Pub Date : 2017-11-01DOI: 10.1109/ISKE.2017.8258755
Faisal Khurshid, Yan Zhu, Chubato Wondaferaw Yohannese, M. Iqbal
Online purchasing became an integral part of our lives in this digital era where E-commerce websites allow people to buy as well as share their experiences about products or services in the form of reviews. Customers as well as companies use these reviews for decision making. This facility helps people to derive their buying decisions whereas malicious users use this as their tool to promote or demote products or services intentionally. This phenomenon is called review spam. Review spam detection is the classification of reviews into malign or benign. Therefore, our aim is to evaluate performance of supervised machine learning algorithms for review spam detection based on different feature sets extracted from real life dataset instead of Amazon Mechanical Turkers (AMT) tailored dataset. We study various factors including Recall, Precision, and Receiver Operating Characteristic (ROC) through experimentation. AdaBoost outperforms all others with 0.83 precision and has correctly identified all spams whereas misclassified minuscule number of normal reviews.
{"title":"Recital of supervised learning on review spam detection: An empirical analysis","authors":"Faisal Khurshid, Yan Zhu, Chubato Wondaferaw Yohannese, M. Iqbal","doi":"10.1109/ISKE.2017.8258755","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258755","url":null,"abstract":"Online purchasing became an integral part of our lives in this digital era where E-commerce websites allow people to buy as well as share their experiences about products or services in the form of reviews. Customers as well as companies use these reviews for decision making. This facility helps people to derive their buying decisions whereas malicious users use this as their tool to promote or demote products or services intentionally. This phenomenon is called review spam. Review spam detection is the classification of reviews into malign or benign. Therefore, our aim is to evaluate performance of supervised machine learning algorithms for review spam detection based on different feature sets extracted from real life dataset instead of Amazon Mechanical Turkers (AMT) tailored dataset. We study various factors including Recall, Precision, and Receiver Operating Characteristic (ROC) through experimentation. AdaBoost outperforms all others with 0.83 precision and has correctly identified all spams whereas misclassified minuscule number of normal reviews.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131838964","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258754
A. Padmanabha, Abhishek M. Appaji, M. Prasad, H. Lu, Sudhanshu Joshi
Early, diagnosis is essential for diabetic patients to avoid partial or complete blindness. This work presents a new analysis method of texture features for classification of Diabetic Retinopathy (DR). The proposed method masks the blood vessels and optic disk segmented and directly extracts the textural features from the remaining retinal region. The proposed method is much simpler with comparison of the other methods that detect the defective regions first and then extract the required features for classification. The Haralick texture measures calculated are used for classification of DR. The proposed method is evaluated through a classification of DR using both Support Vector Machine (SVM) and Artificial Neural Network (ANN). The results of SVM have a better accuracy (87.5%) over ANN (79%). The performance of the proposed method is presented also in terms of sensitivity and specificity.
{"title":"Classification of diabetic retinopathy using textural features in retinal color fundus image","authors":"A. Padmanabha, Abhishek M. Appaji, M. Prasad, H. Lu, Sudhanshu Joshi","doi":"10.1109/ISKE.2017.8258754","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258754","url":null,"abstract":"Early, diagnosis is essential for diabetic patients to avoid partial or complete blindness. This work presents a new analysis method of texture features for classification of Diabetic Retinopathy (DR). The proposed method masks the blood vessels and optic disk segmented and directly extracts the textural features from the remaining retinal region. The proposed method is much simpler with comparison of the other methods that detect the defective regions first and then extract the required features for classification. The Haralick texture measures calculated are used for classification of DR. The proposed method is evaluated through a classification of DR using both Support Vector Machine (SVM) and Artificial Neural Network (ANN). The results of SVM have a better accuracy (87.5%) over ANN (79%). The performance of the proposed method is presented also in terms of sensitivity and specificity.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132491726","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258781
Weitao Xu
This paper extend the α—resolution principle based on classical logic system. An α-generalized resolution method is presented in Linguistic Truth-Valued lattice-valued propositional logic system based on linguistic truth-valued lattice implication algebra. Both soundness and weak completeness theorems for α-generalized resolution method are established in Linguistic Truth-Valued lattice-valued propositional logic system. The proposed approach provides a foundation for α—generalized resolution method under linguistic truth-valued level in a set of general generalized clauses.
