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Sequence-based Protein-Ca2+ Binding Site Prediction Using SVM Classifier Ensemble with Random Under-Sampling 5

Sequence-based Protein-Ca2+ Binding Site Prediction Using SV...

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2017 international conference on progress in informatics and computing(PIC)

作者： Liang Qiao Dongqing Xie School of Mathematics and Information Science Guangzhou University

ISBN: (纸本)9781538619797;9781538619780

Calcium ions(Ca) are crucial for protein *** participate in enzyme catalysis,play regulatory roles,and help maintain protein *** recognizing Ca-binding sites is of significant importance for protein function *** much progress has been made,challenges remain,especially in the post-genome era where large volume of proteins without being functional annotated are quickly *** this study,we design a new ab initio predictor,CaSite,to identify Ca-binding residues from protein *** first uses evolutionary information,predicted secondary structure,predicted solvent accessibility,and Jensen-Shannon divergence information to represent each residue sample feature.A mean ensemble classifier constructed based on support vector machines（SVM） from multiple random under-samplings is used as the final prediction model,which is effective for relieving the negative influence of the imbalance phenomenon between positive and negative training *** results demonstrate that the proposed CaSite achieves a better prediction performance and outperforms the existing sequence-based predictor,TargetS.

关键词： Protein-Ca2+ binding site prediction Imbalanced data learning Random under sampling Support vector machine

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Maximizing Bichromatic Reverse k Nearest Neighbor with Multi-level Tags Queries in Spatial-textual Databases 5

Maximizing Bichromatic Reverse k Nearest Neighbor with Multi...

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2017 international conference on progress in informatics and computing(PIC)

作者： Chengyuan Zhao Yongli Wang Xiaohui Jiang Chi Yuan Yanchao Li Isma Masood Department of Computer Science and Engineering Nanjing University of Science and Technology

ISBN: (纸本)9781538619797;9781538619780

With the popularity of mobile smart devices, location-based services have been more widely used. Bichromatic Reverse k Nearest Neighbor(BRkNN) queries have become a hotspot in spatial-textual databases domain. In this paper, we extend the concept of traditional BRkNN method to process the object with multi-level tags in some specific scenes, and we propose a new type of query, called Maximized Bichromatic Reverse k Nearest Neighbor with Multi-Level Tags queries(MaxBRkNN-MLT), to find the optimal position of object with multi-level tags in the spatial-textual database. Unlike traditional methods, the number of the results of the MaxBRkNN-MLT query is maximized, which can cross the great divide between space and text. The query method proposed in this paper has a wide range of application scenes. For example, in the advertising industry, advertisers expect to find an optimal position, so that the ads with a given tag can attract the most users. Finally, experiments show that the MLT method has better query precision and execution efficiency than the baseline approach.

关键词： spatial-textual databases multi-level tags maximizing Reverse k Nearest Neighbor

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Research Issues and Mining Techniques in Brain informatics

Research Issues and Mining Techniques in Brain Informatics

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4th ieee international Advance computing conference (IACC)

作者： Kaur, Parneet Mangat, Veenu Panjab Univ UIET Chandigarh 160014 India

ISBN: (纸本)9781479925711

This paper discusses the novel area of Brain informatics (BI). BI is an interdisciplinary field that studies Information processing and Neuroscience. First section of the paper discusses this area and identifies major research issues associated with BI. Second section provides a comprehensive literature survey of neuroimaging techniques and their pros and cons. It then relates the most promising technique with its applications in BI. The third section discusses the process of classification. The design of classifiers in the context of BI and use of classification in BI is then discussed. The fifth section discusses the applications of analysis of neuroimaging data. The final section concludes the paper and provides directions for future work.

关键词： Brain informatics Information processing Neuroscience Neuroimaging Classifiers

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Mass Data Processing and Personalized Services in Shanghai E-commerce Credit Evaluation Platform 2

Mass Data Processing and Personalized Services in Shanghai E...

