Relevant bibliographies by topics / Agent mining

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Agent mining'

Author: Grafiati
Published: 2 February 2023

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Journal articles on the topic "Agent mining"

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Cao, Longbing, Vladimir Gorodetsky, and Pericles A. Mitkas. "Agent Mining: The Synergy of Agents and Data Mining." IEEE Intelligent Systems 24, no. 3 (May 2009): 64–72. http://dx.doi.org/10.1109/mis.2009.45.

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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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Sabitha, R., and Karthik. "EMPLOYING AGENTS IN DESCRIPTIVE MINING." International Journal of Research -GRANTHAALAYAH 4, no. 2 (February 29, 2016): 111–20. http://dx.doi.org/10.29121/granthaalayah.v4.i2.2016.2821.

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Agent technology and Data Mining have emerged as two of the prominent areas in information sciences. An effort has been activated towards the interaction and integration between agent technology and data mining which is referred to as “AGENT MINING”. Data Mining is the process of extracting interesting information or patterns from large volumes of data. Agents comprise a powerful technology for the analysis, design and implementation of autonomous intelligent systems that can handle distributed problem-solving, cooperation, coordination, communication, and organization in a multiplayer environment. This agent uses information technology to find trends and patterns in an abundance of information from many different sources. The user can sort through this information in order to find whatever information they are seeking. Intelligent agents are today accepted as powerful tools for data mining in a distributed environment. The interaction and integration between agent and mining has potential to not only strengthen either side, but generate new techniques for developing more powerful intelligence and intelligent information processing systems. This paper discusses how agents are used in the various descriptive models of Data Mining. The various challenges and methodologies are analyzed and it clearly indicates the need for and the promising potential of agent mining for the mutual enhancement of both fields and for the creation of super-intelligent systems. Even though many researchers have been committed, more efforts are required to develop techniques and systems in practical perspectives.
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Yan, Jiaqi, Daning Hu, Stephen S. Liao, and Huaiqing Wang. "Mining Agents’ Goals in Agent-Oriented Business Processes." ACM Transactions on Management Information Systems 5, no. 4 (March 21, 2015): 1–22. http://dx.doi.org/10.1145/2629448.

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P, Rohini, and Sree Lakshmi.P. "Agent-Driven Distributed Data Mining." International Journal of Science and Engineering Applications 2, no. 5 (May 1, 2013): 103–9. http://dx.doi.org/10.7753/ijsea0205.1003.

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Boylu, Fidan, Haldun Aytug, and Gary J. Koehler. "Data mining with agent gaming." Information Technology and Management 11, no. 1 (January 20, 2010): 1–6. http://dx.doi.org/10.1007/s10799-010-0064-3.

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Cao, Longbing, Gerhard Weiss, and Philip S. Yu. "A brief introduction to agent mining." Autonomous Agents and Multi-Agent Systems 25, no. 3 (May 9, 2012): 419–24. http://dx.doi.org/10.1007/s10458-011-9191-4.

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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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Symeonidis, Andreas L., Kyriakos C. Chatzidimitriou, Ioannis N. Athanasiadis, and Pericles A. Mitkas. "Data mining for agent reasoning: A synergy for training intelligent agents." Engineering Applications of Artificial Intelligence 20, no. 8 (December 2007): 1097–111. http://dx.doi.org/10.1016/j.engappai.2007.02.009.

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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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Dissertations / Theses on the topic "Agent mining"

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Chaimontree, Santhana. "Multi-agent data mining with negotiation : a study in multi-agent based clustering." Thesis, University of Liverpool, 2012. http://livrepository.liverpool.ac.uk/7673/.

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Multi-Agent Data Mining (MADM) seeks to harness the general advantages offered by Multi-Agent System (MAS) with respect to the domain of data mining. The research described in this thesis is concerned with Multi-Agent Based Clustering (MABC), thus MADM to support clustering. To investigate the use of MAS technology with respect to data mining, and specifically data clustering, two approaches are proposed in this thesis. The first approach is a multi-agent based approach to clustering using a generic MADM framework whereby a collection of agents with different capabilities are allowed to collaborate to produce a ``best'' set of clusters. The framework supports three clustering paradigms: K-means, K-NN and divisive hierarchical clustering. A number of experiments were conducted using benchmark UCI data sets and designed to demonstrate that the proposed MADM approach can identify a best set of clusters using the following clustering metrics: F-measure, Within Group Average Distance (WGAD) and Between Group Average Distance (BGAD). The results demonstrated that the MADM framework could successfully be used to find a best cluster configuration. The second approach is an extension of the proposed initial MADM framework whereby a ``best'' cluster configuration could be found using cooperation and negotiation among agents. The novel feature of the extended framework is that it adopts a two-phase approach to clustering. Phase one is similar to the established centralised clustering approach (except that it is conducted in a decentralised manner). Phase two comprises a negotiation phase where agents ``swap'' unwanted records so as to improve a cluster configuration. A set of performatives is proposed as part of a negotiation protocol to facilitate intra-agent negotiation. It is this negotiation capability which is the central contribution of the work described in this thesis. An extensive evaluation of the extended framework was conducted using: (i) benchmark UCI data sets and (ii) a welfare benefits data set that provides an exemplar application. Evaluation of the framework clearly demonstrates that, in the majority of cases, this negotiation phase serves to produce a better cluster configuration (in terms of cohesion and separation) than that produced using a simple centralised approach.
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Albashiri, Kamal Ali. "An investigation into the issues of multi-agent data mining." Thesis, University of Liverpool, 2010. http://livrepository.liverpool.ac.uk/1275/.

