Academic literature on the topic 'Computing approach'

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Journal articles on the topic "Computing approach"

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Anjaneyulu, P., and Mr S. Srinivasa Reddy. "Cloud Computing Adoption Approach towards Securing Data in Cloud Computing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 390–93. http://dx.doi.org/10.31142/ijtsrd12995.

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Panhwar, Muhammad Aamir. "Workflow-based Approach in Cloud Computing Environment." Journal of Advanced Research in Dynamical and Control Systems 12, no. 7 (July 20, 2020): 815–22. http://dx.doi.org/10.5373/jardcs/v12i7/20202066.

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Emmert-Streib, Frank. "Biostatistics: A Computing Approach." CHANCE 26, no. 4 (November 2013): 59–60. http://dx.doi.org/10.1080/09332480.2013.868760.

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William Hughes, P. "Biostatistics: a computing approach." Journal of Applied Statistics 39, no. 10 (October 2012): 2306. http://dx.doi.org/10.1080/02664763.2012.692545.

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Baba, Toshio, Yoshi-aki Shimada, Seiichiro Kawamura, Masanori Matoba, Toshikazu Fukushima, Shinichiro Fujii, Toshiki Nagano, Yasuhiro Katsumata, Nobuo Kochi, and Yasunori Kimura. "Proposal of disruptive computing (A computing-domain-oriented approach)." Japanese Journal of Applied Physics 59, no. 5 (April 27, 2020): 050503. http://dx.doi.org/10.35848/1347-4065/ab8577.

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Negi, Adhyayan, Gautam Sharma, and Ankit Mittal. "Green Computing: An Efficient Approach." International Journal of Computer Applications 179, no. 32 (April 17, 2018): 1–3. http://dx.doi.org/10.5120/ijca2018916715.

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Zauner, K. P., and M. Conrad. "Molecular approach to informal computing." Soft Computing 5, no. 1 (February 2001): 39–44. http://dx.doi.org/10.1007/s005000000064.

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GIORGADZE, G. "MONODROMY APPROACH TO QUANTUM COMPUTING." International Journal of Modern Physics B 16, no. 30 (November 30, 2002): 4593–605. http://dx.doi.org/10.1142/s0217979202014607.

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In this work, a gauge approach to quantum computing is considered. It is assumed that there exists a classical procedure for placing certain classical system in a state described by a holomorphic vector bundle with connection with logarithmic singularities. This bundle and its connection are constructed with the aid of unitary operators realizing the given algorithm using methods of the monodromic Riemann–Hilbert problem. Universality is understood in the sense that for any collection of unitary matrices there exists a connection with logarithmic singularities whose monodromy representation involves these matrices.
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Anderson, M., S. L. Anderson, and C. Armen. "An Approach to Computing Ethics." IEEE Intelligent Systems 21, no. 4 (July 2006): 56–63. http://dx.doi.org/10.1109/mis.2006.64.

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Huang, Ching-Huei, Horn-Yong Jan, Chun-Liang Lin, and Chia-Soon Lee. "System identification: DNA computing approach." ISA Transactions 48, no. 3 (July 2009): 254–63. http://dx.doi.org/10.1016/j.isatra.2009.01.006.

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Dissertations / Theses on the topic "Computing approach"

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Constantinescu-Fuløp, Zoran. "A Desktop Grid Computing Approach for Scientific Computing and Visualization." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-2191.

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Scientific Computing is the collection of tools, techniques, and theories required to solve on a computer, mathematical models of problems from science and engineering, and its main goal is to gain insight in such problems. Generally, it is difficult to understand or communicate information from complex or large datasets generated by Scientific Computing methods and techniques (computational simulations, complex experiments, observational instruments etc.). Therefore, support of Scientific Visualization is needed, to provide the techniques, algorithms, and software tools needed to extract and display appropriately important information from numerical data.

Usually, complex computational and visualization algorithms require large amounts of computational power. The computing power of a single desktop computer is insufficient for running such complex algorithms, and, traditionally, large parallel supercomputers or dedicated clusters were used for this job. However, very high initial investments and maintenance costs limit the availability of such systems. A more convenient solution, which is becoming more and more popular, is based on the use of nondedicated desktop PCs in a Desktop Grid Computing environment. Harnessing idle CPU cycles, storage space and other resources of networked computers to work together on a particularly computational intensive application does this. Increasing power and communication bandwidth of desktop computers provides for this solution.

