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  Subjects -> COMPUTER SCIENCE (Total: 2064 journals)
    - ANIMATION AND SIMULATION (31 journals)
    - ARTIFICIAL INTELLIGENCE (101 journals)
    - AUTOMATION AND ROBOTICS (105 journals)
    - CLOUD COMPUTING AND NETWORKS (64 journals)
    - COMPUTER ARCHITECTURE (10 journals)
    - COMPUTER ENGINEERING (11 journals)
    - COMPUTER GAMES (16 journals)
    - COMPUTER PROGRAMMING (26 journals)
    - COMPUTER SCIENCE (1196 journals)
    - COMPUTER SECURITY (46 journals)
    - DATA BASE MANAGEMENT (14 journals)
    - DATA MINING (35 journals)
    - E-BUSINESS (22 journals)
    - E-LEARNING (29 journals)
    - ELECTRONIC DATA PROCESSING (23 journals)
    - IMAGE AND VIDEO PROCESSING (40 journals)
    - INFORMATION SYSTEMS (110 journals)
    - INTERNET (93 journals)
    - SOCIAL WEB (51 journals)
    - SOFTWARE (33 journals)
    - THEORY OF COMPUTING (8 journals)

COMPUTER SCIENCE (1196 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: 20)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 27)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 12)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 15)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 5)
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: 5)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 4)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 19)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 8)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 6)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 29)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Informatica Malaysia     Open Access  
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: 28)
Advanced Science Letters     Full-text available via subscription   (Followers: 10)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 7)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 19)
Advances in Computer Engineering     Open Access   (Followers: 4)
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: 13)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
Advances in Human-Computer Interaction     Open Access   (Followers: 20)
Advances in Materials Sciences     Open Access   (Followers: 14)
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: 44)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 5)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 9)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
AI EDAM     Hybrid Journal  
Air, Soil & Water Research     Open Access   (Followers: 11)
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: 4)
Annals of Data Science     Hybrid Journal   (Followers: 12)
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: 12)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 11)
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: 16)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5)
Applied System Innovation     Open Access  
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: 142)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
arq: Architectural Research Quarterly     Hybrid Journal   (Followers: 7)
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)
Big Data and Cognitive Computing     Open Access   (Followers: 2)
Biodiversity Information Science and Standards     Open Access  
Bioinformatics     Hybrid Journal   (Followers: 294)
Biomedical Engineering     Hybrid Journal   (Followers: 15)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 21)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 37)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 47)
British Journal of Educational Technology     Hybrid Journal   (Followers: 138)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 12)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 1)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal   (Followers: 2)
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 15)
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: 3)
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: 1)
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  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Communication Methods and Measures     Hybrid Journal   (Followers: 12)
Communication Theory     Hybrid Journal   (Followers: 21)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Computational Physics     Full-text available via subscription   (Followers: 2)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 52)
Communications of the Association for Information Systems     Open Access   (Followers: 16)
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: 8)
Computación y Sistemas     Open Access  
Computation     Open Access   (Followers: 1)
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: 12)
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: 16)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 5)
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: 96)
Computer Aided Surgery     Open Access   (Followers: 6)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Communications     Hybrid Journal   (Followers: 16)
Computer Journal     Hybrid Journal   (Followers: 9)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 23)
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: 19)
Computer Physics Communications     Hybrid Journal   (Followers: 7)

        1 2 3 4 5 6 | Last

Journal Cover
Cluster Computing
Journal Prestige (SJR): 0.374
Citation Impact (citeScore): 2
Number of Followers: 1  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1573-7543 - ISSN (Online) 1386-7857
Published by Springer-Verlag Homepage  [2351 journals]
  • Multi-prediction based scheduling for hybrid workloads in the cloud data
           center
    • Authors: Haiou Jiang; Haihong E; Meina Song
      Abstract: Cloud computing can leverage over-provisioned resources that are wasted in traditional data centers hosting production applications by consolidating tasks with lower QoS and SLA requirements. However, the dramatic fluctuation of workloads with lower QoS and SLA requirements may impact the performance of production applications. Frequent task eviction, killing and rescheduling operations also waste CPU cycles and create overhead. This paper aims to schedule hybrid workloads in the cloud data center to reduce task failures and increase resource utilization. The multi-prediction model, including the ARMA model and the feedback based online AR model, is used to predict the current and the future resource availability. Decision to accept or reject a new task is based on the available resources and task properties. Evaluations show that the scheduler can reduce the host overload and failed tasks by nearly 70%, and increase effective resource utilization by more than 65%. The task delay performance degradation is also acceptable.
      PubDate: 2018-06-04
      DOI: 10.1007/s10586-018-2265-1
       
