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Energy-Aware Topology Construction and Maintenance for Energy Harvesting Sensor Networks 2nd

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2nd international conference on advanced Intelligent Systems and Informatics (AISI)

作者： Sung, Tien-Wen Lee, Chao-Yang Lu, You-Te Shiu, Liang-Cheng Lin, Fu-Tian Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou Peoples R China Fujian Univ Technol Coll Informat Sci & Engn Fuzhou Peoples R China Natl Cheng Kung Univ Dept Elect Engn Tainan Taiwan Southern Taiwan Univ Sci & Technol Dept Informat & Commun Tainan Taiwan Natl Pingtung Univ Dept Comp Sci & Informat Engn Pingtung Taiwan Tung Fang Design Univ Dept Elect Engn Kaohsiung Taiwan

ISBN: (纸本)9783319483085;9783319483078

This work designs a topology control approach which can stratify the perpetual energy supply to extend the system lifetime in energy harvesting sensor networks. Topology control is a well-known and energy-efficiency method that aims to reduce the energy consumption and prolong network lifetime in many research fields. The proposed perpetual and distributed topology control (PDTC) algorithm aims to make the harvesting ambient energy usefully and ensure network sustainability, and performs in each sensor which includes two phases, topology construction phase and topology maintenance phase. First, in topology construction phase, each sensor decides a most suitable parent node with maximal working time and adjusts the traffic generating rate to achieve the system sustainability. In the topology maintenance phase, this work adopts a topology maintenance algorithm to trigger the topology construction algorithm and then re-build a new network topology needed. The experimental results demonstrate the superiority of the PDTC algorithm in terms of energy efficient, network lifetime, and system sustainability.

关键词： Topology construction Topology maintenance Network lifetime Energy harvesting Sensor network

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A Taxi Gap Prediction Method via Double Ensemble Gradient Boosting Decision Tree 3

A Taxi Gap Prediction Method via Double Ensemble Gradient Bo...

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IEEE 3rd international conference on Big data Security on Cloud (BigdataSecurity) / 3rd IEEE international conference on High Performance and Smart computing (HPSC) / 2nd IEEE international conference on Intelligent data and Security (IDS)

作者： Zhang, Xiao Wang, Xiaorong Chen, Wei Tao, Jie Huang, Weijing Wang, Tengjiao Peking Univ Sch EECS Key Lab High Confidence Software Technol MOE Beijing 100871 Peoples R China China Elect Power Res Inst Technol & Strategy Res Ctr Beijing 100192 Peoples R China

ISBN: (纸本)9781509062966

Predicting the gap between taxi demand and supply in taxi booking apps is completely new and important but challenging. However, manually mining gap rule for different conditions may become impractical because of massive and sparse taxi data. Existing works unilaterally consider demand or supply, used only few simple features and verified by little data, but not predict the gap value. Meanwhile, none of them dealing with missing values. In this paper, we introduce a Double Ensemble Gradient Boosting Decision Tree Model (DEGBDT) to predict taxi gap. (1) Our approach specifically considers demand and supply to predict the gap between them. (2) Also, our method provides a greedy feature ranking and selecting method to exploit most reliable features. (3) To deal with missing value, our model takes the lead in proposing a double ensemble method, which secondarily integrates different Gradient Boosting Decision Tree (GBDT) model at the different data sparse situation. Experiments on real large-scale dataset demonstrate that our approach can effectively predict the taxi gap than state-of-the-art methods, and shows that double ensemble method is efficacious for sparse data.

关键词： taxi gap double ensemble GBDT data sparsity

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Divisive Hierarchical Bisecting Min-Max Clustering Algorithm 1st

Divisive Hierarchical Bisecting Min-Max Clustering Algorithm

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1st international conference on data Engineering and Communication Technology (ICDECT)

作者： Johnson, Terence Singh, Santosh Kumar AMET Univ Chennai Tamil Nadu India Agnel Inst Technol & Design Assagao Goa India Thakur Coll Sci & Commerce Kandivali E Mumbai Maharashtra India

ISBN: (纸本)9789811016752;9789811016745

The inspiration for the Divisive Hierarchical Bisecting Min-Max Clustering Algorithm came from the Bisecting K-Means clustering Algorithm. To obtain K clusters, bifurcate the set of input values into two clusters, select one of these clusters to split further (each time bisect the selected cluster using the Min-Max Clustering Algorithm), and so on, until K clusters have been produced. The Min-Max Clustering Algorithm initially computes the minimum of the input set and then finds a point which is at the greatest distance from the minimum. The remaining values from the set of data items are then accumulated into twoclusters formed by the maximally disjoint min and max values.

