for Journals by Title or ISSN
for Articles by Keywords
help
  Subjects -> COMPUTER SCIENCE (Total: 2011 journals)
    - ANIMATION AND SIMULATION (30 journals)
    - ARTIFICIAL INTELLIGENCE (98 journals)
    - AUTOMATION AND ROBOTICS (98 journals)
    - CLOUD COMPUTING AND NETWORKS (61 journals)
    - COMPUTER ARCHITECTURE (9 journals)
    - COMPUTER ENGINEERING (9 journals)
    - COMPUTER GAMES (16 journals)
    - COMPUTER PROGRAMMING (24 journals)
    - COMPUTER SCIENCE (1172 journals)
    - COMPUTER SECURITY (46 journals)
    - DATA BASE MANAGEMENT (13 journals)
    - DATA MINING (32 journals)
    - E-BUSINESS (22 journals)
    - E-LEARNING (29 journals)
    - ELECTRONIC DATA PROCESSING (21 journals)
    - IMAGE AND VIDEO PROCESSING (39 journals)
    - INFORMATION SYSTEMS (108 journals)
    - INTERNET (92 journals)
    - SOCIAL WEB (50 journals)
    - SOFTWARE (34 journals)
    - THEORY OF COMPUTING (8 journals)

COMPUTER SCIENCE (1172 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 15)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 24)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 9)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 13)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 6)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 12)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 15)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 6)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 1)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 21)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 8)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 9)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 25)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Advanced Engineering Materials     Hybrid Journal   (Followers: 26)
Advanced Science Letters     Full-text available via subscription   (Followers: 9)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 7)
Advances in Artificial Intelligence     Open Access   (Followers: 16)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 6)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 18)
Advances in Computer Science : an International Journal     Open Access   (Followers: 15)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Advances in Engineering Software     Hybrid Journal   (Followers: 27)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 11)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 27)
Advances in Human-Computer Interaction     Open Access   (Followers: 21)
Advances in Materials Sciences     Open Access   (Followers: 16)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Remote Sensing     Open Access   (Followers: 40)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 4)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 8)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Air, Soil & Water Research     Open Access   (Followers: 9)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 11)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 5)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 5)
American Journal of Sensor Technology     Open Access   (Followers: 4)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 11)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 1)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 13)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Computer Systems     Open Access   (Followers: 2)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 33)
Applied Medical Informatics     Open Access   (Followers: 10)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 15)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5)
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 136)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 7)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access   (Followers: 1)
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 4)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 11)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 9)
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Biodiversity Information Science and Standards     Open Access  
Bioinformatics     Hybrid Journal   (Followers: 285)
Biomedical Engineering     Hybrid Journal   (Followers: 15)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 14)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 18)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 34)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 47)
British Journal of Educational Technology     Hybrid Journal   (Followers: 136)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 2)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal  
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 14)
Capturing Intelligence     Full-text available via subscription  
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
Cell Communication and Signaling     Open Access   (Followers: 2)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
CERN IdeaSquare Journal of Experimental Innovation     Open Access   (Followers: 1)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 14)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Full-text available via subscription   (Followers: 11)
Circuits and Systems     Open Access   (Followers: 15)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Cluster Computing     Hybrid Journal   (Followers: 1)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Communication Methods and Measures     Hybrid Journal   (Followers: 12)
Communication Theory     Hybrid Journal   (Followers: 20)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 55)
Communications of the Association for Information Systems     Open Access   (Followers: 18)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access   (Followers: 1)
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Computación y Sistemas     Open Access  
Computation     Open Access  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 11)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 9)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 15)
Computational Linguistics     Open Access   (Followers: 22)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 14)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 30)
Computer     Full-text available via subscription   (Followers: 90)
Computer Aided Surgery     Hybrid Journal   (Followers: 5)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Communications     Hybrid Journal   (Followers: 10)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 9)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 22)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 12)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 2)
Computer Music Journal     Hybrid Journal   (Followers: 18)
Computer Physics Communications     Hybrid Journal   (Followers: 6)
Computer Science - Research and Development     Hybrid Journal   (Followers: 8)
Computer Science and Engineering     Open Access   (Followers: 19)
Computer Science and Information Technology     Open Access   (Followers: 13)
Computer Science Education     Hybrid Journal   (Followers: 14)
Computer Science Journal     Open Access   (Followers: 22)