  • Enabling secure auditing and deduplicating data without owner-relationship
           exposure in cloud storage
    • Authors: Huiying Hou; Jia Yu; Hanlin Zhang; Yan Xu; Rong Hao
      Abstract: The public cloud storage auditing with deduplication has been studied to assure the data integrity and improve the storage efficiency for cloud storage in recent years. The cloud, however, has to store the link between the file and its data owners to support the valid data downloading in previous schemes. From this file-owner link, the cloud server can identify which users own the same file. It might expose the sensitive relationship among data owners of this multi-owners file, which seriously harms the data owners’ privacy. To address this problem, we propose an identity-protected secure auditing and deduplicating data scheme in this paper. In the proposed scheme, the cloud cannot learn any useful information on the relationship of data owners. Different from existing schemes, the cloud does not need to store the file-owner link for supporting valid data downloading. Instead, when the user downloads the file, he only needs to anonymously submit a credential to the cloud, and can download the file only if this credential is valid. Except this main contribution, our scheme has the following advantages over existing schemes. First, the proposed scheme achieves the constant storage, that is, the storage space is fully independent of the number of the data owners possessing the same file. Second, the proposed scheme achieves the constant computation. Only the first uploader needs to generate the authenticator for each file block, while subsequent owners do not need to generate it any longer. As a result, our scheme greatly reduces the storage overhead of the cloud and the computation overhead of data owners. The security analysis and experimental results show that our scheme is secure and efficient.
      PubDate: 2018-06-01
      DOI: 10.1007/s10586-018-2813-8
       
  • An implementation of matrix–matrix multiplication on the Intel KNL
           processor with AVX-512
    • Authors: Roktaek Lim; Yeongha Lee; Raehyun Kim; Jaeyoung Choi
      Abstract: The second generation Intel Xeon Phi processor codenamed Knights Landing (KNL) have recently emerged with 2D tile mesh architecture and the Intel AVX-512 instructions. However, it is very difficult for general users to get the maximum performance from the new architecture since they are not familiar with optimal cache reuse, efficient vectorization, and assembly language. In this paper, we illustrate several developing strategies to achieve good performance with C programming language by carrying out general matrix–matrix multiplications and without the use of assembly language. Our implementation of matrix–matrix multiplication is based on blocked matrix multiplication as an optimization technique that improves data reuse. We use data prefetching, loop unrolling, and the Intel AVX-512 to optimize the blocked matrix multiplications. When we use a single core of the KNL, our implementation achieves up to 98% of SGEMM and 99% of DGEMM using the Intel MKL, which is the current state-of-the-art library. Our implementation of the parallel DGEMM using all 68 cores of the KNL achieves up to 90% of DGEMM using the Intel MKL.
      PubDate: 2018-06-01
      DOI: 10.1007/s10586-018-2810-y
       
  • Research of the processing technology for time complex event based on LSTM
    • Authors: Qing Li; Jiang Zhong; Yongqin Tao; Lili Li; Xiaolong Miao
      Abstract: With the huge amount of data, it is increasingly meaningful to combine different business system data with potential values. In the traditional event description, the input event flow of the event engine is a single atomic event type. The event predicate constraint contains simple attribute value, comparison operation and simple aggregation operation. The time constraint between events always simply. This makes the traditional detection method cannot meet the requirements such as financial, medical and other relatively accurate time requirements, event predicate constraints require more complex applications. Thus, this paper introduces the long short-term memory network model (LSTM), designs a multivariate event input to process these data based on TCN quantitative timing constraint representation model and predicate constraint representation model. In this paper, an innovative method makes the complex event processing technology more high efficient. By the analysis 200 million records of 2045 stocks, the results show that the processing technology of the complex events is more effective, more efficient.
      PubDate: 2018-05-29
      DOI: 10.1007/s10586-018-2765-z
       
