syedzayyan/paper_mill_benchmark huggingface dataset:
A Hybrid BSC-DEA Model with Indeterminate Information

Strategy is the main source of long-term growth for organizations, and if it is not successfully implemented, even if appropriate ones are adopted, the process is futile.The balanced scorecard which focuses on four aspects such as growth and learning, internal processes, customer, and financial is considered as a comprehensive framework for assessing performance and the progress of the strategy.Moreover, the data envelopment analysis is one of the best mathematical methods to compute the efficiency of organizations.The combination of these two techniques is a significant quantitative measurement with respect to the organization’s performance.However, in the real world, determinate and indeterminate information exists.Henceforth, the indeterminate issues are inescapable and must be considered in the performance evaluation.Neutrosophic number is a helpful tool for dealing with information that is indeterminate and incomplete.In this paper, we propose a new model of data envelopment analysis in the neutrosophic number environment.Furthermore, we attempt to combine the new model with the balanced scorecard to rank different decision-making units.Finally, the proposed method is illustrated by an empirical study involving 20 banking branches.The results show the effectiveness of the proposed method and indicate that the model has practical outcomes for decision-makers.

Journal: Journal of Mathematics (2021)
Publication types: Research Article;
A 516 iμ/iW, 121.2 dB-SNDR, and 125.1 dB-DR Discrete-Time Sigma-Delta Modulator with a 20 kHz BW

This article proposed a discrete-time single-loop 3rd order 5-bit sigma-delta (σδ) modulator for the audio applications.In this modulator, a feed forward path is used to relax the design requirement of amplifier, which can reduce integrator’s output swing.And a 5-bit asynchronous sar adc combined with analog summing is adopted to quantify the summation of feedforward signal and loop filter output, which can significantly reduce power consumption.In addition, chopper stabilization technique is used to alleviate the flicker noise introduced by the first integrator.To eliminate the nonlinearity introduced by multibit quantizer, an improved data weighted average algorithm calibration circuit is proposed.The proposed modulator is implemented in 130 nm technology with a 1.12 mm2 core area.Operating at a 2.56ms/s sampling rate, post layout simulation results show that the modulator realizes 121.2-d b sndr and 125.1-d b dr in a 20 k hz signal bandwidth, it dissipating 516 μw from 1.5 v supply.It also achieves the energy efficiency, as demonstrated by a schreier figure of merit (fo ms) of 197 d b.

Journal: Journal of Sensors (2022)
Publication types: Research Article;
A Blockchain Technique for Trade Credit Maintainability Using the Role of Information and Communication Technology

Recent advances in data analysis and processing methods can improve the ability of computational applications to perform complex steps of different tasks.With the progress of information and communication technologies (ict), blockchain‐based complex data processing for transaction analysis and smart contract agreement has become a new research area in the fields of mathematics and computation.Stability of financial sector based on the ict is a core component for growing the economics of medium and small enterprises.This stability brings the innovation to businesses productivity, while the management of information takes more prospective for improving the efficiency and more ways for innovating the business of products.In this study, we use the autoregressive distribution lag (ardl) model with blockchain‐based complex data processing approach to emphasize the role of ict in the field of trade credit maintainability.Actually, the ict connects the industries in the entire world and makes business sectors that use its technologies be more advanced.Based on the ardl model conducted on the records gathered from 2000 to 2019, the analysis concludes that the ict‐based complex data processing is a critical component of trade credit.The statistics of ict are chosen based on the economy penetrations through the internet and mobile phones.The causality exposed between the trade credit and ict is bidirectional in nature.Also, it is found that the usage of mobile phones has a substantial influence on the business sectors, as a substantial amount of trading and business transactions are conducted over the phone.Therefore, the primary concern is the association between the blockchain and trade credit, which is thoroughly discussed in this work.The trade credit improves the stability of financial sector and the blockchain supports its maintainability by the role of ict.The results of the study can help the business stakeholders and investors to estimate the marketing for future useful execution.

