bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on a novel gesture recognition device
Authors: Huang Jian 
Published: February 2021

Abstract
In order to effectively detect the input gesture information, a new type of capacitive sensing technology, which is of a low power consumption, low cost but high resolution non-contact sensing technology, is adopted. When our finger gets close to the metal plate, it will cause the change of the capacitance value of the metal plate to the ground. That value can be converted into 28-bit binary data for output. The microprocessor is equipped with the IIC interface for acquisition and processing of the output data. Because a variety of gestures produce different capacitance values, their output varies. After many times of training and machine learning, different gestures can be identified. The test results show the rate of different gestures recognition is over 99%, which can be applicable in human-computer interaction equipment.
A method of filtering bad text in network based on topic sensitive words tagging
Authors: Xiong Jianying 
Published: February 2021

Abstract
Bad information filtering is conducive to creating a healthy network. A two-level filtering method based on topic and sensitive words is proposed. In the first stage, the network text is filtered by using thesaurus, by setting the weight of different topics. In the second stage, according to the frequency, position and sensitivity of sensitive words, the value of bad tendency is obtained by weighting the web text. Finally, taking the text set recognition of bad financial publicity content in the network as an example, the result proves that it can improve the efficiency and accuracy of filtering of bad investment information.
Comparative advantage analysis of production of jasmine tea in China
Authors: Wu Qinyao , Yang Jiangfan 
Published: February 2021

Abstract
Based on the analysis of the evolution of the production layout of jasmine tea since the founding of new China, this paper further explores the heterogeneity of the comparative advantage in the main producing areas of jasmine tea from four aspects: comparative advantage index of efficiency, comparative advantage index of scale, comprehensive comparative advantage index and comparative advantage index of development. The research shows that except for Fujian, the traditional producing areas of jasmine tea have lost their comparative advantages, such as Zhejiang and Guangdong. Although the emerging producing areas, led by Guangxi, have absolute advantages in terms of comprehensive comparative advantages, but have no advantages in terms of the development advantage index. Therefore, it is necessary for all parties to focus on the key technical issues in order to achieve the sustainable and healthy development of the jasmine tea industry. Based on the above research results, it is proposed initiative and economic new normal opportunities should be seized to improve the international competitiveness and influence of China’s jasmine tea industry under the background of “One Belt And One Road”. To improve effective supply, promote tea consumption to promote economic growth and other suggestions.
Indoor intelligent driving scene-based traffic signal data set
Authors: Fu En , Pan Weiguo , Pan Feng , Yang Qing 
Published: February 2021

Abstract
In recent years, with the rapid development of intelligent driving technology, vision based traffic signal target detection is one of the urgent problems to be solved; especially with the extensive application of deep learning technology, the traffic signal data set is more and more important. Due to the variety of traffic signals, there is no data set that can cover all categories at home and abroad. This paper presents a traffic signal data set based on indoor traffic scene. The currently disclosed data category includes 22 categories, including traffic signs and traffic lights. Simultaneously, this paper also uses a two-stage deep learning method to conduct experiments. In order to facilitate other researchers to conduct further research on the disclosed data set in this paper.
Evolution of dynamics of omni-channel pre-sale and spot competition based on consumer heterogeneity
Authors: Li Peiqin 
Published: February 2021

Abstract
The paper constructs the pre-sale competition model, the pre-sale and spot hybrid competition model, and the pure spot competition model under the influence of the pre-sale promotion efforts. In the analysis of complete pre-sale competition model, only when the homogeneity of consumers is high, retailers will launch full pre-sale competition. If the pre-sale competition is to be launched, the greater the difference in the response of consumers’ demand to the pre-sale promotion, the higher the requirement for the homogeneity of consumers is. In the hybrid competition model of presale and spot, it is found that the optimal price equilibrium solution to full pre-sale competition is always less than the optimal price equilibrium solution of both pre-sale market and spot market. In the pure spot market competition model, the conditions are given for breaking the hybrid competition equilibrium between pre-sale and spot market, so as to realize the pure spot market competition. Finally, the paper finds the consumer heterogeneity interval optimized by the above three models through numerical analysis. And when heterogeneity of consumers in a medium range, enterprises 1 need more significant effort level to achieve the established earnings rise, on the other hand, when a consumer heterogeneity, within the range of low or high level interval, enterprise 1 only takes significant promotional effort in general level, the rise of yield can be achieved.
Data analysis of relationship between equity concentration and innovation investment
Authors: Xu Qian , Lin Hongzhen 
Published: February 2021

