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Journal articles on the topic 'Agent-based data mining'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

M., Inbavalli. "An Intelligent Agent based Mining Techniques for Distributed Data Mining." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 610–17. http://dx.doi.org/10.5373/jardcs/v12sp4/20201527.

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2

Sridhar, S. "Improving diagnostic accuracy using agent-based distributed data mining system." Informatics for Health and Social Care 38, no. 3 (September 7, 2012): 182–95. http://dx.doi.org/10.3109/17538157.2012.716110.

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Devasekhar, V., and P. Natarajan. "Multi-agent based data mining aggregation approaches using machine learning techniques." International Journal of Engineering & Technology 7, no. 3 (June 23, 2018): 1136. http://dx.doi.org/10.14419/ijet.v7i3.9631.

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Data Mining is an extraction of important knowledge from the various databases using different kinds of approaches. In the multi agent, distributed mining the knowledge aggregation is one of challenging task. This paper tries to optimize the problem of aggregation and boils down into the solution, which is derived based on the machine learning statistical features of each agents. However, in this paper a novel optimization algorithm called Multi-Agent Based Data Mining Aggregation (MABDA) is used for present day’s scenarios. The MBADA algorithm has agents which collect extracted knowledge and summarizes the various levels of agent’s cluster data into an aggregation with maximum accuracies. To prove the effectiveness of the proposed algorithm, the experimental results are compared with relatively existing methods.

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Sherief, Abdallah. "Mining Dynamics: Using Data Mining Techniques to Analyze Multi-agent Learning." Journal of Intelligent Systems 26, no. 4 (September 26, 2017): 613–24. http://dx.doi.org/10.1515/jisys-2016-0136.

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AbstractAnalyzing the learning dynamics in multi-agent systems (MASs) has received growing attention in recent years. Theoretical analysis of the dynamics was only possible in simple domains and simple algorithms. When one or more of these restrictions do not apply, theoretical analysis becomes prohibitively difficult, and researchers rely on experimental analysis instead. In experimental analysis, researchers have used some global performance metric(s) as a rough approximation to the internal dynamics of the adaptive MAS. For example, if the overall payoff improved over time and eventually appeared to stabilize, then the learning dynamics were assumed to be stable as well. In this paper, we promote a middle ground between the thorough theoretical analysis and the high-level experimental analysis. We introduce the concept of mining dynamics and propose data-mining-based methodologies to analyze multi-agent learning dynamics. Using our methodologies, researchers can identify clusters of learning parameter values that lead to similar performance, and discover frequent sequences in agent dynamics. We verify the potential of our approach using the well-known iterated prisoner’s dilemma (with multiple states) domain.

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Lin, Hsueh-Yuan, and Sheng-Yuan Yang. "A cloud-based energy data mining information agent system based on big data analysis technology." Microelectronics Reliability 97 (June 2019): 66–78. http://dx.doi.org/10.1016/j.microrel.2019.03.010.

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Lin, Hsueh-Yuan, and Sheng-Yuan Yang. "A Smart Cloud-Based Energy Data Mining Agent Using Big Data Analysis Technology." Smart Science 7, no. 3 (April 14, 2019): 175–83. http://dx.doi.org/10.1080/23080477.2019.1600112.

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Fuqiang, Yue. "The Research on Distributed Data Stream Mining based on Mobile Agent." Procedia Engineering 23 (2011): 103–8. http://dx.doi.org/10.1016/j.proeng.2011.11.2473.

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Sun, Xiao Yan, Long Li, and Ping Ping Liu. "Research on Fault Diagnosis for Power Transmission Based on Mass Data Mining." Applied Mechanics and Materials 271-272 (December 2012): 1623–27. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.1623.

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A Multi-Agent based transmission fault diagnosis system is researched in this paper. Many data digging analysis methods are employed, combined with data warehouse, OLAP and Multi-Agent technology. An intelligent decision supporting system for monitoring transmission network data is built. Data digging method is used to intelligently analyze and process fault data in the data warehouse, and Agent technology is used to realize data collection, pretreatment, inquiry, knowledge Automatic extraction, mining and other functions, which makes the whole mining process intellectual and intelligent. It aids transmission management with decision-making, thus to make the monitoring and repair of power grid fault more timely and accurate.

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Mazumdar, Bireshwar Dass, and R. B. Mishra. "Multi-Agent Negotiation in B2C E-Commerce Based on Data Mining Methods." International Journal of Intelligent Information Technologies 6, no. 4 (October 2010): 46–70. http://dx.doi.org/10.4018/jiit.2010100104.

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The Multi agent system (MAS) model has been extensively used in the different tasks of e-commerce like customer relation management (CRM), negotiation and brokering. For the success of CRM, it is important to target the most profitable customers of a company. This paper presents a multi-attribute negotiation approach for negotiation between buyer and seller agents. The communication model and the algorithms for various actions involved in the negotiation process is described. The paper also proposes a multi-attribute based utility model, based on price, response-time, and quality. In support of this approach, a prototype system providing negotiation between buyer agents and seller agents is presented.

