首页 | 本学科首页   官方微博 | 高级检索  
文章检索
  按 检索   检索词:      
出版年份:   被引次数:   他引次数: 提示:输入*表示无穷大
  收费全文   10篇
  免费   0篇
无线电   1篇
自动化技术   9篇
  2022年   1篇
  2021年   1篇
  2020年   1篇
  2019年   2篇
  2017年   1篇
  2015年   1篇
  2014年   2篇
  2013年   1篇
排序方式: 共有10条查询结果,搜索用时 15 毫秒
1
1.
With the high availability of digital video contents on the internet, users need more assistance to access digital videos. Various researches have been done about video summarization and semantic video analysis to help to satisfy these needs. These works are developing condensed versions of a full length video stream through the identification of the most important and pertinent content within the stream. Most of the existing works in these areas are mainly focused on event mining. Event mining from video streams improves the accessibility and reusability of large media collections, and it has been an active area of research with notable recent progress. Event mining includes a wide range of multimedia domains such as surveillance, meetings, broadcast, news, sports, documentary, and films, as well as personal and online media collections. Due to the variety and plenty of Event mining techniques, in this paper we suggest an analytical framework to classify event mining techniques and to evaluate them based on important functional measures. This framework could lead to empirical and technical comparison of event mining methods and development of more efficient structures at future.  相似文献   
2.
Multimedia Tools and Applications - Auto understanding of human activities in video is an increasing necessity in some application realms. The existing methods for human’s activity...  相似文献   
3.
Due to rapid development of Internet technology and electronic business, fraudulent activities have increased. One of the ways to cope with damages of them is fraud detection. In this field, there is a need for methods accurate and fast. Therefore, a novel and efficient feature extraction method based on social network analysis called FEMBSNA is proposed for fraud detection in banking accounts. In this method, in order to increase accuracy and control runtime in the first step, features based on network level are considered using social network analysis and extracted feature is combined with other features based on user level in the next phase. To evaluate our feature extraction method, we use PCK-means method as a basic method to learn. The results show using the proposed feature extraction as a pre-processing step in fraud detection improves the accuracy remarkably while it controls runtime in comparison with other methods.  相似文献   
4.
Multimedia Tools and Applications - According to the rapid spread of multimedia data and online observations by users, the importance of researching on machine vision also, analyzing and automatic...  相似文献   
5.

In this paper, a new representation of neural tensor networks is presented. Recently, state-of-the-art neural tensor networks have been introduced to complete RDF knowledge bases. However, mathematical model representation of these networks is still a challenging problem, due to tensor parameters. To solve this problem, it is proposed that these networks can be represented as two-layer perceptron network. To complete the network topology, the traditional gradient based learning rule is then developed. It should be mentioned that for tensor networks there have been developed some learning rules which are complex in nature due to the complexity of the objective function used. Indeed, this paper is aimed to show that the tensor network can be viewed and represented by the two-layer feedforward neural network in its traditional form. The simulation results presented in the paper easily verify this claim.

  相似文献   
6.
7.
8.
Multimedia Tools and Applications - The decrease in fertility rates and the increase in the average age of individuals are the main reasons behind the aging of the population. Challenges that come...  相似文献   
9.
Leveraging supervised learning methods is vital for predictive analysis of crime data, however, because of the complex dependencies of crime behavioral variables, classifying behavioral crime profiles is considered to be a demanding task. This paper presents two classifiers for matching single-offender crimes of the type: Burglary from Dwelling Houses (BDH). The first classifier, Multiclass MLP Crime Classifier (M2C2), leverages a multiclass topology to become capable of matching nonprolific offenders in addition to prolific offenders. This method will be useful for matching crimes to several local offenders in a particular district, and it is not suitable for classifying a large number of offenders. Contrarily, the second method, Ensemble Neural Network Crime Classifier (EN2C2), focuses on automating decision-making processes for crime matching through exploiting expert classifiers’ outputs in a bagging ensemble approach. As demonstrated by evaluative experiments, M2C2 is an efficient approach for classifying small numbers of nonprolific and prolific offenders. The proposed method's performance was proved when compared with other common machine learning techniques.  相似文献   
10.

Dyslexia is a learning disorder in which individuals have significant reading difficulties. Previous studies found that using machine learning techniques in content supplements is vital in adapting the course concepts to the learners' educational level. However, to the best of our knowledge, no research objectively applied machine learning methods to adaptive content generation. This study introduces an adaptive reinforcement learning framework known as RALF through Cellular Learning Automata (CLA) to generate content automatically for students with dyslexia. At first, RALF generates online alphabet models as a simplified font. CLA structure learns each rule of character generation through the reinforcement learning cycle asynchronously. Second, Persian words are generated algorithmically. This process also considers each character's state to decide the alphabet cursiveness and the cells' response to the environment. Finally, RALF can generate long texts and sentences using the embedded word-formation algorithm. The spaces between words are proceeds through the CLA neighboring states. Besides, RALF provides word pronunciation and several exams and games to improve the learning performance of people with dyslexia. The proposed reinforcement learning tool enhances students' learning rate with dyslexia by almost 27% compared to the face-to-face approach. The findings of this research show the applicability of this approach in dyslexia treatment during Lockdown of COVID-19.

  相似文献   
1
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号