  • A novel task scheduling approach based on dynamic queues and hybrid
           meta-heuristic algorithms for cloud computing environment
    • Authors: Hicham Ben Alla; Said Ben Alla; Abdellah Touhafi; Abdellah Ezzati
      Abstract: Task scheduling is one of the most challenging aspects to improve the overall performance of cloud computing and optimize cloud utilization and Quality of Service (QoS). This paper focuses on Task Scheduling optimization using a novel approach based on Dynamic dispatch Queues (TSDQ) and hybrid meta-heuristic algorithms. We propose two hybrid meta-heuristic algorithms, the first one using Fuzzy Logic with Particle Swarm Optimization algorithm (TSDQ-FLPSO), the second one using Simulated Annealing with Particle Swarm Optimization algorithm (TSDQ-SAPSO). Several experiments have been carried out based on an open source simulator (CloudSim) using synthetic and real data sets from real systems. The experimental results demonstrate the effectiveness of the proposed approach and the optimal results is provided using TSDQ-FLPSO compared to TSDQ-SAPSO and other existing scheduling algorithms especially in a high dimensional problem. The TSDQ-FLPSO algorithm shows a great advantage in terms of waiting time, queue length, makespan, cost, resource utilization, degree of imbalance, and load balancing.
      PubDate: 2018-05-25
      DOI: 10.1007/s10586-018-2811-x
       
  • Retraction Note to: Capture-removal model sampling estimation based on big
           data
    • Authors: Zhichao Li; Siyun Gan; Ru Jia; Jun Fang
      Abstract: The authors have retracted “Capture-removal model sampling estimation based on big data”, Vol. 20, No. 2, June 2017. Upon re-review of the data, the authors identified an incorrect time setting of the Tianjin explosion in their data collecting process. Due to problems existing in the data management and analysis process, they are unable to repeat the analysis results in this research with the same data and believe that these errors are sufficient to undermine the conclusions of the article. All authors agree to this retraction.
      PubDate: 2018-05-19
      DOI: 10.1007/s10586-018-2809-4
       
  • DEFAD: ensemble classifier for DDOS enabled flood attack defense in
           distributed network environment
    • Authors: K. Munivara Prasad; A. Rama Mohan Reddy; K. Venugopal Rao
      Abstract: Technological advancements in the information systems and networks are the outcome of potential developments resulting in the networking and communications. The role of Critical Infrastructure is playing a vital role in imparting the condition of effective information systems management. However, with some of the negative developments like DDoS attacks that impact the operations of network application systems, there are adverse set of issues encountered. With the rising number of DDoS attacks phenomenon, researchers have focused on developing contemporary solutions that can support in thwarting such attacks. From the review of such models in the literature review, it is imperative that two distinct dimensions like the detection and mitigation accuracy levels has scope for improvement and profoundly majority of such models were tested on the static datasets which are not pragmatic. Considering such equations, the model proposed in this manuscript focused on a contemporary range of solution that can be high on accuracy rate and also is tested over the dynamic dataset to understand the efficacy of the system. Using the ensemble classifiers comprising drift detection features, at service request stream level, the proposed solution if implemented can lead to better levels of detection. Experimental study of the model carried out using the service request stream that is synthesized is tested based on statistical metrics like accuracy, prediction value and true negative rates. Significance of the model is imperative in terms of results generated and its comparative analysis to the other bench-mark models in the segment.
      PubDate: 2018-05-14
      DOI: 10.1007/s10586-018-2808-5
       
