this book features high-quality research papers presented at the 6thinternationalconference on Computational Intelligence in patternrecognition (CIPR 2024), held at Maharaja Sriram Chandra Bhanja Deo University (MS...
详细信息
ISBN:
(数字)9789819780907
ISBN:
(纸本)9789819780891
this book features high-quality research papers presented at the 6thinternationalconference on Computational Intelligence in patternrecognition (CIPR 2024), held at Maharaja Sriram Chandra Bhanja Deo University (MSCB University), Baripada, Odisha, India, during March 15–16, 2024. It includes practical development experiences in various areas of data analysis and patternrecognition, focusing on softcomputing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big data analytics, and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments.
In social media, many existing websites (e.g., Flickr, YouTube, and Facebook) are for users to share their own interests and opinions of many popular events, and success-fully facilitate the event generation, sharing ...
详细信息
ISBN:
(纸本)9781450328104
In social media, many existing websites (e.g., Flickr, YouTube, and Facebook) are for users to share their own interests and opinions of many popular events, and success-fully facilitate the event generation, sharing and propagation. As a result, there are substantial amounts of user-contributed media data (e.g., images, videos, and textual content) for a wide variety of real-world events of different types and scales. the aim of this paper is to automatically identify the interesting events from massive social media data, which are useful to browse, search and monitor social events by users or governments. To achieve this goal, we propose a novel multi-modal supervised latent dirichlet allocation (mm-SLDA) for social event classification. Our proposed mm-SLDA has a number of advantages. (1) It can effectively exploit the multi-modality and the multi-class property of social events jointly. (2) It makes use of the supervised social event category label information and is able to classify multi-class social event directly. We evaluate our proposed mm-SLDA on a real world dataset and show extensive experimental results, which demonstrate that our model outperforms state-of-the-art methods. Copyright 2014 ACM.
Evolving Fuzzy Neural Networks (EFuNNs) are dynamic connectionist feed forward networks. Several paper can be found in the literature in which EFuNN reach better results than other methods. However, only one paper was...
详细信息
ISBN:
(纸本)9789814619967
Evolving Fuzzy Neural Networks (EFuNNs) are dynamic connectionist feed forward networks. Several paper can be found in the literature in which EFuNN reach better results than other methods. However, only one paper was found in which EFuNN results were analyzed with respect to some statistical distributions of data. this study has as goal to complement the previous study, evaluating the EFuNN performance using four other statistical distributions. Results of assessment are provided and show different accuracy according to the statistical distribution of data.
this paper introduces a newly developed test system for structure inspection and evaluation of cement-based products by applying ultrasonic test with support vector machine (SVM) classifier. In other words, this paper...
详细信息
ISBN:
(纸本)9781467327428
this paper introduces a newly developed test system for structure inspection and evaluation of cement-based products by applying ultrasonic test with support vector machine (SVM) classifier. In other words, this paper represents a novel method based on SVM for defect detection, classification of number of defects, and identification of defect materials. Withthe system, pattern of ultrasonic waves for each case of specimen can be obtained from direct and indirect measurements. Machine learning algorithm called support vector machine and artificial neural network (ANN) are employed for classification and verification of the wave patterns obtained from different samples. By applying the system, the presence or absence of a defect in mortar can be identified. Moreover, the system can also classify the number of defects and identify the defect materials being inside the mortar. For classification, input features are extracted in different ways and the numbers of training sets are varied. Base on the results from SVM, the signals extracted in frequency domain gives better performance than time domain. Using a larger training set can give more satisfactory results. In this article, the methodology is explained and the classification results are discussed. the effectiveness of the developed test system is evaluated. Comparison of the classification results that obtained by between SVM and ANN classifiers is also demonstrated. this study shows that this technique based on patternrecognition has a high potential for practical inspection of concrete structure.
Proceedings oftheSixthinternationalconference on Intelligent System and Knowledge Engineering presents selected papers from the conference ISKE 2011, held December 15-17 in Shanghai, China. this proceedings doesnt on...
详细信息
ISBN:
(纸本)9783642256578
Proceedings oftheSixthinternationalconference on Intelligent System and Knowledge Engineering presents selected papers from the conference ISKE 2011, held December 15-17 in Shanghai, China. this proceedings doesnt only examine original research and approaches in the broad areas of intelligent systems and knowledge engineering, but also present new methodologies and practices in intelligent computing paradigms. the book introduces the current scientific and technical advances in the fields of artificial intelligence, machine learning, patternrecognition, data mining, information retrieval, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, natural-language processing, etc. Furthermore, new computing methodologies are presented, including cloud computing, service computing and pervasive computing with traditional intelligent methods. the proceedings will be beneficial for both researchers and practitioners who want to utilize intelligent methods in their specific research fields. Dr. Yinglin Wang is a professor at the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; Dr. Tianrui Li is a professor at the School of Information Science and Technology, Southwest Jiaotong University, China.
Social network has become a very popular way for internet users to communicate and interact online. Users spend a great deal of time on famous social networks (e.g. Facebook, Twitter, Sina Weibo, etc.), reading news, ...
详细信息
ISBN:
(纸本)9781479940929
Social network has become a very popular way for internet users to communicate and interact online. Users spend a great deal of time on famous social networks (e.g. Facebook, Twitter, Sina Weibo, etc.), reading news, discussing events and posting their messages. Unfortunately, this popularity also attracts a significant amount of spammers who continuously expose malicious behaviors (e.g. post messages containing commercial topics or URLs, following a larger amount of users, etc.), leading to great inconvenience on normal users' social activities. In this paper, a supervised machine learning based spammer filtering method is proposed. We first collected a dataset from Sina Weibo that includes 30,116 users and more than 16 million messages;then, construct a labeled dataset of users and manually classify users into spammers and non-spammers;after that, abstract a set of novel features from message content and users' social behavior, and apply into SVM based spammer classifier. Our experiments show that true positive rate of spammers and non-spammers could reach 99.1% and 99.9%.
暂无评论