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Journal of Applied Data Sciences
Published by Bright Publisher
ISSN : -     EISSN : 27236471     DOI : doi.org/10.47738/jads
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes applied to collect, treat and analyze data will help to render scientific research results reproducible and thus more accountable. The datasets itself should also be accessible to other researchers, so that research publications, dataset descriptions, and the actual datasets can be linked. The journal Data provides a forum to publish methodical papers on processes applied to data collection, treatment and analysis, as well as for data descriptors publishing descriptions of a linked dataset.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 6 Documents
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Search results for , issue "Vol 4, No 2: MAY 2023" : 6 Documents clear
Design and Realization of Computer Image Intelligent Recognition System Guibing Xu
Journal of Applied Data Sciences Vol 4, No 2: MAY 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i2.76

Abstract

The application of intelligent image recognition technology in life is more and more extensive, especially in the field of computer and multimedia, the research of machine vision system is becoming more and more mature, and the demand of human society for information processing is constantly increasing.This article first analyzes the basic knowledge of digital images based on computer technology, including basic knowledge of digital images, basic knowledge of image filtering and image recognition algorithms. Secondly, this paper studies the design and implementation of computer image intelligent recognition system.
Optimization of Decision Support System in Investment Risk Management with Firefly Algorithm Ajang Sopandi
Journal of Applied Data Sciences Vol 4, No 2: MAY 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i2.95

Abstract

This research focuses on optimizing the decision support system in investment risk management using Firefly Algorithm. The data used in this research is obtained from the Kaggle repository. The research aims to compare the performance of Firefly Algorithm with other optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing. This research applies Firefly Algorithm and other optimization algorithms individually to the investment risk management system to analyze and compare their performance. The results show that Firefly Algorithm outperforms other optimization algorithms in terms of finding optimal solutions and minimizing investment risk. Firefly Algorithm can effectively identify the best investment options to avoid risks, which can bring significant benefits to investors and companies. The findings of this study show that Firefly Algorithm can be a useful tool in the investment risk management system. The application of Firefly Algorithm in the investment risk management decision-making process can improve decision-making and help investors avoid risks and maximize their profits. The novelty of this research is the application of the Firefly algorithm to optimize the decision support system in investment risk management. It aims to identify the best option in avoiding investment risk and maximizing profit. In addition, this research also compares the performance of the Firefly algorithm with other algorithms such as Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing in solving optimization problems in the investment field.
Application of Adaptive UKF Algorithm in Multi-target Tracking and Positioning System Guofang Liu; Xiong Wang
Journal of Applied Data Sciences Vol 4, No 2: MAY 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i2.81

Abstract

Adaptive filtering algorithm (FIR) is a design method of adaptive variable target tracking system based on probability density distribution model. The algorithm realizes the target movement in the global range by estimating the parameters of different regions in the image, which improves the real-time performance and effectiveness.
Research on Deep Learning-Based Algorithm and Model for Personalized Recommendation of Resources Yu’e Liu
Journal of Applied Data Sciences Vol 4, No 2: MAY 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i2.85

Abstract

Resource recommendation system is a new type of management system, which uses personalized information to solve business needs such as customer consultation and product recommendation, and provides users with high quality services and achieves accurate marketing, so nowadays resource recommendation system has a pivotal role in modern resource management. In this paper, I study the algorithm and model of resource personalized recommendation based on deep learning, taking human resource recommendation as an example.
Research on New Virtualization Security Protection Management System Based on Cloud Platform Zhihong Li; Guangxu Liu; Yijie Dang; Zhijie Shang; Nan Lin
Journal of Applied Data Sciences Vol 4, No 2: MAY 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i2.89

Abstract

As an emerging product under the condition of informatization, the utilization of cloud platform in many industries has brought fundamental changes to the production and business model in related fields. The cloud platform provides rich and diverse utilization services to terminals through multi-dimensional integration of different IT resources. With the in-depth utilization of cloud platform, the security problems it faces are becoming more and more prominent. The traditional network security protection means have been difficult to effectively adapt to and deal with the security threats under the new situation of cloud platform utilization. As a prominent part of building cloud platform, the construction level of virtualization security protection system will have an intuitive impact on the security of cloud platform. At present, the virtualization security protection management system under cloud platform is facing direct threats from virtual machine deployment, virtual machine communication and virtual machine migration. Based on this, this paper studies the virtualization security protection management system of cloud platform from the perspective of virtualization security tech, so as to ameliorate the stability, reliability and security of cloud platform.
Software Defect Fault Intelligent Location and Identification Method Based on Data Mining Fang Wang; Sungho Park; Cattareeya Suwanasri
Journal of Applied Data Sciences Vol 4, No 2: MAY 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i2.92

Abstract

With the advancement of the times, computer technology is also constantly improving, and people's requirements for software functions are also constantly improving, and as software functions become more and more complex, developers are technically limited and teamwork is not tacitly coordinated. And so on, so in the software development process, some errors and problems will inevitably lead to software defects. The purpose of this paper is to study the intelligent location and identification methods of software defects based on data mining. This article first studies the domestic and foreign software defect fault intelligent location technology, analyzes the shortcomings of traditional software defect detection and fault detection, then introduces data mining technology in detail, and finally conducts in-depth research on software defect prediction technology. Through in-depth research on several technologies, it reduces the accidents of software equipment and delays its service life. According to the experiments in this article, the software defect location proposed in this article uses two methods to compare. The first error set is used as a unit to measure the subsequent error set software error location cost. The first error set 1F contains 19 A manually injected error program, and the average positioning cost obtained is 3.75%.

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