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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 463 Documents
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Algorithm Analysis of Clothing Classification Based on Neural Network Su, Hai Yin
Journal of Applied Data Sciences Vol 3, No 2: MAY 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i2.61

Abstract

With the rapid development of Internet e-commerce, the online transaction volume of clothing has increased day by day, and the importance of clothing images in transactions has also increased. However, there are many clothing categories and different classification standards. It is difficult for consumers and e-commerce merchants to unify the description of clothing categories, which can easily lead to a poor clothing shopping experience. Neural network has an excellent list in the field of computer vision, which can effectively classify clothing. The purpose of this article is to study the algorithm analysis of clothing classification based on neural network. Starting from the neural network, this paper proposes a clothing image classification algorithm based on a multi-task convolutional neural network (Convolutional Neural Network, CNN). Through hierarchical classification data combined with multi-task technology, the basic structure of the network model is not changed. The accuracy of clothing image classification improves the network’s ability to express refined clothing categories. This paper proposes a clothing classification algorithm based on the feature fusion of Hu invariant matrix and CNN network. The feature fusion of the features extracted by the convolutional neural network is initially explored, the information gain of the feature is calculated, and the shape feature is used to eliminate the feature with less information gain. This paper also designs a clothing classification system based on neural network to realize the recognition, detection and classification of clothing images. The experimental results show that the clothing classification accuracy rates under the four combined tasks are 93.54%, 89.26%, 92.14%, 95.66%, and 93.54%, respectively. It can be seen that the model based on convolutional neural network can further improve the accuracy of clothing classification.
Study on Image Classification Method Based on Small Sample Learning Wang, Dongxue
Journal of Applied Data Sciences Vol 3, No 2: MAY 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i2.57

Abstract

Image classification as according to their different features of reflected in the image information, make a distinction between different categories of target image processing methods, and especially for quantitative analysis using the computer, each of the images or image pixels, or regional planning is one of the several categories, in lieu of visual interpretation of the person, It has important practical value for the study of image classification method. However, the current study of image classification method based on small sample learning cannot effectively follow the development needs of society and industry, so it is urgent to carry out effective reform. Based on this, this paper first analyzes the problems existing in the research system construction of image classification method in small sample learning, and then gives the construction strategy of image classification method system according to these problems.
HTTP Traffic Analysis based on Multiple Deep Convolution Network Model Generation Algorithms Liu, Bocheng; Yang, Fan
Journal of Applied Data Sciences Vol 3, No 4: DECEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i4.69

Abstract

In recent years, with the development of the Internet, social networking, online banking, e-commerce and other network applications are growing rapidly. At the same time, all kinds of malicious web pages are constantly emerging. Under the new situation, the network security threats are distributed, large-scale and complex. New network attack modes are emerging. With more and more diverse devices access to the Internet, our life is more intelligent and convenient, but also brings more loopholes and hidden dangers. Some malicious web pages through a variety of means to lure users to open URL links and conduct malicious behavior. However, if we can detect the URL of the malicious web page and identify the malicious web page, we can avoid the problems of content variability and behavior tracking. Therefore, traffic analysis based on various deep convolution network model generation algorithms arises at the historic moment, and becomes an important research issue in the field of Internet security.
Research on saliency detection method based on depth and width neural network He, Guanqi; Lu, Guo
Journal of Applied Data Sciences Vol 3, No 4: DECEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i4.68

Abstract

Image saliency detection is to segment the most important areas in the image. Solving the problem of image saliency detection usually involves knowledge in computer vision, neuroscience, cognitive psychology and other fields. In recent years, as deep learning has made great achievements in the field of computer vision, the application of deep learning has also played a good role in image saliency detection. Therefore, algorithms based on deep convolutional neural networks have become solutions to image saliency The most effective method of detection. For researchers, improving the computational efficiency of neural network-based saliency detection algorithms generally starts from two perspectives. One is to tailor the network structure and combine traditional feature extraction methods for processing. The other is to use a lighter network to solve the saliency detection problem. Based on these two points of thinking, this paper proposes two efficient and accurate neural network-based saliency detection algorithms. In recent years, with the rapid development of multimedia and Internet technologies, a huge amount of picture information is generated on blogs, social networking or shopping platforms every day. Such a lot of information not only enriches people's lives, but also provides efficient and accurate network management platforms. The management of these image information brings difficulties. Therefore, how to understand and process these image information more intelligently and efficiently has become a hot topic for many image processing and computer vision researchers. Among them, the saliency detection technology plays a key role in solving the problem of intelligent understanding and processing of images. To put it simply, saliency detection is a technology to automatically calculate or detect the most important areas in an image, and its processing results provide a basis for understanding and processing the image content. Saliency detection is a basic problem in computer vision, neuroscience and visual perception. The algorithm detects and extracts the most interesting or significant areas in the image.
Research on Obstacle Avoidance and Environment Adaptability of Snake Robot Based on Deep Learning Qi, Wei; Ying, Chun; Yong, Sheng; Zhao, Guizhi; Wang, Lihua
Journal of Applied Data Sciences Vol 3, No 4: DECEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i4.70

