Multimedia Content Tagging Using Multilabel Decision Tree

2009 ◽  
Author(s):  
Aiyesha Ma ◽  
Ishwar Sethi ◽  
Nilesh Patel
Keyword(s):  
Decision Tree ◽  
Multimedia Content ◽  
Content Tagging

scholarly journals A Mobile Picture Tagging System Using Tree-Structured Layered Bayesian Networks

2013 ◽  
Vol 9 (3) ◽  
pp. 209-224 ◽  
Author(s):  
Young-Seol Lee ◽  
Sung-Bae Cho
Keyword(s):  
Bayesian Networks ◽  
Digital Media ◽  
Probabilistic Models ◽  
Semantic Annotation ◽  
Multimedia Content ◽  
Graphic User Interface ◽  
Mobile Environment ◽  
Tree Structures ◽  
Automatic Tagging ◽  
Content Tagging

Advances in digital media technology have increased in multimedia content. Tagging is one of the most effective methods to manage a great volume of multimedia content. However, manual tagging has limitations such as human fatigue and subjective and ambiguous keywords. In this paper, we present an automatic tagging method to generate semantic annotation on a mobile phone. In order to overcome the constraints of the mobile environment, the method uses two layered Bayesian networks. In contrast to existing techniques, this approach attempts to design probabilistic models with fixed tree structures and intermediate nodes. To evaluate the performance of this method, an experiment is conducted with data collected over a month. The result shows the efficiency and effectiveness of our proposed method. Furthermore, a simple graphic user interface is developed to visualize and evaluate recognized activities and probabilities.

Automated Development of Clinical Strategies Using Multistage Decision Analysis

1986 ◽  
Vol 25 (04) ◽  
pp. 207-214 ◽  
Author(s):  
P. Glasziou
Keyword(s):  
Decision Tree ◽  
Decision Analysis ◽  
Decision Trees ◽  
Optimal Strategy ◽  
Cholestatic Jaundice ◽  
Clinical Strategies ◽  
Simultaneous Study

SummaryThe development of investigative strategies by decision analysis has been achieved by explicitly drawing the decision tree, either by hand or on computer. This paper discusses the feasibility of automatically generating and analysing decision trees from a description of the investigations and the treatment problem. The investigation of cholestatic jaundice is used to illustrate the technique.Methods to decrease the number of calculations required are presented. It is shown that this method makes practical the simultaneous study of at least half a dozen investigations. However, some new problems arise due to the possible complexity of the resulting optimal strategy. If protocol errors and delays due to testing are considered, simpler strategies become desirable. Generation and assessment of these simpler strategies are discussed with examples.

Cloud-Based Multimedia Content Protection System

2020 ◽  
pp. 164-169
Author(s):  
B. Aparna ◽  
S. Madhavi ◽  
G. Mounika ◽  
P. Avinash ◽  
S. Chakravarthi
Keyword(s):  
Large Scale ◽  
Protection System ◽  
Multimedia Content ◽  
Computationally Efficient ◽  
Content Protection ◽  
Protection Systems ◽  
Multimedia Content Protection ◽  
High Scalability ◽  
Cloud Infrastructures ◽  
Rapid Deployment

We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of videos, while our system detects more than 98% of them.

KLASIFIKASI SELEKSI ATRIBUT PADA SERANGAN SPAM MENGGUNAKAN METODE ALGORITMA DECISION TREE

2018 ◽  
Vol 14 (2) ◽  
pp. 145
Author(s):  
Aji Sudibyo ◽  
Taufik Asra ◽  
Bakhtiar Rifai
Keyword(s):  
Decision Tree

internet sangat biasa untuk sekarang ini, penggunaaan internetnya tak lepas dari penggunaan email, salah satu ancaman yang terjadi ketika menggunakan email adalah spam, spam  merupakan pesan atau email yang tidak diinginkan oleh penerimanya dan dikirimkan secara massa.        Penelitian tentang serangan spam didapat dari dataset spam sebanyak 4601 record yang terdiri 1813 record dianggap spam dan 278 data bukan spam dengan atribut awal sebanyak 57 atribute dengan 1 atribute class, pada ekperimen yang dilakukan menggunakan select attribute dengan decision tree menjadi 15 atribute dengan 1 atribute class dilakukan 3 percobaan pengujian dengan persentase atribute 30%, 50% dan 70% select atribute didapat hasil fitur select atribute sebesar 70% didapat hasil lebih baik dari 30% ataupun 50% dengan nilai accuracy sebesar 92.469%.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

Sign in / Sign up

Export Citation Format

Share Document