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2nd ieee international conference on progress in informatics and computing (PIC)

作者： Zhao, Shixiong Zhang, Yuchen Shen, Beijun Shen, Xin Chen, Rongbin Shanghai Jiao Tong Univ Sch Software Shanghai 200030 Peoples R China China Telecom Grp Best Tone Informat Co Ltd Shanghai Peoples R China

ISBN: (纸本)9781479920303

E-commerce has been one critical component of information society now;however credit is becoming a bottleneck for its further development. Therefore Shanghai is building a third-party e-commerce credit evaluation platform. The platform will be integrated with many external systems to collect massive credit-related data, do scientific evaluation and provide users with personalized credit services. In order to deal with mass data and provide personalized services to tenants of the platform, a cloud computing model of Shanghai e-commerce credit evaluation platform is designed in this paper. In PaaS layer, we propose a mass data processing technology based on the XML metadata specification;in SaaS layer, we propose a mixed model of data storage and a model of areas-layered tenant customization. Experiments show that these technologies and models can improve the data processing efficiency and resource utilization greatly.

关键词： cloud computing e-commerce mass data multitenant

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Welcome from the General Chairs

Proceedings of the 2021 IEEE International Conference on Pro...

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Proceedings of the 2021 ieee international conference on progress in informatics and computing, PIC 2021 2021年 8-9页

作者： Fujita, Hamido Wang, Yinglin Zhou, Mengqi Iwate Prefectural University Iwate Japan Shanghai University of Finance and Economics China IEEE Beijing Section China

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Enhancing Distributed Sensor Networks for Air-Quality Monitoring: A Social informatics Approach 15

Enhancing Distributed Sensor Networks for Air-Quality Monito...

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15th Annual international conference on Distributed computing in Sensor Systems (DCOSS)

作者： Pee, L. G. Nanyang Technol Univ Wee Kim Wee Sch Commun & Informat Singapore Singapore

ISBN: (纸本)9781728105703

This research-in-progress proposes an approach that accounts for social and institutional issues in the design and implementation of sensor networks for air-quality monitoring. The proposed approach is evaluated through action research, in an initiative that seeks to enhance the air-quality sensor network of a Megalopolis based on data analytics. Preliminary findings indicate that the proposed approach improves access to resources, enhances the validity of design, and increases buy-in for implementation.

关键词： social informatics air quality air pollution data analytics

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Structure Connectivity and Substructure Connectivity of Alternating Group Graphs 6

Structure Connectivity and Substructure Connectivity of Alte...

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ieee international conference on progress in informatics and computing (PIC)

作者： You, Lantao Han, Yuejuan Wang, Xi Zhou, Chen Gu, Rui Lu, Chen Suzhou Ind Pk Inst Serv Outsourcing Sch Informat Engn Suzhou Peoples R China Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China Suzhou Inst Ind Technol Sch Software & Serv Outsourcing Suzhou Peoples R China

ISBN: (纸本)9781538676721

The alternating group graph, denoted by AG(n) , is one of the popular interconnection networks. In this paper, we consider two network connectivities, H-structure-connectivity and H-substructure-connectivity, which are new measures for a network's reliability and fault-tolerability. We say that a set F of connected subgraphs of G is a subgraph-cut of G if G-V (F) is a disconnected or trivial graph. Let H be a connected subgraph of G. Then F is an H-structure-cut, if F is a subgraph-cut, and every element in F is isomorphic to H. And F is an H-substructure-cut if F is a subgraph-cut, such that every element in F is isomorphic to a connected subgraph of H. The H-structure-connectivity(resp. H-substructure-connectivity) of G, denoted by is an element of(G;H)(resp. is an element of s (G;H)), is the minimum cardinality of all H-structure-cuts(resp. H-substructure-cuts) of G. In this paper, we will establish both is an element of(AG(n);H) and is an element of(AG(n);H) for the alternating group graph AG(n) and H is an element of{K-1 ,K-1,K-1 ,K-1,K-2 }.

