scholarly journals An Improved D-S Evidence Theory based on Genetic Algorithm to VIP Intelligent Recognition and Recommendation System

2013 ◽  
Author(s):  
Xiaoyin Xu ◽  
Lihong Ren ◽  
Yongsheng Ding
Keyword(s):  
Genetic Algorithm ◽  
Recommendation System ◽  
Evidence Theory ◽  
Intelligent Recognition

An Improved D-S Evidence Theory Based on Genetic Algorithm to VIP Intelligent Recognition and Recommendation System

2013 ◽  
Vol 347-350 ◽  
pp. 2442-2446
Author(s):  
Xiao Yin Xu ◽  
Li Hong Ren ◽  
Yong Sheng Ding
Keyword(s):  
Genetic Algorithm ◽  
Body Size ◽  
Recommendation System ◽  
Evidence Theory ◽  
Suitable Method ◽  
Improved Method ◽  
The Stability ◽  
Intelligent Recognition

In this paper, we use GA to improve the D-S evidence theory, and apply the improved D-S evidence theory to VIP intelligent recognition and recommendation system. In the VIP intelligent recognition and recommendation system of clothes, there are three main evidences: body size, personal preferences, and purchase records. So collision often happens inevitable. This requirement asks us to find out a suitable method to identify the VIPs needs. D-S evidence theory can improve the rate of identification, but has no idea about the collision. The improved D-S evidence theory based on genetic algorithm can deal with the collision evidence and improve the rate of the identification and the stability. As such we can provide VIP more suitable recommendation. The experiment results of clothes recommendation demonstrate the flexibility of the improved method.

scholarly journals Optimizing Smartphone Recommendation System through Adaptation of Genetic Algorithm and Progressive Web Application

2021 ◽  
Vol 12 (12) ◽  
Author(s):  
Khyrina Airin Fariza Abu Samah ◽  
Nursalsabiela Affendy Azam ◽  
Raseeda Hamzah ◽  
Chiou Sheng Chew ◽  
Lala Septem Riza
Keyword(s):  
Genetic Algorithm ◽  
Web Application ◽  
Recommendation System

Personalized query recommendation system : A genetic algorithm approach

2020 ◽  
Vol 23 (2) ◽  
pp. 523-535 ◽  
Author(s):  
Debaditya Barman ◽  
Ritam Sarkar ◽  
Anil Tudu ◽  
Nirmalya Chowdhury
Keyword(s):  
Genetic Algorithm ◽  
Recommendation System ◽  
System A ◽  
Query Recommendation ◽  
Genetic Algorithm Approach

scholarly journals PENERAPAN TEKNIK KLASIFIKASI PADA SISTEM REKOMENDASI MENGGUNAKAN ALGORITMA GENETIKA

2016 ◽  
Vol 2 (3) ◽  
Author(s):  
Rita Rismala ◽  
Mahmud Dwi Sulistiyo
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Recommendation System ◽  
Classification Model ◽  
Mutation Probability ◽  
Linear Classifier ◽  
Average Accuracy ◽  
Crossover Probability ◽  
Best Parameter

[Id]Sistem rekomendasi yang dibangun dalam penelitian ini adalah sistem rekomendasi yang dapat memberikan rekomendasi sebuah item terbaik kepada user. Dari sisi data mining, pembangunan sistem rekomendasi satu item ini dapat dipandang sebagai upaya untuk membangun sebuah model classifier yang dapat digunakan untuk mengelompokkan data ke dalam satu kelas tertentu. Model classifier yang digunakan bersifat linier. Untuk menghasilkan konfigurasi model classifier yang optimal digunakan Algoritma Genetika (AG). Performansi AG dalam melakukan optimasi pada model klasifikasi linier yang digunakan cukup dapat diterima. Untuk dataset yang digunakan dengan kombinasi nilai parameter terbaik yaitu yaitu ukuran populasi 50, probabilitas crossover 0.7, dan probabilitas mutasi 0.1, diperoleh rata-rata akurasi sebesar 72.80% dengan rata-rata waktu proses 6.04 detik, sehingga penerapan teknik klasifikasi menggunakan AG dapat menjadi solusi alternatif dalam membangun sebuah sistem rekomendasi, namun dengan tetap memperhatikan pengaturan nilai parameter yang sesuai dengan permasalahan yang dihadapi.Kata kunci:sistem rekomendasi, klasifikasi, Algoritma Genetika[En]In this study was developed a recommendation system that can recommend top-one item to a user. In terms of data mining, it can be seen as a problem to develop a classifier model that can be used to classify data into one particular class. The model used was a linear classifier. To produce the optimal configuration of classifier model was used Genetic Algorithm (GA). GA performance in optimizing the linear classification model was acceptable. Using the case study dataset and combination of the best parameter value, namely population size 50, crossover probability 0.7 and mutation probability 0.1, obtained average accuracy 72.80% and average processing time of 6.04 seconds, so that the implementation of classification techniques using GA can be an alternative solution in developing a recommender system, due regard to setting the parameter value depend on the encountered problem.Keywords:Recommendation system, classification, Genetic Algorithm

