scholarly journals Multiagent System for Mutual Collaboration Classification for Cancer Detection

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Mais Haj Qasem ◽  
Amjad Hudaib ◽  
Nadim Obeid
Keyword(s):  
Cancer Detection ◽  
Autonomous Agents ◽  
Multiagent System ◽  
Distributed Data ◽  
Distributed Clustering ◽  
Classification Tasks ◽  
Communication And Coordination ◽  
Prediction Techniques ◽  
Distributed Classification ◽  
And Behavior

A multiagent system (MAS) is a mechanism for creating goal-oriented autonomous agents in shared environments with communication and coordination facilities. Distributed data mining benefits from this goal-oriented mechanism by implementing various distributed clustering, classification, and prediction techniques. Hence, this study developed a novel multiagent model for distributed classification tasks in cancer detection with the collaboration of several hospitals worldwide using different classifier algorithms. A hospital agent requests help from other agents for instances that are difficult to classify locally. The agents communicate their beliefs (calculated classification), and others decide on the benefit of using such beliefs in classifying instances and adjusting their prior assumptions on each class of data. A MAS model state and behavior and communication are then developed to facilitate information sharing among agents. Regarding accuracy, implementing the proposed approach in comparison with typically different noncommunicated distributed classifications shows that sharable information considerably increases the classification task accuracy by 25.77%.

scholarly journals A Survey on Job Scheduling in Big Data

2016 ◽  
Vol 16 (3) ◽  
pp. 35-51 ◽  
Author(s):  
M. Senthilkumar ◽  
P. Ilango
Keyword(s):  
Big Data ◽  
Job Scheduling ◽  
Research Area ◽  
Data Locality ◽  
Distributed Data ◽  
Distributed Data Processing ◽  
Big Data Applications ◽  
Mapreduce Model ◽  
Active Research ◽  
And Behavior

Abstract Big Data Applications with Scheduling becomes an active research area in last three years. The Hadoop framework becomes very popular and most used frameworks in a distributed data processing. Hadoop is also open source software that allows the user to effectively utilize the hardware. Various scheduling algorithms of the MapReduce model using Hadoop vary with design and behavior, and are used for handling many issues like data locality, awareness with resource, energy and time. This paper gives the outline of job scheduling, classification of the scheduler, and comparison of different existing algorithms with advantages, drawbacks, limitations. In this paper, we discussed various tools and frameworks used for monitoring and the ways to improve the performance in MapReduce. This paper helps the beginners and researchers in understanding the scheduling mechanisms used in Big Data.

scholarly journals Bioentity2vec: Attribute- and behavior-driven representation for predicting multi-type relationships between bioentities

GigaScience ◽  
2020 ◽  
Vol 9 (6) ◽  
Author(s):  
Zhen-Hao Guo ◽  
Zhu-Hong You ◽  
Yan-Bin Wang ◽  
De-Shuang Huang ◽  
Hai-Cheng Yi ◽  
...  
Keyword(s):  
Computational Models ◽  
Molecular Level ◽  
Area Under The Curve ◽  
Biological Information ◽  
Precision Recall Curve ◽  
Classification Tasks ◽  
Systematic Understanding ◽  
Biological Entities ◽  
And Behavior ◽  
Recall Curve

Abstract Background The explosive growth of genomic, chemical, and pathological data provides new opportunities and challenges for humans to thoroughly understand life activities in cells. However, there exist few computational models that aggregate various bioentities to comprehensively reveal the physical and functional landscape of biological systems. Results We constructed a molecular association network, which contains 18 edges (relationships) between 8 nodes (bioentities). Based on this, we propose Bioentity2vec, a new method for representing bioentities, which integrates information about the attributes and behaviors of a bioentity. Applying the random forest classifier, we achieved promising performance on 18 relationships, with an area under the curve of 0.9608 and an area under the precision-recall curve of 0.9572. Conclusions Our study shows that constructing a network with rich topological and biological information is important for systematic understanding of the biological landscape at the molecular level. Our results show that Bioentity2vec can effectively represent biological entities and provides easily distinguishable information about classification tasks. Our method is also able to simultaneously predict relationships between single types and multiple types, which will accelerate progress in biological experimental research and industrial product development.

scholarly journals Double Deep Autoencoder for Heterogeneous Distributed Clustering

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 144
Author(s):  
Chen ◽  
Huang
Keyword(s):  
Data Mining ◽  
Clustering Algorithms ◽  
Distributed Data Mining ◽  
Distributed Data ◽  
Distributed Environments ◽  
Distributed Clustering ◽  
Clustering Problem ◽  
Accuracy Index ◽  
Heterogeneous Datasets ◽  
Homogeneous Data