Abstract
Based on the relevant data of A-share listed high-tech enterprises in Shanghai and Shenzhen in 2014-2018, the relationship between ownership structure and innovation input is empirically studied according to the principal-agent theory and Information asymmetry theory, and combined with the intermediary effect model, further analysis of internal control in the relationship between the intermediary role. It is found that the degree of equity concentration has a negative correlation with innovation input, but has a positive correlation with internal control. The degree of equity concentration can weaken the negative influence on innovation input through the positive influence on internal control, it can be seen that internal control plays an intermediary role in the relationship between ownership concentration and innovation input. The research results can provide enlightenment for high-tech enterprises to optimize the ownership structure, improve the effectiveness of internal control, enhance innovation input and enhance the competitive advantage of science and technology.
Study on rms integrated simulation modeling of multi-agent warship propulsion system
Authors: Qian Chao , Bao Jingyuan , Tang Yong 
Published: February 2021

Abstract
As the key design characteristics of warship, reliability, maintainability, and supportability (RMS) are crucial to the successful completion of warship missions. To enrich the RMS design and analysis methods of warship equipment, this paper focuses on the voyage of warship propulsion system, and builds a theoretical model of RMS integrated assessment system for warship on the basis of multi-agent technology, so as to indicate how successfulness of the mission warship propulsion system is dynamically related to equipment and supporting resources. Furthermore, Anylogic is used as a platform to develop a RMS simulation system for warship propulsion system in the mission of voyage. By changing the RMS parameters of warship propulsion system and repeating the simulation of voyage, important factors are identified for the improvement of probability of mission completion success, which is of great military significance.
Research on operation strategy of new energy microgrid theory model containing biomass energy
Authors: Wang Tang , Li Fei , Ge Liangquan , Zeng Guoqiang , Zhang Qingxian , Gu Yi 
Published: February 2021

Abstract
In order to alleviate the energy crisis and improve environmental quality, rational use of renewable energy has become a development trend. This paper proposes a research on the new energy micro-grid operation strategy, and selects the appropriate power generation method according to the development status of each distributed energy in the microgrid. Specially establish the biomass power generation, photovoltaic power generation, wind power generation and storage battery models of the micro-grid, and infer the power generation output characteristics according to the power generation characteristics of each module. A brief analysis of the micro-grid model containing biomass power generation established in the article is carried out, and the basic operation strategy of the micro-grid is proposed, which provides a theoretical basis for the development of the micro-grid in the future.
Research on Simulation and Calculation of Temperature Field and Pressure of High Voltage Transformer
Authors: Fan Houyang , Wu Jian , Fei Xuan , Zhang Peng , Yang Xinchi , Liu Bin 
Published: February 2021

Abstract
In order to ensure the normal and safe operation of high-voltage transformers, this paper uses oil-immersed current transformers as an example to use finite element analysis to simulate the internal temperature field of high-voltage transformers when a fault occurs, and obtain the temperature distribution of the whole body under several typical fault types. Through theoretical calculations, the pressure change at the expander on the top of the high-voltage transformer is obtained. The measured data is compared with the simulation calculation results, and the state of the high-voltage transformer at the time of failure is analyzed, and the safe and controllable temperature and pressure range of the high-voltage transformer are obtained.
Resource allocation method based on massive MIMO NOMA MEC on distribution communication network
Authors: Li Da , Qin Ningli , Li Bo , Jing Xiaosong , Du Changyu , Wan Chunyi 
Published: February 2021

Abstract
The current smart distribution network has problems of slow fault detection and low efficiency. This paper reasonably derives the expression of delay from two aspects of transmission time and calculation time based on technologies such as massive multiple input multiple output, non-orthogonal multiple access, and mobile edge computing (MIMO-NOMA-MEC). In addition, a multi-objective iterative algorithm is designed to minimize the delay by jointly optimizing the transmission power, task allocation coefficient, and the computing resources of the MEC server. Finally, the simulation results show that the processing delay of the system gradually decreases as the number of antennas increases. In addition, the processing delay of the system can still be below 10ms under the extremely bad situation that there are many devices currently requesting services.
Research on influence of excavation parameters of shield tunnel based on ABAQUS
Authors: Liu Yang , Fu Xin , Yang Minghu , Tang Zhendong , Chen Lu , Liu Yuancai 
Published: February 2021