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Ellouzi, Hamdi, Hela Ltifi, and Mounir Ben Ayed. "Multi-agent modelling of decision support systems based on visual data mining." Multiagent and Grid Systems 13, no. 1 (April 12, 2017): 31–45. http://dx.doi.org/10.3233/mgs-170260.

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Mateo, Romeo Mark A., and Jaewan Lee. "Data mining model based on multi-agent for the intelligent distributed framework." International Journal of Intelligent Information and Database Systems 4, no. 4 (2010): 322. http://dx.doi.org/10.1504/ijiids.2010.035579.

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Wan, Li Yong, Jian Xin Chen, and Dong Juan Gu. "An Information Mining Model of Intelligent Collaboration Based on Agent Technology." Advanced Materials Research 998-999 (July 2014): 1096–99. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.1096.

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Information can be collected search engine on the Web, but the current search engines can only interpret the characteristic structure of web page data from the perspective of syntax, is lack of semantic understanding, and thus cannot find the desired information quickly and accurately. In order to solve these problems, this paper proposes an information mining model of intelligent Collaboration based on agent technology. By analyzing the information mining process, more to understand the mechanism of Collaboration mining, thus can help users find faster and better desired information.

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Bell, David, and Chidozie Mgbemena. "Data-driven agent-based exploration of customer behavior." SIMULATION 94, no. 3 (December 8, 2017): 195–212. http://dx.doi.org/10.1177/0037549717743106.

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Customer retention is a critical concern for mobile network operators because of the increasing competition in the mobile services sector. Such unease has driven companies to exploit data as an avenue to better understand changing customer behavior. Data-mining techniques such as clustering and classification have been widely adopted in the mobile services sector to better understand customer retention. However, the effectiveness of these techniques is debatable due to the constant change and increasing complexity of the mobile market itself. This design study proposes an application of agent-based modeling and simulation (ABMS) as a novel approach to understanding customer behavior through the combination of market and social factors that emerge from data. External forces at play and possible company interventions can then be added to data-derived models. A dataset provided by a mobile network operator is utilized to automate decision-tree analysis and subsequent building of agent-based models. Popular churn modeling techniques were adopted in order to automate the development of models, from decision trees, and subsequently explore possible customer churn scenarios. ABMS is used to understand the behavior of customers and detect reasons why customers churned or stayed with their respective mobile network operators. A CART decision-tree method is presented that identifies agents, selects important attributes, and uncovers customer behavior – easily identifying tenure, location, and choice of mobile devices as determinants for the churn-or-stay decision. Word of mouth between customers is also explored as a possible influence factor. Importantly, methods for automating data-driven agent-based simulation model generation will support faster exploration and experimentation – including with those determinants from a wider market or social context.

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Nennuri, Rajasekhar, Atmakuri Krishna Chaitanya, and Lakshmi Prasanthi Malyala. "Implementation of data frame work system based on model driven architecture for MAS and Web based applications." International Journal of Engineering & Technology 7, no. 2.20 (April 18, 2018): 1. http://dx.doi.org/10.14419/ijet.v7i2.20.11730.

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To utilize those Incomprehensible measures from claiming data effectively on the Web to make the data transforming intelligent, customize Furthermore programmed will be those A large portion vital requisitions of the present information mining innovation organization. Model driven Architecture (MDA) which will be utilized for code era need huge numbers profits In conventional product improvement routines. In this paper, Web information mining transform is acquainted from the see for function, a canny mining framework from claiming data is based for joining together those information mining. The idea of Web information mining is presented the place the part of MDA may be characterized. MDA utilizing J2EE (Java to Enterprise Edition) on portray conduct about operators need aid utilized within this suggested structural engineering. Struts skeleton gives a standard to Creating MAS (multi-agent systems) and Web based requisitions

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Lombardo, Gianfranco, Paolo Fornacciari, Monica Mordonini, Michele Tomaiuolo, and Agostino Poggi. "A Multi-Agent Architecture for Data Analysis." Future Internet 11, no. 2 (February 18, 2019): 49. http://dx.doi.org/10.3390/fi11020049.

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ActoDatA (Actor Data Analysis) is an actor-based software library for the development of distributed data mining applications. It provides a multi-agent architecture with a set of predefined and configurable agents performing the typical tasks of data mining applications. In particular, its architecture can manage different users’ applications; it maintains a high level of execution quality by distributing the agents of the applications on a dynamic set of computational nodes. Moreover, it provides reports about the analysis results and the collected data, which can be accessed through either a web browser or a dedicated mobile APP. After an introduction about the actor model and the software framework used for implementing the software library, this article underlines the main features of ActoDatA and presents its experimentation in some well-known data analysis domains.