Given the issues relating to big data and privacy-preserving challenges, distributed data mining (DDM) has received much attention recently. Here, we focus on the clustering problem of distributed environments. Several distributed clustering algorithms have been proposed to solve this problem, however, previous studies have mainly considered homogeneous data. In this paper, we develop a double deep autoencoder structure for clustering in distributed and heterogeneous datasets. Three datasets are used to demonstrate the proposed algorithms, and show their usefulness according to the consistent accuracy index.

scholarly journals On the use of agent technology in intelligent, multisensory and distributed surveillance

2011 ◽  
Vol 26 (2) ◽  
pp. 191-208 ◽  
Author(s):  
José M. Gascueña ◽  
Antonio Fernández-Caballero
Keyword(s):  
Multiagent Systems ◽  
State Of The Art ◽  
Good Choice ◽  
Agent Technology ◽  
Surveillance Systems ◽  
Distributed Data ◽  
Distributed Data Management ◽  
Social Ability ◽  
Basic Characteristics ◽  
Communication And Coordination

AbstractThis article revises the state of the art of the application of agent technology within the scope of surveillance systems. Thus, the potential of the practical use of the concepts and technologies of the agent paradigm can be identified and evaluated in this domain. Current surveillance systems are noted for using several devices, heterogeneous in many instances, distributed along the observed scenario, while incorporating a certain degree of intelligence to alert the operator proactively to what is going on in the observed scenario and prevent the operator from having to observe the monitors continuously. The basic characteristics of the agents (autonomy, reactivity, proactiveness and social ability), along with multiagent systems’ characteristics (distributed data management, low coupling, robustness, communication and coordination between autonomous entities), suggest that the agency is a good choice for solving problems which appear and are dealt with in surveillance systems.

scholarly journals An Approach to Model Based Testing of Multiagent Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Shafiq Ur Rehman ◽  
Aamer Nadeem
Keyword(s):  
Multiagent Systems ◽  
Autonomous Agents ◽  
Multiagent System ◽  
Dynamic Environment ◽  
Proactive Behavior ◽  
Prototype Tool ◽  
Novel Approach ◽  
Model Based Testing ◽  
Coverage Criterion ◽  
Prometheus Methodology

Autonomous agents perform on behalf of the user to achieve defined goals or objectives. They are situated in dynamic environment and are able to operate autonomously to achieve their goals. In a multiagent system, agents cooperate with each other to achieve a common goal. Testing of multiagent systems is a challenging task due to the autonomous and proactive behavior of agents. However, testing is required to build confidence into the working of a multiagent system. Prometheus methodology is a commonly used approach to design multiagents systems. Systematic and thorough testing of each interaction is necessary. This paper proposes a novel approach to testing of multiagent systems based on Prometheus design artifacts. In the proposed approach, different interactions between the agent and actors are considered to test the multiagent system. These interactions include percepts and actions along with messages between the agents which can be modeled in a protocol diagram. The protocol diagram is converted into a protocol graph, on which different coverage criteria are applied to generate test paths that cover interactions between the agents. A prototype tool has been developed to generate test paths from protocol graph according to the specified coverage criterion.

scholarly journals The Natural Philosophy of Economic Information: Autonomous Agents and Physiosemiosis

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 277
Author(s):  
Carsten Herrmann-Pillath
Keyword(s):  
Information Processing ◽  
Autonomous Agents ◽  
Natural Philosophy ◽  
Economic Information ◽  
Heat Engines ◽  
Recent Developments ◽  
Evolutionary View ◽  
And Behavior ◽  
The Brain ◽  
Evolutionary Interpretation

Information is a core concept in modern economics, yet its definition and empirical specification is elusive. One reason is the intellectual grip of the Shannon paradigm which marginalizes semantic information. However, a precise concept of economic information must be based on a theory of semantics, since what counts economically is the meaning, function and use of information. This paper introduces a new principled approach to information that adopts the paradigm of biosemiotics, rooted in the philosophy of Charles S. Peirce and builds on recent developments of the thermodynamics of information. Information processing by autonomous agents, defined as autopoietic heat engines, is conceived as physiosemiosis operating according to fundamental thermodynamic principles of information processing, as elucidated in recent work by Kolchinsky and Wolpert (KW). I plug the KW approach into a basic conceptual model of physiosemiosis and present an evolutionary interpretation. This approach has far-reaching implications for economics, such as suggesting an evolutionary view of the economic agent, choice and behavior, which is informed by applications of statistical thermodynamics on the brain.

