bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analyzing Prediction Performance between Wavelet Neural Network and Product-Unit Neural Network
Authors: Suhailayani Suhaimi Nur , Othman Zalinda , Ridzwan Yaakub Mohd 
Published: January 2020

Abstract
Analyzing the performance of a particular approach in a field very dependent on the problem it’s aimed to solve. Artificial Neural Network (ANN) widely used for prediction in many areas including medical, environment, business intelligence and education. The uniqueness of ANN is the dynamic of hidden layer can be improvised mapped with the data problem and the structure of architecture can be enhanced such as Wavelet Artificial Neural Network (WANN) and Product Unit Neural Network (PUNN). This research aimed to analyzed the performance between WANN and PUNN towards water quality data of Chini Lake. Real world data comes with dynamic stream data and dynamic parameters based on its area of data collection method. Handling dynamic data would be misleading if the approach used very dependent towards data classes. The measurement to analyze the data based on performance accuracy, data sensitivity, data precision and specification of both method with regards of the regular ANN. The findings demonstrate the ability to obtain satisfactory prediction accuracy for both WANN and PUNN compared to regular ANN. The model accuracy for this case study by using WANN and PUNN were 75.34 % and 66.86 %, respectively. Therefore, WANN would be a competitive tool for prediction with conventional ANN.
Corneal arcus classification for hyperlipidemia detection using gray level co-occurrence matrix features
Authors: Ramlee R A , Subramaniam S K , Yaakob S B , Rahman A S F , Saad N M 
Published: January 2020

AbstractThe arcus cornea is an eye problem that is often encountered among older people, but the situation is risky to teenagers and young people, in which it gave a sign the presence of lipid abnormalities in their blood and the risk of disease problems such as diabetes and heart disease. This paper presents the classification of the arcus cornea, using the extraction of texture features of the gray level co-occurrence matrix, along with several models of the classifiers, namely as scale conjugate gradient, Bayesian regulation, and Levenberg-Marquardt. Percentage fractions for training, testing and validation for classifier are 70%, 15%, and 15% respectively. The comparison of the classifiers used by the past researchers for classification the eye abnormalities, also were analyzed and studied in this work. In this experiment, a total of 125 image eyes were used, consisting of two classes of the eye image, which is normal and abnormal. The best result demonstrated in this proposed framework using Bayesian regulation classifier is, a sensitivity of 96%, and a specificity of 100%. However, this classifier did not achieve perfectly classification or an accuracy of 100%. Nevertheless, it is able and evident that the system is effective by the output of 98.4% accuracy.
Machine learning in intelligent manufacturing system for optimization of production costs and overall effectiveness of equipment in fabrication models
Authors: Carla Acosta P , Terán Héctor C. , Arteaga Oscar , Terán María B. 
Published: January 2020

Abstract
The study proposes optimize the production costs with the implementation of an intelligent autonomous system applied to adaptive control and supervision to in computer-integrated manufacturing. For the validation, a horizontal band saw was used with 3 axes of displacement implementing 2 cameras with stereoscopic vision and finding an estimation of depth in the cut. With the dimensional deviations (x, y, z) of the cut, the shape and dimension of the cuts in the pipe, are defined to be manipulated and classified in correct and in-correct cuts by means of a separator coupling. For this purpose, algorithms were developed on two computer platforms: LabVIEW, which obtains the images, controls the automatic separator and the material feeder; Matlab, which processes dimensional deviations by recognizing patterns with the “Principal component analysis” (PCA) technique, in turn compares with an ideal pattern and optimizes the cutting parameters: Cut speed, cutting index, through a derivative Integral Proportional PID algorithm with the interaction of machine learning (ML) based on SVM theory. Autonomously corrects errors without human supervision, obtains the lengths and depths with the optimum cut-offs and result of adaptive supervision, increases production, product quality and reduces operating costs for each cutting cycle by com-plying with Overall Equipment Effectiveness parameters (OEE) and integrating into intelligent manufacturing systems.
Real-time classification of national and international students for ICT and mobile technology: an experimental study on Indian and Hungarian University
Authors: Verma Chaman , Illés Zoltán , Sttofová Veronika 
Published: January 2020

