Results

Found 17 articles

Clear filters

IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning
PDF
2019 | International Journal of Information Management | Citations: 8
Authors: Li, Daming; Deng, Lianbing; Lee, Minchang; Wang, Haoxiang

Abstract: With the development of information technology and economic growth, the Internet ... Expand

Abstract: With the development of information technology and economic growth, the Internet of Things (IoT) industry has also entered the fast lane of development. The IoT industry system has also gradually improved, forming a complete industrial foundation, including chips, electronic components, equipment, software, integrated systems, IoT services, and telecom operators. In the event of selective forwarding attacks, virus damage, malicious virus intrusion, etc., the losses caused by such security problems are more serious than those of traditional networks, which are not only network information materials, but also physical objects. The limitations of sensor node resources in the Internet of Things, the complexity of networking, and the open wireless broadcast communication characteristics make it vulnerable to attacks. Intrusion Detection System (IDS) helps identify anomalies in the network and takes the necessary countermeasures to ensure the safe and reliable operation of IoT applications. This paper proposes an IoT feature extraction and intrusion detection algorithm for intelligent city based on deep migration learning model, which combines deep learning model with intrusion detection technology. According to the existing literature and algorithms, this paper introduces the modeling scheme of migration learning model and data feature extraction. In the experimental part, KDD CUP 99 was selected as the experimental data set, and 10% of the data was used as training data. At the same time, the proposed algorithm is compared with the existing algorithms. The experimental results show that the proposed algorithm has shorter detection time and higher detection eﬃciency. Collapse

Semantic filters: rough set theory

Topics: intrusion detection system internet of things mobile system database system MySQL

Methods: computational algorithm experiment machine learning simulation cluster analysis

Theories: rough set theory
Improving accuracy and lowering cost in crowdsourcing through an unsupervised expertise estimation approach
PDF
2019 | Decision Support Systems | Citations: 7
Authors: Moayedikia, Alireza; Yeoh, William; Ong, Kok-Leong; Boo, Yee Ling

Abstract: Crowdsourcing refers to distributing microtasks to an unknown group of online wo ... Expand

Abstract: Crowdsourcing refers to distributing microtasks to an unknown group of online workers. Given that workers have varying expertise levels, a major research challenge for crowdsourcing is solving the problems of untargeted task assignment and unestimated aggregation of results. Although existing approaches can estimate the expertise of workers and use expertise information to allocate tasks, the effectiveness of these approaches is limited for the following reasons: 1) reliance on human intervention; 2) dependence on the type of answers; 3) non-sparseness; 4) post-expertise estimation. To overcome these limitations of crowdsourcing, this paper introduces an unsupervised approach to expertise estimation in microtask crowdsourcing that is independent of answer type, which is named ROUgh set based eXpertise estimation (ROUX). We consider the problem of expertise estimation as a metaheuristic optimization search problem, and integrate it with a rough set to better estimate the expertise of each online worker. Further, ROUX uses the expertise rating of workers for task assignment to maximize the accuracy of the results and lower the cost. Extensive experimental evaluations using real-world datasets show that ROUX performs remarkably in accuracy improvement and cost efficacy. Collapse

Semantic filters: rough set theory

Topics: crowdsourcing Google+ source code

Methods: computational algorithm machine learning Wilcoxon signed-rank test nonparametric test parametric test

Theories: rough set theory
An upper approximation based community detection algorithm for complex networks
PDF
2017 | Decision Support Systems | Citations: 4
Authors: Kumar, Pradeep; Gupta, Samrat; Bhasker, Bharat

Abstract: The emergence of multifarious complex networks has attracted researchers and pra ... Expand

Abstract: The emergence of multifarious complex networks has attracted researchers and practitioners from various disciplines. Discovering cohesive subgroups or communities in complex networks is essential to understand the dynamics of real-world systems. Researchers have made persistent efforts to investigate and infer community patterns in complex networks. However, real-world networks exhibit various characteristics wherein existing communities are not only disjoint but are also overlapping and nested. The existing literature on community detection consists of limited methods to discover co-occurring disjoint, overlapping and nested communities. In this work, we propose a novel rough set based algorithm capable of uncovering true community structure in networks, be it disjoint, overlapping or nested. Initial sets of granules are constructed using neighborhood connectivity around the nodes and represented as rough sets. Subsequently, we iteratively obtain the constrained connectedness upper approximation of these sets. To constrain the sets and merge them during each iteration, we utilize the concept of relative connectedness among the nodes. We illustrate the proposed algorithm on a toy network and evaluate it on fourteen real-world benchmark networks. Experimental results show that the proposed algorithm reveals more accurate communities and significantly outperforms state-of-the-art techniques. Collapse

Semantic filters: rough set theory

Topics: electronic mail evaluation criteria open source programming language

Methods: computational algorithm experiment cluster analysis cross sectional research single linkage hierarchical clustering

Theories: set theory rough set theory
Decision support to customer decrement detection at the early stage for theme parks
PDF
2017 | Decision Support Systems | Citations: 0
Authors: Yen, Chung-En; Huang, Chun-Che; Wen, Dan-Wei (Marian); Wang, You-Ping