        1 2 3 4 5 6 | Last

Journal Cover Cluster Computing
  [SJR: 0.605]   [H-I: 24]   [1 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1573-7543 - ISSN (Online) 1386-7857
   Published by Springer-Verlag Homepage  [2355 journals]
  • A cloud-based virtual caregiver for elderly people in a cyber physical IoT
           system
    • Authors: Md. Abdur Rahman; M. Shamim Hossain
      Abstract: In this research, we propose virtual caregiver (VCareGiver), a virtual caregiver as a cyber-physical smart home (CPSH) companion to support the daily life activities of elderly people through their gestures. The CPSH will act as a virtual caregiver who will understand the need of an elderly person and be present with him/her ubiquitously at any time and at any location within his/her house. VCareGiver will help in solving two important problems. Firstly, a large population is at their old age who needs help in their daily chores, since the family members are unable to give full support (e.g. reminding the to-do list, maintaining appointments, taking medicine on right time, sending SMS, saving energy due to consumption of electricity, controlling home appliances, etc.) due to busy schedule. Secondly, due to the old age movement problem, they need support in doing activities through gesture. In this research, we want to test the viability of the virtual caregiver as a physical assistant. The gestures of an elderly subject in the physical world are recognized by a multi-sensory IoT-based framework that is passed to the CPSH, which analyses the gestures and generates a command to perform certain physical world activity and finally produce the visualization. A proof of concept CPSH has been implemented through the web, and smartphone based applications. More than 100 different gestures have been incorporated into the CPSH system through which different daily life activities can be performed. Initial test results are very encouraging and show the prospect of such virtual caregiver, which can revolutionize the quality of life of our prospective elderly generation.
      PubDate: 2018-02-07
      DOI: 10.1007/s10586-018-1806-y
       
  • Multiple parameter algorithm approach for adult image identification
    • Authors: R. Balamurali; A. Chandrasekar
      Abstract: Multi-parameter algorithm with statistical approach for adult image discretion is proposed to figure out the obscene images. In this work, we propose an analysis on different color spaces to identify an effective pixel identification for human skin. In this work, an algorithm is incorporated to verify and spirit away the unambiguous image by identifying high skin pixel rate. This framed algorithm is verified in terms of accuracy, true negatives and false positives and the results expressed in this paper show that the algorithm worked well and fast in detecting obscene images.
      PubDate: 2018-02-07
      DOI: 10.1007/s10586-017-1510-3
       
  • Research on O2O take-away restaurant recommendation system: taking ele.me
           APP as an example
    • Authors: Linan Wang; Bo Yi
      Abstract: Online to Offline (O2O) take-away has the great potentialT for development in China, but with a large number of merchants taking part in O2O take-away APP, resulting in the problem of information overload. And the recommended system can effectively relieve the information overload of APP. In this paper, the O2O take-away restaurant recommendation system is studied in detail. By analyzing the current situation of the O2O takeaway restaurant recommendation system, we put forward a recommendation algorithm based on rank-centroid/analytic hierarchy process. Through transforming the recent booking preferences of user into the take-away service standard weight, we establish the model, combined with the case of ele.me APP, compute actual scores of the restaurants by the comprehensive score, and choose the high score of the restaurants to recommend. Compared with the mainstream collaborative filtering recommendation method, the proposed method is simple and computationally smaller.
      PubDate: 2018-02-07
      DOI: 10.1007/s10586-018-1814-y
       