Abstract
This paper focused on the classification of student national-level status such as national and international. A primary survey was conducted in the academic year 2017-2018 to analysis the circumstances of trending ICT and Mobile Technology (MT) in Indian and Hungarian higher education. The main objective was to identify the student’s answers provided in the survey based on their national-level status. For the classification tasks, we used Logistic regression (LR), Support Vector Machine, Multilayer perceptron (MLP) and Random Forest (RF)on both balanced and unbalanced datasets with K-fold Cross-Validation (KCV), Leave One Out (LOO), and Hold Out (HO) methods. Also, Xtreme Gradient Boosting (XGB) classifier was also implemented to enhance the classification accuracy of existing classifiers. The findings of the study showed that the XGB classifier outperformed others with the highest accuracy of 95% with 18 significant features. Also, class balancing improved significantly the accuracy of classification. Further, the authors recommended this predictive model to be implemented as a real-time function utility on the website of the university.
Dimensionality Reduction and Regression Analysis Based on the Data of Educational Participation of Student Party Member
Authors: Li Jing , Wang Dengshan , Xu Xiaojuan 
Published: January 2020

Abstract
This study adopts the multi-stage random sampling method of grade, major and class. Data were obtained through questionnaires. Through the analysis of the questionnaire data, we find that the grade and identity distribution of the respondents are relatively uniform among the respondents. Taking the arithmetic mean value of each secondary index, we construct the dimension index based on the data of questionnaires. By least square and generalized linear regression model, we find that the influence of independent participation on the learning harvest is significantly positive, indicating that the daily party member education activities such as active participation and social practice can significantly improve the learning harvest.
Parameters optimization of SVM based on the swarm intelligence
Authors: Wang Yu-xin , Li Xue-zhen , Wang Zheng-yi 
Published: January 2020

Abstract
In view of the existing optimization method in the optimization of support vector machine (SVM) parameters of low searching efficiency and easy falling into the master problem, put forward a kind of modified pollinate flowers algorithm based on mutation strategy—MFPA algorithm, and applied to SVM parameter optimization problem, through the relevant test data sets from UCI, test results show that MFPA-SVM parameter optimization model of its classification performance is superior to the existing PSO-SVM model and the BA-SVM model.
A survey on key technologies of privacy leakage detection for Android platform
Authors: Yu Fu , Xuming Lv , Xiao Gao , Lin Liu , Jingzhao Luan 
Published: January 2020

Abstract
With the rapid advancement of the mobile internet, smartphones have been the essential tools for communication and entertainment. In recent years, Android, developing quickly, has been the largest mobile operating system in the world. However, due to mass private data generated by Android intelligent devices, privacy disclosure is a serious problem in security for the Android platform. Based on the relevant background, this thesis indicates the problems of privacy protection that the Android platform faces, summarizes the detecting techniques for privacy disclosure on the Android platform at the present stage, and introduces the research status and progress with the aim of offering a reference for relevant practitioners and researchers.
Deep Layer Aggregation with Cross Attention for Lane Detection
Authors: Lu Ming , Liu Duan , Sun Yongteng , Duan Hao 
Published: January 2020

Abstract
As an important part of driverless vehicle research and high-precision map making, lane detection technology needs to be improved urgently. However, it is difficult for current networks to obtain long-distance semantic information, which leads to the confusion of categories in narrow lanes. This paper adds cross attention based on deep layer aggregation to obtain long-distance semantic information, and constructs time series filter to filter in time domain, the method is simple and robust. Our encoder is based on resnet-50. According to the experimental results, slightly changing resnet-50 can achieve better results. Our method is evaluated on Baidu ApolloSpace land segmentation dataset, increases 3.4% relative to DeeplabV3+, and cross attention with time series filter contribute more than 1% mIoU accuracy.
Back-propagation Artificial Neural Network for Early Diabetic Retinopathy Detection Based On A Priori Knowledge
Authors: Wu Huiqun , Zhao Shenghui , Zhang Xiaofeng , Sang Aimin , Dong Jiancheng , Jiang Kui 
Published: January 2020

AbstractPurpose: We aim to develop a back-propagation artificial neural network (BP-ANN) improved by a priori knowledge and to compare its efficacy with other methods in early diabetic retinopathy (DR) detection.Methods: A total of 240 fundus images, composed of 120 early-stage DR and 120 normal images, were obtained with the same 45° field of view camera, with the macula at the center, as a cohort for further training. All retinal images were processed, and a priori knowledge features such as blood vessel width and tortuosity were semi-automatically extracted. An improved BP-ANN with a priori knowledge was developed, and its efficacy was compared with that of the traditional BP network and SVM. Besides, k-fold cross validation method was conducted to demonstrate the efficiency of the proposed methods. We also developed a graphical user interface of our proposed BP-ANN to aid in DR screening.Results: Our 10 randomization and 5-fold cross validation results of SVM, traditional BP, and improved BP were compared. The results indicated that the BP-ANN with a priori knowledge can achieve better detection results. Besides, our results were also comparable with other reported state-of-art algorithms. During the training stage, the epoch in the improved BP-ANN was less than that in the traditional BP group (109 vs 254), indicating that the time cost was shorter when using our improved BP-ANN. Furthermore, the accuracy and epoch of both the traditional BP and our improved BP network obtained better performances when the number of hidden neurons was 20.Conclusions: A priori knowledge-based BP-ANN could be a promising measure for early DR detection.CCS: Information system→Expert system
Research on Key Technologies of Personalized Education Resource Recommendation System Based on Big Data Environment
Authors: Cheng Yunli , Bu Xu 
Published: January 2020