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Laskowski, M., B. C. P. Demianyk, J. Benavides, M. R. Friesen, R. D. McLeod, S. N. Mukhi, and M. Crowley. "Extracting Data from Disparate Sources for Agent-Based Disease Spread Models." Epidemiology Research International 2012 (June 21, 2012): 1–18. http://dx.doi.org/10.1155/2012/716072.

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This paper presents a review and evaluation of real data sources relative to their role and applicability in an agent-based model (ABM) simulating respiratory infection spread a large geographic area. The ABM is a spatial-temporal model inclusive of behavior and interaction patterns between individual agents. The agent behaviours in the model (movements and interactions) are fed by census/demographic data, integrated with real data from a telecommunication service provider (cellular records), traffic survey data, as well as person-person contact data obtained via a custom 3G smartphone application that logs Bluetooth connectivity between devices. Each source provides data of varying type and granularity, thereby enhancing the robustness of the model. The work demonstrates opportunities in data mining and fusion and the role of data in calibrating and validating ABMs. The data become real-world inputs into susceptible-exposed-infected-recovered (SEIR) disease spread models and their variants, thereby building credible and nonintrusive models to qualitatively model public health interventions at the population level.

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Farooqui, Md Faizan, Md Muqeem, and Dr Md Rizwan Beg. "A Comparative study of Multi Agent Based and High-Performance Privacy Preserving Data Mining." International Journal of Computer Applications 4, no. 12 (August 10, 2010): 23–26. http://dx.doi.org/10.5120/876-1247.

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SHINDE, AMIT, MOEED HAGHNEVIS, MARCO A. JANSSEN, GEORGE C. RUNGER, and MANI JANAKIRAM. "SCENARIO ANALYSIS OF TECHNOLOGY PRODUCTS WITH AN AGENT-BASED SIMULATION AND DATA MINING FRAMEWORK." International Journal of Innovation and Technology Management 10, no. 05 (October 2013): 1340019. http://dx.doi.org/10.1142/s0219877013400191.

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A framework is presented to simulate and analyze the effect of multiple business scenarios on the adoption behavior of a group of technology products. Diffusion is viewed as an emergent phenomenon that results from the interaction of consumers. An agent-based model is used in which potential adopters of technology product are allowed to be influenced by their local interactions within the social network. Along with social influence, the effect of product features is important and we ascribe feature sensing attributes to the consumer agents along with sensitivities to social influence. The model encompasses utility theory and discrete choice models in the decision-making process for the consumers. We use expressive machine learning algorithms that can handle complex, nonlinear, and interactive effects to identify important inputs that contribute to the model and to graphically summarize their effects. We present a realistic case study that demonstrates the ability of this framework to model changes in market shares for a group of products in response to business scenarios such as new product introduction and product discontinuation under different pricing strategies. The models and other tools developed here are envisioned to be a part of a recommender system that provides insights into the effects of various business scenarios on shaping market shares of different product groups.

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Sayah, Zaoui, Merouane Zoubeidi, Ahmed Ghenabzia, Abdelhak Merizig, and Okba Kazar. "Multi-agent approach for data mining-based bagging ensembles to improve the decision process for big data." International Journal of Information and Communication Technology 17, no. 4 (2020): 380. http://dx.doi.org/10.1504/ijict.2020.10031572.

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Ghenabzia, Ahmed, Okba Kazar, Abdelhak Merizig, Zaoui Sayah, and Merouane Zoubeidi. "Multi-agent approach for data mining-based bagging ensembles to improve the decision process for big data." International Journal of Information and Communication Technology 17, no. 4 (2020): 380. http://dx.doi.org/10.1504/ijict.2020.110794.

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21

Morejón, Reinier, Marx Viana, and Carlos Lucena. "An Approach to Generate Software Agents for Health Data Mining." International Journal of Software Engineering and Knowledge Engineering 27, no. 09n10 (November 2017): 1579–89. http://dx.doi.org/10.1142/s0218194017400125.

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Data mining is a hot topic that attracts researchers of different areas, such as database, machine learning, and agent-oriented software engineering. As a consequence of the growth of data volume, there is an increasing need to obtain knowledge from these large datasets that are very difficult to handle and process with traditional methods. Software agents can play a significant role performing data mining processes in ways that are more efficient. For instance, they can work to perform selection, extraction, preprocessing, and integration of data as well as parallel, distributed, or multisource mining. This paper proposes a framework based on multiagent systems to apply data mining techniques to health datasets. Last but not least, the usage scenarios that we use are datasets for hypothyroidism and diabetes and we run two different mining processes in parallel in each database.

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Saoud, Manel Saad, Abdelhak Boubetra, and Safa Attia. "A Multi-Agent Based Modeling and Simulation Data Management and Analysis System for the Hospital Emergency Department." International Journal of Healthcare Information Systems and Informatics 12, no. 3 (July 2017): 21–36. http://dx.doi.org/10.4018/ijhisi.2017070102.

