bluuebunny/crossref_metadata_2025_split huggingface dataset:
Determining the Eligibility of Providing Motorized Vehicle Loans by Using the Logistic Regression, Naive Bayes and Decission Tree (C4.5)
Authors: Rianto Harsih , Amrin  , Rudianto  , Pahlevi Omar , Kusumawardhani Paramita , Hadi Seno Sudarmono 
Published: November 2020

Abstract
Evaluating in determining the eligibility of giving credit is very important. Errors in providing credit worthiness assessments can result a bad credit risk. The problem that often occurs is not the application of the system by financial parties but more on HR when making predictions about the determination of consumer credit worthiness. Research in the field of computers has been done to reduce credit risk resulting in losses to the company. In this research a comparison of Logistic Regression (LR), Naïve Bayes (NB) and Decision Tree (C4.5) algorithms is performed to predict the feasibility of granting credit. In order to produce a prediction of the feasibility of granting credit to new consumers, credit data used by the company is used. The data used in this study consists of 481 consumer records that have been classified as consumers with current credit and bad credit. After testing using the same dataset on the three algorithms by comparing the AUC and Confusion Matrix values, it was found that the appropriate algorithm to be applied to the credit worthiness dataset was Logistic Regression with an Area Under Curve (AUC) value of 0.972 and Accuracy or Confusion Matrix of 93.14%. As for the Decision Tree Algorithm (C4.5) from the test results, the AUC value is 0.926 and the Accuracy is 90.85% and the Algortima Naïve Bayes AUC value is 0.905 and the Accuracy is 82.75%.
Hybrid approximation for solutions of high-order integro-differential equations including variable delay
Authors: Gürbüz Burcu 
Published: November 2020

Abstract
In this study, a numerical technique with hybrid approximation is developed for solving high-order linear integro-differential equations including variable delay under the initial conditions. These type of problems are of applications in mathematical physics, mechanics, natural sciences, electronics and computer science. The aim of this work is to investigate an approximation with the matrix forms of Taylor and Laguerre polynomials along with standard collocation points. By the reduction of the solution of this problem with regard to the matrix relations, the solution of a system of algebraic equations has been obtained. The usefulness of this algorithm has been demonstrated by numerical experiments together with an error analysis technique.
Entrepreneurial Mindfulness Based on Artificial Intelligence
Authors: Indrianti Yasinta , Sasmoko  , Mohd Amin Nor Fadila , Rabiha Sucianna Ghadati , Setiadi Nugroho Juli , Handrimurtjahjo Agustinus Dedy , Waspodo Muktiono 
Published: November 2020

Abstract
The ability to self-diagnose to build capacity as an entrepreneur is something that is important in order to bridge the development of self-competence needed in the industrial era 4.0. This ability is expected to bring up a condition in which a person has full active awareness of the awakening of the entrepreneurial spirit within himself to then produce a productive person called Entrepreneurial Mindfulness. This study aims to develop self-detection tools about Entrepreneurial Mindfulness through an artificial intelligence-based website for students and alumni who are entrepreneurs. The research method used is the Neuroresearch method for developing Entrepreneurial Mindfulness measurement tools and the waterfall method for developing Artificial Intelligence-based websites. The results of research in the form of an Artificial Intelligence-based website about Entrepreneurial Mindfulness where the website will be able to provide profiling based on diagnoses made related to the level of Entrepreneurial Mindfulness owned by students and alumni in implementing entrepreneurial practices.
K-Means Algorithm for Clustering The Location Of Accident-Prone On The Highway
Authors: Puspitasari Diah , Wahyudi Mochamad , Rizaldi Muhammad , Nurhadi Acmad , Ramanda Kresna , Sumanto  
Published: November 2020