关键词： data mining Hierarchical clustering Bisecting K-Means clustering Min-Max Clustering Bisecting min max clustering

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An Outlook in Some Aspects of Hybrid Decision Tree Classification Approach: A Survey 1st

An Outlook in Some Aspects of Hybrid Decision Tree Classific...

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1st international conference on data Engineering and Communication Technology (ICDECT)

作者： Panhalkar, Archana Doye, Dharmpal Amrutvahini Coll Engn Sangamner Maharashtra India Shri Guru Gobind Singhji Inst Engn & Technol Nanded Maharashtra India

ISBN: (纸本)9789811016783;9789811016776

Decision tree one of the complex but useful approach for supervised classification is portrayed in this review. Today's research is deemed toward the use of hybridized decision tree for the need of various applications. The recent approaches of decision tree techniques come with hybrid decision tree. This survey, it has been elaborating the various approaches of converting decision tree to hybridized decision tree. For classification of data SVMs and other classifier in decision tree are generally used at the decision node to improve accuracy of decision tree classifier. Then the more penetration is given to some aspects which less likely used by researchers which gives more scope. The ideas of various hybridized approaches of decision tree are given like use of clustering, naive Bayes, and AVL tree, fuzzy and genetic algorithm.

关键词： data mining AVL Fuzzy clustering Naive Bayes SVM Cuckoo optimization

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Time series analysis and crime pattern forecasting of city crime data 17

Time series analysis and crime pattern forecasting of city c...

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2017 international conference on Algorithms, computing and Systems, ICACS 2017

作者： Marzan, Charlie S. De Dios Bulos, Remedios Baculo, Maria Jeseca C. Ruiz, Conrado De la Salle University Manila City Philippines

ISBN: (纸本)9781450352840

Crime analysis using data mining techniques have been a possible solution to aid law enforcement officers to mitigate crime related problems. In this paper, a geospatial data analysis was conducted for detecting the hotspots of criminal activities in Manila City, Philippines. The crime records of 2012-2016 which were manually collected were geocoded and the map was generated using ArcGIS version 10. Association rules mining using Apriori algorithm was also performed on discovering frequent patterns to help the police officers to form a preventive action. This analyzed the different crimes and predicted the chance of each crime that can recur. In addition, analysis of various time series forecasting methods such as Linear Regression, Gaussian Processes, Multilayer Perceptron, and SMOreg to predict future trends of crime was performed. This work provides a solution to help the officers to build a crime controlling strategy to prevent crimes in the future. © 2017 Association for computing Machinery.

关键词： Association rules

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An Ontology-Based Framework for Linking Heterogeneous Medical data 2nd

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2nd international conference on advanced Intelligent Systems and Informatics (AISI)

作者： Salem, Rashed Elsharkawy, Basma Kader, Hatem Abdel Menoufia Univ Dept Informat Syst Fac Comp & Informat Shibin Al Kawm Al Minufiyah Egypt

ISBN: (纸本)9783319483085;9783319483078

Clinical records contain a massive heterogeneity number of data, generally written in free-note without a linguistic standard. Other forms of medical data include medical images with/without metadata (e.g., CT, MRI, radiology, etc.), audios (e.g., transcriptions, ultrasound), videos (e.g., surgery recording), and structured data (e.g., laboratory test results, age, year, weight, billing, etc.). Consequently, to retrieve the knowledge from these data is not trivial task. Handling the heterogeneity besides largeness and complexity of these data is a challenge. The main purpose of this paper is proposing a framework with two-fold. Firstly, it achieves a semantic-based integration approach, which resolves the heterogeneity issue during the integration process of healthcare data from various data sources. Secondly, it achieves a semantic-based medical retrieval approach with enhanced precision. Our experimental study on medical datasets demonstrates the significant accuracy and speedup of the proposed framework over existing approaches.

关键词： data integration Heterogeneity Image retrieval CBIR Semantic ontology

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Content Based Image Retrieval with Hadoop 2nd

引用

2nd international conference on advanced Intelligent Systems and Informatics (AISI)

作者： Gaber, Heba Marey, Mohammed Amin, Safaa E. Tolba, Mohamed F. Ain Shams Univ Comp & Informat Sci Cairo Egypt

ISBN: (纸本)9783319483085;9783319483078

Hadoop has become a widely used open source framework for large scale data processing. MapReduce is the core component of Hadoop. It is this programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. It allows processing of extremely large video files or image files on data nodes. This can be used for implementing Content Based Image Retrieval (CBIR) algorithms on Hadoop to compare and match query images to the previously stored terabytes of an image descriptors databases. This work presents the implementation for one of the well-known CBIR algorithms called Scale Invariant Feature Transformation (SIFT) for image features extraction and matching using Hadoop platform. It gives focus on utilizing the parallelization capabilities of Hadoop MapReduce to enhance the CBIR performance and decrease data input\output operations through leveraging Partitioners and Combiners. Additionally, image processing and computer vision tools such as Hadoop Image Processing (HIPI) and Open Computer Vision (OpenCV) are integration is shown.

