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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 3, No 2: MAY 2022" : 6 Documents clear
Big Data Classification of Personality Types Based on Respondents’ Big Five Personality Traits Jennifer Chi
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.v3i2.32

Abstract

A mixed model was introduced in this study, k-means clustering analysis for data examination, discriminant analysis for classification, and multilayer perceptron neural network analysis for prediction. After deleted inadequate samples and outliers, total number of observations was 1,009,998 for this study that was collected through on interactive online personality (i.e., big five personality traits) test in 2018. Empirical results based on the k-means clustering analysis identified four different personality clusters using the total score of big five personality traits (Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness to Experience). Results of the k-means clustering analysis were tested for accuracy using the discriminant analysis indicated that cluster means were significantly different, and showed that 95.8% of original grouped cases correctly classified. The multilayer perceptron neural network framework was utilized as a predictive model, showed a 5-5-4 neural network construction, in deciding the personality classification of participants: Training 99.5% of training grouped cases and 99.5% of testing grouped cases correctly classified. Results of this study may provide insight into the understanding of the personality of participants for further psychological, social, cultural, and economic considerations.
The Mechanical and System Design of Finger Training Rehabilitation Device Based on Speech Recognition Xiaoxuan Wei; Chen Dong; Yang Xu
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.v3i2.58

Abstract

Aiming at the problem of long diagnosis and treatment recovery period and low recovery efficiency of traditional stroke patients, a finger rehabilitation training control system based on speech recognition is designed to help patients carry out finger exercise training and obtain the perception ability of finger angle, speed and position, and provide rehabilitation physicians with finger rehabilitation evaluation and training data reference. In order to realize the finger rehabilitation training control system, based on the finger movement perception system, the system is divided into hardware circuits, lower computer control systems, and voice recognition human computer interaction systems. Combining the unique advantages of the HMM algorithm, the HMM algorithm is applied to the voice interaction system for pattern matching, and the simulation test of the finger rehabilitation system is performed. The application results show that the rehabilitation training system based on speech recognition proposed in this study meets the design requirements, has good safety and reliability, and has high application value for future finger rehabilitation training.
Research on Short Video Publishing Algorithm and Recommendation Mechanism Based on Artificial Intelligence Lei An
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.v3i2.59

Abstract

Aiming at the problem of poor feature expression ability and model representation effect of traditional video recommendation mechanism, combined with the characteristics of traditional recommendation algorithm, this paper deeply studies the short video publishing algorithm and recommendation mechanism under artificial intelligence, and constructs a two-layer feature representation model BIFR based on attention. Firstly, the basic principle of recommendation algorithm is introduced in detail, and then the internal representation of features is studied through a multi head self attention mechanism to deeply mine the correlation between features and further improve the expressiveness of features. Then adjust the input feature crossover to learn the feature crossover more effectively. Finally, combine the two, add DNN to get the final output results, and then use the corresponding evaluation indicators to test the constructed recommendation model. The test results show that the video recommendation model constructed in this study has high accuracy, strong expressiveness and effectiveness.
Elevator Group Scheduling by Improved Dayan Particle Swarm Algorithm in Computer Cloud Computing Environment Jie Yu; Bo Hu
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.v3i2.60

Abstract

The world is entering the era of cloud computing. Due to the rapid development of computer technology, as the core content of elevator transportation technology, elevator group control dispatching systems and group intelligent algorithms will have a wide range of application prospects due to their significant advantages. The purpose of this paper is to study the elevator group scheduling problem of the improved Dayan particle swarm algorithm in the computer cloud computing environment.This article first summarizes the research status of elevator group control technology and algorithms, and then analyzes and studies the basic theory of cloud computing task scheduling. Combined with the improved Dayan particle swarm algorithm, the elevator prediction model is established. This paper systematically expounds the theory and algorithm principle of the basic particle swarm algorithm, and analyzes the Dayan particle swarm algorithm on this basis. In this paper, the experimental research is carried out by comparing the two algorithms on the simulation software. Research shows that the improved Dayan particle swarm algorithm has better scheduling performance than the traditional basic particle swarm algorithm.
Algorithm Analysis of Clothing Classification Based on Neural Network Hai Yin Su
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 Dongxue Wang
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.

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