{"title":"α-generalized resolution method based on linguistic truth-valued lattice-valued propositional logic system","authors":"Weitao Xu","doi":"10.1109/ISKE.2017.8258781","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258781","url":null,"abstract":"This paper extend the α—resolution principle based on classical logic system. An α-generalized resolution method is presented in Linguistic Truth-Valued lattice-valued propositional logic system based on linguistic truth-valued lattice implication algebra. Both soundness and weak completeness theorems for α-generalized resolution method are established in Linguistic Truth-Valued lattice-valued propositional logic system. The proposed approach provides a foundation for α—generalized resolution method under linguistic truth-valued level in a set of general generalized clauses.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125004358","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258783
Guanfeng Wu, Qingshan Chen, Feng Cao, Yang Xu, Xiaomei Zhong
SAT problem is the first proved NP-complete problems. Heuristic methods on solving the SAT problem although belongs to incomplete method, but it has its advantages. Genetic Algorithm (GA) as one of the heuristic algorithms, was applied to solve the SAT problem of many years, and also got some better results combine with other algorithms. However, there is still room for improvement. In this paper we combine GA with the Local Search Algorithm (LSA) and improve the sort algorithm. Using the Open MP to implement the Parallel Hybrid GA based on the Coarse-Grained Model (CGPHGA). This article describes the design and implementation of CGPHGA in detail, According to the experimental results, CGPHGA improves the success rate and efficiency.
{"title":"Parallel hybrid genetic algorithm for sat problems based on OpenMP","authors":"Guanfeng Wu, Qingshan Chen, Feng Cao, Yang Xu, Xiaomei Zhong","doi":"10.1109/ISKE.2017.8258783","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258783","url":null,"abstract":"SAT problem is the first proved NP-complete problems. Heuristic methods on solving the SAT problem although belongs to incomplete method, but it has its advantages. Genetic Algorithm (GA) as one of the heuristic algorithms, was applied to solve the SAT problem of many years, and also got some better results combine with other algorithms. However, there is still room for improvement. In this paper we combine GA with the Local Search Algorithm (LSA) and improve the sort algorithm. Using the Open MP to implement the Parallel Hybrid GA based on the Coarse-Grained Model (CGPHGA). This article describes the design and implementation of CGPHGA in detail, According to the experimental results, CGPHGA improves the success rate and efficiency.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122675991","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258812
Ity Kaul, É. Martin, V. Puri
Trend detection in financial temporal data is a significant problem, with far-reaching applications, that presents researchers with many challenges. Existing techniques require users to choose a given interval, and then provide an approximation of the data on that interval; they always produce some approximation, namely, a member of a class of candidate functions that is "best" according to some criteria. Moreover, financial analysis can be performed from different perspectives, at different levels, from short term to long term; it is therefore very desirable to be able to indicate a scale that is suitable and adapted to the analysis of interest. Based on these considerations, our objective was to design a method that lets users input a scale factor, determines the intervals on which an approximation captures a significant trend as a function of the scale factor, and proposes a qualification of the trend. The method we use combines various machine-learning and statistical techniques, a key role being played by a change-point detection method. We describe the architecture of a system that implements the proposed method. Finally, we report on the experiments we ran and use their results to stress how they differ from the results than can be obtained from alternative approaches.
{"title":"A model for the detection of underlying trends in temporal data","authors":"Ity Kaul, É. Martin, V. Puri","doi":"10.1109/ISKE.2017.8258812","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258812","url":null,"abstract":"Trend detection in financial temporal data is a significant problem, with far-reaching applications, that presents researchers with many challenges. Existing techniques require users to choose a given interval, and then provide an approximation of the data on that interval; they always produce some approximation, namely, a member of a class of candidate functions that is \"best\" according to some criteria. Moreover, financial analysis can be performed from different perspectives, at different levels, from short term to long term; it is therefore very desirable to be able to indicate a scale that is suitable and adapted to the analysis of interest. Based on these considerations, our objective was to design a method that lets users input a scale factor, determines the intervals on which an approximation captures a significant trend as a function of the scale factor, and proposes a qualification of the trend. The method we use combines various machine-learning and statistical techniques, a key role being played by a change-point detection method. We describe the architecture of a system that implements the proposed method. Finally, we report on the experiments we ran and use their results to stress how they differ from the results than can be obtained from alternative approaches.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128465793","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258792
Wided Ben Abid, M. Mhiri, M. B. Salem, Emna Bouazizi, F. Gargouri
Many solutions were proposed to store and load a large amount of semantic data which are generated from ontologies. These solutions have introduced a new database architecture called Ontology Based DataBases (OBDBs) which presents several drawbacks : it does not take into account the management of non-standard data, it does not consider the modeling of time for data, and it does not propose a specification and modeling of transactions. Based on these limits, the existed OBDBs architecture is not suitable for real-time applications. The real-time ontology, which is the result of temporal constraints integration on real-time context, has been introduced. In this paper, we propose the integration of a real-time ontology on databases. Currently, many real-time applications are geographically distributed, in this case, the distributed real-time DBMS are the best appropriate to be a suitable target for this integration. The performance study of integrating a real-time ontology on distributed real-time DBMS require the use of quality of service (QoS) based architectures which the feedback control scheduling technique is the best case. In this order, the Distributed Feedback Control Scheduling Architecture for Real-Time Ontology (DFCSA-RTO) is proposed.