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Very often data relevant to one search is not located at a single site, it may be widely-distributed and in many different forms. Similarly there may be a number of algorithms that may be applied to a single Knowledge Discovery in Databases (KDD) task with no obvious “best” algorithm. There is a clear advantage to be gained from a software organisation that can locate, evaluate, consolidate and mine data from diverse sources and/or apply a diverse number of algorithms. Multi-agent systems (MAS) often deal with complex applications that require distributed problem solving. Since MAS are often distributed and agents have proactive and reactive features, combining Data Mining (DM) with MAS for Data Mining (DM) intensive applications is therefore appealing. This thesis discusses a number of research issues concerned with the viability of Multi-Agent systems for Data Mining (MADM). The problem addressed by this thesis is that of investigating the usefulness of MAS in the context of DM. This thesis also examines the issues affecting the design and implementation of a generic and extendible agent-based data mining framework. The principal research issues associated with MADM are those of experience and resource sharing, flexibility and extendibility, and protection of privacy and intellectual property rights. To investigate and evaluate proposed solutions to MADM issues, an Extendible Multi-Agent Data mining System (EMADS) was developed. This framework promotes the ideas of high availability and high performance without compromising data or DM algorithm integrity. The proposed framework provides a highly flexible and extendible data-mining platform. The resulting system allows users to build collaborative DM approaches. The proposed framework has been applied to a number of DM scenarios. Experimental tests on real data have confirmed its effectiveness.
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Chau, Michael, Daniel Zeng, Hsinchun Chen, Michael Huang, and David Hendriawan. "Design and evaluation of a multi-agent collaborative Web mining system." Elsevier, 2003. http://hdl.handle.net/10150/105861.

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Artificial Intelligence Lab, Department of MIS, University of Arizona
Most existing Web search tools work only with individual users and do not help a user benefit from previous search experiences of others. In this paper, we present the Collaborative Spider, a multi-agent system designed to provide post-retrieval analysis and enable across-user collaboration in Web search and mining. This system allows the user to annotate search sessions and share them with other users. We also report a user study designed to evaluate the effectiveness of this system. Our experimental findings show that subjectsâ search performance was degraded, compared to individual search scenarios in which users had no access to previous searches, when they had access to a limited number (e.g., 1 or 2) of earlier search sessions done by other users. However, search performance improved significantly when subjects had access to more search sessions. This indicates that gain from collaboration through collaborative Web searching and analysis does not outweigh the overhead of browsing and comprehending other usersâ past searches until a certain number of shared sessions have been reached. In this paper, we also catalog and analyze several different types of user collaboration behavior observed in the context of Web mining.
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Kerns, Kelly Michael Kumar Vijay. "A self-organized data mining agent framework to dynamically discover neural networks." Diss., UMK access, 2004.

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Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2004.
"A thesis in computer science." Typescript. Advisor: Vijay Kumar. Vita. Title from "catalog record" of the print edition Description based on contents viewed Feb. 21, 2006. Includes bibliographical references (leaves 96-98). Online version of the print edition.
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NOVALES, REINIER MOREJON. "A MULTI-AGENT APPROACH TO DATA MINING PROCESSES: APPLICATIONS TO HEALTH CARE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=34660@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
A mineração de dados é um tema em alta que atrai pesquisadores de diferentes áreas, como bancos de dados, aprendizado de máquina e sistemas multiagentes. Como consequência do crescimento do volume de dados, há uma necessidade crescente de obter conhecimento desses grandes conjuntos de dados que são muito difíceis de manipular e processar com os métodos tradicionais. Os agentes de software podem desempenhar um papel significativo ao executar processos de mineração de dados de maneira mais eficiente. Por exemplo, eles podem trabalhar para realizar seleção, extração, pré-processamento e integração de dados, bem como mineração paralela, distribuída ou de múltiplas fontes. Este trabalho propõe uma abordagem (na forma de um framework) que usa agentes de software para gerenciar processos de mineração de dados. Para testar sua aplicabilidade, utilizamos vários conjuntos de dados relacionados ao domínio de saúde, representando alguns cenários de uso (hipotireoidismo, diabetes e arritmia).
Data mining is a hot topic that attracts researchers from different areas, such as databases, machine learning, and multi-agent systems. As a consequence of the growth of data volume, there is a growing need to obtain knowledge from these large data sets 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 work proposes an approach (in the form of a framework) that uses software agents to manage data mining processes. In order to test its applicability, we use several data sets related to health care domain representing some usage scenarios (hypothyroidism, diabetes and arrhythmia).
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Kerr, Wesley. "Learning to Recognize Agent Activities and Intentions." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/193649.