  • Software defect prediction techniques using metrics based on neural
           network classifier
    • Authors: R. Jayanthi; Lilly Florence
      Abstract: Software industries strive for software quality improvement by consistent bug prediction, bug removal and prediction of fault-prone module. This area has attracted researchers due to its significant involvement in software industries. Various techniques have been presented for software defect prediction. Recent researches have recommended data-mining using machine learning as an important paradigm for software bug prediction. state-of-art software defect prediction task suffer from various issues such as classification accuracy. However, software defect datasets are imbalanced in nature and known fault prone due to its huge dimension. To address this issue, here we present a combined approach for software defect prediction and prediction of software bugs. Proposed approach delivers a concept of feature reduction and artificial intelligence where feature reduction is carried out by well-known principle component analysis (PCA) scheme which is further improved by incorporating maximum-likelihood estimation for error reduction in PCA data reconstruction. Finally, neural network based classification technique is applied which shows prediction results. A framework is formulated and implemented on NASA software dataset where four datasets i.e., KC1, PC3, PC4 and JM1 are considered for performance analysis using MATLAB simulation tool. An extensive experimental study is performed where confusion, precision, recall, classification accuracy etc. parameters are computed and compared with existing software defect prediction techniques. Experimental study shows that proposed approach can provide better performance for software defect prediction.
      PubDate: 2018-02-07
      DOI: 10.1007/s10586-018-1730-1
       
  • Map-Reduce framework based cluster architecture for academic student’s
           performance prediction using cumulative dragonfly based neural network
    • Authors: M. R. M. VeeraManickam; M. Mohanapriya; Bishwajeet K. Pandey; Sushma Akhade; S. A. Kale; Reshma Patil; M. Vigneshwar
      Abstract: The major aim of the education institute is to provide the high-quality education to students. The way to attain the high quality in the education system is to determine the knowledge from the educational data and learn the attributes which influence the performance of the students. The extracted knowledge is used to predict the academic performance of the students. This paper presents the student performance prediction model by proposing the Map-reduce architecture based cumulative dragonfly based neural network (CDF-NN). The CDF-NN is proposed by training the neural network by the cumulative dragonfly algorithm (DA). Initially, the marks of the students from semester 1 to semester 7 are collected from different colleges. In the training phase, the features are selected from the student’s information and the intermediate data is generated by the mapper. Then, the intermediate data is provided to the reducer function which is built with the CDF-NN to provide the estimated marks of the students in a forthcoming semester. The proposed method is compared with the existing methods, such as Dragonfly- NN and Back prorogation algorithm for the evaluation metrics, MSE and RMSE. The proposed prediction model obtains the MSE of 16.944 and RMSE of 4.665.
      PubDate: 2018-02-07
      DOI: 10.1007/s10586-017-1553-5
       
  • An improved design for grid based PV control systems using modified fuzzy
           logic with an improved switched capacitor DC-DC converter
    • Authors: J. K. Mohan Kumar; H. Abdul Rauf; R. Umamaheswari
      Abstract: In Grid computing, the interconnecting photo-voltaic (PV) control system needs an efficient converter to convert the low direct current (DC) voltage into DC. Design has been improved for the PV, by using a modified fuzzy logic along with an improved switched capacitor (SC) and a DC Converter. An optimized SC DC–DC converter’s performance in steady state for improving the efficiency and the regulation of output for a class belonging to the SC converters that is applied to the PV systems control is proposed. The model has many power converters for efficiently managing the transfer of power of a long cable that has higher electro-magnetic interference. On the basis of the model proposed, this converter provides a suitable DC voltage conversion ratio. The capacitor’s resistive output on the impedance accounts for the purpose of charging and discharging losses and the resistive conduction losses has been considered. This work will further extend this DC–DC converter into method of control for a maximum power point tracking which belongs to a PV system that makes use of the variable temperature as well as the irradiance conditions and the fuzzy logic controller (FLC). The FLC that is proposed is improved by using a stochastic diffusion search technique.
      PubDate: 2018-02-07
      DOI: 10.1007/s10586-018-1779-x
       