{"title":"A feedback control scheduling architecture for real-time ontology","authors":"Wided Ben Abid, M. Mhiri, M. B. Salem, Emna Bouazizi, F. Gargouri","doi":"10.1109/ISKE.2017.8258792","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258792","url":null,"abstract":"Many solutions were proposed to store and load a large amount of semantic data which are generated from ontologies. These solutions have introduced a new database architecture called Ontology Based DataBases (OBDBs) which presents several drawbacks : it does not take into account the management of non-standard data, it does not consider the modeling of time for data, and it does not propose a specification and modeling of transactions. Based on these limits, the existed OBDBs architecture is not suitable for real-time applications. The real-time ontology, which is the result of temporal constraints integration on real-time context, has been introduced. In this paper, we propose the integration of a real-time ontology on databases. Currently, many real-time applications are geographically distributed, in this case, the distributed real-time DBMS are the best appropriate to be a suitable target for this integration. The performance study of integrating a real-time ontology on distributed real-time DBMS require the use of quality of service (QoS) based architectures which the feedback control scheduling technique is the best case. In this order, the Distributed Feedback Control Scheduling Architecture for Real-Time Ontology (DFCSA-RTO) is proposed.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121018259","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258762
Xiongtao Zhang, Xingguang Pan, Shitong Wang
Although Deep Belief Network (DBN) has been applied to a wide range of practical scenarios, i.e. image classification, signal recognition, remaining useful life estimation, on account of its powerful high classification accuracy, but it has impossible interpretation of functionality (it is desirable to have a high level of interpretability for users also). In this paper, we propose a novel fuzzy DBN system called TSK_DBN which combines DBN and TSK fuzzy system. Firstly, the fuzzy clustering algorithm FCM is used to divide the input space, and the membership function of the fuzzy rule is defined. Then, the implicit feature is created by DBN. Finally, the consequent parameters of the fuzzy rule are determined by LLM(Least Learning Machine). The TSK_DBN fuzzy system has an adaptive mechanism, which can automatically adjust the depth until the optimal accuracy is achieved. The prominent character of the TSK_DBN system is that there is adaptive mechanism to regulate the depth of DBN to get a high accuracy. Several benchmark datasets have been used to empirically evaluate the efficiency of the proposed TSK_DBN in handling pattern classification tasks. The results show that the accuracy rates of TSK_DBN are at least comparable (if not superior) to DBN system with distinctive ability in providing explicit knowledge in the form of high interpretable rule base.
{"title":"Fuzzy DBN with rule-based knowledge representation and high interpretability","authors":"Xiongtao Zhang, Xingguang Pan, Shitong Wang","doi":"10.1109/ISKE.2017.8258762","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258762","url":null,"abstract":"Although Deep Belief Network (DBN) has been applied to a wide range of practical scenarios, i.e. image classification, signal recognition, remaining useful life estimation, on account of its powerful high classification accuracy, but it has impossible interpretation of functionality (it is desirable to have a high level of interpretability for users also). In this paper, we propose a novel fuzzy DBN system called TSK_DBN which combines DBN and TSK fuzzy system. Firstly, the fuzzy clustering algorithm FCM is used to divide the input space, and the membership function of the fuzzy rule is defined. Then, the implicit feature is created by DBN. Finally, the consequent parameters of the fuzzy rule are determined by LLM(Least Learning Machine). The TSK_DBN fuzzy system has an adaptive mechanism, which can automatically adjust the depth until the optimal accuracy is achieved. The prominent character of the TSK_DBN system is that there is adaptive mechanism to regulate the depth of DBN to get a high accuracy. Several benchmark datasets have been used to empirically evaluate the efficiency of the proposed TSK_DBN in handling pattern classification tasks. The results show that the accuracy rates of TSK_DBN are at least comparable (if not superior) to DBN system with distinctive ability in providing explicit knowledge in the form of high interpretable rule base.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115389430","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258816
Binbin Xue, Lu Wang, K. Qin
This paper focuses on triple I inference method based on interval-valued fuzzy soft sets. Computational formulas for both interval-valued fuzzy soft modus ponens (IVF-SMP) and interval-valued fuzzy soft modus tollens (IVFSMT) with respect to left-continuous interval-valued t-norms and its residual interval-valued implication are presented. Besides, the reversibility property of triple I methods of IVFSMP and IVFSMT are analyzed.
