bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimization and Design Based on Data Visualization of Enterprise-class Crawler Systems for Shipping
Authors: Wu Hao 
Published: January 2021

Abstract
The designed crawler scheduling system is a JEE application based on Quartz scheduling framework that sends crawler tasks to the crawler control system automatically or manually at regular intervals. The crawler control system visually manages and controls the shipping crawlers. The HttpCilent-centric crawler system crawls the shipping website. The message queue ActiveMQ is used as a message middleware for the crawler control system and the crawler system to achieve asynchronous communication and reduce coupling. In this system, front-end frameworks such as Bootstrap, AngularJS, and SweetAlert are used to implement a visual scheduling system and data visualization system. Change the crawler deployment environment by deploying containers through Docker. Add Selenium dynamic page processing for shipping website. Adding message queues and MongoDB database data storage. By adopting a series of techniques and designing, testing and quality assurance of the system, we aim to make the crawler more stable and efficient, and to achieve security, stability and ease of maintenance of the system.
Research on Optimal Allocation of Sea Area Utilization Based on Genetic Algorithm: A Case Study of Dengsha Estuary Sea Area
Authors: Wang Xiaowen , Kang Minjie 
Published: January 2021

Abstract
Optimal allocation of sea area utilization is a necessary method to coordinate the conflicts between different departments and realize the dual goals of development and ecological protection. This paper presents an optimal allocation method of sea area utilization based on genetic algorithm. Taking the estuarine sea area of Dengsha Estuary Sea Area as an example, the empirical study was conducted. The results show that the sea area utilization optimization allocation method based on genetic algorithm can produce the optimization result scheme with higher comprehensive benefit and lower ecological loss based on the initial scheme. Under the guidance of constraint conditions, the problem of conflicts between different departments can be solved by optimizing the spatial distribution and the demand of each department can be fully met. Different spatial distribution optimization alternatives can be produced under the same sea area utilization structure.
Research on Optimization Policy Routing Technology of Optical Fiber Communication Network
Authors: Zhang Ning , Yang Yansong , Chen Tingting , Chen Xiaodan , Sun Huijuan , Chen Ming , Xiao Mingming , Chen Bingjun , Luo Yang 
Published: January 2021

Abstract
With the development of global informatization, information has penetrated into every corner of human society. With the popularization of Internet, the transmission bandwidth of information becomes more and more important. Nowadays, people’s demand and dependence on information are becoming stronger and stronger. It is urgent to expand the capacity of wide area backbone communication network, and the optical network composed of wavelength division multiplexing (WDM) can just take this role. In WDM network, it can transmit multiple optical signals of different wavelengths in one fiber at the same time. WDM has the advantages of large transmission capacity, strong technical adaptability, simple implementation and easy expansion. In addition, WDM optical network can also make full use of the huge bandwidth of optical fiber itself to carry out high-speed transmission. Now, the optical network using WDM technology has become the backbone network of long-distance transmission. This paper studies the optical fiber communication system and analyzes the structure of the optical fiber network. For the optical network, how to find the appropriate routing is a very important technology. After analyzing all kinds of basic routing technologies, this paper proposes a new policy based routing technology, and carries out relevant experiments. The experimental results show that the new routing technology has some advantages compared with the old routing technology.
An Improved Dynamic Programming Method in The Optimization of Gas Transmission Pipeline Operation
Authors: He Miao , Xiong Hui , Kou Jiemin , Zhi Shujie 
Published: January 2021

Abstract
The operation and optimization of gas transmission pipe network system is a mixed integer nonlinear programming problem, with nonlinear and nonconvex objective function and constraints. The dynamic programming method has the advantages of not sensitive to the nonlinearity and nonconvexity of the optimization problem, and it can obtain the global optimal solution, which should have convince performance in the operation and optimization applications of gas transmission pipe network system. However, the use of dynamic programming method may lead to dimension disaster and exponentially increasement of the computational cost. In this paper, the traditional dynamic planning method in the operation optimization of gas transmission pipeline is improved, and the idea of two-level optimization and the concept of local search are introduced, which can effectively avoid the dimension disaster and significantly improve calculation efficiency of dynamic planning method. Through the above two improved measures, the dynamic programming method can be applied to the operation optimization of large-scale gas transmission pipe network system to obtain the optimal solution quickly, which provides certain guidance and assists in the formulation of pipeline operation scheme.
Migration towards Bangladesh coastlines projected to increase with sea-level rise through 2100
Authors: Bell A R , Wrathall D J , Mueller V , Chen J , Oppenheimer M , Hauer M , Adams H , Kulp S , Clark P U , Fussell E , Magliocca N , Xiao T , Gilmore E A , Abel K , Call M , Slangen A B A 
Published: January 2021

