph evaluation lead reasonable cbm uses for antimicrobial wastewater treatment.Also, machine learning forecasting model with good generalization capability is useful for building effective cbms with few empirical screens.It evaluates the accuracy, precision, recall, false positive rate (fpr), and false negative rate (fnr) and also reduces the experimental screening.

Journal: Adsorption Science & Technology (2022)
Publication types: Research Article;
Optimization of CO2 Concentration on Mortality of Various Stages of Callosobruchus maculatus and Development of Controlled Atmosphere Storage Structure for Black Gram Grains

Pulse beetle ( callosobruchus maculatus) is a common weevil that is responsible for up to 24% of stored pulse losses.Using black gram grain storage, the efficiency of carbon dioxide gas against all life stages of callosobruchus maculatus insects was tested at various concentrations and exposure times.The trials were carried out in pilot bins with a capacity of 25 kg.At a co 2 concentration of 50%, complete mortality of the egg stage of callosobruchus maculatus was attained after 72 h of continuous exposure.At a co 2 concentration of 60% for 48 h, 100% larva mortality was achieved.At the most tolerant stage of pupa, recorded complete mortality is at a co 2 concentration of 70% for 96 hours of the exposure period.Adult insects are especially vulnerable to the high co 2 concentration.Adult mortality was achieved at a concentration of 20% with an exposure period of 48 h.The co 2 -treated black grams were then stored for three months with the optimized co 2 concentration and exposure period, while physiochemical parameters such as water retention capacity and physiological loss in weight were determined.Grain stored in the silo showing significant 100% mortality of egg was measured after 20-25 days of observation.The use of a controlled atmosphere storage bin increased the mortality of the insect c.Maculatus at all developmental stages, by means of increasing co 2 concentration and exposure time.Grain stored in a controlled atmosphere silo showed minimum losses of grain (4.10%) compared to the gunny bag storage (22.56%).

Journal: Adsorption Science & Technology (2022)
Publication types: Research Article;
Antioxidant, Antimicrobial, and Photocatalytic Potential of Cobalt Fluoride (CoF2) Nanoparticles

Herein, co f 2 nanoparticles (nps) are prepared by simple coprecipitation method and are characterized by various techniques, i.e., xrd, sem/edx, ftir, and uv/vis, for their structure identification.As-prepared nanostructures were used as photocatalyst, as antioxidant, and as antimicrobial agent.The degradation studies of the prepared samples were carried out for specific time for the degradation of methylene blue (mlb) dye under a uv/visible spectrophotometer to determine decolorization and change in concentration of mlb with respect to time.The antibacterial activity against escherichia coli ( e.Coli) and bacillus subtilis ( b.Subtilis) was measured by well diffusion and serial dilution method to determine their efficiency against these two bacteria, through a dose-dependent method.The antibacterial activity was further confirmed against the experimental bacteria through calculation of minimum inhibition concentration (mic).The antioxidant activity (radical scavenging activity) of the prepared co f 2 nps was also assessed.

Journal: Adsorption Science & Technology (2022)
Publication types: Research Article;
An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms

Purification of polluted water and return back to the agriculture field is the wastewater treatment for plants.Contaminated water causes illness and health emergencies of public.Also, health risk due release of toxic contaminants brings problem to all living beings.At present, sensors are used in waste water treatment and transfer data via internet of things (io t).Prediction of wastewater quality content which is presence of total nitrogen (t-n) and total phosphorous (t-p) elements, chemical oxygen demand (cod), biochemical demand (bod), and total suspended solids (tss) is associated with eutrophication that should be prevented.This may leads to algal bloom and spoils aquatic life which is consumed by human.The presence of nitrogen and phosphorous elements is in the content of wastewater, and these elements are associated with eutrophication which should be prevented.Adsorption of t-n and t-p activated carbon was predictable as one of the most promising methods for wastewater treatment.Many research works have been done.The issues are inefficiency in the prediction of wastewater treatment.To overcome this issue, this paper proposed fusion of b-knn with the elm algorithm that is used.The accuracy of the bknn-elm algorithm in classification of water quality status produced the highest accuracy of the highest accuracy which is [formula: see text] and [formula: see text] with rate of accuracy which is 93.56%, and the lowest accuracy is [formula: see text].Experiment evaluation shows that a total suspended solid predicted by proposed model is 91 with accuracy of 93%.The relative error rate of prediction is 12.03 which is lesser than existing models.