scholarly journals Coordination of Multiple Autonomous Agents Using Naturally Generated Languages in Task Planning

2019 ◽  
Vol 9 (17) ◽  
pp. 3571
Author(s):  
Li Wang ◽  
Qiao Guo
Keyword(s):  
Computational Linguistics ◽  
Autonomous Agents ◽  
Evolutionary Games ◽  
Task Planning ◽  
Human Beings ◽  
Language Generation ◽  
Sufficient And Necessary Conditions ◽  
State Abstraction ◽  
And Behavior ◽  
Generation Problem

Language plays a prominent role in the activities of human beings and other intelligent creatures. One of the most important functions of languages is communication. Inspired by this, we attempt to develop a novel language for cooperation between artificial agents. The language generation problem has been studied earlier in the context of evolutionary games in computational linguistics. In this paper, we take a different approach by formulating it in the computational model of rationality in a multi-agent planning setting. This paper includes three main parts: First, we present a language generation problem that is connected to state abstraction and introduce a few of the languages’ properties. Second, we give the sufficient and necessary conditions of a valid abstraction with proofs and develop an efficient algorithm to construct the languages where several words are generated naturally. The sentences composed of words can be used by agents to regulate their behaviors during task planning. Finally, we conduct several experiments to evaluate the benefits of the languages in a variety of scenarios of a path-planning domain. The empirical results demonstrate that our languages lead to reduction in communication cost and behavior restriction.

scholarly journals Semantic Distributed Data for Vehicular Networks Using the Inter-Planetary File System

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6404
Author(s):  
Victor Ortega ◽  
Jose F. Monserrat
Keyword(s):  
Vehicular Networks ◽  
Autonomous Agents ◽  
File System ◽  
Distributed Storage ◽  
Intelligent Vehicles ◽  
Distributed Data ◽  
Distributed Network ◽  
Fast System ◽  
Remote Sensors ◽  
Shared Data

Vehicular networks provide means to distribute data among intelligent vehicles, increasing their efficiency and the safety of their occupants. While connected to these networks, vehicles have access to various kinds of information shared by other vehicles and road-side units (RSUs). This information includes helpful resources, such as traffic state or remote sensors. An efficient and fast system to get access to this information is important but unproductive if the data are not appropriately structured, accessible, and easy to process. This paper proposes the creation of a semantic distributed network using content-addressed networking and peer-to-peer (P2P) connections. In this open and collaborative network, RSUs and vehicles use ontologies to semantically represent information and facilitate the development of intelligent autonomous agents capable of navigating and processing the shared data. In order to create this P2P network, this paper makes use of the Inter-Planetary File System (IPFS), an open source solution that provides secure, reliable, and efficient content-addressed distributed storage over standard IP networks using the new QUIC protocol. This paper highlights the feasibility of this proposal and compares it with the state-of-the-art. Results show that IPFS is a promising technology that offers a great balance between functionality, performance, and security.

AUTONOMY-ORIENTED SOCIAL NETWORKS MODELING: DISCOVERING THE DYNAMICS OF EMERGENT STRUCTURE AND PERFORMANCE

2007 ◽  
Vol 21 (04) ◽  
pp. 611-638 ◽  
Author(s):  
SHIWU ZHANG ◽  
JIMING LIU
Keyword(s):  
Social Network ◽  
Autonomous Agents ◽  
Multiagent System ◽  
Clustering Coefficient ◽  
Local Interaction ◽  
Selection Strategies ◽  
Real World Applications ◽  
Emergent Structure ◽  
And Performance ◽  
Dynamic Relationships

A social network is composed of social individuals and their relationships. In many real-world applications, such a network will evolve dynamically over time and events. A social network can be naturally viewed as a multiagent system if considering locally-interacting social individuals as autonomous agents. In this paper, we present an Autonomy-Oriented Computing (AOC) based model of a social network, and study the dynamics of the network based on this model. In the AOC model, the profile of agents, service-based interactions, and the evolution of the network are defined, and the autonomy of the agents is emphasized. The model can reveal dynamic relationships among global performance, local interaction (partner selection) strategies, and network topology. The experimental results show that the agent network forms a community with a high clustering coefficient, and the performance of the network is dynamically changing along with the formation of the network and the local interaction strategies of the agents. In this paper, the performance and topology of the agent network are analyzed, and the factors that affect the performance and evolution of the agent network are examined.

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