Semantic web service composition using semantic similarity measures and formal concept analysis

Abid, A.; Rouached, Mohsen; Messai, Nizar

doi:10.1007/s11042-019-08441-z

Cited by 12 publications

(3 citation statements)

References 40 publications

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“…The metaphysics incorporates a few relations and induction rules that offer assistance to infer unused relations between parameters of administrations. Abid et al [8] introduced the IDECSE cutting edge approach for composite administrations designing. By considering linguistics web organizations, IDECSE addresses the test of totally automating the characterization, disclosure, and arrangement though decreasing progression time and incurred significant damage.…”

Section: Literature Reviewmentioning

confidence: 99%

Semantic Analysis of Urdu English Tweets Empowered by Machine Learning

Tabassum¹,

Alyas²,

Hamid³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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Development in the field of opinion mining and sentiment analysis has been rapid and aims to explore views or texts on various social media sites through machine-learning techniques with the sentiment, subjectivity analysis and calculations of polarity. Sentiment analysis is a natural language processing strategy used to decide if the information is positive, negative, or neutral and it is frequently performed on literature information to help organizations screen brand, item sentiment in client input, and comprehend client needs. In this paper, two strategies for sentiment analysis is proposed for word embedding and a bag of words on Urdu and English tweets. Word embedding is a notable arrangement of procedures that can remember words linguistics dependent on the spread theory which expresses that word is utilized and happens within the same settings tend to indicate comparable implications. Bag of words is an approach used in natural language processing to retrieve information and features from written documents. For the bag of words, machine learning techniques like naive bayes, decision tree, k-nearest neighbor, and support vector machine is used to enhance the accuracy. For word embedding the neural network technique is proposed by the combination of recurrent neural network (RNN) with long-short term memory (LSTM) for sentimental analysis of tweets. Datasets of Urdu and English tweets are used for negative and positive classification tweets with machine learning techniques. The contribution of this paper involves the implementation of a hybrid approach that focused on a sentiment analyzer to overcome social network challenges and also provided the comparative analysis of different machine learning algorithms. The results indicate improvement while using the combination of RNN with the help of LSTM showed accuracy 87% on the Urdu dataset and 92% on the English dataset.

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Section: Literature Reviewmentioning

confidence: 99%

Semantic Analysis of Urdu English Tweets Empowered by Machine Learning

Tabassum¹,

Alyas²,

Hamid³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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“…This section mainly presents the preliminary knowledge about FCA [9][10][11] and skyline queries [12,13], which we are going to utilize for addressing our problem in this paper. FCA is often used in the field of data analysis as a classic computational intelligence technique.…”

Section: Preliminariesmentioning

confidence: 99%

A Multi-Criteria Multi-Cloud Service Composition in Mobile Edge Computing

et al. 2020

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The development of mobile edge computing (MEC) is accelerating the popularity of 5G applications. In the 5G era, aiming to reduce energy consumption and latency, most applications or services are conducted on both edge cloud servers and cloud servers. However, the existing multi-cloud composition recommendation approaches are studied in the context of resources provided by a single cloud or multiple clouds. Hence, these approaches cannot cope with services requested by the composition of multiple clouds and edge clouds jointly in MEC. To this end, this paper firstly expands the structure of the multi-cloud service system and further constructs a multi-cloud multi-edge cloud (MCMEC) environment. Technically, we model this problem with formal concept analysis (FCA) by building the service–provider lattice and provider–cloud lattice, and select the candidate cloud composition that satisfies the user’s requirements. In order to obtain an optimized cloud combination that can efficiently reduce the energy consumption, money cost, and network latency, the skyline query mechanism is utilized for extracting the optimized cloud composition. We evaluate our approach by comparing the proposed algorithm to the random-based service composition approach. A case study is also conducted for demonstrating the effectiveness and superiority of our proposed approach.

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“…Zhang et al [14] used the method of sequential pattern mining, clustering technology and decision tree to extract learner's feature information, and thus achieved the purpose of grouping learners. Abid et al [15] proposed semantic web framework technology, and used association rules to provide personalized learning materials for current learners. Domestic researchers have done a lot of theoretical and practical research, and developed a number of personalized learning system, such as Knowre [16], Ape question bank [17].…”

Section: Introductionmentioning

confidence: 99%

A study on the adaptive technology of intelligent learning system based on mobile terminal recognition

Zhao

Long

2023

2022 2nd Conference on High Performance Computing and Communication Engineering (HPCCE 2022)

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With the rapid development of the mobile internet technology, m-learning is gradually becoming a key modern remote learning way. With the combination of mobile technology and digitalized technology, a learning mode based on mobile terminals is created to provide a learning environment unlimited by the time and space for learners, thus meeting the real-time learning needs of learners. However, the current mobile terminal oriented learning system is still in the exploratory stage, and how to build an intelligent learning system for mobile terminal access is a hot spot concerned by numerous researchers. Based on this, this paper explores the intelligent mobile learning system (IMLS) solution from the two technical dimensions of user terminal recognition and learning content adaptability, and describes its development environment and key technologies. Experimental results show that users' convenience and access can be enhanced to some extent by the system, which will further enhance their learning quality.

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Semantic web service composition using semantic similarity measures and formal concept analysis

Cited by 12 publications

References 40 publications

Semantic Analysis of Urdu English Tweets Empowered by Machine Learning

Semantic Analysis of Urdu English Tweets Empowered by Machine Learning

A Multi-Criteria Multi-Cloud Service Composition in Mobile Edge Computing

A study on the adaptive technology of intelligent learning system based on mobile terminal recognition

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