Abstract
At present, the mainstream method of urban subway construction is shield construction. What cannot be avoided in shield construction is the settlement and displacement of the ground surface, and the tunneling pressure is closely related to the grouting pressure and the settlement of the soil. For the overlapping tunnel shield construction, this paper takes the shield construction of the overlapped tunnel section of Kunming Rail transit Line 6 as the research object. Using ABAQUS finite element software to simulate overlap shield tunnel construction, the research of shield tunneling parameters and the relationship between the surface displacement of soil. The results show that reasonable selection of excavation parameters can well control ground settlement.
Performance Analysis of Activation Functions for Wheat Crop Yield Prediction
Authors: Bhojani Shital H. , Bhatt Nirav 
Published: February 2021

Abstract
Activation function (AF) plays a vital role in the neural network. This research study focuses on the performance evaluation of the various AFs in multilayer perceptron (MLP) algorithm for the wheat crop yield prediction at the regional level. Nowadays researchers are publishing the new AFs and also proposing the improvement in existing AFs for more accurate results. Sigmoid AF is the default function used in the MLP algorithm. In this research we have applied the various AFs in MLP using the WEKA java libraries and performance analysis has been carried out for the selected districts of the Gujarat state. Based on the mean absolute percentage error (MAPE) performance measure output is discussed. Therefore scientists and researchers can select the appropriate AF to improve the prediction accuracy and hence improve the performance of the network as per the research data.
Neural Network, Activation Function (AF), MLP, Yield Prediction
Regression Model for Prediction of Epidemic Outbreaks
Authors: Shashank Punna Sai Teja , Vaibhavi Akkaladevi , Vaishnavi Jenega , Jabbar M.A 
Published: February 2021

Abstract
Epidemic diseases are contagious diseases that can spread into the entire nation if the contagion measurement had reached the outbreak level and there is a chance to wipe out the entire population. So, there must be a way to track these epidemics to take counter measurements to restrict its spreading. There are many ways to track these epidemics. Nowadays, machine learning is applied in almost all the fields since data is generating rapidly from various sources. The data which is useful for tracking epidemics is also very high around us. Various machine learning techniques can be implemented to find out the status of epidemics. Regression is a machine learning algorithm that is used to find the relationship between a dependent variable and an independent variable. For fitting a regression line, the most common method is the method of least-squares. In this method, the best-fitting line is calculated for the observed data such that the sum of the squares of the vertical deviations from each data point to the line is minimum. In this paper, we proposed a Regression-based prediction system that is used to predict the status of an epidemic in a region. Linear regression is used for this purpose to predict the number of deaths in a region on a specific date. Various libraries of python like NumPy, pandas, matplotlib, and sci-kit-learn are used to develop the model.
A Survey of Machine Learning Techniques Used on Indian Stock Market
Authors: Vohra Archit A. , Tanna Paresh J. 
Published: February 2021

Abstract
Stock market provides nonlinear time series data that has been difficult for making any kind of predictions. Since the advent of various Machine Learning techniques lot of research is covered in this area. Researches have mostly been carried out using Supervised Machine Learning algorithms on predicting the price of a particular stock and also on predicting the stock index movement. The studies also cover various unsupervised machine learning algorithms as well as some statistical methods used in the stock price prediction. Study has been covered on generating portfolio of stocks that maximizes the profit with minimum risk. Social Media today generates a large amount of data and has a significant impact on the buy/sell decision made by experts. Specifically, research has been carried out on how tweets and news have an impact on stock price movement. Recent researches focus more on neural networks, deep learning and hybrid models. This study tries to identify the different approaches used by researchers in order to improve the accuracy of machine learning algorithms on the India Stock Market. The survey emphasizes that all aspects related to stock market need to be considered before making any kind of prediction.
Survey on: Applications of Smart Wearable Technology in Health Insurance
Authors: Shah Apeksha , Ahirrao Swati , Phansalkar Shraddha , Kotecha Ketan 
Published: February 2021

Abstract
Humanity is considered as the most intelligent species on the earth and are health reactive. Today, due to increase in sluggish behavior there is reduction in physical activity and that leads to health problems. To overcome this cause, wearable technology helps human being to get updates about physical health behavior e.g. heart rate, blood pressure, steps count, BMI, calories and many more. Wearables available in market helps to measure all kind of metrics related to health. The aim of this research is to study and analyze the research to check whether wearable technologies with augmented smart AI solutions help to predict health status and its outcome. Leveraging Artificial Intelligence (AI) and wearable technology could help health insurance to cover the risks associated with the uncertainties in the health, increased life span and high cost of treatments. In this study, we first discuss the works where wearable technology and AI can create value in the field of healthcare and importantly discuss the use case of leveraging the insurance domain with a smart health-monitoring service for the customers. Although, there are inhibitions in implementing a smart and fitness monitoring system in health-insurance sector like privacy concerns and hesitations towards sharing the data, health-insurance sector can look at this use-case as an incentivization to the fitness conscious clients.
Real Time Emotion Detection of Humans Using Mini-Xception Algorithm
Authors: Fatima Syed Aley , Kumar Ashwani , Raoof Syed Saba 
Published: February 2021

