{"title":"Network Learning and Training of a Cascaded Link-Based Feed Forward Neural Network (CLBFFNN) in an Intelligent Trimodal Biometric System","authors":"Benson-Emenike Mercy E, Ifeanyi-Reuben Nkechi J","doi":"10.2139/ssrn.3425859","DOIUrl":"https://doi.org/10.2139/ssrn.3425859","url":null,"abstract":"","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43886772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris biometric data. It gives an overview of neural networks
{"title":"Network Learning and Training of a Cascaded Link-Based Feed Forward Neural Network (CLBFFNN) in an Intelligent Trimodal Biometric System","authors":"E. Benson-EmenikeMercy, J. Ifeanyi-ReubenNkechi","doi":"10.5121/IJAIA.2018.9603","DOIUrl":"https://doi.org/10.5121/IJAIA.2018.9603","url":null,"abstract":"Presently, considering the technological advancement of our modern world, we are in dire need for a\u0000system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and\u0000intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris\u0000biometric data. It gives an overview of neural networks","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"9 1","pages":"27-47"},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2018.9603","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49144194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-06DOI: 10.5121/ijaia.2021.12606
Kieran R. C. Greer
This paper presents a batch classifier that splits a dataset into tree branches depending on the category type. It has been improved from the earlier version and fixed a mistake in the earlier paper. Two important changes have been made. The first is to represent each category with a separate classifier. Each classifier then classifies its own subset of data rows, using batch input values to create the centroid and also represent the category itself. If the classifier contains data from more than one category however, it needs to create new classifiers for the incorrect data. The second change therefore is to allow the classifier to branch to new layers when there is a split in the data, and create new classifiers there for the data rows that are incorrectly classified. Each layer can therefore branch like a tree - not for distinguishing features, but for distinguishing categories. The paper then suggests a further innovation, which is to represent some data columns with fixed value ranges, or bands. When considering features, it is shown that some of the data can be classified directly through fixed value ranges, while the rest must be classified using a classifier technique and the idea allows the paper to discuss a biological analogy with neurons and neuron links. Tests show that the method can successfully classify a diverse set of benchmark datasets to better than the state-of-the-art.
{"title":"Category Trees - Classifiers that Branch on Category","authors":"Kieran R. C. Greer","doi":"10.5121/ijaia.2021.12606","DOIUrl":"https://doi.org/10.5121/ijaia.2021.12606","url":null,"abstract":"This paper presents a batch classifier that splits a dataset into tree branches depending on the category type. It has been improved from the earlier version and fixed a mistake in the earlier paper. Two important changes have been made. The first is to represent each category with a separate classifier. Each classifier then classifies its own subset of data rows, using batch input values to create the centroid and also represent the category itself. If the classifier contains data from more than one category however, it needs to create new classifiers for the incorrect data. The second change therefore is to allow the classifier to branch to new layers when there is a split in the data, and create new classifiers there for the data rows that are incorrectly classified. Each layer can therefore branch like a tree - not for distinguishing features, but for distinguishing categories. The paper then suggests a further innovation, which is to represent some data columns with fixed value ranges, or bands. When considering features, it is shown that some of the data can be classified directly through fixed value ranges, while the rest must be classified using a classifier technique and the idea allows the paper to discuss a biological analogy with neurons and neuron links. Tests show that the method can successfully classify a diverse set of benchmark datasets to better than the state-of-the-art.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42157765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Attention mechanism in recurrent neural networks has been widely used in natural language processing. In this paper, the research team explore a new mechanism of extending output attention in recurrent neural networks for dialog systems. The new attention method was compared with the current method in generating dialog sentence using a real dataset. Our architecture exhibits several attractive properties such as better handle long sequences and, it could generate more reasonable replies in many cases.