  • A control theoretical view of cloud elasticity: taxonomy, survey and
           challenges
    • Authors: Amjad Ullah; Jingpeng Li; Yindong Shen; Amir Hussain
      Abstract: The lucrative features of cloud computing such as pay-as-you-go pricing model and dynamic resource provisioning (elasticity) attract clients to host their applications over the cloud to save up-front capital expenditure and to reduce the operational cost of the system. However, the efficient management of hired computational resources is a challenging task. Over the last decade, researchers and practitioners made use of various techniques to propose new methods to address cloud elasticity. Amongst many such techniques, control theory emerges as one of the popular methods to implement elasticity. A plethora of research has been undertaken on cloud elasticity including several review papers that summarise various aspects of elasticity. However, the scope of the existing review articles is broad and focused mostly on the high-level view of the overall research works rather than on the specific details of a particular implementation technique. While considering the importance, suitability and abundance of control theoretical approaches, this paper is a step forward towards a stand-alone review of control theoretic aspects of cloud elasticity. This paper provides a detailed taxonomy comprising of relevant attributes defining the following two perspectives, i.e., control-theory as an implementation technique as well as cloud elasticity as a target application domain. We carry out an exhaustive review of the literature by classifying the existing elasticity solutions using the attributes of control theoretic perspective. The summarized results are further presented by clustering them with respect to the type of control solutions, thus helping in comparison of the related control solutions. In last, a discussion summarizing the pros and cons of each type of control solutions are presented. This discussion is followed by the detail description of various open research challenges in the field.
      PubDate: 2018-05-07
      DOI: 10.1007/s10586-018-2807-6
       
  • A study on supervised machine learning algorithm to improvise intrusion
           detection systems for mobile ad hoc networks
    • Authors: S. Vimala; V. Khanaa; C. Nalini
      Abstract: The security inside the network correspondence is a noteworthy concern. Being the way that information is considered as the profitable asset of an association, giving security against the intruders is exceptionally fundamental. Intrusion Detection Systems tries to recognize security assaults of intruders by researching a few information records saw in forms on the network. In this paper, Intrusion Detection Classification of attacks is done by NNIDS, TSVID and DF-IDS. The proposed algorithms are trained and tested using KDD Cup 1999 dataset . This paper has presented a novel method for an adaptive fault tolerant mobile agent based intrusion detection system. At first the classification of attacks is done by TSVID Classification algorithm. The TSVID makes use of RBF kernel and iterative learning mechanism. Next the classification is done by using NNIDS that makes use of neural network based approach. Advantages of NNIDS method is that it can successfully handle both qualitative, quantitative data, and it handles multiple criteria and easier to understand. Then, finally, the classification is done by using iterative learning mechanism and DF-IDS gives successful results of classification. The performances of the proposed algorithm are evaluated using the classification metrics such as detection rate and accuracy. Comparison graphs of attack detection rate and false-alarm rate reveals that the obtained results of anticipated methods achieve greater detection rate and less computational time for the classification of attacks and protocols. The proposed study is a classification based approach for combining several networks in intrusion detection systems. In evaluation of this model, it has been demonstrated that there is a significant improvement in real time performance without sacrificing efficiency.
      PubDate: 2018-04-28
      DOI: 10.1007/s10586-018-2686-x
       
  • Robust optimization for energy-efficient virtual machine consolidation in
           modern datacenters
    • Authors: Robayet Nasim; Enrica Zola; Andreas J. Kassler
      Abstract: Energy efficient virtual machine (VM) consolidation in modern data centers is typically optimized using methods such as Mixed Integer Programming, which typically require precise input to the model. Unfortunately, many parameters are uncertain or very difficult to predict precisely in the real world. As a consequence, a once calculated solution may be highly infeasible in practice. In this paper, we use methods from robust optimization theory in order to quantify the impact of uncertainty in modern data centers. We study the impact of different parameter uncertainties on the energy efficiency and overbooking ratios such as e.g. VM resource demands, migration related overhead or the power consumption model of the servers used. We also show that setting aside additional resource to cope with uncertainty of workload influences the overbooking ration of the servers and the energy consumption. We show that, by using our model, Cloud operators can calculate a more robust migration schedule leading to higher total energy consumption. A more risky operator may well choose a more opportunistic schedule leading to lower energy consumption but also higher risk of SLA violation.
      PubDate: 2018-04-28
      DOI: 10.1007/s10586-018-2718-6
       