Journal: Adsorption Science & Technology (2022)
Publication types: Research Article;
Design and Realization of Animation Composition and Tone Space Conversion Algorithm

In recent years, with the development of society and the rapid development of the animation industry, people are paying more and more attention to and requirements for animation production.As an indispensable part of animation production, picture composition plays a major role in animation production.It can give full play to the application of color matching and light and shadow design and enhance the depth and space of the animation screen.Tone space conversion refers to the conversion or representation of color data in one color space into corresponding data in another color space.Its purpose is to distinguish and process color components such as hue and saturation in an image.This article first introduces the domestic and foreign research status of digital image preprocessing and analyzes the basic principles of several color space conversions in detail.Then, several color space conversion algorithms are studied, and the performance of the algorithms is compared and analyzed.The paper focuses on the hardware implementation and optimization of the algorithm for converting rgb color space into hsi color space to meet the real‐time requirements.This article focuses on the mutual conversion between the rgb tone space and the hsi tone space and describes in detail how each color component in the hsi tone space is converted from the three rgb color components from a geometric perspective, and then the conversion is derived, and several general conversion methods of rgb to hsi tone space are introduced; two conversion methods of geometric derivation method and standard modulus algorithm are implemented in the software, and the comparison verification is carried out, and the comparison is made from the perspective of hardware implementation.The pros and cons of the two methods are discussed.Finally, the paper summarizes the shortcomings in the design and proposes further research directions in the future.

Journal: Complexity (2021)
Publication types: Research Article;
The Influence and Analysis of Multimedia Interaction on the Development of Dance

Since ancient times, dance has been widely loved by people.The history of dance has a long history, and people’s pursuit of dance has never stopped.However, with the development of modern technology, traditional dance has been unable to keep up with the pace of the times.Therefore, this article studies the influence of multimedia interaction on the development of dance.This article proposes that multimedia interaction has a great influence on the status quo of dance development.This article aims to study the role of multimedia interaction in dance development.In the eleven years since 2010, the technology of stage effects has developed rapidly.As you can imagine, not only the stage effects can bring a visual feast, but also the development space of dancers has been greatly improved.It can be seen that multimedia interaction is of great significance to the development of dance; with the rapid development of science and technology, multimedia technologies such as computer networks are widely used in all walks of life.The application in the field of dance art is gradually popularized, in which dance teaching and stage performance methods have undergone great changes with the in-depth popularization of multimedia.Therefore, this article must analyze the influence of multimedia interaction on the development of dance.

Journal: Journal of Electrical and Computer Engineering (2022)
Publication types: Research Article;
Scene Construction and Application of Panoramic Virtual Simulation in Interactive Dance Teaching Based on Artificial Intelligence Technology

In recent years, with the rapid development of human motion simulation technology and virtual simulation technology, natural human-computer interaction has become the main form of research in the computer industry.Due to the research capabilities and practical value of mobile video technology, it is widely used in advanced fields, such as video animation production, rehabilitation medicine, sports training, and game software, effectively realizing the connection between the three-dimensional world and the simulation world.However, the application of motion imaging technology in teaching activities is not perfect.This article is based on dance teaching, adopts a dance movement analysis method based on vector matching, and uses artificial intelligence technology to create a virtual panoramic scene.Experimental data show that the average recognition rate of gestures is 89.8%, which can meet the gesture switching interaction of working conditions in virtual simulation of equipment operation.

Journal: Journal of Electrical and Computer Engineering (2022)
Publication types: Research Article;
Dance Movement Interference Suppression Algorithm Based upon Contour Model and AdaBoost Algorithm

With the development of computer technology, digital image processing, as an emerging field, has become an indispensable tool in the information society.The action interference effect refers to the phenomenon that an individual reacts to a dangerous object longer than a safe object in the state of preparation for action.Currently, there is no effective anti-jamming technology to recognize dance movements.In order to solve these problems, this paper proposes a dance movement interference suppression algorithm based on contour model and ada boost algorithm, which aims to realize the application of gesture-based behavior recognition technology in the field of dance steps and complete the recognition of some dance steps.The method of this paper is to study the contour model based on the image and then propose to use the ada boost algorithm to detect body movements and finally use the radio frequency interference suppression algorithm to ensure the smooth operation of the technology.The function of the method is to propose a dance action recognition and anti-interference algorithm based on contour model and ada boost algorithm.This article describes the contour model and then uses the ada boost algorithm to recognize the action and adjust the action interference effect.The results show that the algorithm proposed in this paper can accurately identify dance movements, with an average recognition accuracy rate of 99.2%.

Journal: Journal of Electrical and Computer Engineering (2022)
Publication types: Research Article;
Optimization of E-Commerce Supply Chain Management Process Based on Internet of Things Technology

The widespread use of internet technology has changed people’s traditional life mode and created a new market transaction mode, namely, e‐commerce platform.The internet of things can carry out real‐time monitoring and management of any product in e‐commerce and not only can realize information sharing in commercial activities but also can play a greater role in the protection of personal and enterprise information security.In this context, the efficiency and low cost of information flow, cash flow, logistics, and human resources among enterprises are particularly important.The internet of things technology is a means to effectively improve and optimize the operation mode of the supply chain.This paper analyzes the status of traditional supply chain management, which discusses the development of a supply chain management based on the internet of things technology.Finally, this paper analyzes how to realize the optimization strategy of e‐commerce supply chain management based on the internet of things technology, which provides strong support for the informatization of e‐commerce supply chain management.Through the application of the internet of things technology, small‐ and medium‐sized enterprises can optimize the workflow and circulation links of the e‐commerce supply chain management, enhance the matching level of product value and information transmission, improve the supply chain operation mechanism, and increase economic benefits.