关键词： Alternating group graph Structure connectivity Substructure connectivity Parallel computing

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An Approach based on Decision Tree for Analysis of Behavior with Combined Cycle Power Plant 5

An Approach based on Decision Tree for Analysis of Behavior ...

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2017 international conference on progress in informatics and computing(PIC)

作者： Abshukirov Zhandos Jian Guo School of Automation Nanjing university of Science and Technology

ISBN: (纸本)9781538619797;9781538619780

This paper presents about Combined Cycle Power Plant（CCPP） and decision tree. CCPP considered as the best effective power suppliers to the large temperature incline between its gas turbine passage and the environment or the cooling process, and to help of their engineers, who are able to optimally venture the present temperature level. Moreover, in this paper we did comparison of four types of decision tree algorithms like Decision Stump, Hoeffding Tree,logistic model trees（LMT） and J48. Based on these algorithms we analyzed the behavior of Combined Cycle Power Plant（CCPP）, particularly its temperature. The temperature is target variable among other variables. This is why temperature was divided three classes. Theoretical analysis and experimental results have shown that the J48 algorithm is the best algorithm which predict attributes of given instances precisely among other three algorithms. Based on our findings, J48 algorithm can predict precisely the temperature of Combined Cycle Power Plant and predict 97.1676% cases correctly.

关键词： combined cycle power plant(CCPP) decisiontree classification J48(C4.5)

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Particle Swarm Optimization Based Task Scheduling for Multi-core Systems Under Aging Effect 5

Particle Swarm Optimization Based Task Scheduling for Multi-...

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2017 international conference on progress in informatics and computing(PIC)

作者： Jinbin Tu Tianhao Yang Yi Zhang Jin Sun School of Computer Science and Engineering Nanjing University of Science and Technology

ISBN: (纸本)9781538619797;9781538619780

As the size of the transistor continues to shrink, a number of reliability issues have emerged in network-on-chip(NoC) design. Taking into account the performance degradation induced by Negative Bias Temperature Instability(NBTI) aging effect, this paper proposes an aging-aware task scheduling framework for No Cbased multi-core systems. This framework relies on a NBTI aging model to evaluate the degradation of core's operating frequency to establish the task scheduling model under aging effect. Then, we develop a particle swarm optimization(PSO)-based heuristic to solve the scheduling problem with an optimization objective of total task completion time, and finally obtain a scheduling result with higher efficiency compared with traditional scheduling algorithms without considering of NBTI aging effect. Experiments show that the proposed aging-aware task-scheduling algorithm achieves not only shorter makespan and higher throughput, but also better reliability over non-aging-aware ones.

关键词： network-on-chip negative bias temperature instability task scheduling particle swarm optimization

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A Short-Term Marginal Price Forecasting Model Based on Ensemble Learning 5

A Short-Term Marginal Price Forecasting Model Based on Ensem...

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2017 international conference on progress in informatics and computing(PIC)

作者： Kejia Pan Wenbin Shi Xin Wang Jiazhou Li Si Chuan Electric Power Co Rporation Information &Communication Co School of Computer Science and Technology Shanghai University of Electric power

ISBN: (纸本)9781538619797;9781538619780

This The system marginal price reflects the shortterm supply and demand of electricity goods in the electricity market, which is an important economic link to the participating members of the market. The traditional prediction model has a large error and low generalization ability to forecast the shortterm marginal price. Therefore, this paper proposes an ensemble learning algorithm for short-term marginal price forecasting based on Ada Boost. In this paper, the main factors influencing the short-term marginal electricity price are analyzed. Based on the Ada Boost algorithm, the short-term marginal electricity price forecasting problem is modeled. Four prediction models（C4.5, CART, Linear neural network, BP） are compared, and a short-term marginal price forecasting algorithm is proposed. By comparing the actual values with the predicted values, our proposed algorithm is superior to SVM and BP algorithm, which has high application values in power plant engineering.

关键词： AdaBoost integrated learning system marginal price short term

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