{"title":"Extending Output Attentions in Recurrent Neural Networks for Dialog Generation","authors":"Chan Lee","doi":"10.5121/IJAIA.2018.9504","DOIUrl":"https://doi.org/10.5121/IJAIA.2018.9504","url":null,"abstract":"Attention mechanism in recurrent neural networks has been widely used in natural language processing. In this paper, the research team explore a new mechanism of extending output attention in recurrent neural networks for dialog systems. The new attention method was compared with the current method in generating dialog sentence using a real dataset. Our architecture exhibits several attractive properties such as better handle long sequences and, it could generate more reasonable replies in many cases.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45733327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The objective of this work is to enhance the cognitive abilities of an agent robot (ARb), a model of a human. This article considers a new approach to ARb simulation, consisting of the utilization of a direct analogy between the functions of specific organs of the human brain/head and the "brain/head" of a model, between the life of a human and the "life" of a model. The model of a Homo Sapiens is constructed as an intellectual agent integrated with a humanoid robot. The task set is to construct an ARb that must be able to read and write, to understand its situation in the world, the meaning of its actions, and the semantics of the text it processes. An ARb achieves the high cognitive abilities mentioned above because, like a human, from the moment of its "birth" it learns a natural language, first and foremost such as the names of objects and phenomena it sees and recognises. It is proposed that an ARb will be taught, including language teaching, in a group of both fellow robots and humans, under the tutelage of a teacher. The growth of the cognitive abilities of an ARb is also achieved thanks to the evolution of a "population of reproducing agents" (our article [1]). For "reproduction", it is proposed that we should separate the "private life" of an ARb from its operating functions ("service"), i.e. to separate the "private" and the "service" spheres in the ARb software.
{"title":"Enhancement of Cognitive Abilities of an Agent-Robot on the Basis of Image Recognition and Sound Perception","authors":"Wladimir Stalski","doi":"10.5121/IJAIA.2018.9501","DOIUrl":"https://doi.org/10.5121/IJAIA.2018.9501","url":null,"abstract":"The objective of this work is to enhance the cognitive abilities of an agent robot (ARb), a model of a human. This article considers a new approach to ARb simulation, consisting of the utilization of a direct analogy between the functions of specific organs of the human brain/head and the \"brain/head\" of a model, between the life of a human and the \"life\" of a model. The model of a Homo Sapiens is constructed as an intellectual agent integrated with a humanoid robot. The task set is to construct an ARb that must be able to read and write, to understand its situation in the world, the meaning of its actions, and the semantics of the text it processes. An ARb achieves the high cognitive abilities mentioned above because, like a human, from the moment of its \"birth\" it learns a natural language, first and foremost such as the names of objects and phenomena it sees and recognises. It is proposed that an ARb will be taught, including language teaching, in a group of both fellow robots and humans, under the tutelage of a teacher. The growth of the cognitive abilities of an ARb is also achieved thanks to the evolution of a \"population of reproducing agents\" (our article [1]). For \"reproduction\", it is proposed that we should separate the \"private life\" of an ARb from its operating functions (\"service\"), i.e. to separate the \"private\" and the \"service\" spheres in the ARb software.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2018.9501","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70613377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Massaro, A. Calicchio, Vincenzo Maritati, A. Galiano, V. Birardi, L. Pellicani, Maria Gutierrez Millan, Barbara Dalla Tezza, Mauro Bianchi, Guido Vertua, Antonello Puggioni
In this paper, a case study is analyzed. This case study is about an upgrade of an industry communication system developed by following Frascati research guidelines. The knowledge Base (KB) of the industry is gained by means of different tools that are able to provide data and information having different formats and structures into an unique bus system connected to a Big Data. The initial part of the research is focused on the implementation of strategic tools, which can able to upgrade the KB. The second part of the proposed study is related to the implementation of innovative algorithms based on a KNIME (Konstanz Information Miner) Gradient Boosted Trees workflow processing data of the communication system which travel into an Enterprise Service Bus (ESB) infrastructure. The goal of the paper is to prove that all the new KB collected into a Cassandra big data system could be processed through the ESB by predictive algorithms solving possible conflicts between hardware and software. The conflicts are due to the integration of different database technologies and data structures. In order to check the outputs of the Gradient Boosted Trees algorithm an experimental dataset suitable for machine learning testing has been tested. The test has been performed on a prototype network system modeling a part of the whole communication system. The paper shows how to validate industrial research by following a complete design and development of a whole communication system network improving business intelligence (BI).