  • The linear regression method of the influencing factors of cultural
           industry based on the classification of structural data sources
    • Authors: Cao Honggang; Chen Kai; Zhong Xu
      Abstract: This paper takes cultural industry listed companies in China as the research object and selects 127 listed companies as samples. It uses their financial data from the year of 2009 to 2016 and uses factor analysis and linear regression to establish a multiple linear regression model to make an empirical study on the capital structure of listed companies in cultural industry. The results show that the capital structure of listed companies in cultural industry has significant positive correlation with profitability, growth ability, company size, nature of property rights and cash flow, and that the capital structure of listed companies in cultural industry is negatively correlated with dividend policy and irrelevant to asset structure.
      PubDate: 2018-02-07
      DOI: 10.1007/s10586-018-1852-5
       
  • Content based image retrieval using deep learning process
    • Authors: R. Rani Saritha; Varghese Paul; P. Ganesh Kumar
      Abstract: Content-based image retrieval (CBIR) uses image content features to search and retrieve digital images from a large database. A variety of visual feature extraction techniques have been employed to implement the searching purpose. Due to the computation time requirement, some good algorithms are not been used. The retrieval performance of a content-based image retrieval system crucially depends on the feature representation and similarity measurements. The ultimate aim of the proposed method is to provide an efficient algorithm to deal with the above mentioned problem definition. Here the deep belief network (DBN) method of deep learning is used to extract the features and classification and is an emerging research area, because of the generation of large volume of data. The proposed method is tested through simulation in comparison and the results show a huge positive deviation towards its performance.
      PubDate: 2018-02-07
      DOI: 10.1007/s10586-018-1731-0
       
  • A coverage hole detection and repair algorithm in wireless sensor networks
    • Authors: Xin Feng; Xin Zhang; Jing Zhang; Ali Ahmed Muhdhar
      Abstract: Coverage and connectivity are the basic issues in wireless sensor networks. In this paper, a coverage hole detection and repair algorithm is proposed to solve the problem of network disconnection. The algorithm dynamically analyzes the network topology based on the maximum simple subnet, determines the boundary node of the empty area, calculates the polygon area of the boundary node and compares it with the preset area threshold to determine the clustering strategy onto the polygon. Moreover, with activating the valid inactive nodes and make them as the cluster nodes within the empty area, the blank hole could thus be repaired and the connectivity as well as communication of the network is then managed. The simulation results show that the accuracy of hole detection is 7 and 5% higher than that of BFNP and HPA, and the coverage is 10 and 40% higher than that of BFNP and the condition without any repairing solution.
      PubDate: 2018-02-06
      DOI: 10.1007/s10586-017-1665-y
       
  • Short-term load forecasting based on multivariate time series prediction
           and weighted neural network with random weights and kernels
    • Authors: Kun Lang; Mingyuan Zhang; Yongbo Yuan; Xijian Yue
      Abstract: Forecasting short-term load is a basic but indispensable problem for power system operations. This paper treats the forecasting problem as a multivariate time series forecasting problem. The electricity load and the corresponding temperature data are analyzed as correlative time series, and are reconstructed to the multivariate phase space. A neural network with random weights and kernels, which combines the advantages of the neural network and support vector machine including simple training and good generalization performance, is used as the forecasting model. Then, in order to further improve the forecasting performance, different weights are applied to the input data in the phase space according to the predictive value, and the resulting model is called weighted neural network with random weights and kernels. Simulation results based on the real world data set from the EUNITE competition show the effectiveness of the proposed method.
      PubDate: 2018-02-06
      DOI: 10.1007/s10586-017-1685-7
       