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Psychological research has demonstrated that subjects shown animations consisting of nothing more than simple geometric shapes perceive the shapes as being alive, having goals and intentions, and even engaging in social activities such as chasing and evading one another. While the subjects could not directly perceive affective state, motor commands, or the beliefs and intentions of the actors in the animations, they still used intentional language to describe the moving shapes. The purpose of this dissertation is to design, develop, and evaluate computational representations and learning algorithms that learn to recognize the behaviors of agents as they perform and execute different activities. These activities take place within simulations, both 2D and 3D. Our goal is to add as little hand-crafted knowledge to the representation as possible and to produce algorithms that perform well over a variety of different activity types. Any patterns found in similar activities should be discovered by the learning algorithm and not by us, the designers. In addition, we demonstrate that if an artificial agent learns about activities through participation, where it has access to its own internal affective state, motor commands, etc., it can then infer the unobservable affective state of other agents.
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Abdo, Walid A. A. "Enhancing association rules algorithms for mining distributed databases. Integration of fast BitTable and multi-agent association rules mining in distributed medical databases for decision support." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5661.

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Over the past few years, mining data located in heterogeneous and geographically distributed sites have been designated as one of the key important issues. Loading distributed data into centralized location for mining interesting rules is not a good approach. This is because it violates common issues such as data privacy and it imposes network overheads. The situation becomes worse when the network has limited bandwidth which is the case in most of the real time systems. This has prompted the need for intelligent data analysis to discover the hidden information in these huge amounts of distributed databases. In this research, we present an incremental approach for building an efficient Multi-Agent based algorithm for mining real world databases in geographically distributed sites. First, we propose the Distributed Multi-Agent Association Rules algorithm (DMAAR) to minimize the all-to-all broadcasting between distributed sites. Analytical calculations show that DMAAR reduces the algorithm complexity and minimizes the message communication cost. The proposed Multi-Agent based algorithm complies with the Foundation for Intelligent Physical Agents (FIPA), which is considered as the global standards in communication between agents, thus, enabling the proposed algorithm agents to cooperate with other standard agents. Second, the BitTable Multi-Agent Association Rules algorithm (BMAAR) is proposed. BMAAR includes an efficient BitTable data structure which helps in compressing the database thus can easily fit into the memory of the local sites. It also includes two BitWise AND/OR operations for quick candidate itemsets generation and support counting. Moreover, the algorithm includes three transaction trimming techniques to reduce the size of the mined data. Third, we propose the Pruning Multi-Agent Association Rules algorithm (PMAAR) which includes three candidate itemsets pruning techniques for reducing the large number of generated candidate itemsets, consequently, reducing the total time for the mining process. The proposed PMAAR algorithm has been compared with existing Association Rules algorithms against different benchmark datasets and has proved to have better performance and execution time. Moreover, PMAAR has been implemented on real world distributed medical databases obtained from more than one hospital in Egypt to discover the hidden Association Rules in patients¿ records to demonstrate the merits and capabilities of the proposed model further. Medical data was anonymously obtained without the patients¿ personal details. The analysis helped to identify the existence or the absence of the disease based on minimum number of effective examinations and tests. Thus, the proposed algorithm can help in providing accurate medical decisions based on cost effective treatments, improving the medical service for the patients, reducing the real time response for the health system and improving the quality of clinical decision making.
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Abdo, Walid Adly Atteya. "Enhancing association rules algorithms for mining distributed databases : integration of fast BitTable and multi-agent association rules mining in distributed medical databases for decision support." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5661.

