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1 |
Performance analysis of machine learning algorithm of detection and classification of brain tumor using computer vision
تحلیل عملکرد الگوریتم یادگیری ماشین تشخیص و طبقه بندی تومور مغزی با استفاده از بینایی کامپیوتر-2022 Brain tumor is one of the undesirables, uncontrolled growth of cells in all age groups. Classification of tumors
depends no its origin and degree of its aggressiveness, it also helps the physician for proper diagnosis and
treatment plan. This research demonstrates the analysis of various state-of-art techniques in Machine Learning
such as Logistic, Multilayer Perceptron, Decision Tree, Naive Bayes classifier and Support Vector Machine for
classification of tumors as Benign and Malignant and the Discreet wavelet transform for feature extraction on the
synthetic data that is available data on the internet source OASIS and ADNI. The research also reveals that the
Logistic Regression and the Multilayer Perceptron gives the highest accuracy of 90%. It mimics the human
reasoning that learns, memorizes and is capable of reasoning and performing parallel computations. In future
many more AI techniques can be trained to classify the multimodal MRI Brain scan to more than two classes of
tumors. keywords: هوش مصنوعی | ام آر آی | رگرسیون لجستیک | پرسپترون چند لایه | Artificial Intelligence | MRI | Logistic regression | OASIS | Multilayer Perceptron |
مقاله انگلیسی |
2 |
Post-Quantum Blockchain-Based Data Sharing for IoT Service Providers
به اشتراک گذاری داده های مبتنی بر بلاک چین پسا کوانتومی برای ارائه دهندگان خدمات اینترنت اشیا-2022 Quantum technologies have made significant advances and are likely to lead to important security challenges and threats to networks in
the near future. On the other hand, sharing the huge amount of data from the Internet of Things (IoT) in the context of data as a service
could provide new revenue streams for infrastructure providers and service providers. However, post-quantum computing exposes the
entire data sharing ecosystem to a new set of security risks. In this article, we propose a novel blockchain-based system for data sharing in
the post-quantum era. The proposed system facilitates data sharing among multiple organizations while meeting compliance and regulatory
requirements via private blockchain. We implemented the proposed architecture and information flow using three blockchain networks
(namely Hyperledger Fabric, Ethereum, and Quorum) and selected NTRU as our quantum resistant security algorithm (QRSA) to compare
the parallelization performance of Toom-Cook’s and Karatsuba’s computation methods. Experimental results show that parallel computation
has a positive impact when the security level of QRSAs is lowered, and the transaction time savings is almost 50 percent in favor of Quorum. Finally, we outline the main challenges and potential solutions.
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مقاله انگلیسی |
3 |
Post-Quantum Era in V2X Security: Convergence of Orchestration and Parallel Computation
دوران پسا کوانتومی در امنیت V2X: همگرایی ارکستراسیون و محاسبات موازی-2022 Along with the potential emergence of quantum computing, safety and security of new and
complex communication services such as automated driving need to be redefined in the post-quantum era. To ensure reliable, continuous, and
secure operation of these scenarios, quantum-resistant security algorithms (QRSAs) that enable
secure connectivity must be integrated into the
network management and orchestration systems
of mobile networks. This article explores a roadmap study of post-quantum era convergence with
cellular connectivity using the Service & Computation Orchestrator (SCO) framework for enhanced
data security in radio access and backhaul transmission with a particular focus on vehicle-to-everything services. Using NTRU as a QSRA, we
show that the parallelization performance of the
Toom-Cook and Karatsuba computation methods
can vary based on different CPU load conditions
through extensive simulations, and that the SCO
framework can facilitate the selection of the most
efficient computation for a given QRSA. Finally,
we discuss the evaluation results, identify the current standardization efforts, and present possible
directions for the coexistence of post-quantum
and mobile network connectivity through an SCO
framework that leverages parallel computing.