Abstract
In Indonesia, there is a highway which connects Jakarta and Bogor, some accidents have occurred at the highway every year. This paper aims to analyze the location of the accident to classify the high and low levels of accident vulnerability in the Jakarta Bogor Highway. First, the extracted data is then grouped based on some of the same characteristics in the dataset namely cause, location, minor injuries, serious injuries and death. Second, the grouping results are visualized in the form of highway maps that can help highway managers in identifying and evaluating several accident-prone points on Jakarta Bogor Highway. The method to be used in data processing in this study is the K-Means clustering algorithm which is expected to produce useful information for Jakarta Bogor Highway managers. The results of this study indicate that accidents that often occur are in cluster 3 with a total of 80 accidents and at least there are in cluster 2 with a total of 57 accidents, the location of accidents that often occurs in cluster 1 is in KM 24, while cluster 2 is in KM 41 and cluster 3 located at KM 10.6.
Analysis of Perceptron Quantum Artificial Neural Networks to Classify the Feasibility of Prospective Debtors
Authors: Pujiastuti Lise , Wahyudi Mochamad , Solikhun  
Published: November 2020

Abstract
Bank is a business entity whose activities are collecting funds from the public in the form of deposits and channeling them to the public in the form of loans or other forms to improve the lives of many people. This study aims to classify the feasibility data of prospective borrowers with the perceptron algorithm using quantum computing to facilitate the bank in determining the prospective debtor. The results of this study are an analysis of the feasibility of prospective borrowers using the perceptron algorithm with quantum computing.
Generation of Rectangular Matrix Key for Hill Cipher Algorithm Using Playfair Cipher
Authors: Alawiyah Tuti , Hikmah Agung Baitul , Wiguna Wildan , Kusmira Mira , Sutisna Herlan , Simpony Bambang Kelana 
Published: November 2020

Abstract
Hill cipher is a cryptographic algorithm that uses matrix as a key by utilizing modulo operations. The key matrix used is usually made randomly as a square matrix. In this study, a key matrix was created using the Playfair Cipher algorithm as a rectangular matrix. The use of the Playfair Cipher algorithm makes it easy for users to remember key matrices, while remaining safe when distributed. Beside the key matrix security, use of a rectangular key matrix produces a longer and more complicated ciphertext to find the linear equation.
Gojek and Grab User Sentiment Analysis on Google Play Using Naive Bayes Algorithm And Support Vector Machine Based Smote Technique
Authors: Hermanto  , Kuntoro Antonius Yadi , Asra Taufik , Pratama Eri Bayu , Effendi Lasman , Ocanitra Ridatu 
Published: November 2020

Abstract
As the online Ojek services, people often talk about them by giving their opinions and opinions through various media, one of which is Google Play opinion given by the public to the services of online Ojek also diverse. Users provide review reviews or comments about the application, of course users will choose an app that has a good review. But monitoring the reviews of the general public is not easy, because the amount is very much to be processed so that researchers want to know the extent of the user review analysis of Gojek and Grab applications based on the review of user comments using the classification technique is using the NB algorithm and SVM based technique Smote. The results of the test with the highest accuracy result 81.09% and AUC value = 0.922 is the application Gojek while for application test results grab accuracy value of 73.20% and AUC value = 0.848. To that end, the implementation of the Support Vector Machine based Smote technique in this study has higher accuracy so that it can be used to provide solution to the sentiment analysis problems in the review user comments online Ojek application
Popular Content Prediction Based on Web Visitor Data With Data Mining Approach
Authors: Iskandar I D , Basjaruddin N Ch , Supriadi D , Ratningsih  , Purnia D S , Wibisono T 
Published: November 2020

Abstract
A quality website has five parameters that must be considered are: information, security, convenience, comfort, quality of service. But of course, the fifth parameter does not always guarantee the amount through its Web page will increase, from that problem. So research is conducted to predict website content based on visitor data with a data mining approach, this research aims to improve the quality of content on target according to the interest of website visitors. Evaluation of random forest algorithm has the value the accuracy of classification of 71 percent by value of Kappa 0.712 whereas the k-NN algorithm has higher accuracy values of random Forest algorithm i.e. worth 84.88 percent and kappa values of the mean 0.847 the k-NN algorithm performs data processing process predictions against data of web content more effectively than the random forest
Expert System for Diagnosing Osteoarthritis with Fuzzy Tsukamoto Method
Authors: Hardi SM , Triwiyono A , Amalia  
Published: November 2020