In a desktop grid system, the execution of an application is orchestrated by a central scheduler node, which distributes the tasks amongst the worker nodes and awaits workers’ results. An application only finishes when all tasks have been completed. The attractiveness of exploiting desktop grids is further reinforced by the fact that costs are highly distributed: every volunteer supports her resources (hardware, power costs and internet connections) while the benefited entity provides management infrastructures, namely network bandwidth, servers and management services, receiving in exchange a massive and otherwise unaffordable computing power. The usefulness of desktop grid computing is not limited to major high throughput public computing projects. Many institutions, ranging from academics to enterprises, hold vast number of desktop machines and could benefit from exploiting the idle cycles of their local machines.

In the work presented in this thesis, the central idea has been to provide a desktop grid computing framework and to prove its viability by testing it in some Scientific Computing and Visualization experiments. We present here QADPZ, an open source system for desktop grid computing that have been developed to meet the above presented needs. QADPZ enables users from a local network or Internet to share their resources. It is a multi-platform, heterogeneous system, where different computing resources from inside an organization can be used. It can be used also for volunteer computing, where the communication infrastructure is the Internet. QADPZ supports the following native operating systems: Linux, Windows, MacOS and Unix variants. The reason behind natively supporting multiple operating systems, and not only one (Unix or Windows, as other systems do), is that often, in real life, this kind of limitation restricts very much the usability of desktop grid computing.

QADPZ provides a flexible object-oriented software framework that makes it easy for programmers to write various applications, and for researchers to address issues such as adaptive parallelism, fault-tolerance, and scalability. The framework supports also the execution of legacy applications, which for different reasons could not be rewritten, and that makes it suitable for other domains as business. It also supports low-level programming languages as C/C++ or high-level language applications, (e.g. Lisp, Python, and Java), and provides the necessary mechanisms to use such applications in a computation. Consequently, users with various backgrounds can benefit from using QADPZ. The flexible object-oriented structure and the modularity allow facile improvements and further extensions to other programming languages.

We have developed a general-purpose runtime and an API to support new kinds of high performance computing applications, and therefore to benefit from the advantages offered by desktop grid computing. This API directly supports the C/C++ programming language. We have shown how distributed computing extends beyond the master-worker paradigm (typical for such systems) and provided QADPZ with an extended API that supports in addition lightweight tasks and parallel computing (using the message passing paradigm - MPI). This extends the range of applications that can be used to already existing MPI based applications - e.g. parallel numerical solvers used in computational science, or parallel visualization algorithms.

Another restriction of existing systems, especially middleware based, is that each resource provider needs to install a runtime module with administrator privileges. This poses some issues regarding data integrity and accessibility on providers computers. The QADPZ system tries to overcome this by allowing the middleware module to run as a non-privileged user, even with restricted access, to the local system.

QADPZ provides also low-level optimizations, such as on-the-fly compression and encryption for communication. The user can choose from different algorithms, depending on the application, improving both the communication overhead imposed by large data transfers and keeping privacy of the data. The system goes further, by providing an experimental, adaptive compression algorithm, which can transparently choose different algorithms to improve the application. QADPZ support two different protocols (UDP and TCP/IP) in order to improve the efficiency of communication.

Free source code allows its flexible installations and modifications based on the particular needs of research projects and institutions. In addition to being a very powerful tool for computationally intensive research, the open sourceness makes QADPZ a flexible educational platform for numerous smallsize student projects in the areas of operating systems, distributed systems, mobile agents, parallel algorithms, etc. Open source software is a natural choice for modern research as well, because it encourages effectively integration, cooperation and boosting of new ideas.

This thesis proposes also an improved conceptual model (based on the master-worker paradigm), which makes contributions in several directions: pull vs. push work-units, pipelining of work-units, more work-units sent at a time, adaptive number of workers, adaptive time-out interval for work-units, and multithreading. We have also demonstrated that the use of desktop grids should not be limited to only master-worker applications, but it can be used for more fine-grained parallel Scientific Computing and Visualization applications, by performing some specific experiments. This thesis makes supplementary contributions: a hierarchical taxonomy of the main existing desktop grids, and an adaptive compression algorithm for remote visualization. QADPZ has also pioneered autonomic computing approach for desktop grids and presents specific self-management features: self-knowledge, self-configuration, selfoptimization and self-healing. It is worth to mention that to the present the QADPZ has over a thousand users who have download it (since July, 2001 when it has been uploaded to sourceforge.net), and many of them use it for their daily tasks (see the appendix). Many of the results have been published or are in course of publishing as it can be seen from the references.