Abstract
To date, projections of human migration induced by sea-level change (SLC) largely suggest large-scale displacement away from vulnerable coastlines. However, results from our model of Bangladesh suggest counterintuitively that people will continue to migrate toward the vulnerable coastline irrespective of the flooding amplified by future SLC under all emissions scenarios until the end of this century. We developed an empirically calibrated agent-based model of household migration decision-making that captures the multi-faceted push, pull and mooring influences on migration at a household scale. We then exposed ∼4800 000 simulated migrants to 871 scenarios of projected 21st-century coastal flooding under future emissions pathways. Our model does not predict flooding impacts great enough to drive populations away from coastlines in any of the scenarios. One reason is that while flooding does accelerate a transition from agricultural to non-agricultural income opportunities, livelihood alternatives are most abundant in coastal cities. At the same time, some coastal populations are unable to migrate, as flood losses accumulate and reduce the set of livelihood alternatives (so-called ‘trapped’ populations). However, even when we increased access to credit, a commonly-proposed policy lever for incentivizing migration in the face of climate risk, we found that the number of immobile agents actually rose. These findings imply that instead of a straightforward relationship between displacement and migration, projections need to consider the multiple constraints on, and preferences for, mobility. Our model demonstrates that decision-makers seeking to affect migration outcomes around SLC would do well to consider individual-level adaptive behaviors and motivations that evolve through time, as well as the potential for unintended behavioral responses.
AI and Blockchain: potential and challenge for building a smart E-Learning system in Vietnam
Authors: Hung Nguyen Quoc , Phung Thai Kim , Hien Phan , Hoang Thanh Dang Ngoc 
Published: January 2021

Abstract
Smart E-Learning is an education form, in which information technology plays an important role. Smart E-Learning utilizes the achievements of modern technologies such as artificial intelligence (AI). So, it can help to gain the ability of connectivity, sharing, convenience, and flexibility for learners. All activities including teaching, learning, discussing, testing, and grading, are well done over the internet environment. However, some potential risks can cause participants, especially, about data privacy. The issue is a big challenge for developing applications for smart E-Learning. Therefore, the goal to combine E-Learning with Blockchain technology to improve security for smart E-Learning is necessary. In this paper, we discuss potential and challenge for applying AI and Blockchain for developing applications for smart E-Learning in Vietnam. We also propose to combine independent testing models integrated with AI and Blockchain into the same smart E-Learning system.
A Glimpse on Key Applications of Smart City under M2M Communication
Authors: Kaushik Sonam , Aggarwal Gaurav , Tejasvee Sanjay 
Published: January 2021

Abstract
In India, It is very necessary to make normal cities as smart cities near future. So, Develop a smart city is one of the most recent fields in today’s digital era to researches. Machine to Machine (M2M ) communication is the latest innovative-fangled type of communiqué that enables full automation with the help of integrated applications to make a normal city to a smart city. Many devices are connected in a smart city to monitoring many tasks and it is a very challenging task to integrate and manage these connected devices. Interconnection of several devices and machines using the internet to form an M2M Area Network (M2MAN) provides controlling and monitoring smart applications like smart-metering, weather, environment, home, air quality, water supply with quality management, power supply with bill monitoring, traffic, and healthcare, etc. under M2M communication and integrated single level umbrella structured applications. Out of several M2M monitoring and controlling applications towards smart cities, few key applications are presented in this paper with their basic approach, workflow, and concerning issues and used technologies for the implementation with the brief growth analysis.
Study of Deep Learning Techniques on Image Denoising
Authors: Gupta Kanika 
Published: January 2021

Abstract
With the advancement in the area of Artificial Intelligence, the technology of deep learning is widely used in the field of Digital Image Processing and it is showing good results in the domain of Image denoising. To understand the area and progress of the field of deep learning in the domain of denoising, the research work is to be reviewed on various techniques, so that research scholars, academicians and industry professionals can take benefits out of this. The three models are introduced in this paper, such as wavelet neural network, pulse coupled neural network and convolutional neural network which are typically used in the field of noise reduction i.e. image denoising. The method of reduction in noise which is nonlocal in nature is considered as the heart of this technique. The aim of this paper is to better understand the recent developments in the field of machine learning and deep learning with the domain of reduction in noise of digital images.
A Review on Control System Applications in Industrial Processes
Authors: Juneja P K , Sunori S K , Sharma A , Sharma A , Pathak H , Joshi V , Bhasin P 
Published: January 2021