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Over the past few years, mining data located in heterogeneous and geographically distributed sites have been designated as one of the key important issues. Loading distributed data into centralized location for mining interesting rules is not a good approach. This is because it violates common issues such as data privacy and it imposes network overheads. The situation becomes worse when the network has limited bandwidth which is the case in most of the real time systems. This has prompted the need for intelligent data analysis to discover the hidden information in these huge amounts of distributed databases. In this research, we present an incremental approach for building an efficient Multi-Agent based algorithm for mining real world databases in geographically distributed sites. First, we propose the Distributed Multi-Agent Association Rules algorithm (DMAAR) to minimize the all-to-all broadcasting between distributed sites. Analytical calculations show that DMAAR reduces the algorithm complexity and minimizes the message communication cost. The proposed Multi-Agent based algorithm complies with the Foundation for Intelligent Physical Agents (FIPA), which is considered as the global standards in communication between agents, thus, enabling the proposed algorithm agents to cooperate with other standard agents. Second, the BitTable Multi-Agent Association Rules algorithm (BMAAR) is proposed. BMAAR includes an efficient BitTable data structure which helps in compressing the database thus can easily fit into the memory of the local sites. It also includes two BitWise AND/OR operations for quick candidate itemsets generation and support counting. Moreover, the algorithm includes three transaction trimming techniques to reduce the size of the mined data. Third, we propose the Pruning Multi-Agent Association Rules algorithm (PMAAR) which includes three candidate itemsets pruning techniques for reducing the large number of generated candidate itemsets, consequently, reducing the total time for the mining process. The proposed PMAAR algorithm has been compared with existing Association Rules algorithms against different benchmark datasets and has proved to have better performance and execution time. Moreover, PMAAR has been implemented on real world distributed medical databases obtained from more than one hospital in Egypt to discover the hidden Association Rules in patients' records to demonstrate the merits and capabilities of the proposed model further. Medical data was anonymously obtained without the patients' personal details. The analysis helped to identify the existence or the absence of the disease based on minimum number of effective examinations and tests. Thus, the proposed algorithm can help in providing accurate medical decisions based on cost effective treatments, improving the medical service for the patients, reducing the real time response for the health system and improving the quality of clinical decision making.
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Sainani, Varsha. "Hybrid Layered Intrusion Detection System." Scholarly Repository, 2009. http://scholarlyrepository.miami.edu/oa_theses/44.

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The increasing number of network security related incidents has made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). Detecting intrusion in a distributed network from outside network segment as well as from inside is a difficult problem. IDSs are expected to analyze a large volume of data while not placing a significant added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel hybrid layered multiagent-based intrusion detection system is created, particularly with the support of a multi-class supervised classification technique. In agent-based IDS, there is no central control and therefore no central point of failure. Agents can detect and take predefined actions against malicious activities, which can be detected with the help of data mining techniques. The proposed IDS shows superior performance compared to central sniffing IDS techniques, and saves network resources compared to other distributed IDSs with mobile agents that activate too many sniffers causing bottlenecks in the network. This is one of the major motivations to use a distributed model based on a multiagent platform along with a supervised classification technique. Applying multiagent technology to the management of network security is a challenging task since it requires the management on different time instances and has many interactions. To facilitate information exchange between different agents in the proposed hybrid layered multiagent architecture, a low cost and low response time agent communication protocol is developed to tackle the issues typically associated with a distributed multiagent system, such as poor system performance, excessive processing power requirement, and long delays. The bandwidth and response time performance of the proposed end-to-end system is investigated through the simulation of the proposed agent communication protocol on our private LAN testbed called Hierarchical Agent Network for Intrusion Detection Systems (HAN-IDS). The simulation results show that this system is efficient and extensible since it consumes negligible bandwidth with low cost and low response time on the network.
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Kritzinger, Jacob Johannes. "The game of diminishing returns : Architecture as a regenerative agent of man and nature." Diss., University of Pretoria, 2018. http://hdl.handle.net/2263/63622.