Abstract
Osteoarthritis is a degenerative joint disease involving the cartilage, the lining of the joints, ligaments, and bones, causing pain and stiffness in the joints that suffer from these symptoms. People often ignore pain that occurs when moving the shoulder, plate, knee and hand pain when it has great potential are the symptoms of the disease Osteoarthritis. Expert systems can be used as a system to identify disease Osteoarthritis. With Osteoarthritis disease diagnosis expert system then expected to help people to detect diseases early Osteoarthritis and can consult with a doctor before continuing to a more dangerous stage. In this research using Fuzzy Tsukamoto Algorithm method, which method is an extension of the reasoning Tsukamoto monotonous, every consequent formed IF-Then rules should be presented with a fuzzy set. As a result, output inference of each rule is given explicitly by predicate. Result is obtained by using a weighted average (center average defuzzifier). Symptoms of Osteoarthritis serve as fuzzy values which will then be sorted according to a predetermined membership function. After fuzzy value of the calculation has been obtained, then fuzzy value will be converted into firm value using a weighted average. From these results it can be concluded that the accuracy of the expert system that has been built to have an accuracy of 90% that is 18 of the 20 patients who had been tested using an expert system that has been built
Elaboration Factors of Success in The Application of Community-Based Solid Waste Management and Composting Technology
Authors: Ramadhayanti Ana , Nurhidayati  , Sari Imelda , Kuspriyono Taat 
Published: November 2020

Abstract
This study aims to describe the elaboration of success factors in community-based waste management in Waste Bank and composting technology in Flamboyan Indah RW 05 Kelurahan Rawa Badak Selatan, Koja in 2020. This study uses descriptive qualitative methods using primary data to become research objects in RW 05, Desa Rawa Badak Selatan and secondary data from sources and literature. We conduct field studies, including observations and interviews. Koja area is an area with a high population increase accompanied by an increase in organic and inorganic waste. This condition requires community involvement to help manage and reduce waste by processing waste directly from the source. The action taken is to divide the two forms of garbage, inorganic waste is processed by the Community-Based Waste Management System (CBSWM) or waste bank and organic waste with composting technology. Composting technology uses a fermentation process in it. The results of this study concluded that the successful implementation of the CBSWM system and composting technology invited active community involvement by providing economic benefits. But on the other hand this system has weaknesses such as limited land and economies of scale that have not made it possible for technology investments that require large funds.
The Issues on Time Synchronization Technology in Time Triggered Ethernet
Authors: Liu Shijia , Hamdulla Askar 
Published: November 2020

Abstract
Based on the idea of time-triggered, global unified high precision time synchronization is the foundation of network deterministic communication. In the process of time synchronization, clock synchronization algorithm, transmission delay, clock drift and so on will affect the accuracy of clock synchronization. It is a great technical challenge to realize high precision and reliable clock synchronization. This paper collates and analyses the relevant literature, first introduces the time-triggered Ethernet, then analyses the time synchronization protocol that can be used for high-precision clock synchronization, reviews the development status of time synchronization technology and makes an overall analysis. Finally summarizes the clock synchronization difficulty and prospects the development trend of the time synchronization technology.
An Automated Method for Bacterial Flora Counting Based on Image Analysis
Authors: Wu Xiyuan , Liu Jinke , Zheng Xingkai , Chen Xi , Que Yutao , Ma Borong 
Published: November 2020