Abstract

With the development and popularization of computer artificial intelligence technology, more and more intelligent machines are gradually produced. These intelligent machines have brought great convenience to people's lives.This paper studies the control method of snake robots based on environment adaptability, which mainly explains the construction and stability of multi-modal CPG model. In addition, this paper also studies the trajectory tracking and dynamic obstacle avoidance of mobile robot based on deep learning.
Design and Computer Analysis of a Road Load Detection Machine Li, Shuo; Zhao, Yan; Hwang, Don-Ha
Journal of Applied Data Sciences Vol 3, No 4: DECEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i4.71

Abstract

In this paper, an experimental device is designed for measuring vehicle dynamic load,the structure and stress of the equipment are analyzed by computer technology. The device design mainly includes vehicle, road surface, vehicle transmission, and control [1]. The vehicle is designed based on a 2-DOF vehicle model, the road is designed based on the Pasternak foundation model, and the control mainly uses a single-chip microcomputer. The dynamic response of vehicles to the road at different speeds is analyzed through the experiment.
Applying the Apriori Algorithm to Analyze and Optimize Medical Device Inventory Management Avizenna, Meidar Hadi
Journal of Applied Data Sciences Vol 3, No 4: DECEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i4.33

Abstract

Extracting data or an effort to retrieve valuable knowledge and information in a large database is called data mining or Knowledge Discovery in Database or usually shortened as KDD . One of the most popular algorithm in data mining technic is Apriori Algorithm, while the discovery of “relational combination pattern among itemset used Association Rules”. Data mining has been implemented into the various fields like : business or trade, education and telecommunication. In business for instance, the implementation result of data mining use ‘algorithm Apriori which can give a hand to help the Businessmen make decision on supplies. For example, the necessity of supplies system in a drugstore as one of the medical stuff supplier, and to determine which product as the priority should be supplied to anticipate out of stock of supplies availability in the store, as the results will also affect to the consumer service and daily income. Medical tools are essential unit should be supplied and being and essential factor which will impact to the consumer trust to a hospital or another medical service. That is why the availability of medical tools in drugstores is completely needed to support the success of distribution to the consumers, so the activity of medical service to consumers run thoroughly. In this case, data mining is seen as able to built intelligent business environment as solution for competing increated competition among the drugstores in future.
Design of Computer Recognition System Based on Graphic Image Luo, Qun; Liu, Zhendong; He, Ruiying
Journal of Applied Data Sciences Vol 4, No 1: JANUARY 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i1.79

Abstract

With the advancement of computer technology and the rapid development of the Internet, every bit of life has begun to become electronic. The recognition of informatized graphic images is particularly important. The image is collected through graphic images and the elements in the image are detected and recognized autonomously. Some characteristic elements of the image are used to identify and correspond to the characteristics in real life. This article first analyzes the research and discussion on the accuracy of the image and graphic information. After reading it through the computer, the image quality is improved accordingly. Finally, the design of the computer recognition system based on the graphic image is obtained.
Analysis of Efficient Optimization Algorithm for Information Nodes in Wireless Network Communication Chaos Qi, Jing; Cao, Tru
Journal of Applied Data Sciences Vol 4, No 1: JANUARY 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i1.77

Abstract

Due to the poor node optimization effect of traditional mathematical model methods, an efficient node optimization algorithm based on ant colony genetic algorithm is proposed. The ant colony algorithm is a kind of bionic optimization algorithm, and its dynamics and self-similarity are very similar to the optimization principles of messy information nodes. "Chaos node efficient optimization algorithm" is dedicated to effectively aggregating various resources such as computing, storage, knowledge, communication, information, distributed around the world, serving the public, and realizing resource sharing and collaborative work. Among them, chaos nodes efficiently search the excellent problem is particularly prominent. If the number of parent nodes and the order of the nodes are known, the ant colony genetic algorithm is used to find the largest supporting tree, so as to obtain the best node to obtain the largest number of iterations, thereby effectively optimizing the information node.Wireless Network Communication, Chaotic Nodes, Nodes, Efficient Optimization
Development of Computer Intelligent Control System Based on Modbus and WEB Technology Ran, Longyi; Wu, Yenchun Jim
Journal of Applied Data Sciences Vol 4, No 1: JANUARY 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i1.75

Abstract

With the increasing popularity of intelligent computer control systems in our country, the accuracy and efficiency of intelligent control in the current computer control systems have attracted more and more attention. Modbus and WEB technology have a simple chassis format, compact and powerful functions. On this basis, based on the current research status of intelligent computer control technology, this article analyzes the problem of optimizing intelligent computer control systems based on Modbus bus and WEB technology in the application process, and improves the intelligent computer control systems based on Modbus bus and WEB technology.

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