Abstract
Present paper attempts to review the literature related to design of P-I-D control for time delayed complex industrial process for single as well as for multivariable process with interaction considerations, their decoupler design and time delay compensators. General instrumentation of the industrial feedback control systems along with control system analysis has been covered. Also it covers, control features of some paper mill sub-processes like headbox operation, basis weight and retention. The importance of eliminating the effects of interactions, among the process control loops inside a multi input multi output industrial control system, has been discussed with the help of literature study. The importance of the process dynamics knowledge for designing a control system has also been investigated. This paper also investigates the significance and effectiveness of PID controllers through various literature studies. The problems of the classical PID controllers such as constraints, presence of disturbances etc.) can be removed by designing in combination with soft computing techniques. Moreover, possibility of further enhancements in the PID controller with the utilization of various schemes available, has been presented. The present status of control systems in industrial processes in terms of various control parameters such as stability, dead time compensation etc. has been presented and the future improvements have been stated.
Big Data: Big Data Analysis, Issues and Challenges and Technologies
Authors: Rawat R , Yadav R 
Published: January 2021

Abstract
The data generated at an exponential rate has resulted in Big Data. This data has many characteristics and consists of structured, unstructured, and semi-structured data formats. It contains valuable information for the different types of stakeholders based on their need however it is not possible to meet them with the help of traditional tools and techniques. Here the big data technologies play a crucial role to handle, store, and process this tremendous amount of data in real-time. Big data analytics is used to extract meaningful information or patterns from the voluminous data. It can be further divided into three types i.e. text analytics, audio analytics, video analytics, and social media analytics. Big data analytics if followed by big data analysis process plays a significant role in generating meaningful information from big data. Big data analysis process consists of data acquisition, data storage, data management, data analytics, and finally data visualization. However, it is not simple and brings many challenges that need to be resolved. This paper presents the issues and challenges related to big data, prominent characteristics of big data, big data analytics, big data analysis process, and technologies used for processing the massive data.
Implementation of SimpleRNN and LSTMs based prediction model for coronavirus disease (Covid-19)
Authors: Priyanka  , Kumari A , Sood M 
Published: January 2021

Abstract
Deep learning is a powerful technique which is inspired by the structure as well as processing power of the human brain. This technique uses deep neural network to perform complex tasks such as time series prediction, image classification, and cancer detection. In this research work, we used Covid-19 time series datasets and with the help of deep learning we built the model for prediction of Covid-19 cases. For the model building, we used two deep learning neural networks, Recurrent Neural Networks (RNN) and Long Short Term Memory Networks (LSTMs). We built a prediction model using RNN in the first instance and subsequently the second model was built using LSTMs. Out of these two neural networks, we got promising results from the model based on LSTMs with an overall accuracy of 98%. As the cases of Covid-19 are increasing day-by-day at a very high rate, we proposed these models using neural networks to help in predicting the future trends of Covid-19 confirmed, deaths and recovered cases.
A Study on Different Aspects of Web Mining and Research Issues
Authors: Kumar Santosh , Kumar Ravi 
Published: January 2021

Abstract
Web is huge and dynamically increasing day by day with exponential rate. If query is little bit complex then often or sometimes it becomes difficult to get relevant information in response. Several search engines try to optimize the users query and/or search results to provide the relevant information. Also many websites are ecommerce sites which are also competing to attract their customers and some are recommendation based. Now almost all good sites are using web mining techniques to improve their performance. In this paper, an attempt has been made to cover the key aspects used in web mining research. Initially problems faced by users and/or owners have been highlighted. Further it discusses types of data and techniques used by different categories of web mining are discussed. Finally research issues and application areas have been discussed.
Product Recommendation System a Comprehensive Review
Authors: Sharma Jatin , Sharma Kartikay , Garg Kaustubh , Sharma Avinash Kumar 
Published: January 2021

Abstract
In today’s technical era, every startup or a company attempt to establish a better sort of communication between their products and the users, and for that purpose, they require a type of mechanism which can promote their product effectively, and here the recommender system serves this motive. It is basically a filtering system that tries to predict and show the items that a user would like to purchase. By analyzing the preference of the users, companies can decide which product to be launched in the market to procure more benefits. These systems are proved to be very beneficial in variety of domains involving music, books, movies, research articles and products in common. In this paper, we review various mechanisms and techniques that are required for recommender systems for recommending the products or items in the domain of fashion and books.
Performance Evaluation of QoS metrics in Software Defined Networking using Ryu Controller
Authors: Babbar Himanshi , Rani Shalli 
Published: January 2021