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In the last decades, multi-agent based modeling and simulation systems have become more increasingly used to model the dynamic and the complex healthcare systems which contain many variabilities and uncertainties such as the hospital emergency departments (ED). Modeling and creating virtual societies almost identical and similar to the reality are considered as the strongest advantages of these agents systems. However, during the dynamic development of the artificial societies, a massive volume of data, which generally contains non-express and shrouded information and even knowledge, is involved. Therefore, dealing with this data, to study and to analyze the unclear relationships and the emerging phenomena, is a well-known weakness and bottleneck that the multi-agent systems is suffering from. In conjunction, data mining techniques are the most powerful tools that can help simulation experts to tackle this issue. This paper presents an ongoing research that combines the multi-agent based modeling and simulation systems and data mining techniques to develop a decision support system to improve the operation of the emergency department.

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Wang, Zhi Ming, Min Xia Liu, Hui Hu, and Wu Xue Jiang. "Study on Network Security Audit System Based on Agent Technology and Log Mining." Advanced Materials Research 798-799 (September 2013): 534–37. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.534.

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Aiming to increase network security auditing efficiency, an audit system with good efficiency has been generated through Agent and log mining technology. This paper improves the traditional association rule mining algorithm Apriori and designs a new data structure in which each brother node is in a parallel alignment, each child node has a pointer directing to their children node and sibling node, the audit system can only scan the database once. Experiments show that compared with traditional Apriori, the improved algorithm has greatly reduced the I/O expenditure of the system, having obvious superiority.

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Owadally, Iqbal, Feng Zhou, Rasaq Otunba, Jessica Lin, and Douglas Wright. "An agent-based system with temporal data mining for monitoring financial stability on insurance markets." Expert Systems with Applications 123 (June 2019): 270–82. http://dx.doi.org/10.1016/j.eswa.2019.01.049.

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Li, Jing. "The Application of Data Warehouse Model in Business Data Mining Based on the Integration of Multi Agent Systems." Journal of Computational and Theoretical Nanoscience 13, no. 12 (December 1, 2016): 9942–47. http://dx.doi.org/10.1166/jctn.2016.6091.

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Sulastri, Heni, and Acep Irham Gufroni. "PENERAPAN DATA MINING DALAM PENGELOMPOKAN PENDERITA THALASSAEMIA." Jurnal Nasional Teknologi dan Sistem Informasi 3, no. 2 (September 26, 2017): 299–305. http://dx.doi.org/10.25077/teknosi.v3i2.2017.299-305.

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Thalassaemia is the genetic disease caused by deficiency and syinthesis of globin chains. It influences our body by decreasing eroticist and hemoglobin degree. People with Thalassaemia in 2015 at Tasikmalaya, Garut, and ciamis west java were 203 people. They organized in POPTI Tasikmalaya branch that placed in Dr. Soekardjo and Preasetya Bunda hospital. On the therapy process, they have different time needs and blood volume needs in every transfusion process. On the other hand, the difference transfusion levels also influence in giving iron chelation medicine. Furthermore, the method needed to help POPTI committee and health staff in appropriating blood volume and Iron Chelating Agent trough Thalassaemia people. Datamining method used by applying clustering method used K-means algorithm. Furthermore, this research conducted to categorized people with Thalassaemia based on blood volume need and HB in every transfusion process. Moreover, the pattern known by minor Thalassaemia, intermediate Thalassaemia, and mayor Thalassaemia based on age pattern, HB level in transfusion process, and blood volume needs. The research method in this research is begin by pre observation and data mining analysis method to analyze data on data mining using 3 steps of KDD such as data cleaning, data integration, data selection, data transformation, and data knowledge presentation. Further, the result of this research has 374 data that divided into 3 cluster. They are cluster 1 that has 214 data, cluster 2 has 137 data, and cluster 3 that has 23 data with the pattern that shows that the transfusion blood volume increase based on patient’s age.

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Mesbahi, Nadjib, Okba Kazar, Saber Benharzallah, Merouane Zoubeidi, and Samir Bourekkache. "Multi-Agents Approach for Data Mining Based k-Means for Improving the Decision Process in the ERP Systems." International Journal of Decision Support System Technology 7, no. 2 (April 2015): 1–14. http://dx.doi.org/10.4018/ijdsst.2015040101.

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Today the enterprise resource planning (ERP) became a tool that enables uniform and consistent management of information system (IS) of the company with a large single database. In addition, Data Mining is a technology whose purpose is to promote information and knowledge extraction from a large database. In this paper, an agent-based multi-layered approach for data mining based k-Means through the ERP to extract hidden knowledge in the ERP database is proposed. To achieve this, the authors call the paradigm of multi-agent system to distribute the complexity of several autonomous entities called agents, whose goal is to group records or observations on similar objects classes using the k-means technique that is dedicated the task of clustering. This will help business decision-makers to take good decisions and provide a very good response time by the use of multi-agent system. To implement the proposed architecture, it is more convenient to use the JADE platform while providing a complete set of services and agents comply with the specifications FIPA.