Abstract
This paper focuses on the construction of personalized education resource recommendation system and the research of key technologies in the big data environment, designs a personalized recommendation system architecture that can handle PB education resources, and implements an educational resource personalized recommendation system based on this architecture. A hybrid recommendation algorithm based on content and collaborative filtering is designed to meet the recommendation novelty requirements and solve the cold start problem of the recommendation system. Research a flexible, reliable, high-performance personalized recommendation system architecture that can store and process PB-level data and can be recommended in real time, research personalized recommendation engine, data pre-processing and data mining model construction, and implement the recommendation system. It is important to determine the availability of the system by performing related functions and performance verification.
Extremely Short Chinese Text Classification Method Based on Bidirectional Semantic Extension
Authors: Yue Yongzeng , Zhang Yuhong , Hu Xuegang , Li Peipei 
Published: January 2020

Abstract
Short text classification methods have achieved significant progress and wide application on text data such as Twitter and Weibo. However, the extremely short chinese texts like tax invoice data are different with traditional short texts in lackness of contextual semantic information, feature sparseness and extremely short length. The existing short text classification methods are difficult to achieve a satisfactory performance in these texts. To address these problems, this paper proposes a text classification method based on bidirectional semantic extension for extremely short texts like Chinese tax invoice data. More specifically, firstly, the Chinese knowledge graph is introduced for extending bidirectional semantic of texts and label data to expand the extremely short texts and ease the problem of feature sparseness; secondly, the hash vectorization is used to avoid the semantic problem caused by the lackness of contextual information. Experimental results conducted the real tax invoice dataset demonstrate the effectiveness of our proposed method.
The Facilitation of Modern Technics for English Pronunciation Class in Foreign Language Learning in China
Authors: Zhou Qian , Li Yonghong , Guo Lei 
Published: January 2020

Abstract
English is learned in China as a foreign language, the foreign language setting made it very difficult to learn the standard pronunciation. But the short comings can be greatly overcome by the proper use and facilitation of modern technics. This passage focused on what and how software and mobile phone Apps can be used in English class to improve the performance of English pronunciation.

CCS Concept

• Social and professional topics➝Adult education
Research on User Knowledge Acquisition and Application in Software Ecology
Authors: Qiuyu Ding , Fuhua Shang 
Published: January 2020

Abstract
In the process of software development, users play a crucial role. User knowledge plays a role of energy driver in the software ecology, and user knowledge plays a guiding and reference role in software development. From the perspective of knowledge chain, improving the acquisition rate and utilization rate of user knowledge can better complete the upgrading work of large software platform. Based on this starting point, this paper puts forward the idea of actively acquiring user behavior information, mining association rules of software platform components, and finally applying knowledge mapping technology to the knowledge representation of user knowledge and inherent knowledge of software platform, so as to strengthen the extensibility and continuity of knowledge. Experiments show that above methods can improve the acquisition rate and utilization rate of user knowledge, quickly locate relevant entities in the software to improve the speed and quality of large software platforms.
Research on Access Control Model Based on RBAC Model in Microservice Environment
Authors: Zhao Jiantao , Sun Jin 
Published: January 2020

Abstract
With the development of cloud platform technology, micro-service has become a very popular software development architecture mode. The micro-service architecture reduces the coupling between application modules, which is conducive to the development, deployment and operation of complex application systems, but also brings many system security problems. Access control model is an important guarantee. This paper discusses the basic requirements of access control in micro-service environment. Through the research of traditional access control technology, the limitations and shortcomings of access control in micro-service environment are analyzed, and an optimization model of access control based on RBAC is proposed.
Research on UAV image denoising effect based on improved Wavelet Threshold of BEMD
Authors: Pingjuan NIU , Xueru MA , Run MAO , Pan Jie , Wang Shan , Hao Shi , She Huanlin 
Published: January 2020