  • Study on learning effect prediction models based on principal component
           analysis in MOOCs
    • Authors: Wei Zhang; Shiming Qin; Baolin Yi; Peng Tian
      Abstract: In recent years, the rise and development of massive open online courses (MOOCs) have promoted the boom of online education and also promoted the research on learning analysis and mining based on big data of education. However, while offering a large number of high quality courses, there is also a phenomenon that the overall learning effect is not ideal. How to make effective use of MOOCs for teaching activities poses urgent practical requirements for educators and researchers. MOOCs store massive learners’ learning behavior data, and mining these data is of great significance to learners’ learning effects. Due to the deviation caused by the correlation between many behavior indexes, the paper analyze the nine measurable performance indexes by principal component analysis (PCA), and get three principal components factors in order to reduce the computational dimension and increase the comprehensibility, and then obtain a model of learning effect prediction, namely the PCA prediction model (PCA_PM). Logistic regression algorithm has a good fitting effect on behavioral indexes. Therefore, the paper use the logistic regression method to construct another model of learning effect prediction, namely the principal component logistic regression prediction model (PCL_PM), and predictive effects of two models were compared and analyzed. The results show that it is an effective way to help learners to improve the passing rate of the course by establishing the predictive models. The prediction accuracy of both PCA_PM and PCL_PM is high, but overall, the prediction effect of PCA_PM is better.
      PubDate: 2018-04-10
      DOI: 10.1007/s10586-018-2594-0
       
  • Correction to: A variable neighborhood search based genetic algorithm for
           flexible job shop scheduling problem
    • Authors: Guohui Zhang; Lingjie Zhang; Xiaohui Song; Yongcheng Wang; Chi Zhou
      Abstract: The original version of this article unfortunately contained a mistake in the acknowledgement statement. It should read as follow “This paper presents work funded by the National Natural Science Foundation of China (No. 61203179), Excellent Youth Foundation of Science & Technology Innovation of Henan Province (No. 184100510001), and Key scientific research projects of Henan Province (No. 182102210457). Also, we would like to thank the Collaborative Innovation Center for Aviation Economy Development of Henan Province.”. The oricd of the corresponding author is revised to http://orcid.org/0000-0001-9143-2922. Also the e-mail of the corresponding author is revised to zgh_hust@qq.com.
      PubDate: 2018-04-02
      DOI: 10.1007/s10586-018-2328-3
       
  • Product pricing considering the consumer preference based on Internet of
           Things
    • Authors: Xin-yu Pan; Jing-zhong Ma; Cheng-xia Wu
      Abstract: With the rapid development of information technology, Internet of things is rapidly penetrated into the manufacturing industry, some manufacturers began to adopt the Internet of things technology to produce intelligent products. Focusing on the pricing of products in the home market under the Internet of things technology environment, the network effect and technological innovation are introduced in the condition that consumers’ preferences are evenly distributed and a pricing game model which can reflect the relationship among manufacturers are constructed. On this basis, the optimal subsidy rate of government is designed based on the principle of social welfare maximization. The results show that, with the advantage of low price of ordinary products, the manufacturer 1 (the ordinary household product manufacturer) can also obtain the corresponding market share. The network effect and the degree of product innovation are the important influencing factors for the manufacturer 2 (the intelligent household product manufacturer) to gain the competitive advantage. Increasing the subsidy rate can effectively stimulate the manufacturer 2 to carry out technological innovation, but it will lead to the decline of the total social welfare level.
      PubDate: 2018-04-02
      DOI: 10.1007/s10586-018-2601-5
       