Abstract
In the past few decades there has been operating analysis on emotion expression recognition due to the great intra-class deviation also it is still challenging. Maximum number of research work performs the best on controlled datasets (i.e., small datasets with less features), whereas it fails to operate well and it’s still challenging on datasets varies variations in images and even in partial faces. In modern years, many works have introduced an end-to-end plan for emotion expression recognition, utilizing deep learning models. Although emotion recognition is a great task, there still seems to be a huge area for development. In this paper, we developed a mini-Xception based on Xception and Convolution Neural Network (CNN), which is easy to concentrate on great parts like the face, and conclude important improvements to earlier works. We validated our model by creating a real-time vision system which accomplishes the tasks of face detection, and emotion classification simultaneously in one blended step using our proposed mini-Xception architecture. We still utilize a visualization technique that is ready to detect important face sectors because recognizing various emotions, based on the classifier’s output. For experimental analysis we had used FER-2013 dataset and results manifest that the proposed method can efficiently perform all the tasks like detection and classification with seven different emotions using with Mini-Xception algorithm and achieved accuracy around 95.60%.
AutoAssociative Memory for Face Recognition using High-Dimensional Octonion Data
Authors: Salvadi Shoba Rani , Rao D. Nagendra , Vatsal S 
Published: February 2021

Abstract
Uniqueness or individuality of an individual face is the representation of one’s identity. Recent research in color image processing focusing on octonion algebra, which is the generalization of complex and quaternion algebra. In this paper, the face of an individual is used for identification making automatically. For this, an autoassociative memory is used for the recognition of images or videos with 8-dimensions. Octonion algebra is not commutative and not associative but it is an alternate algebra for representing the phase and angle of images effectively. The prospective experiments are conducted on color images for image reconstruction, denoising and efficiently retrieving blurred images in true color images.
RETRACTED: Detecting Sybil Attack In Wireless Sensor Networks Using Machine Learning Algorithms
Authors: Mounica Mandala , Vijayasaraswathi R. , Vasavi R. 
Published: February 2021

Abstract
In sensitive areas such as battlefields, a Wireless Sensor Network (WSN) is especially in military and civilian applications and it is of utmost importance to develop security in these networks. In various respects, this can improve the quality of life. But to be used for protection reasons in multiple situations such as implementation. There is a high risk of being exposed to multiple viruses and hacking attacks. Unauthorized APs for information protection needs to be detected. Any malicious attacks against these networks, Like Sybil Attack leads to breach of security by enacting as a node that illegitimately declares several false identities at the same time. This misleads valid nodes, and they presume each of those identifiers as actual independent nodes by accident. Thus we proposed a machine learning model to detect Sybil attack in network where rawtraffic data has been collected and is used identify authorized and unauthorized APs in an integrated wired/wireless environment.
Layered double hydroxides intercalated with methyl orange as a controlled-release corrosion inhibitor for iron in chloride media
Authors: Bakhtaoui N , Benali O , Mazarío E , Recio Francisco J , Herrasti P 
Published: February 2021

Abstract
In this study, the corrosion inhibition properties of nanocontainer-type layered double hydroxide (LDH) are evaluated on iron that is immersed in a 3.5% NaCl aqueous solution. LDH ZnAl-NO3 was synthesized via coprecipitation. The material presents satisfactory crystallinity with a Zn/Al ratio of 2:1. Methyl orange (MO) has been added into the synthesis process by exchange with nitrate ions and/or by adsorption of MO onto LDH surfaces (LDH-MO). Iron was immersed in solutions with various concentrations of LDH and LDH-MO ranged 1–6 gl−1, and the corrosion inhibition properties were investigated using linear sweep votammetry, electrochemical impedance spectroscopy (EIS), and SEM. Based on pitting potential studies, LDH has demonstrated inhibition of the pitting corrosion process, and the optimal concentration was identified as 2 gl−1. The presence of MO in LDH provides excellent anticorrosive properties with a mixed inhibition mechanism. The corrosion potential of LDH-MO presents more noble values and exchange current densities that are one order of magnitude less than those of the bare iron after 72 h of immersion in a 3.5% NaCl aqueous solution. EIS results corroborated that the corrosion resistance increased when 2 gl−1 of LDH-MO was in solution. SEM images support the anticorrosive behaviour of the LDH-MO.