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Abukmail, Ahmed Ahed. "Pervasive computing approach to energy management." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0013060.

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Al-Shammaa, Mohammed. "Granular computing approach for intelligent classifier design." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13686.

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Granular computing facilitates dealing with information by providing a theoretical framework to deal with information as granules at different levels of granularity (different levels of specificity/abstraction). It aims to provide an abstract explainable description of the data by forming granules that represent the features or the underlying structure of corresponding subsets of the data. In this thesis, a granular computing approach to the design of intelligent classification systems is proposed. The proposed approach is employed for different classification systems to investigate its efficiency. Fuzzy inference systems, neural networks, neuro-fuzzy systems and classifier ensembles are considered to evaluate the efficiency of the proposed approach. Each of the considered systems is designed using the proposed approach and classification performance is evaluated and compared to that of the standard system. The proposed approach is based on constructing information granules from data at multiple levels of granularity. The granulation process is performed using a modified fuzzy c-means algorithm that takes classification problem into account. Clustering is followed by a coarsening process that involves merging small clusters into large ones to form a lower granularity level. The resulted granules are used to build each of the considered binary classifiers in different settings and approaches. Granules produced by the proposed granulation method are used to build a fuzzy classifier for each granulation level or set of levels. The performance of the classifiers is evaluated using real life data sets and measured by two classification performance measures: accuracy and area under receiver operating characteristic curve. Experimental results show that fuzzy systems constructed using the proposed method achieved better classification performance. In addition, the proposed approach is used for the design of neural network classifiers. Resulted granules from one or more granulation levels are used to train the classifiers at different levels of specificity/abstraction. Using this approach, the classification problem is broken down into the modelling of classification rules represented by the information granules resulting in more interpretable system. Experimental results show that neural network classifiers trained using the proposed approach have better classification performance for most of the data sets. In a similar manner, the proposed approach is used for the training of neuro-fuzzy systems resulting in similar improvement in classification performance. Lastly, neural networks built using the proposed approach are used to construct a classifier ensemble. Information granules are used to generate and train the base classifiers. The final ensemble output is produced by a weighted sum combiner. Based on the experimental results, the proposed approach has improved the classification performance of the base classifiers for most of the data sets. Furthermore, a genetic algorithm is used to determine the combiner weights automatically.
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Ingram, Colin. "Computing education in FE : a systems approach." Thesis, University of the West of Scotland, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.419787.

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Hiziroglu, Abdulkadir. "A soft computing approach to customer segmentation." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.503072.

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Improper selection of segmentation variables and tools may have an effect on segmentation results and can cause a negative financial impact (Tsai & Chiu, 2004). With regards to the selection of segmentation variables, although general segmentation variables such as demographics are frequently utilised based on the assumption that customers with similar demographics and lifestyles tend to exhibit similar purchasing behaviours (Tsai & Chiu, 2004), it is believed the behavioural variables of customers are more suitable to use as segmentation bases (Hsieh, 2004). As far as segmentation techniques are concerned, two conclusions can be made. First, the cluster-based segmentation methods, particularly hierarchical and non-hierarchical methods, have been widely used in the related literature. But, the hierarchical methods are criticised for nonrecovery while the non-hierarchical ones are not able to determine the initial number of clusters (Lien, 2005). Hence, the integration of hierarchical and partitional methods (as a two-stage approach) is suggested to make the clustering results powerful in large databases (Kuo, Ho & Hu, 2002b). Second, none of those traditional approaches has the ability to establish non-strict customer segments that are significantly crucial for today's competitive consumer markets. One crucial area that can meet this requirement is known as soft computing. Although there have been studies related to the usage of soft computing techniques for segmentation problems, they are not based on the effective two-stage methodology. The aim of this study is to propose a soft computing model for customer segmentation using purchasing behaviours of customers in a data mining framework. The segmentation process in this study includes segmentation (clustering and profiling) of existing consumers and classification-prediction of segments for existing and new customers. Both a combination and an integration of soft computing techniques were used in the proposed model. Clustering was performed via a proposed neuro-fuzzy two stage-clustering approach and classification-prediction was employed using a supervised artificial neural network method. Segmenting customers was done according to the purchasing behaviours of customers based on RFM (Recency, Frequency, Monetary) values, which can be considered as an important variable set in identifying customer value. The model was also compared with other two-stage methods (Le., Ward's method followed by k-means and self-organising maps followed by k-means) based on select segmentability criteria. The proposed model was employed in a secondary data set from a UK retail company. The data set included more than 300,000 unique customer records and a random sample of approximately 1 % of it was used for conducting analyses .. The findings indicated that the proposed model provided better insights and managerial implications in comparison with the traditional two-stage methods with respect to the select segmentability criteria. --' The main contribution of this study is threefold. Firstly it has the potential benefits and implications of having fuzzy segments, which enables us to have flexible segments through the availability of membership degrees of each customer to the corresponding customer segments. Secondly the development of a new two-stage clustering model could be considered to be superior to its peers in terms of computational ability. And finally, through the classification phase of the model it was possible to extract knowledge regarding segment stability, which was utilised to calculate customer retention or chum rate over time for corresponding segments.
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Mallett, Jacky 1963. "Kami : an anarchic approach to distributed computing." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/61847.