Abstract: In recent years, a theme park drives significant attention in tourism industry d ... Expand

Abstract: In recent years, a theme park drives significant attention in tourism industry due to the provision of quality and integrated service, and issuing annual pass cards help the theme park to differentiate long-term customers from short-term ones. Customer Value Analysis is demanded for theme parks to identify potential customers as well as to appraise customer value through the setting of the annual pass. Moreover, customer value often alters from time to time since theme park industry is relevantly competitive and innovation demanded than other industries, and customer preferences are frequently changed. This study provides an early warning system to support the theme park to detect, monitor and analyze the changes of customer value. By applying the aggregated approach based on Rough Set Theory and Recency, Frequency and Monetary architecture, the tourist satisfaction levels can be captured after the aforementioned approach is executed. In addition, the rule comparison approach is contributed to predicting customer behavior from technical viewpoint. This study aims at providing an early correction strategy for the theme park to avoid losing VIP customers and identify latent customers. Collapse

Semantic filters: rough set theory

Topics: customer satisfaction decision making criminality decision support system marketing management

Methods: case study computational algorithm survey

Theories: rough set theory
Algorithm for the detection of outliers based on the theory of rough sets
PDF
2015 | Decision Support Systems | Citations: 0
Authors: Maciá-Pérez, Francisco; Berna-Martinez, Jose Vicente; Fernández Oliva, Alberto; Abreu Ortega, Miguel Alfonso

Abstract: Outliers are objects that show abnormal behavior with respect to their context o ... Expand

Abstract: Outliers are objects that show abnormal behavior with respect to their context or that have unexpected values in some of their parameters. In decision-making processes, information quality is of the utmost importance. In specific applications, an outlying data element may represent an important deviation in a production process or a damaged sensor. Therefore, the ability to detect these elements could make the difference between making a correct and an incorrect decision. This task is complicated by the large sizes of typical databases. Due to their importance in search processes in large volumes of data, researchers pay special attention to the development of efficient outlier detection techniques. This article presents a computationally efficient algorithm for the detection of outliers in large volumes of information. This proposal is based on an extension of the mathematical framework upon which the basic theory of detection of outliers, founded on Rough Set Theory, has been constructed. From this starting point, current problems are analyzed; a detection method is proposed, along with a computational algorithm that allows the performance of outlier detection tasks with an almost-linear complexity. To illustrate its viability, the results of the application of the outlier-detection algorithm to the concrete example of a large database are presented. Collapse

Semantic filters: rough set theory

Topics: database system computer hardware decision support big data

Methods: computational algorithm anomaly detection machine learning

Theories: rough set theory
A web recommendation system considering sequential information
PDF
2015 | Decision Support Systems | Citations: 11
Authors: Mishra, Rajhans; Kumar, Pradeep; Bhasker, Bharat

Abstract: With the rapid growth of information technology, the current era is witnessing a ... Expand

Abstract: With the rapid growth of information technology, the current era is witnessing an exponential increase in the generation and collection of web data. Projecting the right information to the right person is becoming more difficult day by day, which in turn adds complexity to the decision making process. Recommendation systems are intelligent systems that address this issue. They are widely used in e-commerce websites to recommend products to users. Most of the popular recommendation systems consider only the content information of users and ignore sequential information. Sequential information also provides useful insights about the behavior of users. We have developed a novel system that considers sequential information present in web navigation patterns, along with content information. We also consider soft clusters during clustering, which helps in capturing the multiple interests of users. The proposed system has utilized similarity upper approximation and singular value decomposition (SVD) for the generation of recommendations for users. We tested our approach on three datasets, the MSNBC benchmark dataset, simulated dataset and CTI dataset. We compared our approach with the first order Markov model as well as random prediction model. The results validate the viability of our approach. Collapse

Semantic filters: rough set theory

Topics: recommender system web page spreadsheet internet technology website

Methods: cluster analysis experiment data transformation singular value decomposition Markov model

Theories: rough set theory
Rough set based classification of real world Web services
PDF
2015 | Information Systems Frontiers | Citations: 1
Authors: Own, Hala S.; Yahyaoui, Hamdi

Abstract: We propose in this paper a rough set based classification technique for real wor ... Expand

Abstract: We propose in this paper a rough set based classification technique for real world Web services. The set of Web service quality attributes is reduced to a core set, which represents the most relevant set of attributes. Such reduction is achieved through the analysis of the QWS dataset using rough set theory. The core set is then used during the training phase to derive a minimal set of decision rules. These rules are applied during the classification phase to classify real world Web services into one of four categories: Platinum, Gold, Silver, and Bronze. Our experimental results show that the use of rough set theory to classify QWS services allows to have better classification accuracy results compared to other classification techniques, which have been recently applied on QWS. Collapse

Semantic filters: rough set theory

Topics: web service service quality

Methods: computational algorithm experiment machine learning descriptive statistic literature study

Theories: rough set theory
A Machine Learning Approach to Improving Dynamic Decision Making
PDF
2014 | Information Systems Research | Citations: 43
Authors: Meyer, Georg; Adomavicius, Gediminas; Johnson, Paul E.; Elidrisi, Mohamed; Rush, William A.; Sperl-Hillen, JoAnn M.; O'Connor, Patrick J.