  • A fully homomorphic–elliptic curve cryptography based encryption
           algorithm for ensuring the privacy preservation of the cloud data
    • Authors: G. Prabu Kanna; V. Vasudevan
      Abstract: Enabling a security and privacy preservation for the cloud data is one of the demanding and crucial tasks in recent days. Because, the privacy of the sensitive data should be safeguard from the unauthorized access for improving its security. So, various key generation, encryption and decryption mechanisms are developed in the traditional works for privacy preservation in cloud. Still, it remains with the issues such as increased computational complexity, time consumption, and reduced security. Also, the traditional works use the symmetric key cryptography based. Thus, this paper aims to develop a new privacy preservation mechanism by implementing a fully homomorphic–elliptic curve cryptography (FH-ECC) algorithm. The data owner encrypts the original data by converting it into the cipher format with the use of ECC algorithm, and applies the FH operations on the encrypted data before storing it on the cloud. When the user gives the data request to the cloud, the Cloud Service Provider verifies the access control policy of the user for enabling the restricted access on the data. If the access policy is verified, the encrypted data is provided to the user, from that the cipher text is extracted. Then, the ECC decryption and FH operations are applied to generate the original text. Based on the several analysis, the research work is evaluated with the help of different performance measures such as execution time, encryption time, and decryption time. In addition the effectiveness of the novel FHE technique is justified by the comparative analysis made with the traditional techniques.
      PubDate: 2018-04-02
      DOI: 10.1007/s10586-018-2723-9
       
  • Research on image segmentation method using a structure-preserving region
           model-based MRF
    • Authors: Chenghua Fan; Qunjing Wang
      Abstract: This paper proposes a structure-preserving region model for machine images. Under the Bayesian framework, the proposed model is combined with MRF (Markov random field) to offer a new method for the segmentation of machine images. The structure-preserving region model aims to deal with problems with MRF-based segmentation on parameter estimation and optimization. Construction of the structure-preserving region model involves two processes. The bilateral filter algorithm is first applied to machine images to remove noise and restore image structures, followed by an initial segmentation by applying MRF on the images and represented by a region adjacency graph (RAG). The proposed segmentation method has been evaluated using machine images. Relative to existing MRF-based methods, testing results have demonstrated that our proposed method substantially improves the segmentation performance.
      PubDate: 2018-04-02
      DOI: 10.1007/s10586-018-2592-2
       
  • Construction of a knee osteoarthritis diagnostic system based on X-ray
           image processing
    • Authors: Yongping Li; Ning Xu; Qiang Lyu
      Abstract: In order to accurately diagnose knee osteoarthritis, a detection technique as well as its quantitative assessment based on X-ray image processing is proposed in this study. First, image segmentation is implemented on the basis of maximum between-class variance and region growing method. Second, the edge of the image concerned is filled based on calculations of mathematical morphology, followed by edge extraction, which realizes extraction of the image in the region of interest. Finally, processing and judgment concerning four indicators to determine knee osteoarthritis, namely, joint space asymmetry, articular sclerosis, rugged articular surface, and intra-articular loose bodies, were judged and judged. Our experimental results show that this technique can effectively detect and describe the features of knee osteoarthritis, which can be used as a tool for clinical diagnosis.
      PubDate: 2018-04-02
      DOI: 10.1007/s10586-018-2677-y
       
  • Intrusion detection system using SOEKS and deep learning for in-vehicle
           security
    • Authors: Lulu Gao; Fei Li; Xiang Xu; Yong Liu
      Abstract: With the continuous development of the intelligent vehicle, vehicle security events occur frequently, therefore, the vehicle information security is particularly important. In this paper, the in-vehicle security measures are analyzed, especially the current situation of in-vehicle intrusion detection system, which are mainly aimed at specific vehicles and are not enough to meet the need of vehicle security. Then, a new in-vehicle intrusion detection mechanism is proposed based on deep learning and the set of experience knowledge structure (SOEKS), which is a knowledge representation structure. Utilizing SOEKS and information entropy to increase the versatility of intrusion detection for different vehicle. In practice, the more precise model for specific vehicle can formed by training a large amount of specific vehicle data through deep learning. It is demonstrated with experimental results that the proposed approach is able to have 98% accuracy and detect a wide range of in-vehicle attacks.
      PubDate: 2018-04-02
      DOI: 10.1007/s10586-018-2385-7
       