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Thesis (S.M.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 2000.
Includes bibliographical references (p. 83-84).
This thesis presents a distributed computing system, Kami, which provides support for applications running in an environment of heterogeneous workstations linked together by a high speed network. It enables users to easily create distributed applications by providing a backbone infrastructure of localized daemons which operate in a peer-to-peer networking environment, providing support for software distribution, network communication, and data streaming suitable for use by coarse grained distributed applications. As a collective entity, kami daemons, each individually run on a single machine, form a cooperating anarchy of processes. These support their applications using adaptive algorithms with no form of centralized control. Instead of attempting to provide a controlled environment, this thesis assumes a heterogeneous and uncontrolled environment, and presents a model for distributed computation that is completely decentralized and uses multicast communication between workstations to form an ecology of co-operating processes, which actively attempt to maintain an equilibrium between the demands of their users and the capabilities of the workstations on which they are running.
by Jacky Mallett.
S.M.
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Millard, Ian C. "Contextually aware pervasive computing : a semantic approach." Thesis, University of Southampton, 2008. https://eprints.soton.ac.uk/266002/.

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We live in a world which is becoming increasingly rich in technology, with a wide array of portable and embedded devices being readily available and surrounding us in everyday use. Similarly, advances in communications technologies and the explosive growth of data being published on the Internet have provided access to information on an unparalleled scale. However, device interoperability is often poor at best, and accessing data which is relevant to any given situation can be difficult due to the sheer quantity of information which is available. A contextually aware environment is envisioned as one in which integrated computer systems have an understanding or representation of not only the physical space and the resources within it, but also the activities, interests, actions and intent of the human occupants at any given time. Given such knowledge, a contextually aware and technology rich pervasive environment may offer services and applications which attempt to adapt the surroundings in a manner which assists its users, such as by configuring devices or assimilating information which is relevant to activities currently being undertaken. The research presented in this thesis combines the fields of knowledge management, semantic technologies, logic and reasoning with those from the predominantly hardware and communications oriented field of pervasive computing, in order to facilitate the creation of contextually aware environments. Requirements for such a system are discussed in detail, resulting in the development of a generic framework of components and data representations from which domain specific deployments can be created. To demonstrate and test the proposed framework, experimentation has been conducted in the example domain of an academic environment, including the development of two contextually aware applications. The experiences and lessons learned during this research are documented throughout, and have influenced the proposed avenues for future related research in this area.
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Craven, Stephen Douglas. "Structured Approach to Dynamic Computing Application Development." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/27730.

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The ability of some configurable logic devices to modify their hardware during operation has long held great potential to increase performance and reduce device cost. However, despite many research projects and a decade of research, the dynamic reconfiguration of Field Programmable Gate Arrays (FPGAs) is still very much an art practiced by few. Previous attempts to automate the many low-level details that complicate Run-Time Reconfigurable (RTR) application development suffer severe limitations. This dissertation describes a comprehensive approach to dynamic hardware development, providing a designer with appropriate models for computation, communication, and reconfiguration integrated with a high-level design environment. In this way, many manual and time consuming tasks associated with partial reconfiguration are hidden, permitting a designer to focus instead on a design's behavior. This design and implementation environment has been validated on a variety of relevant applications, quantifying the effects of high-level design.
Ph. D.
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Taylor, Daniel Kyle. "A Model-Based Approach to Reconfigurable Computing." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/36202.

