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检索条件"任意字段=3rd International Conference on Soft Computing in Data Science, SCDS 2017"

共 694 条记录，以下是341-350 订阅

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Periodic Scheduling of Profiling-Based Floating-Window Bandwidth Reservations for Scientific Collaboration 19

Periodic Scheduling of Profiling-Based Floating-Window Bandw...

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19th IEEE international conference on High Performance computing and Communications (HPCC) / 15th IEEE international conference on Smart City (SmartCity) / 3rd IEEE international conference on data science and Systems (DSS)

作者： Wang, Yongqiang Wu, Chase Q. Hou, Aiqin Northwest Univ Sch Informat Sci & Technol Xian 710127 Shaanxi Peoples R China New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA

ISBN: (纸本)9781538625880

An increasing number of scientific applications carry out big data transfer through dedicated networks for global collaboration, where bandwidth scheduling plays a critical role in improving the utilization of network resources and meeting diverse user requests. In this paper, we formulate a periodic bandwidth scheduling problem to maximize the number of satisfied user requests for profiling-based floating-window bandwidth reservations under deadline constraint on a network path, referred to as PFWR-DC. We prove that PFWR-DC is NP-complete, and propose an integrated bandwidth scheduling algorithm based on the product of floating-window size and dynamic transport throughput, referred to as ProductWT-BS. Extensive simulation results shed light on the performance superiority of ProductWT-BS in terms of scheduling success ratio over live heuristic algorithms designed for performance comparison. The proposed scheduling algorithm has great potential to improve the performance of collaborative scientific applications that require the floating-window bandwidth provisioning service for coordinated network-based operations.

关键词： High-performance networks data transfer bandwidth scheduling resource utilization

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Prediction of new bioactive molecules of chemical compound using boosting ensemble methods 3rd

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3rd international conference on soft computing in data science, scds 2017

作者： Hashim, Haslinda Saeed, Faisal Kolej Yayasan Pelajaran Johor KM16 Jalan Kulai-Kota Tinggi Kota TinggiJohor81900 Malaysia Information Systems Department Faculty of Computing Universiti Teknologi Malaysia SkudaiJohor81310 Malaysia College of Computer Science and Engineering University of Taibah Medina Saudi Arabia

ISBN: (纸本)9789811072413

Virtual screening (VS) methods can be categorized into structure-based virtual screening (SBVS) that involves knowledge about the target’s 3D structure and ligand-based virtual screening (LBVS) approaches that utilize information from at least one identified ligand. However, the activity prediction of new bioactive molecules in highly diverse data set is still less accurate and the result is not comprehensive enough since only one approach is applied at one time. This paper aims to recommend the boosting ensemble method, MultiBoost, into LBVS using the well-known chemoinformatics database, the MDL Drug data Report (MDDR). The experimental results were compared with Support Vector Machines (SVM). The final outcomes showed that MultiBoost ensemble classifiers had improved the effectiveness of the prediction of new bioactive molecules in high diverse data. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Adaptive boosting

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Protection policy in networked locations using machine learning and data science approach 3

Protection policy in networked locations using machine learn...

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3rd international conference on Applied and Theoretical computing and Communication Technology, iCATccT 2017

作者： Reddy, S. Sai Satyanarayana Shahabadkar, Ramesh Mamatha, Ch. Chatterjee, Priyadarshini Vardhaman College of Engineering Kacharam Shamshabad Hyderabad Telangana501 218 India

ISBN: (纸本)9781538611449

A protection policy employs diverse techniques and mechanisms to sense protection related glitches and coercions in a networked location. The protection policy is big-data focused and services machine learning technique to perform protection analytics using data science. The protection policy performs entity behavioral analytics to sense the protection related glitches and coercions, regardless of whether such glitches/coercions were previously known. The protection policy can include both real-time and group modes for sensing glitches and coercions. By visually presenting diagnostic results scored with jeopardy assessments and auxiliary indication, the protection policy enables network protection administrators to respond to a sensed glitch or coercions, and to take action promptly. © 2017 IEEE.