Abstract
How to quickly detect the characteristics of water quality flora, such as bacterial flora number, is a key problem of water quality safety, the traditional manual counting method has some serious defects such as low efficiency, low accuracy, easy to cause visual fatigue and hard to deal with large samples. This paper designs an automated method for bacterial flora counting based on image analysis, including such modules as pretreatment of bacterial image classification and colony counting of culture medium images by filter method (using Hough circle detection algorithm and a circle deduplication algorithm). The accuracy of experimental results is above 90%, which is in line with international standards, and the time complexity of the algorithms is up to the order of square. This method can solve the problems of heavy workload and low efficiency in traditional manual counting method.
Research on Image Fuzzy Enhancement Algorithm with Tanh Operator
Authors: Liu Xiwen 
Published: November 2020

Abstract
Improvements are proposed in this paper to overcome the drawbacks of traditional fuzzy enhancement algorithms. Tanh operator is used as membership function, and fuzzy enhancement function are made up of simple power functions, and the image is divided into two regions by adaptive segmentation method, one is high grey region, the other is low grey region, pixels in one region are enhanced, and pixels in the other region are reduced. Simulation results show that this algorithm has good ability to increase image’s contrast, and it is a simple and efficient way to enhance blur edges.
Research on the Construction Strategy of Landscape Architecture Space Experience Based on the Concept of Cartography
Authors: Tang Fangli 
Published: November 2020

Abstract
As a medium connecting landscape, architecture and city, cartography provides a new design tool for architectural painting and a new way of intervention in the study of landscape architecture space experience. This paper will analyze the development process of “Cartography”, mainly introduce the relationship with landscape architecture, extract relevant information, and explore the strategy of Cartography in landscape architecture space experience.
Research on Location Privacy Protection Mechanism Based on No-Trusted User Cooperation
Authors: Wang Kunfu , Feng Wei , Mao Wanfeng , Wang Hui 
Published: November 2020

Abstract
With the development of Internet and wireless location technology, location-based service makes people’s life more convenient, and the threat to user privacy is growing. After analyzing the advantages and disadvantages of the existing location privacy protection models, this paper proposes a new MHT model based on untrusted users. Through the multi hop transmission of the request information, the user’s location information can be rid of the correlation with the user, and the query request can be desensitized. It can provide precise location service and resist the collusion attack and reasoning attack, which effectively protects the user Location privacy.
The Past, Present and Future of Computer-aided Design
Authors: Tang Dan 
Published: November 2020

Abstract
Under the background of rapid development of digital design, this paper systematically introduces the development origin and technical strategy of computer-aided architectural design, discusses the origin of CAAD thought in depth, puts forward CAAD influence factors, and makes clear the different influence degree of CAAD strategy on architectural design process, and looks forward to the future development direction of CAAD based on this.
Design and Development of Welding Expert System
Authors: Gao Yulong , Zhang Shixin , He Jianying 
Published: November 2020

Abstract
With the gradual increase of welding laboratory business in recent years and the detection content is gradually widened, the development the informatization of welding related business scheduling, management, as well as experimental data analysis has become a crucial part of the whole welding work. This paper presents a welding expert system, which integrates the functions of experimental data collection, customer entrusting test project recommendation, test data detection, etc., so that users can conveniently and quickly realize customer entrusting information recommendation, welder qualification management and other related work, and test and evaluate the test data rapidly.
The Maximum Entropy Method for Bilevel Stochastic Programming
Authors: Wanna Zhou 
Published: November 2020

Abstract
Bi-level stochastic programming is a process of taking the optimal value of the lower level as the feedback to the upper level, and the constraint that the lower level has one single optimal solution can be relaxed in calculation. Using the maximum entropy method, the present study proposed an approximate calculation method for stochastic programming. The proposed method constructs a maximum entropy function for lower-level programming to approximately express the lower-level optimal value function, converts the bi-level stochastic programming problem into a single-level stochastic programming program, and hence obtains a calculation method for an approximation optimal solution (ε-optimal solution) of the bi-level stochastic programming problem.
Online multi-core ranking model for image retrieval
Authors: Zhao Xuezhuan , Pei Lishen , Zhang Zheng , Li Tao 
Published: November 2020