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Throughout the history of software development, advances have been made that improve the ability of developers to create systems by enabling them to work closer to their application domain. These advances have given programmers higher level abstractions with which to reason about problems. A separation of concerns between logic and implementation allows for reuse of components, portability between implementation platforms, and higher productivity. Parallels can be drawn between the challenges that the field of reconfigurable computing (RC) is facing today and what the field of software engineering has gone through in the past. Most RC work is done in low level hardware description languages (HDLs) at the circuit level. A large productivity gap exists between the ability of RC developers and the potential of the technology. The small number of RC experts is not enough to meet the demands for RC applications. Model-based engineering principles provide a way to reason about RC devices at a higher level, allowing for greater productivity, reuse, and portability. Higher level abstractions allow developers to deal with larger and more complex systems. A modeling environment has been developed to aid users in creating models, storing, reusing and generating hardware implementation code for their system. This environment serves as a starting point to apply model-based techniques to the field of RC to tighten the productivity gap. Future work can build on this model-based framework to take advantage of the unique features of reconfigurable devices, optimize their performance, and further open the field to a wider audience.
Master of Science
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Andersson, Casper. "Reservoir Computing Approach for Network Intrusion Detection." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54983.

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Identifying intrusions in computer networks is important to be able to protect the network. The network is the entry point that attackers use in an attempt to gain access to valuable information from a company or organization or to simply destroy digital property. There exist many good methods already but there is always room for improvement. This thesis proposes to use reservoir computing as a feature extractor on network traffic data as a time series to train machine learning models for anomaly detection. The models used in this thesis are neural network, support vector machine, and linear discriminant analysis. The performance is measured in terms of detection rate, false alarm rate, and overall accuracy of the identification of attacks in the test data. The results show that the neural network generally improved with the use of a reservoir network. Support vector machine wasn't hugely affected by the reservoir. Linear discriminant analysis always got worse performance. Overall, the time aspect of the reservoir didn't have a huge effect. The performance of my experiments is inferior to those of previous works, but it might perform better if a separate feature selection or extraction is done first. Extracting a sequence to a single vector and determining if it contained any attacks worked very well when the sequences contained several attacks, otherwise not so well.
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Books on the topic "Computing approach"

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Velte, Anthony T. Cloud computing: A practical approach. New York: McGraw-Hill, 2010.

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1945-, Voyce Stanley, ed. Teaching computing: A practical approach. Englewood Cliffs, N.J: Prentice-Hall, 1988.

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Velte, Anthony T. Cloud computing: A practical approach. New York: McGraw-Hill, 2010.

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Velte, Anthony T. Cloud computing: A practical approach. New York: McGraw-Hill, 2010.

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Heathcote, P. M. Computing: An active-learning approach. London: DP Publications, 1991.

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Heathcote, P. M. Computing: An active-learning approach. 2nd ed. London: DP Publications, 1996.

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Velte, Anthony T. Cloud computing: A practical approach. New York: McGraw-Hill, 2010.

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J, Velte Toby, and Elsenpeter Robert C. 1970-, eds. Cloud computing: A practical approach. New York: McGraw-Hill, 2010.

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Hidary, Jack D. Quantum Computing: An Applied Approach. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83274-2.

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Hidary, Jack D. Quantum Computing: An Applied Approach. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23922-0.

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Book chapters on the topic "Computing approach"

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Raheem, Nasir. "Cloud Computing." In Big DataA Tutorial-Based Approach, 137–43. First edition. | Boca Raton, FL : Taylor & Francis Group, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429060939-10.

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Hidary, Jack D. "Quantum Computing Methods." In Quantum Computing: An Applied Approach, 131–88. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23922-0_9.

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Hidary, Jack D. "Quantum Computing Methods." In Quantum Computing: An Applied Approach, 143–205. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83274-2_9.

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Stigler, Maddie. "An Agnostic Approach." In Beginning Serverless Computing, 175–95. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3084-8_6.

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Heflin, Jeff, and Zhengxiang Pan. "Semantic Integration: The Hawkeye Approach." In Semantic Computing, 199–227. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470588222.ch11.

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Goudarzi, Alireza, Matthew R. Lakin, and Darko Stefanovic. "DNA Reservoir Computing: A Novel Molecular Computing Approach." In Lecture Notes in Computer Science, 76–89. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01928-4_6.

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Stepney, Susan, Rosalind Barden, and David Cooper. "Schuman & Pitt Approach." In Workshops in Computing, 95–104. London: Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-3552-4_8.

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Bisht, Shivankit, and Pratyush Shukla. "Edge Computing Approach to DEVOPS." In Lecture Notes in Electrical Engineering, 23–30. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7961-5_3.

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Herlihy, Maurice. "Topology Approach in Distributed Computing." In Encyclopedia of Algorithms, 2239–42. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_424.