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Yu, Yan, and Jian Hua Wang. "Research on Network Examination System Model Based on Multi-AGENT." Advanced Materials Research 566 (September 2012): 685–90. http://dx.doi.org/10.4028/www.scientific.net/amr.566.685.

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With the development of artificial intelligence, Multi-agent technology is applied more and more in reality. In line with multi-user intelligent network examination test system, this paper proposes multi-agent-based general examination system model constituted by multiple mutually independent but collaborated AGENTs. In addition to fulfill their respective responsibilities, every single sub-Agent communicates among each other to gain information and complete missions in a collaborative form. Also, dedicated data mining AGENT is deployed in the system to conduct intelligent analysis and processing on various data, thereby providing decision support for teachers’ teaching and student’s study duties. And this paper presents a new clue for establishing intelligent network examination system under distributed environment.

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Arroyo, Javier, Samer Hassan, Celia Gutiérrez, and Juan Pavón. "Re-thinking simulation: a methodological approach for the application of data mining in agent-based modelling." Computational and Mathematical Organization Theory 16, no. 4 (October 21, 2010): 416–35. http://dx.doi.org/10.1007/s10588-010-9078-y.

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Sun, Hongpu, and Qianru Hu. "A Novel Deep Web Data Mining Algorithm based on Multi-Agent Information System and Collaborative Correlation Rule." International Journal of Future Generation Communication and Networking 9, no. 11 (November 30, 2016): 81–94. http://dx.doi.org/10.14257/ijfgcn.2016.9.11.08.

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Zhatkina, Kristina, and Oksana Kreider. "Application of intellectual data analysis methods for digital educational platform." System Analysis in Science and Education, no. 2 (2020) (June 30, 2020): 1–5. http://dx.doi.org/10.37005/2071-9612-2020-2-1-5.

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This article describes the possibility of using data mining techniques. In order to join new carpet participants, it is necessary to understand that the system of interaction with them is public educational services. To implement digital educational platforms, it is proposed to create an agent that collects information about sites, and also selects and tests the architecture of the neural network to build an individual trajectory that is trained using the competency-based model.

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Yang, Chang Hui. "Supplier Selection of Multi-Agent Logistic System." Key Engineering Materials 467-469 (February 2011): 614–19. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.614.

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Choosing supplier with better quick response ability becomes more and more important. In this paper, the criterion of evaluating supplier is put forward and a method of evaluating supplier is introduced. To improve the efficiency of selecting supplier, a multi-agent system of supplier selection based on evaluating supplier is developed. Recurring to the supplier’s related data collected by data-mining agent from external web-server, the weights of criteria can be confirmed. And using the system, the supplier with better QRA can be selected based on the measuring results.

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Ahmed, Bashar Shahir, Mohamed Larabi Ben Maâti, and Mohammed Al-Sarem. "Predictive Data Mining Model for Electronic Customer Relationship Management Intelligence." International Journal of Business Intelligence Research 11, no. 2 (July 2020): 1–10. http://dx.doi.org/10.4018/ijbir.2020070101.

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The rising adoption of e-CRM strategies in marketing and customer relationship management has necessitated to more needs especially where a specific customer segment is targeted and the services are personalized. This paper presents a distributed data mining model using access-control architecture in a bid to realize the needs for an online CRM that intends to deliver web content to a specific group of customers. This hybrid model utilizes the integration of the mobile agent and client server technologies that could easily be updated from the already existing web platforms. The model allows the management team to derive insights from the operations of the system since it focuses on e-personalization and web intelligence hence presenting a better approach for decision support among organizations. To achieve this, a software approach made of access-control functions, data mining algorithms, customer-profiling capability, dynamic web page creation, and a rule-based system is utilized.

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Tangod, Kiran, and Gururaj Kulkarni. "Secure Communication through MultiAgent System-Based Diabetes Diagnosing and Classification." Journal of Intelligent Systems 29, no. 1 (June 29, 2018): 703–18. http://dx.doi.org/10.1515/jisys-2017-0353.

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Abstract The main objective of the research is to provide a multi-agent data mining system for diagnosing diabetes. Here, we use multi-agents for diagnosing diabetes such as user agent, connection agent, updation agent, and security agent, in which each agent performs their own task under the coordination of the connection agent. For secure communication, the user symptoms are encrypted with the help of Elliptic Curve Cryptography and Optimal Advanced Encryption Standard. In Optimal Advanced Encryption Standard algorithm, the key values are optimally selected by means of differential evaluation algorithm. After receiving the encrypted data, the suggested method needs to find the diabetes level of the user through multiple kernel support vector machine algorithm. Based on that, the agent prescribes the drugs for the corresponding user. The performance of the proposed technique is evaluated by classification accuracy, sensitivity, specificity, precision, recall, execution time and memory value. The proposed method will be implemented in JAVA platform.

