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检索条件"任意字段=2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016"

共 3354 条记录，以下是561-570 订阅

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Detecting Targeted Malicious E-Mail Using Linear Regression Algorithm with data mining Techniques 1

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3rd international conference on Computational Intelligence in data mining (ICCIDM)

作者： Rao, A. Sesha Avadhani, P. S. Chaudhuri, Nandita Bhanja AU Coll Engn Dept Comp Sci & Syst Engn Visakhapatnam Andhra Prades India AU Coll Engn Dept Comp Sci & Engn Visakhapatnam Andhra Prades India Vignans Inst Engn Women Dept Informat Technol Visakhapatnam Andhra Prades India

ISBN: (数字)9789811038747

ISBN: (纸本)9789811038747;9789811038730

E-mail is the most fundamental means of communication. It is the focus of attack by the terrorists, e-mail spammers, imposters, business fraudsters, and hackers. To combat this, different data mining classifiers are used to identify the spam mails. This paper introduces a system that imports data from the e-mail accounts and performs preprocessing techniques like file conversions that are appropriate to conduct the experiments, searching for frequency of a word by Knuth-Morris-Pratt (KMP) string searching algorithm, and feature selection using principal component analysis (PCA) are applied. Next, linear regression classification is used to predict the spam mails. Then, association rule mining is performed. The mean absolute error and root mean squared error for the training data and test data are computed. The errors of the training and test data sets are negligible which indicates the classifier is well trained. Finally, the results are displayed by the visualization techniques.

关键词： Preprocessing KMP PCA Linear regression Association rule mining Mean absolute error Root mean squared error Visualization

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Biweight mid variance based energy-efficient virtual machine provisioning

Biweight mid variance based energy-efficient virtual machine...

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international conference on Communication and computing Systems, ICCCS 2016

作者： Nhapi, L. Yadav, A.K. Rao, R.S. Department of Computer Science and Engineering ITM University GwaliorMP India Ambedkar Institute of Advanced Communication Technologies and Research Geeta Colony Delhi India

ISBN: (纸本)9781138029521

The purpose of this study is to investigate the effect of applying the biweight midvariance for automatically adjusting the server utilization threshold, so as to adapt to workload variation. We consider energy saving and service Quality, (QoS), as key performance parameters of our algorithm. The proposed method is implemented in CloudSim and the outcome proves that it can lower electricity consumed by data centers, and achieve acceptable QoS. Thus it can be made use of, to economically allocate resources and therefore increase profit for cloud data centers. © 2017 Taylor & Francis Group, London.

关键词： Energy efficiency

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Comparative analysis in between the k-means algorithm, k-means using with Gaussian mixture model and fuzzy c means algorithm

Comparative analysis in between the k-means algorithm, k-mea...

引用

international conference on Communication and computing Systems, ICCCS 2016

作者： Shrivastava, Pooja Kavita Singh, Sarvesh Shukla, Manoj Jayoti Vidyapeeth Women’s University Jaipur India Amity University NoidaUttar Pradesh India

ISBN: (纸本)9781138029521

K-Means clustering algorithm is partitioning method. Gaussian mixture model is multivariate distribution which is consists of a mixture of one or more multivariate Gaussian distribution component. We are used forest fire data set from UCI machine learning. In this paper we present the comparative analysis of k-means and k-means algorithm using a Gaussian mixture model. Secondly, we presented comparative analysis of k-means algorithm and fuzzy c means algorithm. Firstly, we are taking data points through k-means algorithm. Secondly, we are taking data points through Gaussian mixture model and lastly, we are taking data points through fuzzy c means. In this paper we will improve the accuracy and no of iteration of both algorithms and evaluated the performance of algorithms. The main objective of this research we will found which method is better. © 2017 Taylor & Francis Group, London.

关键词： data mining

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Security as a Service Model for Cloud Storage 2nd

引用

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

作者： Abo-alian, Alshaimaa Badr, Nagwa L. Tolba, M. F. Ain Shams Univ Fac Comp & Informat Sci Cairo Egypt

ISBN: (纸本)9783319483085;9783319483078

Along with the widespread interest in cloud computing, there are still concerns that hinder the proliferation and the adoption of cloud services. One of the main concerns is data security in cloud storage environments. Numerous research problems belonging to cloud storage security have been studied intensively before. However, addressing the three dimensions of outsourced data security (i.e., confidentiality, availability, and integrity) as a cloud service is still a challenge in cloud storage. As there is always a tradeoff between maintaining security and obtaining efficiency, it is difficult but nevertheless essential to explore how to efficiently address security challenges over dynamic cloud data. This paper proposes an integrated security model for data storage in the cloud that provides authentication, access control, auditing and data management services. A strong authentication sub-system that is based on the traditional password and the user keystroke dynamics is presented. A dynamic access control system is proposed to ensure data confidentiality in cloud computing. A public auditing system is proposed to delegate the integrity verification of outsourced data in the cloud storage to a third party auditor while maintaining the data privacy. Experimental results demonstrate the effectiveness and efficiency of the proposed model.