本文以一个案例进行分析。本案例研究是关于按照Frascati研究指南开发的工业通信系统的升级。行业知识库(KB)是通过不同的工具获得的，这些工具能够将不同格式和结构的数据和信息提供到连接到大数据的独特总线系统中。研究的最初部分侧重于战略工具的实施，这些工具能够升级知识库。本研究的第二部分涉及基于KNIME (Konstanz Information Miner)梯度提升树工作流的创新算法的实现，该工作流处理传输到企业服务总线(ESB)基础设施的通信系统数据。本文的目标是证明所有收集到Cassandra大数据系统中的新知识库都可以通过ESB进行处理，并通过预测算法解决硬件和软件之间可能存在的冲突。冲突是由于不同的数据库技术和数据结构的集成。为了检验梯度增强树算法的输出，对一个适合机器学习测试的实验数据集进行了测试。在一个原型网络系统上进行了测试，该系统是整个通信系统的一部分。本文展示了如何通过跟踪整个通信系统网络的完整设计和开发来验证工业研究，从而提高商业智能(BI)。
{"title":"A Case Study of Innovation of an Information Communication System and Upgrade of the Knowledge Base in Industry by ESB, Artificial Intelligence, and Big Data System Integration","authors":"A. Massaro, A. Calicchio, Vincenzo Maritati, A. Galiano, V. Birardi, L. Pellicani, Maria Gutierrez Millan, Barbara Dalla Tezza, Mauro Bianchi, Guido Vertua, Antonello Puggioni","doi":"10.5121/IJAIA.2018.9503","DOIUrl":"https://doi.org/10.5121/IJAIA.2018.9503","url":null,"abstract":"In this paper, a case study is analyzed. This case study is about an upgrade of an industry communication system developed by following Frascati research guidelines. The knowledge Base (KB) of the industry is gained by means of different tools that are able to provide data and information having different formats and structures into an unique bus system connected to a Big Data. The initial part of the research is focused on the implementation of strategic tools, which can able to upgrade the KB. The second part of the proposed study is related to the implementation of innovative algorithms based on a KNIME (Konstanz Information Miner) Gradient Boosted Trees workflow processing data of the communication system which travel into an Enterprise Service Bus (ESB) infrastructure. The goal of the paper is to prove that all the new KB collected into a Cassandra big data system could be processed through the ESB by predictive algorithms solving possible conflicts between hardware and software. The conflicts are due to the integration of different database technologies and data structures. In order to check the outputs of the Gradient Boosted Trees algorithm an experimental dataset suitable for machine learning testing has been tested. The test has been performed on a prototype network system modeling a part of the whole communication system. The paper shows how to validate industrial research by following a complete design and development of a whole communication system network improving business intelligence (BI).","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47420788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASA’s turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods.
{"title":"A Novel Scheme for Accurate Remaining Useful Life Prediction for Industrial IoTs by Using Deep Neural Network","authors":"Abdurrahman Pektas, ElifNurdan Pektas","doi":"10.5121/IJAIA.2018.9502","DOIUrl":"https://doi.org/10.5121/IJAIA.2018.9502","url":null,"abstract":"In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASA’s turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47816272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The goal of this research project is to design and implement a mobile application and machine learning techniques to solve problems related to the security of mobile devices. We introduce in this paper a behavior-based approach that can be applied in a mobile environment to capture and learn the behavior of mobile users. The proposed system was tested using Android OS and the initial experimental results show that the proposed technique is promising, and it can be used effectively to solve the problem of anomaly detection in mobile devices.
{"title":"Behavior-Based Security for Mobile Devices Using Machine Learning Techniques","authors":"S. Rashad, Jonathan M. R. Byrd","doi":"10.5121/IJAIA.2018.9401","DOIUrl":"https://doi.org/10.5121/IJAIA.2018.9401","url":null,"abstract":"The goal of this research project is to design and implement a mobile application and machine learning techniques to solve problems related to the security of mobile devices. We introduce in this paper a behavior-based approach that can be applied in a mobile environment to capture and learn the behavior of mobile users. The proposed system was tested using Android OS and the initial experimental results show that the proposed technique is promising, and it can be used effectively to solve the problem of anomaly detection in mobile devices.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42230771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The performance of an aircraft can be improved by predicting the possible complications associated with the system. Prognostics and Health Management (PHM) methodology includes fault detection, diagnosis, and prognosis. In this paper, a comparison of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Artificial Neural Network (ANN) based fault prognosis tool for a typical aircraft fuel system is proposed. The ANFIS is an expert system which works on logical rules. The inputs of both ANFIS and ANN are trained by considering the same input data and generate the corresponding control signal. These methods identify the presence of faults and mitigate them to maintain a proper fuel flow to the engine. Overlooking the presence of any faults in time could potentially be catastrophic which can lead to possible loss of lives and the aircraft as well. These proposed tools work on the logical rules developed as per the engine’s fuel consumption and quantity of fuel flow from the tanks. The results are compared and analyzed which demonstrate the superiority of ANFIS tool compared to ANN.