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RIXEN, MARTIN, and JÜRGEN WEIGAND. "AGENT-BASED SIMULATION OF CONSUMER DEMAND FOR SMART METERING TARIFFS." International Journal of Innovation and Technology Management 10, no. 05 (October 2013): 1340020. http://dx.doi.org/10.1142/s0219877013400208.

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An agent-based model simulates consumer demand for smart metering tariffs. It utilizes the Bass Diffusion Model and Rogers's adopter categories to locate demand-side barriers and drivers. Integration of empirical census microdata enables a validated socio-economic background for each consumer. The key performance indicators diffusion-speed and diffusion-level measure the effectiveness of regulatory interventions to induce diffusion. Pricing, promotion and quantity-regulation policies are tested. Scenario results emphasize the impact of both epidemic and probit effects. Speed of adoption is mainly triggered via interactions and consumer awareness. Level of diffusion primarily depends on pricing, willingness-to-pay and cost-benefit-thresholds. Data mining on agent's attributes highlight weaknesses in current regulatory requirements due to disadvantages in consumer acceptance and policy effectiveness. A "cash-for-clunkers" program could tackle major barriers for adoption and boost diffusion through synergies of pricing and promotion interventions.

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Lee, Jang Hee, and Sang Chan Park. "Agent and data mining based decision support system and its adaptation to a new customer-centric electronic commerce." Expert Systems with Applications 25, no. 4 (November 2003): 619–35. http://dx.doi.org/10.1016/s0957-4174(03)00101-5.

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Lee, Minhyun, and Taehoon Hong. "Hybrid agent-based modeling of rooftop solar photovoltaic adoption by integrating the geographic information system and data mining technique." Energy Conversion and Management 183 (March 2019): 266–79. http://dx.doi.org/10.1016/j.enconman.2018.12.096.

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Liang, Chao, Bharanidharan Shanmugam, Sami Azam, Asif Karim, Ashraful Islam, Mazdak Zamani, Sanaz Kavianpour, and Norbik Bashah Idris. "Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems." Electronics 9, no. 7 (July 10, 2020): 1120. http://dx.doi.org/10.3390/electronics9071120.

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With the popularity of Internet of Things (IoT) technology, the security of the IoT network has become an important issue. Traditional intrusion detection systems have their limitations when applied to the IoT network due to resource constraints and the complexity. This research focusses on the design, implementation and testing of an intrusion detection system which uses a hybrid placement strategy based on a multi-agent system, blockchain and deep learning algorithms. The system consists of the following modules: data collection, data management, analysis, and response. The National security lab–knowledge discovery and data mining NSL-KDD dataset is used to test the system. The results demonstrate the efficiency of deep learning algorithms when detecting attacks from the transport layer. The experiment indicates that deep learning algorithms are suitable for intrusion detection in IoT network environment.

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Chemchem, Amine, Habiba Drias, and Youcef Djenouri. "Multilevel Clustering of Induction Rules." International Journal of Systems and Service-Oriented Engineering 4, no. 3 (July 2014): 1–25. http://dx.doi.org/10.4018/ijssoe.2014070101.

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The tremendous size of data in nowadays world web invokes many data mining techniques. The recent emergence of some new data mining techniques provide also many interesting induction rules. So, it's important to process these induction rules in order to extract some new strong patterns called meta-rules. This work explores this concept by proposing a new support for induction rules clustering. Besides, a new clustering approach based on multilevel paradigm called multilevel clustering is developed for the purpose of treating large scale knowledge sets. The approach invokes k-means algorithm to cluster induction rules using new designed similarity measures. The developed module have been implemented in the core of the cognitive agent, in order to speed up its reasoning. This new architecture called Multilevel Miner Intelligent Agent (MMIA) is tested on four public benchmarks that contain 25000 rules, and compared to the classical one. As foreseeable, the multilevel clustering outperforms clearly the basic k-means algorithm on both the execution time and success rate criteria.

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Pouladi, Parsa, Abbas Afshar, Amir Molajou, and Mohammad Hadi Afshar. "Socio-hydrological framework for investigating farmers’ activities affecting the shrinkage of Urmia Lake; hybrid data mining and agent-based modelling." Hydrological Sciences Journal 65, no. 8 (April 30, 2020): 1249–61. http://dx.doi.org/10.1080/02626667.2020.1749763.

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Bosse, Stefan, and Uwe Engel. "Real-Time Human-In-The-Loop Simulation with Mobile Agents, Chat Bots, and Crowd Sensing for Smart Cities." Sensors 19, no. 20 (October 9, 2019): 4356. http://dx.doi.org/10.3390/s19204356.