Journal: Journal of Mathematics (2022)
Publication types: Research Article;
A 146-dB-Ohm Gain 14-pARMS Noise Patch-Clamp Amplifier for Whole-Cell Ion Current Detection

The membrane clamp technique is an important tool to reflect the electrophysiological characteristics of cells by recording the ionic currents of cellular channels.Embedded systems work in systems designed for specific user groups and are used to implement specific functions.The membrane clamp amplifier built with embedded technology has the advantages of miniaturization, specialization, low power consumption, high integration, high resource utilization, and a long life cycle, which can avoid the inconvenience to developers and experimenters due to the update of general-purpose computer software and hardware.In this paper, a patch-clamp amplifier (pca) based on a transimpedance amplifier (tia) is proposed, which includes a glass microelectrode series resistance/capacitance compensation circuit and feedback resistor parasitic capacitance compensation.The prototype is designed using 180 nm cmos technology and occupies an area of 720 μ m × 630 μ m .The prototype achieves a transimpedance gain of 145.6 d b-ohm, a -3 d b bandwidth greater than 15 k hz, and an input reference noise current of 13.98 p arms.Experimental results show that the proposed pca is capable of compensating up to 30 p f of electrode capacitance and up to 10 mω for 86% of the series resistance, respectively.In addition, when the feedback resistor parasitic capacitance compensation circuit is enabled, the overshoot phenomenon disappears, overcoming the shortcomings of the conventional diaphragm clamp amplifier current clamp and maintaining the original key performance.

Journal: Journal of Sensors (2022)
Publication types: Research Article;
Stability Evaluation of Rock Slope in Hydraulic Engineering Based on Improved Support Vector Machine Algorithm

The safety problem of the slope has always been an important subject in engineering geology, which has a wide range of application background and practical significance in reality.How to correctly evaluate the stability of the slope and obtain the parameters of the slope has always been the focus of research and production personnel at home and abroad.In recent years, various artificial intelligence calculation methods have been applied to the field of rock engineering and engineering geology, providing some new ideas for the solution of slope stability analysis and parameter back analysis.Support vector machine (svm) algorithm has unique advantages and generalization in dealing with finite samples and highly complex and nonlinear problems.At present, it has become a research hotspot of intelligent methods and has been widely paid attention to in various application fields of slope engineering.In this paper, a cuckoo search algorithm‐improved support vector machine (cs‐svm) method is applied to slope stability analysis and parameter inversion.Aiming at the problem of selecting kernel function parameters and penalty number of svm, a method of using cuckoo search algorithm to improve support vector machine was proposed, and the global optimization ability of cuckoo search algorithm was used to improve the algorithm.Aiming at the slope samples collected, the classification algorithm of support vector machine (svm) was used to identify the stable state of the test samples, and the improved svm algorithm was used to analyze the safety factor of the test samples.The results show that the proposed method is reasonable and reliable.Based on the inversion of the permeability coefficient of the test samples by the improved support vector machine, the comparison between the inversion value and the theoretical value shows that it is basically feasible to invert the permeability coefficient of the dam slope by the improved support vector machine.

Journal: Complexity (2021)
Publication types: Research Article;
Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food

Skin cancer is one of the most common types of cancer that has a high mortality rate.Majorly, two types of skin cancer are the most common, which are melanoma and nonmelanoma skin cancer.Each year, approximately 55% of individuals die due to skin cancer.Early detection of skin cancer enhances the survival rate of individuals.There are various antioxidants like vitamins c, e, and a, zinc, and selenium present in various foods that can be helpful in preventing skin cancer.“deep learning” (dl) is an effective method to detect cancerous lesions.The study’s purpose is to comprehend the vital function performed by dl methods in supporting healthcare professionals in easier skin cancer detection using big data networks.The present research analyzes the accuracy, sensitivity, and specificity of “convolutional neural network” (cnn) for dl in the early detection of skin cancer.A statistical analysis has been done with ibm spss software to understand how the accuracy, sensitivity, and specificity of cnn change with the change in image number, augmentation number, epochs, and resolution of images.These factors have been considered independent variables, and accuracy, sensitivity, and specificity have been considered the dependent variables.After that, a linear regression analysis was carried out to obtain t and p values.The major scope of the study is to analyze the major role played by the dl models through the big data network in the medical industry.The researchers also found that when additional characteristics are present, image resolution does not have the potential to reduce image accuracy, specificity, or sensitivity.The scope of the study is more focused on using a dl-based big data network for supporting healthcare workers in detecting skin cancer at an early stage and the role of technology in supporting medical practitioners in rendering better treatment.Findings showed that the number of training images increases the accuracy, sensitivity, and specificity of cnn architecture when various and effective augmentation techniques are used.Image resolution did not show any significant relationship with accuracy.The number of epochs positively affected the accuracy, sensitivity, and specificity; however, more than 98% accuracy has been observed with epochs between 50 and 70.