关键词： Hadoop HIPI OpenCV Big data Crowd sourcing MapReduce Combiners Partitioners

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Cloud Learning Community of Engineering Drawing 1

引用

1st international conference on Smart computing and Communication (SmartCom)

作者： Zhang, Fenna Qi, Yaoguang Pan, Long Yang, Yong Zhang, Hao Yao, Yao China Univ Petr East China Coll Mech & Elect Engn Qingdao 266555 Shandong Peoples R China

ISBN: (数字)9783319520155

ISBN: (纸本)9783319520155;9783319520148

General online teaching system cannot afford the huge information data exchange, held back in the modern teaching method and technology support. This paper aims at establishing the structure of the cloud learning community on big data. The Oracle server on cloud computing is selected to provide the data processing support. Based on the investigation on the students' browse online and the homework completion situation, the existing teaching resource is integrated, and the frame work of the cloud learning community on big data is established to improve the communication and integration. Cloud platform layers and the key data processing technology are analyzed. The cloud learning community can match the data processing technology and expose the students in the advanced cloud teaching stimulate the study enthusiasm.

关键词： Cloud learning community Engineering drawing Big data Resource integration Online teaching

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Incremental Updating of Mined Association Rules for Reflecting Record Insertions 1st

Incremental Updating of Mined Association Rules for Reflecti...

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1st international conference on Computational Intelligence and Informatics (ICCII)

作者： Satyavathi, N. Rama, B. Nagaraju, A. Vaagdevi Coll Engn Dept Comp Sci & Engn Warangal Telangana India Kakatiya Univ Dept Comp Sci Warangal Telangana India Rajasthan Cent Univ Dept Comp Sci & Engn Ajmer Rajasthan India

ISBN: (纸本)9789811024719;9789811024702

Many algorithms came into existence for mining association rules. Since the databases in the real world are subjected to frequent changes, the algorithms need to be rerun to generate association rules that can reflect record insertions. It causes overhead the algorithm needs to scan entire database every time and repeat the process. Incremental updating of mined association rules is challenging. Recently, Deng and Lv proposed an algorithm named FIN (Frequent Itemsets using Nodesets) for fast mining of frequent itemsets. They proposed a data structure named Nodesets which consume less memory. In this paper, we proposed an algorithm named FIN_INCRE based on FIN which updates mined association rules without reinventing the wheel again. When new records are inserted, only the nodes in the data structure are updated adaptively using the concept of pre-large itemsets that effectively avoid re-scanning original data set. We built a prototype application to demonstrate the proof of concept. The empirical results reveal that the proposed algorithm improves the performance significantly.

关键词： FIN FIN_INCRE Incremental data set Itemset

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1st international conference on Advances in computing and data Sciences, ICACDS 2016

1st International Conference on Advances in Computing and Da...

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1st international conference on Advances in computing and data Sciences, ICACDS 2016

ISBN: (纸本)9789811054266

The proceedings contain 64 papers. The special focus in this conference is on Advances in computing and data Sciences. The topics include: A survey on location recommendation systems;a comparative study and performance analysis of classification techniques;a novel technique for segmenting platelets by k-means clustering;additive noise removal by combining non local means filtering and a local fuzzy filter - a fusion approach;an incremental verification paradigm for embedded systems;assembling swarm with limited visibility in presence of line obstacles;clustering proficient students using data mining approach;designing of a gender based classifier for western music;detecting malwares using dynamic call graphs and opcode patterns;development of secured trust SLA model from SLA life cycle phases;dimensionality reduction by distance covariance and Eigen value decomposition;hybrid segmentation technique using wavelet packet and watershed transform for medical images;implementation of medical image watermarking technique using FPGA;measuring branch coverage for the SOA based application using concolic testing;mutation analysis of stateflow to improve the modelling analysis;prioritization of near-miss incidents using text mining and Bayesian network;security integration in big data life cycle;trust based energy efficient clustering protocol in wireless sensor networks for military applications;using morphological features to simplify complex sentences in Punjabi language;a comparative study of internet protocols in MANET;contact dynamics emulation using leap motion controller and scalable online analytics on cloud infrastructures.

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