  • Jaw fracture classification using meta heuristic firefly algorithm with
           multi-layered associative neural networks
    • Authors: Mohamed Hashem; Azza S. Hassanein
      Abstract: Jaw fracture is one of the common facial injuries in human body. The jaw fracture disease is one of the tenth most injuries in human body. Jaw fracture may result from high physical injuries impacts and medical conditions that weaken the looseness of the teeth, jaw stiffness, swelling and so on. Due to the various symptoms of this disease, diagnosis is difficult at an early stage. Therefore, different machine learning and data mining techniques are used to detect jaw fracture because of the severe effects of the disease. Initially, jaw related data is randomly collected from the patient, which includes information such as the patient name, age, height, weight, osteoporosis risk factors, prediction, fracture level, and so on. After collecting jaw fracture information, unwanted data is discarded by applying normalization techniques; optimized features are then selected using the metaheuristic firefly algorithm. The selected data is then classified using the multi-layer neural network training based on associative neural networks. The classifier then successfully categorizes the abnormal jaw fracture feature. Then, the efficiency of the system is examined with the help of the mean square error rate, precision, recall, and accuracy.
      PubDate: 2018-04-02
      DOI: 10.1007/s10586-018-2668-z
       
  • Enhanced continuous and discrete multi objective particle swarm
           optimization for text summarization
    • Authors: V. Priya; K. Umamaheswari
      Abstract: Reviews from various domains is being posted in web increasingly day by day. Analyzing this enormous content would be useful in decision making for various stakeholders. Text summarization techniques generate concise summaries including sentiments which are useful in analyzing the large content. So text summarization systems become significant in analyzing this huge content. The summaries are generated based on important features using multi objective approaches where sufficient literature is not available. Major limitations of text summarization systems are scalability and performance. Two variants of multi objective optimization techniques such as Discrete and Continuous which work under the principles of particle swarm optimization (PSO) for extractive summarization of reviews had been proposed for performance improvement. The performance is validated using Recall-Oriented Understanding for Gisting Evaluation (ROUGE), Success Counting (SC) and Inverted Generational Distance (IGD). Based on the experimental results it is found that the system is effective using multi-objective PSO algorithm when compared to other state-of-art approaches like Liu’s approach feature based binary particle swarm optimization and etc. for feature based review summarization.
      PubDate: 2018-04-02
      DOI: 10.1007/s10586-018-2674-1
       
  • Semi-Markov chain-based grey prediction-based mitigation scheme for
           vampire attacks in MANETs
    • Authors: P. Balaji Srikaanth; V. Nagarajan
      Abstract: Vampire attacks are considered to be the most vulnerable resource draining attack that is potential in disabling the connectivity of the network by draining mobile node’s energy at a faster rate. This vampire attack is generic as they exploit the characteristic features of the base protocol used for enabling communication in mobile ad hoc networks (MANETs). The core objective of this paper is an attempt to formulate an energy forecasting mechanism using grey theory that ensures reliable network connectivity that gets influenced through the vampire behaviour of mobile nodes under active communication. This Semi-Markov chain-based grey prediction-based mitigation (SMCGPM) is an enhanced Markov chain model that integrates the characteristic features of stochastic theory and grey theory for improving the efficacy in detecting a specific kind of vampire attack called as stretch attack. In this technique, the elucidated data from each mobile node are initially modeled based on Grey model. Then, the residual error is calculated between the forecasted and observed values of energy possessed by the mobile nodes based on their packet forwarding rates. SMCGPM has the capability of predicting the possible transition behaviour of mobile nodes through the estimated residual error derived from the Markov chain matrices. Simulation results confirm that SMCGPM is predominant than the baseline prediction schemes by facilitating an effective detection rate of 29% as they achieve correctness and accuracy in prediction through Semi-Markov chain stochastic properties inspired energy factor prediction.
      PubDate: 2018-03-31
      DOI: 10.1007/s10586-018-2698-6
       
 
 
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