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Mining related activities are more expansive than ever before, with population growth data showing an exponential increase. These societies form part of the world consumer market in which people are constantly buying products with a short life-expectancy, such as consumer electronic devices. More often than not, these mining activities occur in developing countries or rural areas. A repercussion of mines located long distances from active communities is the formation of new communities based solely on the monetary system of a mine. Various precedents show the societal damage caused by the inevitable closure of mines. This eventuality is not the only societal concern associated with mining communities; communities with few social binding factors. These spaces are often made up of individuals from geographically and culturally disparate spaces who are brought together solely in their united search for monetary stability. This often leaves these communities rife with xenophobic tendencies and unsustainably weak community identities. Can a system be created in which community identity can act as a social catalyst preventing the disintegration of communities abandoned by mines? The close-out plans, drawn up by mines, leaves a lot to be desired. Based on outdated environmental data and environmental laws, these closure plans offer very little environmental protection or land rehabilitation options, and nearly no protection of any kind for communities involved in mining activities. There lies irreversible societal and environmental damage in our near future, not only for South Africa, but worldwide, if we do not start offering up innovative ideas and possible solutions for these mining communities. This dissertation will investigate architecturally based solutions for community stability, and environmental stability, through the formation of a strong communal identity which will be based on environmental rehabilitation as the catalyst for stability. The proposed site exists as a no-man’s-land between the Refilwe Community and Cullinan diamond mines' largest slime dam. The constant expansion of the Refilwe community has pushed property development to the absolute limit, in terms of proximity to the slime dam. This community currently borders on this slime dam, hazardous to the humans and animals that live there.The history of Refilwe exemplifies pre-Apartheid and Apartheid laws of segregation; a political system which still affects the identity of the residents and the community as a whole today. Recent community-directed architectural failures leads this dissertation to reconsider how architecture can rehabilitate or assist a community. As a point of departure, this dissertation examines utilizing existing potential within a damaged space and analyses the significant community requirements at hand. The architectural response will primarily be based on information collected from various sources, including but not limited to: sites studies, environmental analyses, precedents and historical information. The programme was developed to utilize architecture as a response method for remediation of both man and nature.
Myn gebasseerde aktiwiteite het 'n hoër produksie en verwagting as ooit tevore met die bevolking se eksponensiële toename. Hierdie gemeenskappe vorm deel van die wêreld se vebruikersmark. Die publiek koop aanhoudend produkte wat binne 'n paar jaar op die vullishoop beland, byvoorbeeld elektroniese objekte. Die meerderheid myne word gevind in onontwikkelde lande of in 'n plattelandse omgewing. Die nadeel van myne wat ver van stedelike gebiede is, is dat die nuwe gemeenskappe alleenlik gevorm word om die stigting van die myn. Dit is bewys dat die onvermydelike staking van produksie van myne veroorsaak sosiale verwoesting in sulke gemeenskappe. Dit is nie die enigste probleem nie. Hierdie gemeenskappe het geen sosiale groepseenheid nie en bestaan meestal van mense van verskillende agtergronde en plekke. Hulle is saamgegooi as gevolg van werksgeleenthede. Die gevolg daarvan is dat die gemeenskap se individuele identiteit swak is, en xenofobiese gevoelens ontstaan. Die vraag is, kan 'n sisteem ontwerp word om 'n gemeenskaplike identiteit te help vorm? Wat sal help om die gemeenskap bymekaar te hou en te ontwikkel na die myn toemaak? Die finale plan vir die toemaak van 'n myn is gewoonlik nie bevredigend genoeg nie. Gebaseer op verouderde omgewingsinformasie en omgewingswette word te min beskermings opsies aangebied vir die rehabilitasie van die gebied. Onomkeerbare sosiale en omgewingsvernietiging sal in die toekoms volg, nie net vir Suid-Afrika nie, maar wêreldwyd, indien ons nie begin kyk na nuwe kreatiewe idees, en moontlike oplossings vir hierdie myn gemeenskappe nie. Die verhandeling versoek en beoog 'n oplossing met argitektuur as basis en agtergrond, vir die gemeenskap en die omgewings stabiliteit, deur die vorming van 'n sterker gemeenskaps identiteit wat gebaseer word of omgewings rehabilitasie. Die voorgestelde terrein plan en ontwikkeling skep 'n buffer tussen die Refilwe gemeenskap en die Cullinan Diamant Myn se grootste slyk dam. Die aanhoudende populasie aanwas van die Refilwe gemeenskap het eindomsontwikkeling tot aan sy grense gestoot in terme van sy ligging tot die slyk dam. Huidiglik grens die gemeenskap aan die slyk dam wat 'n gesondheids risiko is vir mens en dier wat daar lewe. Die geskiedenis van Refilwe vertoonbeeld die tydperk van Apartheid en Apartheidswette van segregasie, 'n politiese sisteem wat tot vandag toe die identiteit van die inwoners en die gemeenskap negatief beinvloed. Onlangse gemeenskapsgedrewe argitektuur mislukkings, lei hierdie verhandeling na die heroorweging van hoe argitektuur 'n gemeenskap kan rehabiliteer. Ter afsluiting, ondersoek die verhandeling die moontlikheid om die huidige potensiaal te gebruik in 'n geaffekteerde ruimte en om die toepaslike gemeenskapbehoeftes te analiseer. Die argiteksgedrewe antwoord sal primêr gebaseer wees op data versamel van verskeie oorde, insluitende maar nie beperk tot: terreinstudies, omgewings analises, feite en historiese inligtig. Die projek was ontwikkel om argitektuur te gebruik as 'n moontlike antwoord vir die herstel van mens en die natuur en om 'n nuwe simbiose te skep.
Mini Dissertation MArch(Prof)--University of Pretoria, 2018.
Architecture
MArch (Prof)
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Books on the topic "Agent mining"

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service), SpringerLink (Online, ed. Data Mining and Multi-agent Integration. Boston, MA: Springer-Verlag US, 2009.

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Cao, Longbing, ed. Data Mining and Multi-agent Integration. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0522-2.

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Duarte, Bouça, and Gafagnão Amaro, eds. Agent-based computing. Hauppauge, N.Y: Nova Science Publishers, 2010.

