The synergistic combination of particle swarm optimization and fuzzy sets to design granular classifier

Salehi, Saber; Selamat, Ali; Mashinchi, M.

doi:10.1016/j.knosys.2014.12.017

Cited by 33 publications

(10 citation statements)

References 46 publications

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“…Computational intelligence [64], the combination of artificial neural networks, fuzzy systems, and evolutionary computation (including machine learning and probabilistic methods) has received impressive accomplishment in handling complex real-world problems. Undoubtedly, this is an emerging trend for KnoSys communities, and related studies will be widely explored in KnoSys [25,57,[65][66][67]. Note.…”

Section: Predicting the Trend Of Six Hotspots In Knosysmentioning

confidence: 99%

Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016

Zhang

Chen

et al. 2017

Knowledge-Based Systems

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The journal Knowledge-based Systems (KnoSys) has been published for over 25 years, during which time its main foci have been extended to a broad range of studies in computer science and artificial intelligence. Answering the questions: "What is the KnoSys community interested in?" and "How does such interest change over time?" are important to both the editorial board and audience of KnoSys. This paper conducts a topic-based bibliometric study to detect and predict the topic changes of KnoSys from 1991 to 2016. A Latent Dirichlet Allocation model is used to profile the hotspots of KnoSys and predict possible future trends from a probabilistic perspective. A model of scientific evolutionary pathways applies a learning-based process to detect the topic changes of KnoSys in sequential time slices. Six main research areas of KnoSys are identified, i.e., expert systems, machine learning, data mining, decision making, optimization, and fuzzy, and the results also indicate that the interest of KnoSys communities in the area of computational intelligence is raised, and the ability to construct practical systems through knowledge use and accurate prediction models is highly emphasized. Such empirical insights can be used as a guide for KnoSys submissions.

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Section: Predicting the Trend Of Six Hotspots In Knosysmentioning

confidence: 99%

Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016

Zhang

Chen

et al. 2017

Knowledge-Based Systems

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“…It uses real-numbers randomness and global communication among the swarm particles rather than use the mutation, crossover or pheromone. Due to its simplicity and attractive search efficiency, PSO has been successfully applied to various domains such as: combinatorial optimization [15], data mining [29], clustering [37], neural networks [32], image processing [17], scheduling [47], and fuzzy logic [26].…”

Section: The Necessity Of Soft Computing For Qe: Apso As An Interestimentioning

confidence: 99%

An accelerated PSO for query expansion in web information retrieval: application to medical dataset

Khennak

Drias

2017

Appl Intell

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Swarm intelligence algorithms are now among the most widely used soft computing techniques for optimization and computational intelligence. One recent swarm intelligence algorithm that has begun to receive more attention is Accelerated Particle Swarm Optimization (APSO). It is an enhanced version of PSO with global optimization capability, sufficient simplicity and high flexibility. In this paper, we propose the application of the APSO technique to efficiently solve the problem of Query Expansion (QE) in Web Information Retrieval (IR). Unlike prior studies, we introduce a new modelling of QE that aims to find the suitable expanded query from among a set of expanded query candidates. Nevertheless, due to the large number of potential expanded query candidates, it is extremely complex to produce the best one through conventional hard computing methods. Therefore, we propose to consider the problem of QE as a combinatorial optimization problem and address it with APSO. We thoroughly evaluate the proposed APSO for QE using MEDLINE, the world Web's largest medical library. We first conduct a preliminary experiment to tune the APSO parameters. Then, we compare the results to a recent swarm intelligence algorithm called Firefly Algorithm (FA). We also compare the results with three recently published methods for QE that involved Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Bat Algorithm (BA). The experimental analysis demonstrates that the proposed APSO for QE is very competitive and yields substantial improvement over the other methods in terms of retrieval effectiveness and computational complexity.

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“…Knowledge is extracted from big data by categorization based on parallel ensemble learning modular classification. (6) The cornerstone on new research directions in user interfaces to allow semantic queries aiding the user to ask and retrieve data through ontology concepts, (not just using keywords) such that to retrieve the best profiles fits with the user situation. For such purpose we need to define a set of domain ontologies to annotate semantically a repository of web semantics (WS), like the development of VDS (Virtual Doctor System) [33] in which new innovative technology was introduced to establish multimodal interaction between human outpatients and medical doctor avatar.…”

Section: Corners Stones To Achieve: Health_risks-forecast@people-mentioning

confidence: 99%

Big data based clouds healthcare web service forecasting: Research challenge

Fujita

2015

2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics

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Web service predictions are increasingly evolving in crucial manner in service providing in intelligent systems, in health care. This article highlights research challenges in health care forecasting based on cloud computing utilizing preferences project on users using subjective criteria (from user preferences) and objective criteria (statistical data in the clouds). The objective is to forecast user health situation for risk predictions.

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The synergistic combination of particle swarm optimization and fuzzy sets to design granular classifier

Cited by 33 publications

References 46 publications

Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016

Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016

An accelerated PSO for query expansion in web information retrieval: application to medical dataset

Big data based clouds healthcare web service forecasting: Research challenge

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