关键词： Location

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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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N2DLOF: A New Local Density-Based Outlier Detection Approach for Scattered data 19

N2DLOF: A New Local Density-Based Outlier Detection Approach...

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作者： Su, Shubin Xiao, Limin Zhang, Zhoujie Gu, Fei Ruan, Li Li, Shupan He, Zhenxue Huo, Zhisheng Yan, Baicheng Wang, Haitao Liu, Shaobo Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China Space Star Technol Co Ltd Beijing 100086 Peoples R China

ISBN: (纸本)9781538625880

Since the Local Outlier Factor (LOF) was first proposed, there is a large family of approaches that is derived from it. For the reason that the existing local outliers detection approaches only focus on the extent of overall separation between an object and its neighbors, and do not pay attention to the degree of dispersion between them, the precision of these approaches will be seriously affected in the scattered data sets for outlier detection. In this paper, we redefine the local outliers by combining the degree of dispersion of the object and its neighbors, and propose a new local outlier detection approach (N2DLOF). Compared to conventional approaches, the outliers obtained by N2DLOF are more sensitive to the degree of anomaly of the scattered data sets. Experiments show that our approach has a significant improvement on outlier detection precision in the case of scattered datasets with similar time complexity. In short, we extend the ecosystem of the local outlier detection approaches from a new perspective.

关键词： Outlier detection local outliers neighborhood variance m-tree scattered data

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Fast and efficient data acquisition in radiation affected large WSN by predicting transfaulty nodes 3rd

Fast and efficient data acquisition in radiation affected la...

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3rd international conference on Next Generation computing Technologies, NGCT 2017

作者： Pandey, Manish Dhanoriya, Sachin Bhagat, Amit Maulana Azad National Institute of Technology Bhopal462003 India

ISBN: (纸本)9789811086595

Wireless Sensor Network is a collection of spatially distributed sensor nodes/motes those are connected wirelessly to form network topology. However, despite their great usefulness they are also vulnerable to misbehaviours. One such misbehaviour is Transfaultyness in which a sensor node is unable to transmit the data correctly under the effect of radiation, and it is called as Transfaulty node. Sensor node affected by radiation becomes isolated from the network, thus a dynamic hole is formed in the network. Formation of these dynamic holes in the wireless sensor network leads to data loss. To handle loss of data due to transfaulty nodes in WSN few algorithms have been proposed but none of them is efficient in large network having few thousands of sensor nodes. In our method, we have considered a large wireless sensor network having more than 1000 sensor nodes. We have used sensor nodes with dual mode of communication i.e. (1) Radio Frequency (RF) mode of communication and (2) Acoustic (AC) mode of communication. The nodes inside dynamic hole communicate using acoustic mode, nodes at the boundary of holes communicate using both RF and acoustic mode and all other nodes communicate using RF mode. Determination of transmission failure nodes is also predicted based on its previous observations. Simulation results show that our proposed algorithm achieves better throughput, energy efficiency, running time as compared to other proposed algorithms. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Sensor nodes

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A Blockchain-based Process Provenance for Cloud Forensics 3

A Blockchain-based Process Provenance for Cloud Forensics

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3rd IEEE international conference on Computer and Communications (ICCC)

作者： Zhang, Yong Wu, Songyang Jin, Bo Du, Jiaying Minist Publ Secur Res Inst 3 Shanghai 201204 Peoples R China

ISBN: (纸本)9781509063529

Because of the distributed nature of cloud computing, the data of cloud may be stored and processed across jurisdictional borders. In such case, collecting digital evidence from cloud computing requires multiparty cooperation. However, as far as we known, there is still no available technic approach for improving the trustworthy of the interaction records of all stakeholders in cloud forensics. This work proposed a process provenance, which provides proof of existence and privacy preservation for process records by using technologies of blockchain and cryptography group signature. The process provenance enhances the trustworthy of the chain of custody for cloud forensics.