Abstract
With the popularity of social media services, efficient online image retrieval urgently needs to meet the diverse needs of network users. How to use the existing semantic information and social label information to establish a content model to bridge the semantic gap is an urgent problem to be solved. In this article, we propose an efficient online multi-core ranking model (OMKR), which is trained by minimizing the triplet loss of hard negative samples based on multiple query dimensions and complementary feature channels. By optimizing the sorting performance of multi-dimensional queries, the semantic consistency between image sorting and text query input is directly maximized without relying on the intermediate semantic annotation process. A large number of experiments on two social media data sets prove the advantages of our method in terms of retrieval performance.
Comparative Study of Short-Term Forecasting Methods for Soybean Oil Futures Based on LSTM, SVR, ES and Wavelet Transformation
Authors: Li Ganqiong , Chen Wei , Li Denghua , Wang Dongjie , Xu Shiwei 
Published: November 2020

Abstract
Short-term forecasting of futures market is valuable and is also a technical challenge. In this paper, a hybrid approach for soybean oil futures price forecasting is proposed based on time-series analysis methods. The method combines wavelet transformation and exponential smoothing so that the characteristics of the time series can be captured at different time scales, and forecasting based on exponential smoothing is applied at each time scale. A comparative case study is then conducted that compares the proposed method with other three methods which are an RNN network with Long Short-Term Memory units, a Support-Vector Regression model, and an Exponential Smoothing model without wavelet decomposition to the time series. It could be concluded that the forecasting error performance of ES and Wavelet-ES was better than LSTM and SVR, and the Wavelet-ES achieved the best results for the direction forecasting. The case study provides valuable reference for application of short-term futures price forecasting.
Dangerous goods identification algorithm based on Generative Adversarial Networks
Authors: Wang Chao , Li Zhiyuan , Huang Lei 
Published: November 2020

Abstract
With the continuous progress of deep learning, the application has been extended to the identification of dangerous goods in the security inspection system. The quality of the identification algorithm directly determines the quality of the security inspection system. In this paper, a recognition algorithm based on Generative Adversarial Networks is proposed, which can continuously train the generation model and the discrimination model, and collect the image data samples that are blocked or too small, and then generate images with high similarity, so as to achieve the purpose of detecting and tracking the hidden or small dangerous goods. The experimental results show that the algorithm can effectively detect the dangerous goods and greatly improve the tracking effect.
Explicit Runge-Kutta scheme with high efficiency and energy dissipation for the Allen-Cahn equation
Authors: Li Ke , Zhang Huan 
Published: November 2020

Abstract
This paper presents a series of explicit Runge-Kutta solution to the Allen-Cahn equation, using the same energy and the square of the newly developed method were analyzed. Based on the explicit Runge-Kutta, we find that these schemes not only have high accuracy, high-order convergence and efficiency, but also have energy-consuming characteristics. We first convert the nonlinear Allen-Cahn equation into an equivalent equation that satisfies the law of quadratic energy dissipation. Then, we discretize the reconstruction by using the Runge-Kutta scheme in the time direction. Theoretical analysis and numerical simulation results verify the algorithm’s dissipation characteristics, convergence order and long-term simulation stability.
Deep learning based target detection algorithm for motion capture applications
Authors: Wang Haitao , Tong Xin , Lu Fengyun 
Published: November 2020

Abstract
Motion capture technology is the use of external devices to perform data recording and posture reproduction of the displacement of human structures. Deep learning algorithms are playing an increasingly important role in motion capture technology as the technology involves data that can be directly understood and processed by computers in terms of dimensional measurements, positioning of objects in physical space, and orientation determination. This paper presents an application of a convolutional neural network system, YOLO-V4, in the field of motion capture. YOLO-V4 system weight files are small and do not require high hardware requirements. It can also be implemented in PyTorch so that it can be deployed on mobile devices, enabling edge devices to run these models as well, relieving the space constraint of immovable signal capture devices and providing the advantages of high accuracy and high detection rate.