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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281-290 订阅

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Improved reversible data embedding in medical images using I-IWT and pairwise pixel difference expansion 3rd

Improved reversible data embedding in medical images using I...

引用

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

作者： Geetha, R. Geetha, S. SENSE VIT University Chennai Campus Chennai India SCSE VIT University Chennai Campus Chennai India

ISBN: (纸本)9789811086595

Reversible data Hiding is a tactic of conveying secret message by embedding the same in any of the multimedia content and after extracting the hidden information, original cover can be recovered without any deformation to it. In this paper an unique way of embedding the secret data reversibly by applying pairwise prediction error expansion on Integer-Integer Wavelet transformed (I-IWT) image points is proposed for medical images. Considering the statistics of the medical images the neighboring points are considered to be similar and hence the prediction errors are perceived to be close to zero/minimum. The proposed scheme employs I-IWT which is applied to the sub bands of cover image and secret bits are embedded into all sub bands. Experimental analysis is carried out on various test medical images and compared with other recent reversible data hiding schemes. Superior image quality for considerable payload determines the efficiency of the proposed approach. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Embeddings

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Message from the conference chair

Proceedings - 2018 IEEE/ACIS 3rd International Conference on...

引用

Proceedings - 2018 IEEE/ACIS 3rd international conference on Big data, Cloud computing, data science and Engineering, BCD 2018 2018年 vii页

作者： Tsunoda, Masateru Kindai University Japan

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Big data analysis - an approach to improve power system data analysis and load research 3rd

Big data analysis - an approach to improve power system data...

引用

3rd international conference on Cognitive computing and Information Processing, CCIP 2017

作者： Shankarlinga, Sandhya S. Veeramanju, K.T. Nagaraja, R. Karnataka Power Transmission Corporation Limited Bengaluru India Sri Jayachamarajendra College of Engineering Mysuru India Power Research & Development Consultants Pvt. Ltd. Bengaluru India

ISBN: (纸本)9789811090585

The introduction of various intelligent electronic devices (IEDs), sensors and other network controls for smarter operation of the electric grid has resulted in massive data explosion. With an exponential growth in volume and diversity of data sources, developing an effective data management system is challenging and also imperative. This paper explores the current state of data analysis and associated problems in power sector with reference to Indian scenario and narrates how Big data Analysis and statistics analysis tools could be adapted to improve power system data analysis and load research by acting on the deluge of big data and leveraging various statistical algorithms. We leverage Apache Hadoop Bigdata ecosystem for large volume of load research data and ‘R’ for pattern recognition and load forecasting. The paper also suggests an approachable roadmap to the power utility for sub-station data analysis embracing identification of peaks and valleys in sub-station demand, detection of anomalies in the data and also conducting short term load forecasting of the sub-station peak demand using these technologies. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Big data

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Semantic cloud community framework for services provision 3rd

Semantic cloud community framework for services provision

引用

3rd international conference on Advanced Intelligent Systems and Informatics, AISI 2017

作者： Al-Mashat, Tamer M. El-Licy, Fatma A. Salah, Akram I. Computer Sciences Department Institute of Statistical Studies and Research Cairo University Giza Egypt Computer Sciences Department Faculty of Computer Science and Information Cairo University Giza Egypt

ISBN: (纸本)9783319648606

Cloud computing represents an evolution paradigm that enables information technology (IT) capabilities to be delivered "as a service". In the last decade number of cloud-based services has grown intensely and rapidly. The diversification of cloud service providers has generated the diversification of their offers. Therefore, end-users face a huge challenge while choosing the appropriate cloud provider. Furthermore, the battle for dominance between the big vendors, like Amazon, Google and Salesforce, makes them reluctant to agree on widely accepted standards promoting their own incompatible formats, thus increases the lock-in effect and affects the competition. Interoperability is the missing element that will recover this situation and allows switch between cloud providers whenever needed without setting data and applications at risk. In this paper, we present an approach that will help strengthen semantic and technical interoperability of services. The approach presents a Cloud Community that acts as a broker to mediate between service providers and service consumers based on web ontology language OWLS. This concept would enable end users to select the right services and compose services across multiple Clouds. It would, also, to provide cloud arbitration services that allow users to shift and to choose between existing platforms. © 2018, Springer international Publishing AG.