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Herlihy, Maurice. "Topology Approach in Distributed Computing." In Encyclopedia of Algorithms, 956–58. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-30162-4_424.

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Conference papers on the topic "Computing approach"

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Iwata, Yoshimi. "Phylogenetic Approach for Estimating Noh Archetypes." In 2011 Second International Conference on Culture and Computing (Culture Computing). IEEE, 2011. http://dx.doi.org/10.1109/culture-computing.2011.52.

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Yahiro, Wataru, Nathanael Aubert-Kato, and Masami Hagiya. "A reservoir computing approach for molecular computing." In The 2018 Conference on Artificial Life. Cambridge, MA: MIT Press, 2018. http://dx.doi.org/10.1162/isal_a_00013.

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Paunescu, Doru, Oana Pop, Petru Andea, Eugen Raduca, and Cristian Craciun. "Power flow computing probabilistic approach." In 2011 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2011. http://dx.doi.org/10.1109/saci.2011.5873076.

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Pant, Udit, and Sanjay Kumar Dubey. "Perspective approach in quantum computing." In 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence). IEEE, 2016. http://dx.doi.org/10.1109/confluence.2016.7508207.

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Pittman, Todd B., and James D. Franson. "Optical approach to quantum computing." In Aerospace/Defense Sensing and Controls, edited by Steven P. Hotaling and Andrew R. Pirich. SPIE, 1998. http://dx.doi.org/10.1117/12.312646.

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Nami, Mohammad Reza, and Mohsen Sharifi. "Autonomic Computing: A New Approach." In First Asia International Conference on Modelling & Simulation (AMS'07). IEEE, 2007. http://dx.doi.org/10.1109/ams.2007.20.

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Madhusudan and Aman Kumar Sharma. "Affective computing: A fuzzy approach." In 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2016. http://dx.doi.org/10.1109/pdgc.2016.7913134.

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Al Yami, Mohammed, and Dirk Schaefer. "Fog Computing as a Complementary Approach to Cloud Computing." In 2019 International Conference on Computer and Information Sciences (ICCIS). IEEE, 2019. http://dx.doi.org/10.1109/iccisci.2019.8716402.

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Li, Jiacong, Hang Lv, Bo Lei, and Yunpeng Xie. "A Computing Power Resource Modeling Approach for Computing Power Network." In 2022 International Conference on Computer Communications and Networks (ICCCN). IEEE, 2022. http://dx.doi.org/10.1109/icccn54977.2022.9868931.

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Jasuja, Kumari Parul, and Khushdeep Kaur. "Hybrid soft computing approach for energy efficiency in cloud computing." In 2016 International Conference on Communication and Electronics Systems (ICCES). IEEE, 2016. http://dx.doi.org/10.1109/cesys.2016.7889945.

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Reports on the topic "Computing approach"

1

Franson, James D. Optical Approach to Quantum Computing. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada396429.

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Franson, J. D. Linear Optics Approach to Quantum Computing. Fort Belvoir, VA: Defense Technical Information Center, October 2005. http://dx.doi.org/10.21236/ada440858.

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Fiala, John, and Ronald Lumia. An approach to telerobot computing architecture. Gaithersburg, MD: National Institute of Standards and Technology, 1990. http://dx.doi.org/10.6028/nist.ir.4357.

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Birman, Kenneth P. The Process Group Approach to Reliable Distributed Computing. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada238065.

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Mabuchi, Hideo. Control and Dynamic Approach to Robust Quantum Computing. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada470909.

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Birman, Kenneth P. The Process Group Approach to Reliable Distributed Computing. Revision. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada256787.

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Liu, Huan. Assessing Trustworthiness in Social Media: A Social Computing Approach. Fort Belvoir, VA: Defense Technical Information Center, October 2015. http://dx.doi.org/10.21236/ad1007384.

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Dixit, Arati M., Harpreet Singh, and Thomas Meitzler. Soft Computing Approach to Crack Detection and FPGA Implementation. Fort Belvoir, VA: Defense Technical Information Center, October 2010. http://dx.doi.org/10.21236/ada570058.

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Kagal, Lalana, Sasikanth Avancha, Vladimir Korolev, Anupam Joshi, and Tim Finin. An Approach to Dynamic Service Management in Pervasive Computing Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada440419.

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Dutt, Nikil, Rajesh Gupta, Alex Nicolau, and Alex Veidenbaum. COPPER: Compiler-Controlled On-Demand Approach to Power-Efficient Computing. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada418787.

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