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مقاله انگلیسی |
4 |
Classical Computer Assisted Analysis of Small Multiqudit Systems
تحلیل کامپیوتری کلاسیک سیستمهای چند کیوبیتی کوچک-2022 Quantum computation model is regarded as a model which can overcome barriers in calculations efficiency of problems which appear in modern science. In spite of hardware development, in particular
a recent emergence of several different physical installations of the pioneering quantum machines, the
contemporary and numerical analysis of problems concerning quantum computing is very important. In the
first part of this article, some useful computing techniques for quantum registers processed by a quantum
circuits are presented. Applied classical parallel computational techniques are utilised to shorten the whole
computational time. New methods of processing state vectors for qudits and density matrices are presented,
indicating which operations may be performed in parallel in the context of the implementation of local
unitary operations. There is also shown, how to use the reduction operation in parallel implementation of
the von Neumann measurement by performing local measurements on a system of qudits. In addition to the
purely technical results as described above, the paper includes also a bunch of purely theoretical results which
substitute a solid mathematical ground for the computations performed with the help of the computational
routines as described in Section III. In particular, a discussion concerning general multi-qudit quantum states
through the prism of Entropy and Negativity measures of entanglement included in has been presented.
Additionally, the notion of the total entanglement has been introduced. For certain classes of popular multiqudit states, the introduced deficits of entanglement defined with the use of von Neumann Entropy and
Negativity have been discussed. In particular, by the use of Gram matrix technique, the corresponding deficits
of entanglement in the analysed states have been computed in an explicite way. Additionally, some new
results on AME states for some multiqudit systems are also included in Section V. The last part of the article
presents some numerical experiments on multi-qudit entanglement and determination of total entanglement
values for convex combinations of GHZ and W states. Some details concerning technical nature of results
are included in the attached Appendix.
INDEX TERMS: Quantum computing | quantum circuits | entanglement | entropy | Schmidt decomposition | parallel computations. |
مقاله انگلیسی |
5 |
Parallelizing pairings on Hessian elliptic curves
موازی سازی جفتی بر روی منحنی بیضوی هسیان-2019 This paper considers the computation of the Ate pairing on the Hessian model
of elliptic curves. Due to the many important properties making the model attractive in
cryptography, we compute for the first time the Ate pairing on this model and show how
both the Tate and the Ate pairings can be parallelized on this curve. We wrote codes in the
Sage software to ensure the correctness of formulas in this work. Keywords: Hessian curves | Tate and ate pairings | Parallel computation |
مقاله انگلیسی |
6 |
Peak operation of hydropower system with parallel technique and progressive optimality algorithm
بهره برداری از سیستم نیروی برق آبی با تکنیک موازی و الگوریتم بهینه سازی پیشرفته-2018 With the rapid economic growth in recent years, the peak operation of hydropower system (POHS) is
becoming one of the most important optimization problems in power system. However, the rapid expansion of system scale, refined management and operational constraints has greatly increased the optimization difficult of POHS. As a result, it is of great importance to develop effective methods that can ensure
the computational efficiency of POHS. The progressive optimality algorithm (POA) is a commonly used
technique for solving hydropower operation problem, but its execution time still grows sharply with
the increasing number of hydropower plants, making it difficult to satisfy the efficiency requirement
of POHS. To address this problem, a novel efficient method called parallel progressive optimality algorithm (PPOA) is presented in this paper. In PPOA, the complex problem is firstly divided into several two-stage optimization subproblems, and then the classical Fork/Join framework is used to realize parallel computation of subproblems, making a significant improvement on execution efficiency. The simulations in a real-world hydropower system demonstrate that as compared with the standard POA, PPOA can use abundant multi-core resources to reduce execution time while keeping the quality of solution,
providing a new alternative to solve the complex hydropower peak operation problem.