Journal: Journal of Food Quality (2022)
Publication types: Research Article;
Novel Concepts of q-Rung Orthopair Fuzzy Topology and WPM Approach for Multicriteria Decision-Making

A q -rung orthopair fuzzy set (q-rofs) is a robust approach for fuzzy modeling, computational intelligence, and multicriteria decision-making (mcdm) problems.The aim of this paper is to study the topological structure on q-rofss and define the idea of q -rung orthopair fuzzy topology (q-rof topology).The characterization of q-rof α -continuous mappings between q-rof topological spaces and q-rof connectedness is investigated.Some relationships of different types of q -rung orthopair fuzzy connectedness are also investigated.Additionally, the “ q -rung orthopair fuzzy weighted product model” (q-rof wpm) is developed for mcdm of a hierarchical healthcare system.Due to limited and insufficient resources, a hierarchical healthcare system (hhs) is very effective to deal with the increasing problems of healthcare.Recognizing the stage of a disease with the symptoms, ranking the critical condition of patients, and referring patients to feasible hospitals are key features of hhs.A hhs will provide healthcare services in three levels, a primary health centers for initial stage of disease, secondary hospitals for secondary stage of disease, and tertiary hospital for the third-order stage.A numerical example is illustrated to demonstrate the efficiency of q-rof wpm and advantages of hhs.

Journal: Journal of Function Spaces (2022)
Publication types: Research Article;
Research on Behavior Monitoring of Elderly Living Alone Based on Wearable Devices and Sensing Technology

Human behavior recognition and status monitoring are current research hotspots, especially in the fields of medical monitoring, smart home, and elderly care.With the development of sensor technology, low-power ic chips, and wireless body sensors, miniature sensor networks can be popularized and applied in daily life.Since the energy consumption of sensor networks is a bottleneck problem that limits its development, this paper designs a multimodal collaborative sensing method for the application scenarios of elderly people living alone to reduce energy consumption in the process of daily behavior perception of the elderly.This method subdivides behavior perception into behavior recognition and status monitoring, determines the optimal sensor combination for identifying monitoring different behaviors based on information theory, and then uses a behavior recognition model modeled by a multiclassifier and a status model modeled by a plurality of two classifiers that are used to perceive user behavior.A large number of experimental results show that compared with the traditional sensor network method, our proposed solution can achieve effective sensing while reducing the energy consumption in the process of data transmission and model calculation, thereby prolonging the working life of the sensing network and realizing long-term and reliable daily behavior perception.

Journal: International Journal of Antennas and Propagation (2021)
Publication types: Research Article;
Morphological Bias of Ancient Artifacts: A Case Study of Incense Burners in Ming and Qing Dynasties

This study verifies the relevance of the combination of traditional chinese artifacts and perceptual semantic research.It provides new research ideas to study the chinese artifact culture.Moreover, it helps people to understand the cultural spirit better and design crystallization of traditional artifacts.This study considered chinese traditional incense burners in ming and qing dynasties to adopt morphological analysis and affinity diagram to select representative experimental samples.Furthermore, this research applied the perceptual engineering theory to explore the relation between design group’s description of perceptual semantics and the shape of incense burners.The focuses were the design group on the shape and style of ancient artifacts in aesthetic consideration.According to the results of semantic principal component analysis, the perceptual semantic bias of the design group towards incense burners was concentrated, which is related to the style acceptance of incense burners.Among these related incense burner styles, the design group paid more attention to the proportional design of “incense burner foot” in the perceptual bias of incense burner shape and preferred the proportional incense burner shape of “long foot.”

Journal: Journal of Mathematics (2021)
Publication types: Research Article;
Loading Gentamicin and Zn2+ on TiO2 Nanotubes to Improve Anticoagulation, Endothelial Cell Growth, and Antibacterial Activities

Titanium and its alloys are widely used in blood-contacting implantable and interventional medical devices; however, their biocompatibility is still facing great challenges.In the present study, in order to improve the biocompatibility and antibacterial activities of titanium, ti o2 nanotubes were firstly in situ prepared on the titanium surface by anodization, followed by the introduction of polyacrylic acid (paa) and gentamicin (gs) on the nanotube surface by layer-by-layer assembly, and finally, zinc ions were loaded on the surface to further improve the bioactivities.The nanotubes displayed excellent hydrophilicity and special nanotube-like structure, which can selectively promote the albumin adsorption, enhance the blood compatibility, and promote the growth of endothelial cells to some degree.After the introduction of paa and gs, although the superhydrophilicity cannot be achieved, the results of platelet adhesion, cyclic guanosine monophosphate (c gmp) activity, hemolysis rate, and activated partial thromboplastin time (aptt) showed that the blood compatibility was improved, and the blood compatibility was further enhanced after zinc ion loading.On the other hand, the modified surface showed good cytocompatibility to endothelial cells.The introduction of paa and zinc ions not only promoted the adhesion and proliferation of endothelial cells but also upregulated expression of vascular endothelial growth factor (vegf) and nitric oxide (no).The slow and continuous release of gs and zn2+ over 14 days can significantly improve the antibacterial properties.Therefore, the present study provides an effective method for the surface modification of titanium-based blood-contacting materials to simultaneously endow with good blood compatibility, endothelial growth behaviors, and antibacterial properties.