关键词： Access control Auditing Authentication Cloud computing data security

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A Hybrid Central Force Optimization Algorithm for Optimizing Ontology Alignment 4th

A Hybrid Central Force Optimization Algorithm for Optimizing...

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4th Euro-China conference on Intelligent data Analysis and Applications, ECC 2017

作者： Xue, Xingsi Liu, Shijian Wang, Jinshui College of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China

ISBN: (纸本)9783319685267

Ontology is regarded as an effective solution to data heterogeneity on the semantic web. However, different ontology engineers might use different ways to define the concept, which causes the ontology heterogeneity problem and raises the heterogeneous problem to a higher level. Ontology matching technology, which is able to identify the same concepts in two heterogeneous ontologies, is recognized as a ground solution to tackle the ontology heterogeneity problem. Since different ontology matchers do not necessarily find the same correct correspondences, usually several competing matchers are applied to the same pair of entities in order to increase evidence towards a potential match or mismatch. How to select, combine and tune various ontology matchers to obtain the high quality ontology alignment becomes a crucial challenges in ontology matching domain. Recently, swarm intelligent algorithms are appearing as a suitable methodology to face this challenge, but the slow convergence and premature convergence are two main shortcomings that makes them incapable of effectively searching the optimal solution for large scale and complex ontology matching problems. To improve the ontology alignment’s quality, our work investigates a new emergent class of swarm intelligent algorithm, named Central Force Optimization (CFO) algorithm. To balance CFO’s exploration and exploitation, we proposes a Hybrid CFO (HCFO) by introducing the local search strategy into CFO’s evolving process, and utilize HCFO to automatically select, combine and tune various ontology matchers to optimize the ontology alignment. The experimental results show that our approach can significantly improve the ontology alignment’s quality of existing swarm intelligent algorithm based ontology matching technologies. © 2018, Springer international Publishing AG.

关键词： Ontology

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A Fuzzy Knowledge Based Mechanism for Secure data Aggregation in Wireless Sensor Networks 1

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3rd international conference on Computational Intelligence in data mining (ICCIDM)

作者： Acharya, Sasmita Tripathy, C. R. VSS Univ Technol Dept Comp Applicat Burla India VSS Univ Technol Dept Comp Sci & Engn Burla India

ISBN: (数字)9789811038747

ISBN: (纸本)9789811038747;9789811038730

Wireless Sensor Networks (WSNs) consist of a number of limited energy sensor nodes deployed randomly over an area. The paper proposes a fuzzy knowledge based secure tree-based data aggregation mechanism for WSNs called the Fuzzy knowledge based data Aggregation Scheme (FDAS). In the proposed FDAS mechanism, a combination of fuzzy rules is applied to predict the node status of each node in the aggregation tree. The faulty nodes are then isolated from the process of data aggregation. The proposed FDAS mechanism also ensures the security of the network by the application of the privacy homomorphic cryptography technique. It has been found to give better performance characteristics than the other existing algorithms as validated through the results obtained from simulation.

关键词： Wireless Sensor Networks Fuzzy Logic Privacy Homomorphic Cryptography data Aggregation

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Multicore Framework for Finding Frequent Item-Sets Using TDS 16th

Multicore Framework for Finding Frequent Item-Sets Using TDS

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16th international conference on Hybrid Intelligent Systems (HIS) / 8th World Congress on Nature and Biologically Inspired computing (NaBIC)

作者： Khawaja, Sajid Gul Tehreem, Amna Akram, M. Usman Khan, Shoab Ahmed Natl Univ Sci & Technol Coll Elect & Mech Engn Rawalpindi Pakistan

ISBN: (纸本)9783319529417;9783319529400

mining of frequent items from a dataset is a prime problem in the field of data mining. It plays a pivotal role in many of the data mining applications. In recent years, technological improvements have provided us with cheaper storage spaces capable of handling gigantic amount of digital data of human activities. This humongous data becomes a bottleneck for data analysts as mining algorithms often suffer from performance issues while running larger datasets or where number of items become very large. In this paper, we propose a novel tree based data structure (TDS) for saving itemsets as candidates for time effective application of Apriori property for pruning. The TD structure shows significant improvement in timing as compared to traditional structure. Furthermore, we propose a multicore framework for the processing of TDS based Apriori Algorithm. The framework is based on divide and conquer approach, where all cores work in parallel on their allocated subset of data. Each core shares their local results with other cores to get the global results leading to a collaborative working environment. The proposed framework is highly scalable which requires no change in the overall working of the algorithm. In order to thoroughly test the proposed framework, experimentation is performed using 4 benchmark datasets and its evaluation is carried out on the bases of number of cycles, execution time and comparative speedup. The results indicate that TDS is significantly faster and while working with multicore framework a direct relationship exists in speedup for all datsets with the number of working cores.

