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N. V, K.M. AK. E-learning course recommendation based on sentiment analysis using hybrid Elman similarity. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Alagarsamy R, Arunpraksh R, Ganapathy S, Rajagopal A, Kavitha R. A fuzzy content recommendation system using similarity analysis, content ranking and clustering. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-210246] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Recently, the e-learners are drastically increased from the last two decades. Everything is learnt through internet without help of the tutor as well. For this purpose, the e-learners are required more e-learning applications that are able to supply optimal and satisfied data based on their capability. No content recommendation system is available for recommending suitable contents to the learners. For this purpose, this paper proposes a new semantic and fuzzy aware content recommendation system for retrieving the suitable content for the users. In this content recommendation system, we propose two content pre-processing algorithms namely Target Keyword based Data Pre-processing Algorithm (TKDPA) and Intelligent Anova-T Residual Algorithm (IAATRA) for selecting the more relevant features from the document. Moreover, a new Fuzzy rule based Similarity Matching algorithm (FRSMA) is proposed and used in this system for finding the similarity between the two terms and also rank them by using the newly proposed Similarity and Temporal aware Weighted Document Ranking Algorithm (STWDRA). In addition, a content clustering process is also incorporated for gathering relevant content. Finally, a new Fuzzy, Target Keyword and Similarity Score based Content Recommendation Algorithm (FTKSCRA) is also proposed for recommending the more relevant content to the learners accurately. The experiments have been conducted for evaluating the proposed content recommendation system and proved as better than the existing recommendation systems in terms of precision, recall, f-measure and prediction accuracy.
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Affiliation(s)
| | - R. Arunpraksh
- University College of Engineering, Ariyalur, Tamilnadu, India
| | - Sannasi Ganapathy
- Centre for Cyber-Physical Systems, Vellore Institute of Technology, Chennai, Tamilnadu, India
| | | | - R.J. Kavitha
- University College of Engineering, Panruti, Tamilnadu, India
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RETRACTED ARTICLE: A user preference tree based personalized route recommendation system for constraint tourism and travel. Soft comput 2021. [DOI: 10.1007/s00500-021-06289-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Karthik R, Ganapathy S. A fuzzy recommendation system for predicting the customers interests using sentiment analysis and ontology in e-commerce. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107396] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Wang K, Cui Y, Hu J, Zhang Y, Zhao W, Feng L. Cyberbullying Detection, Based on the FastText and Word Similarity Schemes. ACM T ASIAN LOW-RESO 2021. [DOI: 10.1145/3398191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
With recent developments in online social networks (OSNs), these services are widely applied in daily lives. On the other hand, cyberbullying, which is a relatively new type of harassment through the internet-based electronic devices, is rising in online social networks. Accordingly, scholars are attracted to investigating cyberbullying behaviors. Studies show that cyberbullying has a devastating effect on mental health, especially for teenagers. In order to reduce or even stop cyberbullying, different machine learning techniques are applied and numerous studies have been conducted so far. However, conventional detection schemes still have challenges, such as low accuracy. Therefore, it is of significant importance to find an efficient detection solution in the natural language processing and machine learning communities. In the present study, characteristics of cyberbullying are initially analyzed from vocabulary and syntax points of view. Then a new detection algorithm is proposed based on FastText and word similarity schemes. Finally, experiments are carried out to evaluate the effectiveness and performance of the proposed method. Obtained results show that the proposed algorithm can effectively improve the detection accuracy and recall rate of cyberbullying detection.
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Affiliation(s)
- Kun Wang
- Xidian University, Xi’an, Shannxi
| | | | | | - Yu Zhang
- Xidian University, Xi’an, Shannxi
| | - Wei Zhao
- Xidian University, Xi’an, Shannxi
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Antony Rosewelt L, Arokia Renjit J. A content recommendation system for effective e-learning using embedded feature selection and fuzzy DT based CNN. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191721] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- L. Antony Rosewelt
- Department of Information Technology, Jerusalem College of Engineering, Chennai, India
| | - J. Arokia Renjit
- Department of Computer Science and Engineering, Jeppiaar Engineering College, Chennai, India
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Prediction of user interest fluctuation using fuzzy neural networks in web search. INTERNATIONAL JOURNAL OF INTELLIGENT UNMANNED SYSTEMS 2020. [DOI: 10.1108/ijius-02-2019-0013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThe user interest in content searching in the web will be changed over by time.Design/methodology/approachThe system is in need to find the content of user over the temporal aspects.FindingsSo, predicting the user interest over the time by analyzing the fluctuations of their search keyword is important.Research limitations/implicationsSo, predicting the user interest over the time by analyzing the fluctuations of their search keyword is important.Practical implicationsIn this work, fuzzy neural network techniques are used to predict the user interest fluctuation in different times in different scenarios.Social implicationsIn this proposed work, both the long-term and short-term interest are evaluated using the specialized user interface designed to retrieve the user interest based on the user searching activities.Originality/valueThis work also categorizes the future needs of users using this proposed system.
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Semantic analysis-based relevant data retrieval model using feature selection, summarization and CNN. Soft comput 2020. [DOI: 10.1007/s00500-020-04990-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Senthilkumar N C, Pradeep Reddy Ch. Collaborative Search Engine for Enhancing Personalized User Search Based on Domain Knowledge. J Med Syst 2019; 43:243. [DOI: 10.1007/s10916-019-1350-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 05/20/2019] [Indexed: 12/01/2022]
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Selvaraj S, Sivaraman S. Prediction model for optimized self-compacting concrete with fly ash using response surface method based on fuzzy classification. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3575-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Abstract
Users of Web search engines are generally confronted to numerous responses that are rarely structured, making it difficult to analyze the available results. Indeed, the linear results displayed through lists ordered according to a relevance criterion, although still widely used, seem often limitless. A solution to this problem is to improve the interfaces for better visualization of large number of results. In this paper, we propose modeling and implementation of a tool for graphical visualization and manipulation of results returned by search engines. The goal is to facilitate the analysis, the interpretation and the supervision of users' information needs. The architecture of the ‘Gravisor' tool is based on Multi-Agent paradigm. It is composed of four agents working in full cooperation and coordination. We hope that besides the web information retrieval field, the three graphical visualization modes offered by the ‘Gravisor' tool will be a promising alternative for better information visualization in other areas.
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Affiliation(s)
- Abdelkrim Bouramoul
- Constantine 2 University, Department of Fundamental Computer Science and Its Applications, Constantine, Algeria
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