{"title":"Comparison of ANFIS and ANN Techniques in the Simulation of a Typical Aircraft Fuel System Health Management","authors":"Vijaylakshmi S. Jigajinni, V. Upendranath","doi":"10.5121/ijaia.2018.9404","DOIUrl":"https://doi.org/10.5121/ijaia.2018.9404","url":null,"abstract":"The performance of an aircraft can be improved by predicting the possible complications associated with the system. Prognostics and Health Management (PHM) methodology includes fault detection, diagnosis, and prognosis. In this paper, a comparison of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Artificial Neural Network (ANN) based fault prognosis tool for a typical aircraft fuel system is proposed. The ANFIS is an expert system which works on logical rules. The inputs of both ANFIS and ANN are trained by considering the same input data and generate the corresponding control signal. These methods identify the presence of faults and mitigate them to maintain a proper fuel flow to the engine. Overlooking the presence of any faults in time could potentially be catastrophic which can lead to possible loss of lives and the aircraft as well. These proposed tools work on the logical rules developed as per the engine’s fuel consumption and quantity of fuel flow from the tanks. The results are compared and analyzed which demonstrate the superiority of ANFIS tool compared to ANN.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49327514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Massaro, Vincenzo Maritati, A. Galiano, V. Birardi, L. Pellicani
In this paper are discussed some results related to an industrial project oriented on the integration of data mining tools into Enterprise Service Bus (ESB) platform. WSO2 ESB has been implemented for data transaction and to interface a client web service connected to a KNIME workflow behaving as a flexible data mining engine. In order to validate the implementation two test have been performed: the first one is related to the data management of two relational database management system (RDBMS) merged into one database whose data have been processed by KNIME dashboard statistical tool thus proving the data transfer of the prototype system; the second one is related to a simulation of two sensor data belonging to two distinct production lines connected to the same ESB. Specifically in the second example has been developed a practical case by processing by a Multilayered Perceptron (MLP) neural networks the temperatures of two milk production lines and by providing information about predictive maintenance. The platform prototype system is suitable for data automatism and Internet of Thing (IoT) related to Industry 4.0, and it is suitable for innovative hybrid system embedding different hardware and software technologies integrated with ESB, data mining engine and client web-services.
{"title":"ESB Platform Integrating Knime Data Mining Tool Oriented on Industry 4.0 Based on Artificial Neural Network Predictive Maintenance","authors":"A. Massaro, Vincenzo Maritati, A. Galiano, V. Birardi, L. Pellicani","doi":"10.5121/IJAIA.2018.9301","DOIUrl":"https://doi.org/10.5121/IJAIA.2018.9301","url":null,"abstract":"In this paper are discussed some results related to an industrial project oriented on the integration of data mining tools into Enterprise Service Bus (ESB) platform. WSO2 ESB has been implemented for data transaction and to interface a client web service connected to a KNIME workflow behaving as a flexible data mining engine. In order to validate the implementation two test have been performed: the first one is related to the data management of two relational database management system (RDBMS) merged into one database whose data have been processed by KNIME dashboard statistical tool thus proving the data transfer of the prototype system; the second one is related to a simulation of two sensor data belonging to two distinct production lines connected to the same ESB. Specifically in the second example has been developed a practical case by processing by a Multilayered Perceptron (MLP) neural networks the temperatures of two milk production lines and by providing information about predictive maintenance. The platform prototype system is suitable for data automatism and Internet of Thing (IoT) related to Industry 4.0, and it is suitable for innovative hybrid system embedding different hardware and software technologies integrated with ESB, data mining engine and client web-services.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2018.9301","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42239832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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