Abstract
With assistance from Software Defined Networking (SDN), networks have become more creative to build and maintain over the last few years. The inflexibility of modern network architecture is presenting researchers with a tough achievement. SDN replaces existing inescapable and complicated networks with a creative way of separating the control plane from the data plane and fixing those constraints, namely configurations done manually, monitoring, protection, usability, and functionality. SDN controllers (e.g., POX, RYU, ONOS, OpenDaylight, Floodlight, etc.) have therefore developed as a core for the management of these networks. The performance evaluation of the SDN controller has an outstanding effect on improving the flexibilities and abilities of a network topology of SDN infrastructures. This research summarizes the SDN controller performance tests using criteria (e.g., Bandwidth, Round-Trip Time). This study employs a Mininet emulator to enforce an SDN architecture that comprises an RYU controller with a switching part, one OpenFlow switch, and multiple nodes. The goal is to determine performance QoS (Quality of Service) including Bandwidth, Throughput using the iperf test.
Denoising of images using Thresholding Based on Wavelet Transform Technique
Authors: Kaur Gurjinder , Garg Meenu , Gupta Sheifali , Gupta Rupesh 
Published: January 2021

Abstract
This paper presents an efficient method based on thresholding for images that are corrupted due to Gaussian noise. In order to achieve this task, wavelet transforms and various thresholding techniques have been applied. But the selection of the thresholding technique is restricted due to their widespread use in image denoising application. In this paper, a more efficient thresholding scheme named as neigh shrink sure is studied by incorporating the neighboring wavelet coefficients. Different thresholding techniques like Bayes shrink and Neigh shrink sure algorithms are applied to different images. The results are obtained using 3 different wavelets db-4, sym-4, and coif-4. It has been observed that the use of coif-4 transform produces better results as compared to other wavelet transforms. Further, it has been analyzed that Neigh Shrink sure method performs better in de-noising of corrupted images in comparison to Bayes shrink thresholding technique by possessing higher Peak Signal-to-Noise Ratio (PSNR) and lower Mean Square Error (MSE).
Study of techniques to improve performance of patch antennas for 5G applications at millimeter wave (mmW) frequencies
Authors: Juneja Sagar , Sharma Rajnish 
Published: January 2021

Abstract
Millimeter wave (mmW) frequency signals of 28 GHz and 38 GHz that are being explored for implementing next generation cellular communication network (i.e. 5G) suffer from severe attenuation in the outdoor environment. Therefore, efficient and intelligent antenna design techniques are needed for proper transmission and reception of signals at mmW frequencies. Beamforming, multiple input multiple output (MIMO) and space division multiple access (SDMA) are important requirements of mmW 5G network, and these technologies are heavily dependent upon antenna design and implementation. This paper presents various contemporary techniques for improving performance parameters of patch antennas for 5G implementation at mmW frequencies. These performance parameters include gain, bandwidth, directivity, suppression of surface waves, ease of design, compactness, and mutual coupling between antenna elements for MIMO system. This study also highlights the benefits and drawbacks of each of these techniques discussed in the paper.
Loan default prediction using decision trees and random forest: A comparative study
Authors: Madaan Mehul , Kumar Aniket , Keshri Chirag , Jain Rachna , Nagrath Preeti 
Published: January 2021

Abstract
With the improving banking sector in recent times and the increasing trend of taking loans, a large population applies for bank loans. But one of the major problem banking sectors face in this ever-changing economy is the increasing rate of loan defaults, and the banking authorities are finding it more difficult to correctly assess loan requests and tackle the risks of people defaulting on loans. The two most critical questions in the banking industry are (i) How risky is the borrower? and (ii) Given the borrower’s risk, should we lend him/her? In light of the given problems, this paper proposes two machine learning models to predict whether an individual should be given a loan by assessing certain attributes and therefore help the banking authorities by easing their process of selecting suitable people from a given list of candidates who applied for a loan. This paper does a comprehensive and comparative analysis between two algorithms (i) Random Forest, and (ii) Decision Trees. Both the algorithms have been used on the same dataset and the conclusions have been made with results showing that the Random Forest algorithm outperformed the Decision Tree algorithm with much higher accuracy.
Comparison between attendance system implemented through haar cascade classifier and face recognition library
Authors: Malhotra Samiksha , Aggarwal Vaibhav , Mangal Himanshu , Nagrath Preeti , Jain Rachna 
Published: January 2021

Abstract
Face detection and face recognition are the most widely used features of machine learning(ML) and deep learning(DL). These features are slowly gaining popularity in fields like surveillance through CCTV cameras, mobile phone security (biometric locks), etc. This paper presents a college attendance system based on the above mentioned feature which automatically marks the attendance of the students through the live feed by the CCTV cameras in the classroom. This system saves time, works more efficiently, then manually marking the attendance by the roll call. System was built with the haar cascade features, open CV and face recognition library. These are discussed in the proposed paper. We have presented a comparison between the two models discussed and concluded that haar cascade features work more efficiently than the inbuilt face recognition library for the college attendance system.