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Zili, Zhang. Agent-based hybrid intelligent systems: An agent-based framework for complex problem solving. Berlin: Springer, 2004.

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Mario, Kušek, Nguyễn Ngọc Thanh, Howlett Robert J, Jain Lakhmi C, and SpringerLink (Online service), eds. Agent and Multi-Agent Systems. Technologies and Applications: 6th KES International Conference, KES-AMSTA 2012,Dubrovnik, Croatia, June 25-27, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Chengqi, Zhang, ed. Agent-based hybrid intelligent systems: An agent-based fromework for complex problem solving. New York: Springer, 2004.

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S, Yu Philip, Weiss Gerhard, Liu Jiming, Gorodetski Vladimir I. 1937-, and SpringerLink (Online service), eds. Agents and Data Mining Interaction: 4th International Workshop, ADMI 2009, Budapest, Hungary, May 10-15,2009, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Thanh, Nguyẽ̂n Ngọc, ed. Agent and multi-agent systems: Technologies and applications : second KES international symposium, KES-AMSTA 2008 : Inchʻŏn, Korea, March 26-28, 2008 : proceedings. Berlin: Springer, 2008.

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KES-AMSTA 2010 (2010 Gdynia, Poland). Agent and multi-agent systems: technologies and applications: 4th KES International Symposium, KES-AMSTA 2010, Gdynia, Poland, June 23-25, 2010 ; proceedings. Berlin: Springer, 2010.

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Meyer, John-Jules Ch. Knowledge Representation for Agents and Multi-Agent Systems: First International Workshop, KRAMAS 2008, Sydney, Australia, September 17, 2008, Revised Selected Papers. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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Book chapters on the topic "Agent mining"

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Cao, Longbing, Chengqi Zhang, Philip S. Yu, and Yanchang Zhao. "Agent-Driven Data Mining." In Domain Driven Data Mining, 145–69. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-5737-5_7.

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Selmi, Safa, and Wahiba Ben Abdessalem Karaa. "Multi-Agent System for Text Mining." In Mining Multimedia Documents, 53–66. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2017. http://dx.doi.org/10.1201/9781315399744-5.

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Selmi, Safa, and Wahiba Ben Abdessalem Karaa. "Multi-Agent System for Text Mining." In Mining Multimedia Documents, 53–66. Boca Raton : CRC Press, [2017]: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b21638-4.

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Baik, Sung Wook, Jerzy Bala, and Ju Sang Cho. "Agent Based Distributed Data Mining." In Parallel and Distributed Computing: Applications and Technologies, 42–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30501-9_11.

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Cao, Longbing, Dan Luo, and Chengqi Zhang. "Ubiquitous Intelligence in Agent Mining." In Lecture Notes in Computer Science, 23–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03603-3_3.

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Yang, Cheng-Lin, and Yun-Heh Chen-Burger. "A Hybrid On-line Topic Groups Mining Platform." In Agent and Multi-Agent Systems: Technologies and Applications, 205–15. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19728-9_17.

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Shoham, Yoav, and Rob Powers. "Multi-agent Learning." In Encyclopedia of Machine Learning and Data Mining, 857–60. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_568.

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Chen, Renlong, and Ying Tan. "A Multi-branch Ensemble Agent Network for Multi-agent Reinforcement Learning." In Data Mining and Big Data, 485–98. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7502-7_44.

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Kim, Jae Kyeong, and Yoon Ho Cho. "Using Web Usage Mining and SVD to Improve E-commerce Recommendation Quality." In Intelligent Agents and Multi-Agent Systems, 86–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39896-7_8.

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Muyeba, Maybin, Keeley Crockett, and John Keane. "A Hybrid Interestingness Heuristic Approach for Attribute-Oriented Mining." In Agent and Multi-Agent Systems: Technologies and Applications, 414–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22000-5_43.

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Conference papers on the topic "Agent mining"

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Skorupka, Dariusz, Stanisław Stanek, and Mariusz Żytniewski. "Agent supported process mining." In CENTRAL EUROPEAN SYMPOSIUM ON THERMOPHYSICS 2019 (CEST). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5114159.

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Kumar, M. Naresh, and B. Eswara Reddy. "Improved classification association rule mining." In Multi-Agent Systems (IAMA 2009). IEEE, 2009. http://dx.doi.org/10.1109/iama.2009.5228045.

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Madiraju, Praveen, and Yanqing Zhang. "Web usage data mining agent." In AeroSense 2002, edited by Belur V. Dasarathy. SPIE, 2002. http://dx.doi.org/10.1117/12.460231.

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Anand, T., S. Padmapriya, and E. Kirubakaran. "Terror tracking using advanced web mining perspective." In Multi-Agent Systems (IAMA 2009). IEEE, 2009. http://dx.doi.org/10.1109/iama.2009.5228034.

