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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 5 Documents
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Search results for , issue "Vol 3, No 3: SEPTEMBER 2022" : 5 Documents clear
Data Analysis of Student Attitude Survey Based on Internet Analysis Technology Cao, Yongcheng; Liu, Quanguo; Chen, Huajie
Journal of Applied Data Sciences Vol 3, No 3: SEPTEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i3.63

Abstract

Language attitude is people’s understanding and evaluation of languages, which has important effect on language learning. Based upon investigation into 605 tibetan college students from 5 colleges in Tibet areas, and combined with network data, this paper mainly analyses their attitude towards Tibetan, Chinese and English from four dimensions: recognizing, instrumental, integrative and transferring attitude. This paper also discusses the relationship between students’ language attitude and their gender and grade.
Intangible Cultural Heritage Based on AR Technology Tong, Chaoran; Hee-Gyun, Kim
Journal of Applied Data Sciences Vol 3, No 3: SEPTEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i3.64

Abstract

With the development of society and the improvement of people's material living standards, Internet technology has penetrated into all aspects of people's lives, and AR technology has gradually entered people's lives. At the same time, all sectors of society are paying more and more attention to the development and protection of ICH and other traditional cultures. Although many experts and scholars have conducted research and discussion in this area before and after, due to the late start time, shortage of technology and incomplete technical personnel and other issues, no good solution has been found, and little gain has been achieved. Based on this, this article adopts a new perspective, using the advantages of the modern big data society, through the current advanced AR technology and digital management, aiming to find the best plan for the development and protection of ICH, with a view to the domestic ICH Provide reference and reference for development and protection. This article uses data analysis, comparison and experiment methods. It first introduces the theoretical aspects of ICH and proposes specific measures for its digital development and protection, and then quotes the traditional cultural heritage of shadow play as as a specific example, 60 audiences were randomly surveyed by questionnaire survey, and divided into 3 groups according to age, and they were subjected to issues such as viewing attitude and effect of traditional shadow play based on AR technology and traditional shadow play according to the investigation, it is concluded that the traditional shadow play based on AR technology is more popular with the audience, and compared with the traditional shadow play, the viewing effect is better.
Implementation of the K-Nearest Neighbor Algorithm for the Classification of Student Thesis Subjects Paramita, Adi Suryaputra; Maryati, Indra; Tjahjono, Laura Mahendratta
Journal of Applied Data Sciences Vol 3, No 3: SEPTEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i3.66

Abstract

Students who have studied for a considerable amount of time and will complete a lecture process must complete the necessary final steps. One of them is writing a thesis, a requirement for all students who wish to graduate from college. Each student's choice of topic or specialization will be enhanced if it not only corresponds to their interests but also to their skills. K-Nearest Neighbor is one of the classification techniques used. K-Nearest Neighbor (KNN) operates by determining the shortest distance between the data to be evaluated and the K-Nearest (neighbor) from the training data. K-Nearest Neighbor is utilized to classify new objects based on the learning data closest to the new object. Therefore, KNN is ideally suited for classifying data to predict student thesis topics. This research concludes that optimizing the k value using k-fold cross-validation yields an accuracy rate of 79.37% using k-fold cross-validation = 2 and the K-5 value. Based on the K-Nearest Neighbor Algorithm classification results, 45 students are interested in computational theory thesis (RPL) topics, 32 students are interested in artificial intelligence (AI) thesis topics, and 21 students are interested in software development topics.
Policy Optimization Recommendation Algorithm Based on Mapping Network for Behavior Enhancement Shan, Linlin; Jiang, Guisong; Li, Shuang; Zhao, Shuai; Luo, Kunjie; Zhang, Long; Li, Yi
Journal of Applied Data Sciences Vol 3, No 3: SEPTEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i3.67

Abstract

The algorithm of policy optimization with learning behavior enhancement based on mapping network technology was proposed, aiming to address the issues of lack and sparsity of learning behavior data and weak generalization ability of the model in AI education. Based on the basic recommendation algorithm and the framework of rein- forcement learning, and model introduces the correlation mapping network to realize the transformation of strong and weak correlation, so as to optimize the input agent policy to improve the performance model of course recommendation. Experiment on MOOC da- tasets show that the proposed algorithm model has a stable improvement compared with the baseline models, and can effectively improve the accuracy of course recommendation.
Embedded Image Recognition System for Lightweight Convolutional Neural Networks Fang, Jie; Zhang, Xiangping
Journal of Applied Data Sciences Vol 3, No 3: SEPTEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i3.62

Abstract

In this paper, we design and implement an embedded image recognition system based on STM32 for the problem of limited storage space of embedded systems to run convolutional neural networks efficiently, and for the loading of lightweight convolutional neural network and the hook-up requirement of the quadrotor. The system hardware adopts the idea of modular design to improve the compatibility of the system, and the system software adopts the training of handwritten image recognition based on convolutional neural network, lightweight processing of the convolutional neural network, and transplanting the trained network to the embedded system. Finally, the system can finish the recognition of handwritten images stably and efficiently. This system can provide a low-cost and highly integrated solution for such image processing systems. The lightweight target detection model CED-Det is designed by combining CED-Net and dense feature pyramid network, which firstly performs feature extraction by CED-Net, then performs feature fusion by stacking two layers of dense pyramid network, and finally, the fused feature maps are used for classification prediction and position prediction by two 3×3 convolutions, respectively. CED-Det is used in VOC and Experimental results on COCO datasets show that CED-Det is more suitable for embedded platforms in terms of accuracy, inference speed, and a total number of parameters compared with other target detection models.

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