关键词： cloud forensics process provenance blockchain

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Secured data transmission using improvised Arnold Cat map in WSN 3

Secured data transmission using improvised Arnold Cat map in...

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3rd international conference on Advances in computing, Communication and Automation (Fall), ICACCA 2017

作者： Istwal, Yasha Verma, Shashi Kant Computer Science and Engg. GBPEC Pauri Garhwal246194 India

ISBN: (纸本)9781509064038

This paper is based on secured transmission of data using modified Arnold Cat map using 802.11a/g in WSN. For secure transmission of data, information is transformed in the coded form using data encoded method. Because data send over public domain is secure is primary concern. In Arnold Cat map, firstly data is encrypted, then it is transformed into linear array, after that data modulated and demodulated using OQPSK then using decryption it back into its original format. Analysis of our work is based on MSE and PSNR values. In this paper the value MSE between the original and decrypted image observed as 0.0521 and PSNR observed as 97.1184 which is near perfect retrieval. The histogram of results shows the result after encryption of data. © 2017 IEEE.

关键词： Cryptography

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Cluster-based Best Match Scanning for Large-Scale Missing data Imputation 3

Cluster-based Best Match Scanning for Large-Scale Missing Da...

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3rd international conference on Big data computing and Communications (BIGCOM)

作者： Yu, Weiqing Zhu, Wendong Liu, Guangyi Kan, Bowen Zhao, Ting Liu, He GEIRI North Amer San Jose CA 95134 USA GEIRI Beijing Peoples R China

ISBN: (纸本)9781538633496

High-quality data are the prerequisite for analyzing and using big data to guarantee the value of the data. Missing values in data is a common yet challenging problem in data analytics and data mining, especially in the era of big data. Amount of missing values directly affects the data quality. Therefore, it is critical to properly recover missing values in the dataset. This paper presents a new imputation algorithm called Cluster-based Best Match Scanning (CBMS) designed for Big data. It is a modification of k-NN imputation. CBMS focuses on recovering continuous numeric missing values, and aims at balancing computational complexity and accuracy. As an imputation algorithm, it can potentially reduce the time complexity of k-NN from to, and also reduce the space/memory usage, while perform no worse than k-NN imputation. On top of that CBMS is highly parallelizable. Simulation of CBMS is conducted on smart meter reading data. data is manually divided into training set and testing set, and testing accuracy is evaluated by computing the mean absolute deviation. Comparison with linear interpolation and k-NN imputation is made to demonstrate the power and effectiveness of our proposed CBMS algorithm.

关键词： big data cluster-based best match scanning data imputation k-NN

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Pattern recognition of balinese carving motif using learning vector quantization (LVQ) 3rd

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3rd international conference on soft computing in data science, scds 2017

作者： Mahawan, I. Made Avendias Harjoko, Agus Department of Computer Science and Electronics University of Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9789811072413

Bali is a world tourism destination with its cultural uniqueness, one of the Balinese cultural products that need to be maintained is the art of Balinese carvings in traditional buildings and sacred buildings, to inherit the culture it needs a management, documentation and dissemination of information by utilizing technology. Digital image processing and pattern recognition can be utilized to preserve arts and culture, the technology can be utilized to classify images into specific classes. Balinese carving is one of the carvings that have many variations, if these carvings are analyzed then required an appropriate method for feature extraction process to produce special features in the image. So they can be recognized and classified well and provide information that helps preserve Bali. The aim of this research is to get the right feature extraction method to recognize and classify Bali carving pattern image based on the accuracy of HOG feature extraction method with PCA trained using LVQ. The results of the test data obtained the best accuracy of HOG is 90% with cell size 32 × 32 and block size 2 × 2, PCA obtained 23.67% with threshold 0.01 and 0.001, from training input with learning rate = 0.001 and epoch = 1000. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Principal component analysis

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