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Oliveira, Gustavo H. B. S., Josenildo C. da Silva, Omar A. C. Cortes, and Luciano R. Coutinho. "A Multi-Agent Architecture for Distributed Data Mining Systems." In Brazilian e-Science Workshop. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/bresci.2022.222487.

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The agent-based approach is appealing to Distributed Data Mining (DDM) systems since the concept of agency offers some relevant features, such as scalability, flexibility, robustness, and modularity. This paper investigates whether and how the multi-agent system metaphor might be used for Distributed Data Mining Systems. We proposed and implemented a multi-agent architecture called SeAMS, which is capable of mining patterns efficiently using the DPDTS algorithm. The system was developed in JADE and designed to be easily extensible and protect any local datasets' privacy. Results show that the agentbased approach was able to identify patterns efficiently using three different popular scientific datasets in a distributed time series: Sunspot, Power, and TEK.
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Khashfeh, Mouayad, Moamin A. Mahmoud, and Mohd Sharifuddin Ahmad. "A Text Mining Algorithm Optimising the Determination of Relevant Studies." In 2018 International Symposium on Agent, Multi-Agent Systems and Robotics (ISAMSR). IEEE, 2018. http://dx.doi.org/10.1109/isamsr.2018.8540553.

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Mangla, Monika. "Exploiting OLAP and data mining for augmenting e-business." In Multi-Agent Systems (IAMA 2009). IEEE, 2009. http://dx.doi.org/10.1109/iama.2009.5228057.

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Talib, Ramzan, Bernhard Volz, and Stefan Jablonski. "Agent Assignment for Process Management: Agent Performance Evaluation Framework." In 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.99.

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Patel, Darshana, and J. S. Shah. "Mobile agent and distributed data mining." In 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT). IEEE, 2016. http://dx.doi.org/10.1109/icatcct.2016.7912060.

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Kusumura, Yukitaka, Yoshinori Hijikata, and Shogo Nishida. "Text mining agent for net auction." In the 2004 ACM symposium. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/967900.968124.

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Reports on the topic "Agent mining"

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Kozachenko, Nadiia. Artificial relevance as a way to strengthen an argument : presentation. Department of Philosophy, April 2022. http://dx.doi.org/10.31812/123456789/6686.

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The justification chains supporting the argument descend to beliefs that were not explicated at the start of the argumentation. They are present in the agent`s view, but they are not spoken out and are sometimes not realized. These additional beliefs are not explicitly relevant to the thesis of the argument, but they are taken out of necessity, in the process of argument mining.
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Cytryn, Eddie, Mark R. Liles, and Omer Frenkel. Mining multidrug-resistant desert soil bacteria for biocontrol activity and biologically-active compounds. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598174.bard.

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Control of agro-associated pathogens is becoming increasingly difficult due to increased resistance and mounting restrictions on chemical pesticides and antibiotics. Likewise, in veterinary and human environments, there is increasing resistance of pathogens to currently available antibiotics requiring discovery of novel antibiotic compounds. These drawbacks necessitate discovery and application of microorganisms that can be used as biocontrol agents (BCAs) and the isolation of novel biologically-active compounds. This highly-synergistic one year project implemented an innovative pipeline aimed at detecting BCAs and associated biologically-active compounds, which included: (A) isolation of multidrug-resistant desert soil bacteria and root-associated bacteria from medicinal plants; (B) invitro screening of bacterial isolates against known plant, animal and human pathogens; (C) nextgeneration sequencing of isolates that displayed antagonistic activity against at least one of the model pathogens and (D) in-planta screening of promising BCAs in a model bean-Sclerotiumrolfsii system. The BCA genome data were examined for presence of: i) secondary metabolite encoding genes potentially linked to the anti-pathogenic activity of the isolates; and ii) rhizosphere competence-associated genes, associated with the capacity of microorganisms to successfully inhabit plant roots, and a prerequisite for the success of a soil amended BCA. Altogether, 56 phylogenetically-diverse isolates with bioactivity against bacterial, oomycete and fungal plant pathogens were identified. These strains were sent to Auburn University where bioassays against a panel of animal and human pathogens (including multi-drug resistant pathogenic strains such as A. baumannii 3806) were conducted. Nineteen isolates that showed substantial antagonistic activity against at least one of the screened pathogens were sequenced, assembled and subjected to bioinformatics analyses aimed at identifying secondary metabolite-encoding and rhizosphere competence-associated genes. The genome size of the bacteria ranged from 3.77 to 9.85 Mbp. All of the genomes were characterized by a plethora of secondary metabolite encoding genes including non-ribosomal peptide synthase, polyketidesynthases, lantipeptides, bacteriocins, terpenes and siderophores. While some of these genes were highly similar to documented genes, many were unique and therefore may encode for novel antagonistic compounds. Comparative genomic analysis of root-associated isolates with similar strains not isolated from root environments revealed genes encoding for several rhizospherecompetence- associated traits including urea utilization, chitin degradation, plant cell polymerdegradation, biofilm formation, mechanisms for iron, phosphorus and sulfur acquisition and antibiotic resistance. Our labs are currently writing a continuation of this feasibility study that proposes a unique pipeline for the detection of BCAs and biopesticides that can be used against phytopathogens. It will combine i) metabolomic screening of strains from our collection that contain unique secondary metabolite-encoding genes, in order to isolate novel antimicrobial compounds; ii) model plant-based experiments to assess the antagonistic capacities of selected BCAs toward selected phytopathogens; and iii) an innovative next-generation-sequencing based method to monitor the relative abundance and distribution of selected BCAs in field experiments in order to assess their persistence in natural agro-environments. We believe that this integrated approach will enable development of novel strains and compounds that can be used in large-scale operations.
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Thomashow, Linda, Leonid Chernin, Ilan Chet, David M. Weller, and Dmitri Mavrodi. Genetically Engineered Microbial Agents for Biocontrol of Plant Fungal Diseases. United States Department of Agriculture, 2005. http://dx.doi.org/10.32747/2005.7696521.bard.