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Modelling and simulation of social interaction and networks are of high interest in multiple disciplines and fields of application ranging from fundamental social sciences to smart city management. Future smart city infrastructures and management are characterised by adaptive and self-organising control using real-world sensor data. In this work, humans are considered as sensors. Virtual worlds, e.g., simulations and games, are commonly closed and rely on artificial social behaviour and synthetic sensor information generated by the simulator program or using data collected off-line by surveys. In contrast, real worlds have a higher diversity. Agent-based modelling relies on parameterised models. The selection of suitable parameter sets is crucial to match real-world behaviour. In this work, a framework combining agent-based simulation with crowd sensing and social data mining using mobile agents is introduced. The crowd sensing via chat bots creates augmented virtuality and reality by augmenting the simulated worlds with real-world interaction and vice versa. The simulated world interacts with real-world environments, humans, machines, and other virtual worlds in real-time. Among the mining of physical sensors (e.g., temperature, motion, position, and light) of mobile devices like smartphones, mobile agents can perform crowd sensing by participating in question–answer dialogues via a chat blog (provided by smartphone Apps or integrated into WEB pages and social media). Additionally, mobile agents can act as virtual sensors (offering data exchanged with other agents) and create a bridge between virtual and real worlds. The ubiquitous usage of digital social media has relevant impact on social interaction, mobility, and opinion-making, which has to be considered. Three different use-cases demonstrate the suitability of augmented agent-based simulation for social network analysis using parameterised behavioural models and mobile agent-based crowd sensing. This paper gives a rigorous overview and introduction of the challenges and methodologies used to study and control large-scale and complex socio-technical systems using agent-based methods.

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Yi, Zhen Zhen, Ke Zhao, Ya Tao Li, and Wei Xu. "Research on Knowledge-Based Intelligent Tutoring System." Applied Mechanics and Materials 55-57 (May 2011): 1424–29. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.1424.

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Aiming at students’ learning process, and based on the analysis of tutorship rules of students’ learning after class, a Knowledge-Based Intelligent Tutoring System is given. The system comprehensively uses agent technology, the knowledge-based automatic reasoning, resource modeling for knowledge classification, field natural language understanding, data mining, computer networks, databases and other technologies. It creates a student-oriented self-motivated learning environment in which students can learn abundant knowledge of one or many subjects, send the problems encountered in their own learning to the system server by network, and get real-time multiple tutorship information as an excellent teacher do.

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Shukla, Sanjay Kumar, M. K. Tiwari, and Young Jun Son. "Bidding-based multi-agent system for integrated process planning and scheduling: a data-mining and hybrid tabu-SA algorithm-oriented approach." International Journal of Advanced Manufacturing Technology 38, no. 1-2 (June 14, 2007): 163–75. http://dx.doi.org/10.1007/s00170-007-1087-8.

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Srinivasan, Sujatha, and Sivakumar Ramakrishnan. "A hybrid agent based virtual organization for studying knowledge evolution in social systems." Artificial Intelligence Research 1, no. 2 (September 24, 2012): 99. http://dx.doi.org/10.5430/air.v1n2p99.

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Social modeling applies computational methods and techniques to the analysis of social processes and human behavior.Cultural algorithms (CA’s) are evolutionary systems which utilize agent technology and which supports any evolutionarystrategy like genetic algorithm, evolutionary algorithm or swarm intelligence or ant algorithms. CA’s have been used formodeling the evolution of complex social systems, for re-engineering rule based systems, for data mining, and for solvingoptimization problems. In the current study a cultural algorithm framework is used to model an Agent Based VirtualOrganization (ABVO) for studying the dynamics of a social system at micro as well as macro level. Research gap exists indefining a concrete and systematic method for evaluating and validating Agent Based Social Systems (ABSS). Also theknowledge evolution process at micro and macro levels of an organization needs further exploration. The proposed CA isapplied to the problem of multi-objective optimization (MOO) of classification rules. The evolutionary knowledgeproduced by the agents in creating the rules is accepted into the belief space of the CA and macro evolution takes place.The belief space in turn influences the agents in successive generations. The rules created by the individuals and theknowledge sources created during evolution provide a concrete method to evaluate both the individuals as well as thewhole social system. The feasibility of the system has been tested on bench mark data sets and the results are encouraging.

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LUCK, MICHAEL, and EMANUELA MERELLI. "Agents in bioinformatics." Knowledge Engineering Review 20, no. 2 (June 2005): 117–25. http://dx.doi.org/10.1017/s0269888905000433.

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The scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarize and reflect on the presentations and discussions.

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ZHONG, NING. "IMPENDING BRAIN INFORMATICS RESEARCH FROM WEB INTELLIGENCE PERSPECTIVE." International Journal of Information Technology & Decision Making 05, no. 04 (December 2006): 713–27. http://dx.doi.org/10.1142/s0219622006002283.