Abstract: Decision strategies in dynamic environments do not always succeed in producing d ... Expand

Abstract: Decision strategies in dynamic environments do not always succeed in producing desired outcomes, particularly in complex, ill-structured domains. Information systems often capture large amounts of data about such environments. We propose a domain-independent, iterative approach that (a) applies data mining classification techniques to the collected data in order to discover the conditions under which dynamic decision-making strategies produce undesired or suboptimal outcomes and (b) uses this information to improve the decision strategy under these conditions. In this paper, we formally develop this approach and illustrate it by providing detailed examples of its application to a chronic disease care problem in a healthcare management organization, specifically the treatment of patients with type 2 diabetes mellitus. In particular, the proposed iterative approach is used to improve treatment strategies by predicting and eliminating treatment failures, i.e., insufficient or excessive treatment actions, based on information that is available in electronic medical record systems. We also apply the proposed approach to a manufacturing task, resulting in substantial decision strategy improvements, which further demonstrates the generality and flexibility of the proposed approach. Collapse

Semantic filters: rough set theory

Topics: decision making health information system electronic health record business process design

Methods: computational algorithm decision tree classification machine learning experiment simulation

Theories: control theory rough set theory
Similarity reasoning for the semantic web based on fuzzy concept lattices: An informal approach
PDF
2013 | Information Systems Frontiers | Citations: 0
Authors: Formica, Anna

Abstract: Similarity Reasoning in the presence of vague information is becoming fundamenta ... Expand

Abstract: Similarity Reasoning in the presence of vague information is becoming fundamental in several research areas and, in particular, in the Semantic Web. Fuzzy Formal Concept Analysis (FFCA) is a generalization of Formal Concept Analysis (FCA) for modeling uncertainty information. Although FFCA has become very interesting for supporting different activities for the development of the Semantic Web, in the literature it is usually addressed at a technical level and intended for a restricted audience. This paper proposes a similarity measure for FFCA concepts. The key notions underlying the proposed approach are presented informally, in order to reach a broad audience of readers. Collapse

Semantic filters: rough set theory

Topics: knowledge representation fuzzy logic semantic web database system

Methods: ontological modelling WordNet literature study experiment

Theories: fuzzy set theory rough set theory
Putting Dominance-based Rough Set Approach and robust ordinal regression together
PDF
2013 | Decision Support Systems | Citations: 0
Authors: Greco, Salvatore; Słowiński, Roman; Zielniewicz, Piotr

Abstract: We propose to apply the Dominance-based Rough Set Approach (DRSA) on the results ... Expand

Abstract: We propose to apply the Dominance-based Rough Set Approach (DRSA) on the results of multiple criteria decision aiding (MCDA) methods, in order to explain their recommendations in terms of rules involving conditions on evaluation criteria. The rules represent a decision model which is transparent and easy to interpret for the DM. In fact, decision rules give arguments to justify and explain the decision and, in a learning perspective, they can be the starting point for an interactive procedure for analyzing and constructing the DM's preferences. It enables his/her understanding of the conditions for the suggested recommendation, and provides useful information about the role of particular criteria or their subsets. DRSA can be used in junction with any MCDA method producing a classification result or a preference relation in the set of alternatives. In this paper, we apply DRSA to a recently proposed MCDA methodology, called Robust Ordinal Regression (ROR). The ROR approach to MCDA, also called disaggregation–aggregation approach, aims at inferring parameters of a preference model representing some holistic preference comparisons of alternatives provided by the decision maker (DM). Contrary to the usual ordinal regression approaches to MCDA, ROR takes into account the whole set of possible value of preference model parameters compatible with the DM's preference information, to work out a final recommendation. In consequence, ROR gives a recommendation in terms of necessary and possible consequences of the application of all the compatible sets of parameter values to the considered set of alternatives. UTAGMS and GRIP methods apply this approach, considering general monotonic additive value functions, and produce as a result the necessary and possible preference relations. In this paper we show how DRSA completes the decision aiding process started with ROR, providing a very useful interpretation of the preference relations in terms of decision rules. Collapse

Semantic filters: rough set theory

Topics: evaluation criteria

Methods: nonparametric test robust regression

Theories: rough set theory

Page 1 of 2 Next

AIS11

Journals

Conferences

Semantic Filter

Found 17 articles

IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning

Improving accuracy and lowering cost in crowdsourcing through an unsupervised expertise estimation approach

An upper approximation based community detection algorithm for complex networks

Decision support to customer decrement detection at the early stage for theme parks

Algorithm for the detection of outliers based on the theory of rough sets

A web recommendation system considering sequential information

Rough set based classification of real world Web services

A Machine Learning Approach to Improving Dynamic Decision Making

Similarity reasoning for the semantic web based on fuzzy concept lattices: An informal approach

Putting Dominance-based Rough Set Approach and robust ordinal regression together