关键词： data mining Apriori Parallel processing Multiple core Accelerator

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international conference on Communication and computing Systems, ICCCS 2016

International Conference on Communication and Computing Syst...

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international conference on Communication and computing Systems, ICCCS 2016

ISBN: (纸本)9781138029521

The proceedings contain 192 papers. The special focus in this conference is on Communication and computing Systems. The topics include: Temporal effects in collaborative filtering for recommendation systems;study on the formation of wrinkles in the panel drawing operation using fem;metaheuristics: Modeling variant of ant colony optimization for image edge detection using self adaptive approach;improving medical image classification model using data mining technique;iris recognition using hybrid transform;simo and miso universal filters employing otra;modeling of brain tumor detection using image processing;improved advanced modified decision based unsymmetric trimmed median filter for removal of salt and pepper noise;threshold segmentation technique for tumor detection using morphological operator;fpga implementation of low power & high speed accurate ecg signal detection using dablms algorithm;robust vehicle detection and localization using normalized color illumination;digital image forgery detection using the wavelet decomposition and low level feature points;binary image reconstruction using the adaptive hybrid mechanism with swarm optimization;application of signal processing to optimize em attack on aes;resistorless realization of universal filter employing cciii and ota;face detection from digital images: A comparative study;improved iris biometric system by exploring iris texture features;feature extraction of hyperspectral face images using pca in nir;design and analysis of high gain-low power operational-amplifier for bio-medical applications;early detection of symptoms in tongue cancer through image processing;a study on adaptive wavelet technique for speckle noise removal;image enciphering using modified aes with secure key transmission;trust version of aasr.

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data and Decision Intelligence for Internet of Things: Putting Human in the Loop 3

Data and Decision Intelligence for Internet of Things: Putti...

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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： Ma, Meng Lin, Weilan Pan, Disheng Wang, Ping Zhou, Yuchen Liang, Xiaoxing Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China Peking Univ Sch Software & Microelect Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Software Beijing Peoples R China Peking Univ Natl Engn Res Ctr Software Engn Beijing Peoples R China IBM Res China Beijing Peoples R China

ISBN: (纸本)9781509062966

The requirement and expanding needs of bridging the physical and cyber world promote the emergence of a new global internet-based information architecture, the Internet of Things. The main challenge in Internet of Things is how to make fast and accurate decisions based on multi-modal sensing data. In this paper, we put human into the process and formulate a closed-loop computing paradigm for IoT data and decision intelligence. We first propose a human-in-the-loop reference model, which extends the traditional cyber-physical interaction extended into a closed-loop process based on cyber, physical and human three factors. We defined the three-factor decision paradigm in the model. On the basis of the reference model, we analyze the feature requirement in the data intelligent process and summarize it as three key aspects: semantic, interactive and iterative. Further, we analyze several major challenges facing IoT data and decision intelligence from the perspective of data characteristics. We select and review the recent progress in three typical application domains and analyzed whether the current research has solved the target issues. According to the results of the review and analysis, the paper finally summarizes and addresses some key research directions in the future.

关键词： Internet of Things data intelligence data mining decision-making human-in-the-loop

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Efficient Fast Updated Frequent Pattern tree algorithm and its parallel implementation 2

Efficient Fast Updated Frequent Pattern tree algorithm and i...

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2nd international conference on Image, Vision and computing, ICIVC 2017

作者： Lv, Detao Fu, Bo Sun, Xiao Qiu, Hang Liu, Xiaobing Yanlong, Zhang Department of Computer Science and Engineering UESTC Chengdu China Big Data Research Center UESTC Chengdu China Shulianyikang Science and Technology Co. LTD Chengdu China

ISBN: (纸本)9781509062379

With the rapid expansion of data, incremental update mining for associated rule learning in large scale volume of data processing is concerned. The support count or support ratio are often used as a threshold for the associated rule learning. The support ratio is sensitive to the threshold, especially, adapted to build anomaly detection models learned with unbalanced samples. It might result in missing anomalies or data failures. However, the support count with the characteristic of good explanatory is easy to use. Therefore, the support ratio always needs to be converted into the support count. In order to mine large-scale frequent patterns, based on the FUFP algorithm, an efficient algorithm of incremental frequent item mining is proposed in this paper, named EFUFP (Efficient Fast Updated Frequent Pattern) tree algorithm. The minimum support count and a small item cache are used to effectively detect unbalanced abnormal data and improve the efficiency of query access. The parallelization of EFUFP is implemented on the Spark environment. The experimental results show that the EFUFP algorithm is suitable for the current large scale frequent item mining. © 2017 IEEE.

关键词： Trees (mathematics)

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