{"title":"An interval-valued fuzzy soft set based triple I method","authors":"Binbin Xue, Lu Wang, K. Qin","doi":"10.1109/ISKE.2017.8258816","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258816","url":null,"abstract":"This paper focuses on triple I inference method based on interval-valued fuzzy soft sets. Computational formulas for both interval-valued fuzzy soft modus ponens (IVF-SMP) and interval-valued fuzzy soft modus tollens (IVFSMT) with respect to left-continuous interval-valued t-norms and its residual interval-valued implication are presented. Besides, the reversibility property of triple I methods of IVFSMP and IVFSMT are analyzed.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113960576","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258839
Dongwei Li, Linfeng Liu, Daoliang Chen, Jing Wen
With the increasing concern over marine applications in recent years, the technology of underwater wireless sensor networks (UWSNs) has received considerable attention. In UWSNs, the gathered data is sent to terrestrial control center through multi-hops for further processing. UWSNs usually consists of three types of nodes: ordinary nodes, anchor nodes, and sink nodes. The data messages are transferred from an ordinary node or an anchored node to one of the sink nodes by discrete hops. Thus, we propose a Data Forwarding Algorithm based on estimated Hungarian method (DFAH) to improve delivery ratio and reduce transmission delay. The estimated Hungarian method is applied to solve the assignment problem in data forwarding process, where the anchor nodes receive the forwarding requests from ordinary nodes and optimize the waiting queue. Both analysis and simulation results indicate that DFAH has advantages in delivery success rate and transmission delay.
{"title":"A data forwarding algorithm based on estimated Hungarian method for underwater sensor networks","authors":"Dongwei Li, Linfeng Liu, Daoliang Chen, Jing Wen","doi":"10.1109/ISKE.2017.8258839","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258839","url":null,"abstract":"With the increasing concern over marine applications in recent years, the technology of underwater wireless sensor networks (UWSNs) has received considerable attention. In UWSNs, the gathered data is sent to terrestrial control center through multi-hops for further processing. UWSNs usually consists of three types of nodes: ordinary nodes, anchor nodes, and sink nodes. The data messages are transferred from an ordinary node or an anchored node to one of the sink nodes by discrete hops. Thus, we propose a Data Forwarding Algorithm based on estimated Hungarian method (DFAH) to improve delivery ratio and reduce transmission delay. The estimated Hungarian method is applied to solve the assignment problem in data forwarding process, where the anchor nodes receive the forwarding requests from ordinary nodes and optimize the waiting queue. Both analysis and simulation results indicate that DFAH has advantages in delivery success rate and transmission delay.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114092822","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258760
Leilei Chang, Tianjun Liao, Jiang Jiang
In pushing the social and military developments towards its new frontiers and boundaries are the technologies. As a collection of multiple technologies driven by capability/system requirements, the concept of the Technology System of Systems (TSoS) is proposed. This study further investigates the generation, description, modeling and assessment of TSoS. Specifically, the generation of TSoS is based on the hierarchy hypothesis of TSoS. The description of TSoS is based on concept of views with each to describe a part of TSoS. The readiness assessment of TSoS is to assess the comprehensive readiness of a technology group while the satisfaction assessment of TSoS is to obtain a comprehensive and quantitative result on how much a technology (group) can meet the TSoS requirement. A TSoS with 60 technologies is derived from the 2017 US physical year budget estimates and it is further studied to verify the efficiency of the proposed TSoS generation, description, modeling and assessment methodology.
{"title":"A case study of the generation, description, modeling and assessment of technology system of systems","authors":"Leilei Chang, Tianjun Liao, Jiang Jiang","doi":"10.1109/ISKE.2017.8258760","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258760","url":null,"abstract":"In pushing the social and military developments towards its new frontiers and boundaries are the technologies. As a collection of multiple technologies driven by capability/system requirements, the concept of the Technology System of Systems (TSoS) is proposed. This study further investigates the generation, description, modeling and assessment of TSoS. Specifically, the generation of TSoS is based on the hierarchy hypothesis of TSoS. The description of TSoS is based on concept of views with each to describe a part of TSoS. The readiness assessment of TSoS is to assess the comprehensive readiness of a technology group while the satisfaction assessment of TSoS is to obtain a comprehensive and quantitative result on how much a technology (group) can meet the TSoS requirement. A TSoS with 60 technologies is derived from the 2017 US physical year budget estimates and it is further studied to verify the efficiency of the proposed TSoS generation, description, modeling and assessment methodology.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121075591","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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