  • Optimization using Artificial Bee Colony based clustering approach for big
           data
    • Authors: S. Sudhakar Ilango; S. Vimal; M. Kaliappan; P. Subbulakshmi
      Abstract: As one of the major problems is that the time taken for executing the traditional algorithm is larger and that it is very difficult for processing large amount of data. Clusters possess high degree of similarity among each cluster and have low degree of similarity among other clusters. Optimization algorithm for clustering is the art of allocating scarce resources to the best possible effect. The traditional optimization algorithm is not suitable for processing high dimensional data. The main objective of proposed Artificial Bee Colony (ABC) approach is to minimize the execution time and to optimize the best cluster for the various sizes of the dataset. To deal with this, we are normalizing to distributed environment for time efficiency and accuracy. The proposed ABC algorithm simulates the behavior of real bees for solving numerical optimization problems particularly in clustering. The dataset size is varied for the algorithm and is mapped with its appropriate timings. The result is observed for various fitness and probability value which is obtained from the employed and the onlooker phase of ABC algorithm from which the further calibrations of classification error percentage is done. The proposed ABC Algorithm is implemented in Hadoop environment using mapper and reducer programming. An experimental result reveals that the proposed ABC scheme reduces the execution time and classification error for selecting optimal clusters. The results show that the proposed ABC scheme gives a better performance than PSO and DE in terms of time efficiency.
      PubDate: 2018-02-06
      DOI: 10.1007/s10586-017-1571-3
       
  • A generalized framework for disruption tolerant secure opportunistic
           routing during emergency situations using MANETs
    • Authors: K. Pushpalatha; M. Karthikeyan
      Abstract: Mobile ad-hoc networks (MANETs) play a constitutive role in the field of telecommunication. It enables communication between moving nodes by using auto-node configuration and multi hop wireless routing strategies. During disasters, the existing telecommunication structure is either completely destroyed or partially collapsed. Thus, it is very difficult to perform data communications during that period and also it takes more time to restore it to its original structure. The purpose of this research paper is to ensure reliable and secured communication using opportunistic routing between mobile nodes during such emergency situations. The existing methods of the disaster routing protocols do not guarantee reliable delivery of data in a secured manner and it assumes that the nodes are configured with a unique IP address before participating in the communication. This paper presents a novel framework disruption tolerant secure opportunistic routing (DTSOR) method that ensures secure communication between high mobility nodes. Cluster based address auto configuration mechanism and trace analysis are preferred methods used to select the optimal path for forwarding the packets. Selected path further ensures security using puzzle based data anonymization method. Performance analysis and simulation results shows that proposed framework outperforms the state of the art approaches in terms of the packet delivery ratio, network overhead and throughput.
      PubDate: 2018-02-06
      DOI: 10.1007/s10586-018-1849-0
       
  • Automatic sedimentary microfacies identification from logging curves based
           on deep process neural network
    • Authors: Hui Liu; Shaohua Xu; Xinmin Ge; Jianyu Liu; Muhammad Aleem Zahid
      Abstract: An automatic sedimentary microfacies identification technique is developed based on the deep process neural network (DPNN), which consist several neurons and general non-time-varying neurons arranged in a certain topological structure. In this technique, the features of the shape and amplitude of logging curves are considered to form the category outputs. Combined with the deep learning theory, the diversity of the process features of logging curves and the complexity of combined features of multiple geophysical logging information are considered, and DPNN is created through the stacked superimposition of deep belief network and BP classifier. The technique maintains the structure and information relevance of process signal data and can characterize the distribution features of logging curves automatically, and classify the process signals directly. The theoretical nature and performance of the improved algorithm is tested and validated by some field examples.
      PubDate: 2018-02-06
      DOI: 10.1007/s10586-017-1656-z
       
  • Secondary parallel automatic parking of endpoint regionalization based on
           genetic algorithm
    • Authors: Bo Su; Junhan Yang; Lijuan Li; Yu Wang
      Abstract: Automatic parking technology can overcome the difficulties of drivers in tight spaces during parking. In this paper, in order to improve the accuracy of parallel parking in tight spaces, a secondary parallel automatic parking method of endpoint regionalization based on genetic algorithm is proposed. Firstly, a secondary parallel parking method of endpoint regionalization is proposed and a collision constraint function is established by planning the secondary parallel automatic parking path and analyzing the possible collisions during parking. Secondly, the secondary parallel parking path is planed by estimating the minimum parking length and designing a reasonable terminal area for parking. The simulation made on MATLAB verifies the feasibility of the method. In order to improve the efficiency of secondary parallel automatic parking of endpoint regionalization and achieve the shortest automatic parking path, a parking path function with constraint conditions is established in this paper and optimizing is done by taking the parking path function as target function with genetic algorithm. The simulation results show that the secondary parallel automatic parking of endpoint regionalization based on genetic algorithm can enable cars to park in the designed terminal area correctly without collision and the parking path is shortened by 4.1% compared with that of the original one.
      PubDate: 2018-02-06
      DOI: 10.1007/s10586-018-1809-8
       