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The objectives of the project were: a) to construct the site-specific integrative expression cassettes carrying: (i) the chiA gene for a 58-kDa endochitinase, (ii) the pyrrolnitrin biosynthesis operon, and (iii) the acdS gene encoding ACC deaminase; b) to employ these constructs to engineer stable recombinant strains with an expanded repertoire of beneficial activities; c) to evaluate the rhizosphere competence and antifungal activity of the WT and modified strains against pathogenic fungi under laboratory and greenhouse conditions; and d) to monitor the persistence and impact of the introduced strains on culturable and nonculturable rhizosphere microbial populations in the greenhouse and the field. The research generally support our concepts that combining strategically selected genes conferring diverse modes of action against plant pathogens into one organism can improve the efficacy of biological control agents. We hypothesized that biocontrol agents (BCAs) engineered to expand their repertoire of beneficial activities will more effectively control soilborne plant pathogens. In this work, we demonstrated that biocontrol activity of Pseudomonas fluorescens Q8r1-96 and Q2-87, both producing the antibiotic 2,4-diacetylphloroglucinol (2,4-DAPG) effective against the plant pathogenic fungus Rhizoctonia solani, can be improved significantly by introducing and expressing either the 1.6-kb gene chiA, encoding the 58-kDa endochitinase ChiA from the rhizosphere strain SerratiaplymuthicaIC1270, or the 5.8-kb prnABCDoperon encoding the broad-range antibiotic pyrrolnitrin (Prn) from another rhizosphere strain, P. fluorescens Pf-5. The PₜₐcchiAandPₜₐcprnABCDcassettes were cloned into the integrative pBK-miniTn7-ΩGm plasmid, and inserted into the genomic DNA of the recipient bacteria. Recombinant derivatives of strains Q8r1-96 and Q2-87 expressing the PₜₐcchiA or PₜₐcprnABCD cassettes produced endochitinase ChiA, or Prn, respectively, in addition to 2,4-DAPG, and the recombinants gave significantly better biocontrol of R. solani on beans under greenhouse conditions. The disease reduction index increased in comparison to the parental strains Q8r1-96 and Q2-87 to 17.5 and 39.0% from 3.2 and 12.4%, respectively, in the case of derivatives carrying the PₜₐcchiAcassette and to 63.1 and 70% vs. 2.8 and 12,4%, respectively, in the case of derivatives carrying the PₜₐcprnABCDcassette. The genetically modified strains exhibited persistence and non-target effects comparable to those of the parental strains in greenhouse soil. Three integrative cassettes carrying the acdS gene encoding ACC deaminase cloned under the control of different promoters were constructed and tested for enhancement of plant growth promotion by biocontrol strains of P. fluorescens and S. plymuthica. The integrative cassettes constructed in this work are already being used as a simple and efficient tool to improve biocontrol activity of various PGPR bacteria against fungi containing chitin in the cell walls or highly sensitive to Prn. Some parts of the work (e. g., construction of integrative cassettes) was collaborative while other parts e.g., (enzyme and antibiotic activity analyses) were fully synergistic. The US partners isolated and provided to the Israeli collaborators the original biocontrol strains P. fluorescens strains Q8r1-96 and Q2-87 and their mutants deficient in 2,4-DAPG production, which were used to evaluate the relative importance of introduction of Prn, chitinase or ACC deaminase genes for improvement of the biocontrol activity of the parental strains. The recombinant strains obtained at HUJI were supplied to the US collaborators for further analysis.
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