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Web Intelligence (WI)-based portal techniques (e.g. the wisdom Web, data mining, multi-agent, and data/knowledge grids) will provide a new powerful platform for Brain Sciences. New understanding and discovery of the human intelligence models in Brain Sciences (e.g. cognitive science, neuroscience, brain informatics) will yield new WI research and development. In this paper, we briefly investigate three high-impact research issues as well as present a case study, to demonstrate the potentials of Brain Informatics (BI) research from WI perspective.

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Wang, Chen Shu. "Hybrid Intelligence Agents Architecture Design for Product Return Administration." Advanced Materials Research 403-408 (November 2011): 3339–43. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3339.

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Return is a critical but controversial issue. To deal with such a vague return problem, business must improve information transparency about end users’ return activities. This research proposed an agent-based architecture for return administration. The intelligent return administration expert system (iRAES) architecture consists of two KDD mechanisms and two intelligent agents that can predict the possibility of the end user will return the product (via return diagnosis agent, RDA) and provide return centre staff with recommendations for return administration (via return recommender agent, RRA). iRAES is implemented successfully and adopts hybrid artificial intelligent algorithms, including the following: data mining is employed to implement the RDA agent, and case-based reasoning is adopted to design the RRA agent. A demonstrated 3C-iShop scenario is presented to illustrate the feasibility and efficiency of iRAES architecture. As the experiment results show, iRAES can decrease the 70% effort for return administration evaluation and improve performance with return administration suggestions by 37%. Therefore, return administration and the knowledge management about product return can be accelerated via iRAES.

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Tang, Kai. "Research on the Construction of Personalized Active Information Service Model in Digital Library." Advanced Materials Research 753-755 (August 2013): 3071–74. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.3071.

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Based on study of users behaviors, interests, hobbies, professional fields and habits, personalized active information service in digital library is a kind of information service which takes the user's personalized needs as the center. Its key technologies includes user modeling technology, intelligent agent technology, ontology technology, Web data mining, personalized recommendation technology and information push technology etc. Based on the above technologies, this paper constructs a kind of multi-technology personalized active information service model, which can give full play to superiority of various technologies, and has the feature of intelligence. The model applies the technology of user modeling, ontology, intelligent agent, and draws on the general principle of personalized active information service, which is characterized by self-learning, self-renewal and other intelligent elements.

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Zhou, Suyang, Zijian Hu, Zhi Zhong, Di He, and Meng Jiang. "An Integrated Energy System Operating Scenarios Generator Based on Generative Adversarial Network." Sustainability 11, no. 23 (November 27, 2019): 6699. http://dx.doi.org/10.3390/su11236699.

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The convergence of energy security and environmental protection has given birth to the development of integrated energy systems (IES). However, the different physical characteristics and complex coupling of different energy sources have deeply troubled researchers. With the rapid development of AI and big data, some attempts to apply data-driven methods to IES have been made. Data-driven technologies aim to abandon complex IES modeling, instead mining the mapping relationships between different parameters based on massive volumes of operating data. However, integrated energy system construction is still in the initial stage of development and operational data are difficult to obtain, or the operational scenarios contained in the data are not enough to support data-driven technologies. In this paper, we first propose an IES operating scenario generator, based on a Generative Adversarial Network (GAN), to produce high quality IES operational data, including energy price, load, and generator output. We estimate the quality of the generated data, in both visual and quantitative aspects. Secondly, we propose a control strategy based on the Q-learning algorithm for a renewable energy and storage system with high uncertainty. The agent can accurately map between the control strategy and the operating states. Furthermore, we use the original data set and the expanded data set to train an agent; the latter works better, confirming that the generated data complements the original data set and enriches the running scenarios.

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Klongthong, Worasak, Veera Muangsin, Chupun Gowanit, and Nongnuj Muangsin. "Chitosan Biomedical Applications for the Treatment of Viral Disease: A Data Mining Model Using Bibliometric Predictive Intelligence." Journal of Chemistry 2020 (December 28, 2020): 1–12. http://dx.doi.org/10.1155/2020/6612034.

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Chitosan has attracted increasing attention from researchers in the pharmaceutical and biomedical fields as a potential agent for the prevention and treatment of infectious diseases. However, identifying the development of emerging technologies related to this biopolymer is difficult, especially for newcomers trying to understand the research streams. In this work, we designed and implemented a research process based on a bibliometric predictive intelligence model. Our aim is to glean detailed scientific and technological trends through an analysis of publications that include certain word phrases and related research areas. Cross correlation, factor mapping, and the calculation of “emergent” scores were also used. A total of 1,612 scientific papers on chitosan technology related to viral disease treatment published between 2010 and 2020 were retrieved from the Web of Science. Results from the keyword modelling quantitatively highlight three major frontier research and development topic groups: drug delivery and adjuvants, vaccines and immune response, and tissue engineering. More specifically, the emergent scores show that much of the chitosan-based treatment for viral diseases is in the in vitro stage of development. Most chitosan applications are in pharmacology/pharmacy and immunology. All results were confirmed by experts in the field, which indicates that the validated process can be applied to other fields of interest.

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