  • An efficient and low power impulsive noise suppressor architecture for
           OFDM system
    • Authors: N. Manikanda Devarajan; M. Chandrasekaran
      Abstract: Orthogonal frequency division multiplexing is wireless communication technology which is used for high reliable and high datarate communication. The conventional OFDM system is often affected by noise contents when the symbols are passed through frequency selective fading channel.This paper proposes low power impulsive noise suppressor architecture for detecting and suppressing the noise contents in the received OFDM symbols.This proposed impulsive noise suppression architecture consumes less hardware resources and consumes low power. The proposed system is designed using Verilog high definition language and synthesized in Xilinx Project Navigator 12.1i.
      PubDate: 2018-02-06
      DOI: 10.1007/s10586-018-1758-2
       
  • Application of an intelligent early-warning method based on DBSCAN
           clustering for drilling overflow accident
    • Authors: Liang Haibo; Wang Zhi
      Abstract: Oil and gas are still the necessities of production in today’s society. However, the exploration and mining of them are extremely complex and dangerous. Overflow accidents are undoubtedly one of the biggest threats to safe drilling operations during the oil and gas exploration. Due to the complexity of geological information or lack of adjacent well data in drilling process, the problem of overflow warning model based on sample information can not be established. Data mining is the process of revealing meaningful new patterns, relationships and trends by analyzing data, therefore, based on the correlation between the occurrence of overflow accidents and the change trend of casing pressure, a method of intelligent warning based on improved DBSCAN clustering method for drilling overflow accidents is proposed. The early warning method uses time-series scanning and stratification to rule the idea of clustering, not only improve the efficiency of clustering, but also enhance the clustering effect. According to the results of clustering fitting and the sensitivity of overflow accident, output the warning result of overflow accident. The data analysis is made by using the field data. The experimental results show that the flood warning method based on improved DBSCAN clustering can effectively predict the overflow accidents.
      PubDate: 2018-02-06
      DOI: 10.1007/s10586-017-1687-5
       
  • Artificial bee colony optimization-based weighted extreme learning machine
           for imbalanced data learning
    • Authors: Xiaofen Tang; Li Chen
      Abstract: The imbalanced datasets are common in real-world application and the problem of imbalanced dataset affect classification performance of many standard learning approaches. To address imbalanced datasets, a weighted extreme learning machine (WELM) solving the L \(_{2}\) -regularized weighted least squares problem is presented to avoid the generation of an over-fitting model and obtain better generalization ability compared with ELM. However, the weight generated according to class distribution of training data leads to lack of finding optimal weight with good generalization performance and the randomness of input weight and hidden biases of network makes the algorithm produce suboptimal classification model. In this paper, a weighted extreme learning machine based on hybrid artificial bee colony (HABC) is proposed to obtain better performance than WELM, in which input weights and hidden bias of WELM and the weight assigned to training samples are optimized by the hybrid artificial bee colony algorithm. HABC combines the diversities of the perturbed parameter vectors of differential evolution with the best solution information of the artificial bee colony effectively. In the empirical study, different class imbalance data handling methods including four WELM-based methods, weighted support vector machine, four ensemble methods which combine data sampling and the Bagging or Boosting are compared with our method. The experimental results on 15 imbalanced datasets show that the proposed method outperforms most methods, which indicates its superiority.
      PubDate: 2018-02-06
      DOI: 10.1007/s10586-018-1808-9
       