关键词： Ontology

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Deep learning based adaptive linear collaborative discriminant regression classification for face recognition 3rd

Deep learning based adaptive linear collaborative discrimina...

引用

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

作者： Shailaja, K. Anuradha, B. Venkatapur HyderabadTelangana501301 India Computer Science Engineering Abhinav Hi-Tech College of Engineering Chilkur Balaji Temple Rd Himayat Nagar HyderabadTelangana501301 India

ISBN: (纸本)9789811086564

Face recognition is a popular research problem in the domain of image analysis. The steps involved in face recognition are mainly face verification and face classification. Face verification algorithms have been well-defined in recent years, but face classification researchers are still facing problems. In this regard we propose face recognition using Adaptive Linear Collaborative Discriminant Regression Classification (ALCDRC) with Deep Learning (DL) algorithm by considering different sets of the training images. In particular, ALCDRC makes utilization of various weights to describe different training sets and uses such weighting data to compute between-class and with-in-class reconstruction errors and after that ALCDRC endeavors to locate an ideal projection matrix which maximize the ratio of Between-Class Reconstruction Error (BCRE) over With-in-Class-Reconstruction Error (WCRE). Experimentation was done on two challenging face databases called ORL and YALE-B and prominent results are obtained. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Face recognition

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Cluster based mechanism for avoidance of duplicate tag data in RFID networks 3rd

Cluster based mechanism for avoidance of duplicate tag data ...

引用

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

作者： Bagirathi, S. Sankar, Sharmila Sandhya B.S Abdur Rahman University Chennai India

ISBN: (纸本)9789811086595

Radio Frequency Identification (RFID) is a wireless technology that is used to determine the objects that has been lost or that has to be tracked through the use of radio waves. These objects contain electronically stored information and each tag has a unique identification number to distinguish one from another. RFID is used for automatic information monitoring and management during the tracking process in many fields. Clustering is the technique of dividing the set of data into groups (i.e.) data segregation. The objective of clustering is to reduce the amount of data by grouping similar data items together. Readers read the same tag data during each cycle of tag detection. To avoid this problem and to enhance the energy efficiency of the system, the Duplicate data avoidance algorithm is proposed in this paper. To filter the duplicate data in the network local cluster head node and inter cluster head nodes are used. The proposed work shows improved performance in duplicate data filtering and in terms of energy efficiency which increases the throughput of the system. Also the collisions among tag are also reduced as the same tag need not reply to the readers again and again. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Energy efficiency

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Tumor Segmentation from Multimodal MRI Using Random Forest with Superpixel and Tensor Based Feature Extraction 3rd

引用

3rd international Workshop on Brain-Lesion (BrainLes) held jointly at the conference on Medical Image computing for Computer Assisted Intervention (MICCAI)

作者： Bharath, H. N. Colleman, S. Sima, D. M. Van Huffel, S. Katholieke Univ Leuven STADIUS Ctr Dynam Syst Signal Proc & Data Analyt Dept Elect Engn ESAT Leuven Belgium IMEC Leuven Belgium Katholieke Univ Leuven Dept Elect Engn ESAT Leuven Belgium

ISBN: (纸本)9783319752389;9783319752372

Identification and localization of brain tumor tissues plays an important role in diagnosis and treatment planning of gliomas. A fully automated superpixel wise two-stage tumor tissue segmentation algorithm using random forest is proposed in this paper. First stage is used to identify total tumor and the second stage to segment sub-regions. Features for random forest classifier are extracted by constructing a tensor from multimodal MRI data and applying multi-linear singular value decomposition. The proposed method is tested on BRATS 2017 validation and test dataset. The first stage model has a Dice score of 83% for the whole tumor on the validation dataset. The total model achieves a performance of 77%, 50% and 61% Dice scores for whole tumor, enhancing tumor and tumor core, respectively on the test dataset.