Abstract
Aiming at the noise generated by external or internal factors reduces the sharpness of unmanned aerial vehicle (UAV) images, an improved bi-dimensional empirical mode decomposition (BEMD) de-noising algorithm is proposed to solve the problem. Firstly, an intrinsic mode function (IMF) is obtained after BEMD decomposition of noisy image, and then the high frequency apply particle swarm optimization (PSO) after wavelet decomposition to the high-frequency IMF to obtain the optimal threshold filtering. As for the low frequency, it is filtered by the exponential attenuation threshold algorithm of wavelet semi-soft threshold. Finally, the filtered both high-frequency and low-frequency IMF are reconstructed after inverse wavelet transformation. This algorithm is applied to the processing of UAV images, and compared with the traditional UAV image denoising method and the advanced UAV image denoising method. Simulation results demonstrate that the proposed denoising method outperforms other de-noising methods in terms of peak signal-to-noise ratio and mean square error.
Study on Joint Regulation and Safety Supervision of Urban Rail Transit System
Authors: Cai Xiaolei , Gao Fan 
Published: January 2020

Abstract
The integration of uiban rail transit system is an important stage of urban rail construction. By analyzing the relationship between the urban rail subsystems, this paper studies the project, content and process of each electromechanical system. From the perspective of large scale system, the linkage technology and specifications of the electromechanical equipment are verified to ensure the overall coordination function of the system. In addition, the system joint supervision mode is introduced, and the whole process management is carried out to ensure the orderly and safe operation of the system. The debugging service process of EMU is analyzed emphatically. And the contents of the safety supervision work are also studied. From the perspective of risk analysis of motor vehicle commissioning, taking the risk hidden danger and prevention and control measures of a certain Beijing rail transit line as an example, the main points of safety supervision are studied. It can provide reference for urban rail transit industry system joint management.
Multi-empirical Discriminant Multi-Agent Reinforcement Learning Algorithm Based on Intra-group Evolution
Authors: Zhang Zhong-lei , Zheng Jiao-ling , Zou Chang-jie 
Published: January 2020

Abstract
In order to find the optimal target in the unknown environment more quickly, a multi-empirical discriminant multi-agent reinforcement learning algorithm based on intra-group evolution is proposed based on the deep deterministic policy gradient algorithm (DDPG). In the unknown environment, the agent can find the target faster by performing Information exchange, group genetics and other mechanisms. The comparison with the traditional reinforcement learning algorithm shows that the proposed algorithm is superior to the traditional reinforcement learning algorithm in solving time and solving accuracy, and can quickly and effectively find the optimal target in the environment.

CCS Concepts

•Computing methodologies → Multi-agent systems
Research on Performance Optimization of Web Application System based on JAVA EE
Authors: Cai Zengyu , Li Jingxiao , zhang Jianwei 
Published: January 2020

Abstract
With the development of informatization, the Web application system is being used by more and more enterprises and institutions. With the increase of the user group, the concurrent visits are increasing, which leads to the poor service quality of Web system. Several high-concurrency solutions existed, but they address only one aspect of the performance problem, and small and medium enterprises need to consider the cost. This paper will analyze the performance factors affecting the Web application system through multiple levels, aiming to build a Web application system with high availability, high concurrency, high expansion and less cost. Based on the factors affecting the performance of the Web application system, the project of the online mall system was optimized, and the service support of the Web application system was adjusted to make it more suitable for the system requirements. Besides, related SQL statements were optimized, Java code layer was optimized, user experience layer was optimized, and performance testing and analysis were completed.
Thinking on the Teaching Reform of Electronic Technology
Authors: Shengchang Lin 
Published: January 2020

Abstract
with the rapid development of electronic technology, this paper starts with the design and manufacture of a kind of equipment in the reform of electronic technology teaching, and develops an electronic product. The reason is that the memory of the elderly is declining year by year, and people often forget to bring things when they go out of the house. For example, cell phones, door keys, etc., more serious is to forget to close the electric kettle in use, electromagnets, gas switches and so on. In view of the above phenomenon, I designed a warning device to go out, from shallow to deep in teaching, case teaching, to enhance studentsundefined interest in learning. The product can give a reminder when you go out, check to see if you have everything in place, and remind you to turn off the electrical appliances and gas switches that are in use. So that people can work in peace of mind, happy home.
Analysis of the status quo and trend of agricultural economic management under the background of big data
Authors: Kun-Fa Lee , Hui-ni Chen , Ying Lu 
Published: January 2020

Abstract
With the global economic network, the collection and processing of big data information data is more and more important, and the development trend of big data makes appropriate adjustments and choices in the management of agricultural economy. The use of big data in the combination of agricultural economic management environment has increased the information value of agricultural economic management. On the basis of science, big data as a development economic forecast not only provides a powerful guiding role for agricultural production, but also provides an effective basis for the management and decision-making of agricultural production. Based on the analysis of the development status of big data agricultural economic management, this paper provides a new direction for the innovation and development of China’s agricultural economic management.