  • Dyadic product and crow lion algorithm based coefficient generation for
           privacy protection on cloud
    • Authors: Ashok George; A. Sumathi
      Abstract: Cloud systems are powerful computing resources used inevitably for data subscription and publication. Even though the cloud platform can handle the huge volume of data, privacy becomes a critical issue during data publishing. Hence, an effective technique for the privacy preservation of the data is required in the cloud computing environment. Accordingly, this paper proposes a technique for privacy protection using the dyadic product and an optimization algorithm. The privacy of the original database is protected by the construction of privacy preserved database using a dyadic square matrix obtained taking the dyadic product of two vectors, namely sensitive-utility (SU) coefficient and cumulative data key product. The selection of SU coefficient vector is based on the proposed (Crow search based Lion) C-Lion algorithm, which is designed by combining crow search algorithm with lion algorithm. The fitness of the proposed C-Lion algorithm is designed based on privacy and utility for the feasible selection of SU coefficient vector. The performance of the proposed privacy protection technique based on the C-Lion algorithm is evaluated using two factors, privacy, and utility. The experimental analysis shows that the proposed technique could attain the maximum utility of 0.909 with privacy 0.864 for the breast cancer dataset.
      PubDate: 2018-02-06
      DOI: 10.1007/s10586-017-1589-6
       
  • An object tracking method based on Mean Shift algorithm with HSV color
           space and texture features
    • Authors: Jinhang Liu; Xian Zhong
      Abstract: Mean Shift is a powerful and versatile non-parametric iterative algorithm that can be used for lot of purposes like finding modes, clustering etc. It has been widely used in target tracking field because of some advantages like fewer iteration times and better real-time performance for many years. However, due to only single-color histogram representation of target feature has been used in traditional Mean Shift algorithm, it cannot track very well in some cases, especially under very complicated conditions. There are mainly two problems that can cause traditional Mean Shift algorithm to be unstable. The first problem is when the background color and target color are similar, the tracking performance is significantly insufficient, the second is the partial occlusion problem. In this paper, we have proposed a solution to solve these two issues, which contains three improvements. For the first problem, we transformed original color features in traditional Mean Shift algorithm into HSV color space, At the same time, we will also consider texture features and integrate into algorithm to improve tracking performance. and, we applied four neighborhood searching method to solve partial occlusion problem. We tested our algorithms on a variety of standard datasets and a video data collected from real-world environment. The result of experiments show that our proposed algorithm has higher accuracy than traditional Mean Shift algorithm and the background weighted Mean Shift algorithm in test case of complex conditions. Besides, our proposed algorithm also has a good operating efficiency then traditional one.
      PubDate: 2018-02-06
      DOI: 10.1007/s10586-018-1818-7
       
  • The methods of big data fusion and semantic collision detection in
           Internet of Thing
    • Authors: Ruo Hu; Hui-min Zhao; Yantai Wu
      Abstract: We sometimes find ourselves with plenty of data fusion in Internet of Thing, which necessitates an automatic removing semantic collision. For this, it is necessary to detect semantic collision, with a fairly reliable method to find many semantic collision and powerful enough to run in a reasonable time. Big data fusion in Internet of Thing represents today an important data quality challenge which leads to bad decision-making. This paper proposes and compares on real data effective fusion matching methods for automatic removing semantic collision of files based on names, working with Chinese texts or English texts, and the names of people or places, in East or in the West. After conducting a more complete classification of big data fusion than the usual classifications, we introduce several methods for big data fusion. Through a simple model, we highlight a global efficiency, accuracy and recover. We propose a new measuring mechanism between records, as well as rules for automatic big data fusion. Analyses made on Internet of Thing containing real data in western cities, and on a known standard Internet of Thing containing names of companies in the China, have shown better results than those of known methods, with a lesser complexity.
      PubDate: 2018-02-06
      DOI: 10.1007/s10586-017-1577-x
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs
Your IP address: 54.227.48.147
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-