关键词： Superpixel Multilinear singular value decomposition Random forest MRI Tumor segmentation

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Quantified self using IoT wearable devices 3rd

Quantified self using IoT wearable devices

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3rd international conference on Advanced Intelligent Systems and Informatics, AISI 2017

作者： Shehab, Abdulaziz Ismail, Ahmed Osman, Lobna Elhoseny, Mohamed El-Henawy, I.M. Information Systems Department Faculty of Computers and Information Mansoura University Mansoura Egypt Department of Electronics and Communications Engineering Delta Higher Institute of Engineering and Technology Talkha Egypt Faculty of Computer Science and Information Systems Zagazig University Zagazig Egypt Cairo Egypt

ISBN: (纸本)9783319648606

Nowadays, designing and developing wearable devices that could detect many types of diseases has become inevitable for E-health field. The decision-making of those wearable devices is done by various levels of analysis of enormous databases of human health records. Systems that demand a huge number of input data to decide to require real-time data collected from devices, processes, and analyzing the data. Many researchers utilize the Internet of Things (IoT) in medical wearable devices to detect different diseases by using different sensors together for one goal. The IoT promises to revolutionize the lifestyle using a wealth of new services, based on interactions between large numbers of devices data. The proposed work is human monitor system to track the human body troubles. Smart wearable devices can provide users with overall health data, and alerts from sensors to notify them on their mobile phones accordingly. The proposed system developed a technique using Internet of Things technique to decrease the load on IOT network and decrease the overall cost of the users. The simulation results proved that the proposed system could provide identical communication for IOT devices even if many nodes are used. © 2018, Springer international Publishing AG.

关键词： Internet of things

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A data mining approach towards healthcare recommender system 3rd

A data mining approach towards healthcare recommender system

引用

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

作者： Sharma, Mugdha Ahuja, Laxmi Amity School of Engineering and Technology Amity University NoidaUP201313 India Amity Institute of Information Technology Amity University NoidaUP201313 India

ISBN: (纸本)9789811086564

Healthcare recommender systems are meant to provide accurate and relevant predictions to the patients. It is very difficult for people to explore various online sources to find some useful recommendations as per their medical conditions. So the proposed approach is an effort to provide accurate recommendations to the patients on the basis of their current medical condition as well as their medical history and constraints. First of all, patients are categorized into different groups based on their profiles and then rules predicting the medical condition of each group are mined. The proposed approach is unique in the way that it provides accurate treatments to the patients in the form of recommendations based on content based matching. It also considers the preferences of the patient, which are stored in the system as mined rules or estimated from the medical history of patient. The results of experimental setup also demonstrate that the proposed system provides more accurate outcomes over other healthcare recommendation systems. © Springer Nature Singapore Pte Ltd. 2018.

关键词： data mining

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Controlled replication based bubble rap routing algorithm in delay tolerant network 3rd

Controlled replication based bubble rap routing algorithm in...

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

作者： Jain, Sweta Yadav, Pavan Department of Computer Science and Engineering Maulana Azad National Institute of Technology BhopalMadhya Pradesh462051 India

ISBN: (纸本)9789811086595

Delay-tolerant or opportunistic networks (DTNs) [1, 2] are special types of networks which allow transmission of data where there may be no end to end connection between source and destination. DTNs may lack continuous network connectivity and there may be very large delay in message delivery because of sparse node density. Thus routing in DTNs is a very challenging task because it must handle very long delays, frequent network partitions and resultant inconsistent network topology. Social-based routing approaches have arouse great interest in the context of DTN, where different social characteristics of DTN nodes are used to make better routing decisions. A controlled replication based scheme has been used to improve the performance of Bubble Rap routing algorithm. The proposed algorithm has been tested in both heterogeneous and real world mobility scenarios. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Routing algorithms

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