Journal: Stem Cells International (2021)
Publication types: Research Article;
Intelligent System of Somatosensory Music Therapy Information Feedback in Deep Learning Environment

In the treatment of children with autistic spectrum disorder (asd) through music perception, the perception effect and the development of the disease are mainly reflected in the fluctuations of the electroencephalogram (eeg), which is clinically effective on the brain.There is an inaccuracy problem in electrogram judgment, and deep learning has great advantages in signal feature extraction and classification.Based on the theoretical basis of deep belief network (dbn) in deep learning, this paper proposes a method that combines the optimized restricted boltzmann machine (rbm) feature extraction model with the softmax classification algorithm.Brain wave tracking analysis is performed on children with autism who have received different music perception treatments to improve classification accuracy and achieve the purpose of accurately judging the condition.Through continuous adjustment and optimization of the weight matrix in the model, a stable recognition model is obtained.The simulation results show that this optimization algorithm can effectively improve the recognition performance of dbn, with an accuracy of 94% in a certain environment, and has a better classification effect than other traditional classification methods.

Journal: Complexity (2021)
Publication types: Research Article;
Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Several industries have recently seen the replacement of human labor by automated machinery and equipment.Across the globe, farmers’ attitudes on the use of technology in agriculture are divergent.However, although some people are excited and ready to embrace technology, others are cautious and wary of trying new technologies for the first time.The third category is particularly prevalent in underdeveloped nations such as india, owing to a lack of competence, a lack of effective translation, and most crucially, a lack of financial resources.It is fruitless for the government to attempt to resolve these difficulties due to the fact that they do not take into consideration the changing circumstances and input needs of each agricultural group.Smart tillage is a cutting-edge framework that was developed to solve the challenges listed above.In india, a decision-based smart engine for the rental and sharing of tools and equipment has been developed, which leverages machine learning methods to proceed towards a selection of tools and equipment.The option is entirely reliant on a variety of input variables, including crop kind, harvest time/month, crop equipment needed, harvest type, and the amount of money available for rental.Additionally, an ideal recommendation engine driven by content and collaborative-based filtering will provide the farmer’s requirements depending on their requirements.In terms of escalation, the proposals would be cost-effective and excellent since they would need little changes in training, technique improvements, and resource management via a new rent-share model similar to that used by uber.In this work, demand and supply algorithms are used to define market equilibrium, and the results are shown in graphs.This includes discussion of a variety of demand and supply parameters, their impact on market equilibrium prices and quantities, and their effect on shifting demand and supply curves.The many sorts of elasticities (demand, cross-price, supply, income, and so on) are examined, as well as the ramifications for pricing systems that may result from these elasticities.

Journal: Journal of Food Quality (2022)
Publication types: Research Article;
Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

Farmers’ physical labor and debt are reduced as a result of agricultural automation, which emphasizes efficient and effective use of various machines in farming operations with the purpose of reducing physical labor and debt.It is a revolutionary idea in agriculture to create custom hiring centers, which are intended to make it easier for like-minded farmers to embrace technology/machinery for enhanced resource management practices.The study in question examines the significance of tool renting and sharing in the workplace.Rental and sharing equipment are two approaches that might be used to enable farmers to borrow equipment at a cheaper cost than they would otherwise have to pay for it.The following is a manual pilot study of 562 farmers in india to address the numerous challenges farmers face when looking for tools and equipment, as well as to determine their strong interest in the process of renting and sharing equipment.The study was conducted to address the numerous challenges farmers face when looking for tools and equipment and to determine their strong interest in the process of renting and sharing equipment.Farmers are divided into three groups according to the results of this poll: small, moderate, and large.Training and testing splits were used on the same data set in order to get a better understanding of the target variables.The data set for the survey was standardized in order to remove ambiguity.In this research, three different machine learning models were utilized: nearest neighbors, logistic regression, and decision trees.K-nearest neighbors was the most often used model, followed by logistic regression and decision trees.In order to get the best possible result, a comparison of the aforementioned algorithm models was carried out, which revealed that the decision tree is the better model among the others in this regard.Because the decision tree model is completely reliant on a large number of input factors, such as the kind of crop, the time/month of harvest, and the type of equipment necessary for the crops, it has the potential to have a social and economic impact on farmers and their livelihoods.

Journal: Journal of Food Quality (2022)
Publication types: Research Article;