Design and Development of Computer Specification Recommendation System Based on User Budget With Genetic Algorithm

2018 ◽  
Vol 5 (1) ◽  
pp. 25-29 ◽  
Author(s):  
Michael Michael ◽  
Winarno Winarno
Keyword(s):  
Genetic Algorithm ◽  
User Satisfaction ◽  
Recommendation System ◽  
Brute Force ◽  
Long Time ◽  
Computer Based ◽  
Optimum Solution ◽  
Alternative Solutions ◽  
Crossover And Mutation ◽  
The Greedy Algorithm

There are a lot of things that must be considered when determining specifications of computer components to make sure those components chose are working compatible. According to a survey conducted to 78 respondents, about 72.5% of the respondents prefer to buy a built-up computer. The reason is because of a lack of knowledge of computer components and how to assemble computer properly. This research aimed to develop   a recommendation system that able to give recommendation of buying computer based on compatible components to be assembled, with the available budget, so that people who do not know computer components can also buy a assembled computer. The Genetic Algorithm was chosen for making this recommendation system because this Algorithm gives more alternative solutions through the process of crossover and mutation compared to the Greedy Algorithm which doesn’t produce a solution by trying all alternative solution nor  Exhaustive Search on Brute Force Algorithm which takes a long time to find optimum solution. The recommendation system of computer components specifications based on the budget available has been successfully developed using Genetic Algorithm and achieved 75.75% user satisfaction.

scholarly journals Embedded QR Code Intelligent Recognition Platform Based on Team Progress Algorithm

2016 ◽  
Vol 12 (02) ◽  
pp. 46
Author(s):  
Yuehong Wu
Keyword(s):  
Genetic Algorithm ◽  
General Condition ◽  
Recognition System ◽  
Experimental Results ◽  
Qr Code ◽  
Graphic Display ◽  
Acquisition Process ◽  
Mixed Programming ◽  
Intelligent Recognition ◽  
The Impact

In general condition, QR code often encounters uneven illumination, complex background, contamination and deformation for the reason of the impact on the image acquisition process to make that it is difficult to identify to them in later period and to compare with the recognition results of OR code in the team progress algorithm and genetic algorithm. After that, QR code recognition platform is established to achieve the powerful image and graphic display processing function and to save Visual C++ coding time by using the team genetic algorithm and mixed programming of Matlab and Visual C++ in the article. The experimental results demonstrate the validity of QR code decoding algorithm proposed in the article and the experimental results also demonstrate the feasibility of the design scheme of the embedded QR code recognition system platform proposed in the article.

Classification of Fashion Article Images Based on Improved Random Forest and VGG-IE Algorithm

2019 ◽  
Vol 34 (03) ◽  
pp. 2051004
Author(s):  
Jian Liu ◽  
Yuchen Zheng ◽  
Ke Dong ◽  
Haitong Yu ◽  
Jianjun Zhou ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Random Forest ◽  
Image Recognition ◽  
Classification Accuracy ◽  
Recommendation System ◽  
Computation Time ◽  
Training Data ◽  
Computational Time ◽  
Time Problem

In classification of fashion article images based on e-commerce image recommendation system, the classification accuracy and computation time cannot meet the actual requirements. Herein, for the first time to our knowledge, we present two diverse image recognition approaches for classification of fashion article images called random-forest method based on genetic algorithm (GA-RF) and Visual Geometry Group-Image Enhancement algorithm (VGG-IE) to solve classification accuracy and computation time problem. In GA-RF, the number of segmentation times and the decision trees are the key factors affecting the classification results. Improved genetic algorithm is introduced into the parameter optimization of forests to determine the optimal combination of the two parameters with minimal manual intervention. Finally, we propose six different Deep Neural Network architectures, including VGG-IE, to improve classification accuracy. The VGG-IE algorithm uses batch normalization and seven kinds training-data augmentation for ease and promotion of learning process. We investigate the effectiveness of the proposed method using Fashion-MNIST dataset and 70[Formula: see text]000 pictures, Experimental results demonstrate that, in comparison with the state-of-the-art algorithms for 10 categories of image recognition, our VGG algorithm has the shortest computational time when it satisfies certain classification accuracy. VGG-IE approach has the highest classification accuracy.

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