Journal: Complexity (2021)
Publication types: Research Article;
Meta-Analysis on Cognitive Benefit of Exercise after Stroke

The objective of this paper is to evaluate the cognitive benefit of exercise after stroke, so as to provide more accurate and reliable guidance for targeted exercise intervention.Methods .Randomized controlled trials of the relationship between exercise and cognition after stroke were identified in cochrane library and pub med.Methodological quality was assessed using the cochrane tool of bias.Smd and 95% confidence intervals were calculated, and chi‐squared test ( q ) was adopted to estimate the heterogeneity.Results.(a) twenty‐three studies met inclusion criteria, including 1528 participants.Heterogeneity was from low to high such as attention ( i 2 = 0.00%), executive function ( i 2 = 0.00%), cognition ( i 2 = 64%), and working memory ( i 2 = 77%).(b) the overall effect on cognition was small (smd = 0.16 [0.04, 0.28]) but significant and there is a difference between cognitive domains in attention (smd = −0.35 [−0.57, −0.14]), executive function (smd = −0.24 [−0.40, −0.08]), and working memory (smd = 0.36 [0.20, 0.53]).(c) exercise training was less effective before the 18th month after stroke.Higher benefits on cognition were found after combined therapy compared with other exercise programs, and the older the stroke survivors, the less the cognitive benefit of exercise.Conclusion .Small‐to‐moderate effect of exercise on cognitive benefit after stroke was found, and the effect was moderated by treatment protocols and sample characteristics.

Journal: Complexity (2021)
Publication types: Meta-Analysis;Research Article;
Digital Art Feature Association Mining Based on the Machine Learning Algorithm

With the development of computer hardware and software, digital art is a new discipline.It uses computers and digital technology as tools to perform artistic expression.It can be expanded to various binary numerical codes with computers as the center and can also be refined to various categories of creation with computers.The research scope is set in the field of digital art, and all kinds of accidental factors of digital art creation based on the machine learning algorithm are mined and analyzed for feature correlation.Based on the hidden association relationship of massive data, the study focuses on the implicit association mining of digital art features of data for the recommendation algorithm.The classification and continuous data feature attributes are introduced and discretized, and the binary representation of data features is extended to ensure the diversity of data feature attributes.In order to mine some correlation features in data, a heuristic feature mining method based on minimum support was studied to discover the frequency of correlation features and construct the optimal feature subset.Based on the frequent items of data features, this study observes the heuristic algorithm of digital art feature association mining based on minimum confidence and carries out feature matching based on digital art feature association mining under different situation modes.The validity of the proposed algorithm is verified by using the experimental data of health and medical situations in the machine learning library.

Journal: Complexity (2021)
Publication types: Research Article;
Identification of Driving Factors of Scientific and Technological Innovation in the New Material Industry Based on the Theory of Complex Adaptive System: Taking the Construction of Green Innovation System as an Example

Complex adaptation systems are the main development direction of china’s current green innovation research.New material industry is one of the key entry points to accelerate the construction of modern industrial system and promote innovation, green, and efficient development.Under the requirements of china’s current low‐carbon development, china’s green innovation system is developing rapidly.Green innovation complicates traditional innovation models and their functions and improves economic development.The purpose of this paper is to study the theoretical analysis framework of applying complex adaptive systems and to analyze and calculate the characteristics of the current status of green innovation systems in multiple industries in china’s manufacturing industry.Analysis is as follows.The experimental group and the control group are set for comparison, and the data are organized by applying complex adaptive system theory and data analysis methods.Experimental data show that the coordinated development of the green innovation systems in various industries in china is not balanced, with a difference of more than three times; the parameters of the various green innovation systems within the industry are unbalanced, and the difference is more than five times.The experimental results show that the green innovation subject can strategically interact by adjusting the ratio of resource allocation through the constructed green innovation system model.The green innovation system with complex adaptive system theory is conducive to the development of the green system.It can save about 23% of green innovation time and improve efficiency by about 15%, to promote the development of green innovation in china.

Journal: Complexity (2021)
Publication types: Research Article;
Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

This study is to explore the gesture recognition and behavior tracking in swimming motion images under computer machine vision and to expand the application of moving target detection and tracking algorithms based on computer machine vision in this field.The objectives are realized by moving target detection and tracking, gaussian mixture model, optimized correlation filtering algorithm, and camshift tracking algorithm.Firstly, the gaussian algorithm is introduced into target tracking and detection to reduce the filtering loss and make the acquired motion posture more accurate.Secondly, an improved kernel‐related filter tracking algorithm is proposed by training multiple filters, which can clearly and accurately obtain the motion trajectory of the monitored target object.Finally, it is proposed to combine the kalman algorithm with the camshift algorithm for optimization, which can complete the tracking and recognition of moving targets.The experimental results show that the target tracking and detection method can obtain the movement form of the template object relatively completely, and the kernel‐related filter tracking algorithm can also obtain the movement speed of the target object finely.In addition, the accuracy of camshift tracking algorithm can reach 86.02%.Results of this study can provide reliable data support and reference for expanding the application of moving target detection and tracking methods.

Journal: Complexity (2021)
Publication types: Research Article;