Keywords: Hydropower reservoirs | Peak operation | Progressive optimality algorithm | Fork/Join framework | Parallel computing | Curse of dimensionality |
مقاله انگلیسی |
7 |
A multi-factor monitoring fault tolerance model based on a GPU cluster for big data processing
مدل تحمل نظارت بر گسل چند عامل بر اساس یک خوشه GPU برای پردازش داده های بزرگ-2018 High-performance computing clusters are widely used in large-scale data mining applica
tions, and have higher requirements for persistence, stability and real-time use and sre
therefore computationally intensive. To support large-scale data processing, we design a
multi-factor real-time monitoring fault tolerance (MRMFT) model based on a GPU clus
ter. However, the higher clock frequency of GPU chips results in excessively high energy
consumption in computing systems. Moreover, the ability to support a long-lasting high
temperature operation varies greatly between different GPUs owing to the individual dif
ferences between the chips. In this paper, we design a GPU cluster energy consumption
monitoring system based on wireless sensor networks (WSNs) and propose an energy con
sumption aware checkpointing (ECAC) for high energy consumption problems with the
following two advantages: the system sets checkpoints according to actual energy con
sumption and the device temperature to improve the utilization of checkpoints and re
duce time cost; and it exploits the parallel computing features of CPU and GPU to hide
the CPU detection overhead in GPU parallel computation, and further reduce the time and
energy consumption overhead in the fault tolerance phase. Using ECAC as the constraint
and aiming for a persistent and reliable operation, the dynamic task migration mechanism
is designed, and the reliability of the cluster is greatly improved. The theoretical analysis
and experiment results show that the model improves the persistence and stability of the
computing system while reducing checkpoint overhead.
Keywords: Big data processing ، GPU cluster ، Persistence computing ، Energy consumption ، Fault tolerance ، Energy consumption aware heckpointing ، Task migration |
مقاله انگلیسی |
8 |
A Big Data Scale Algorithm for Optimal Scheduling of Integrated Microgrids
الگوریتم مقیاس داده های بزرگ برای زمانبندی بهینه میکرو شبکه های یکپارچه-2018 The capability of switching into the islanded operation mode of microgrids has been advocated as a viable solution
to achieve high system reliability. This paper proposes a new
model for the microgrids optimal scheduling and load curtailment problem. The proposed problem determines the optimal
schedule for local generators of microgrids to minimize the
generation cost of the associated distribution system in the normal operation. Moreover, when microgrids have to switch into
the islanded operation mode due to reliability considerations,
the optimal generation solution still guarantees for the minimal
amount of load curtailment. Due to the large number of constraints in both normal and islanded operations, the formulated
problem becomes a large-scale optimization problem and is very
challenging to solve using the centralized computational method.
Therefore, we propose a decomposition algorithm using the alternating direction method of multipliers that provides a parallel
computational framework. The simulation results demonstrate
the efficiency of our proposed model in reducing generation cost,
as well as guaranteeing the reliable operation of microgrids in
the islanded mode. We finally describe the detailed implementation of parallel computation for our proposed algorithm to run
on a computer cluster using the Hadoop MapReduce software
framework.
Index Terms: Alternating direction method of multipliers (ADMM), big data, Hadoop, integrated microgrid, islanded operation, load curtailment, MapReduce |
مقاله انگلیسی |
9 |
Parallel algorithms for fitting Markov arrival processes
الگوریتم های موازی برای متناسب سازی فرآیندهای ورود مارکوف-2018 The fitting of Markov arrival processes (MAPs) with the expectation–maximization (EM) algorithm is a computationally demanding task. There are attempts in the literature to reduce the computational complexity by introducing special MAP structures instead of the general representation. Another possibility to improve the efficiency of MAP fitting is to reformulate the inherently serial classical EM algorithm to exploit modern, massively parallel hardware architectures.
In this paper we present three different EM-based fitting procedures that can take advantage of the parallel hardware (like Graphics Processing Units, GPUs) and apply a special MAP structure, the Erlang distributed-continuous-time hidden Markov chain (ER-CHMM) structure for reducing the computational complexity.
All the proposed parallel algorithms have their strengths: the first one traverses the samples only once per iteration, the second one is memory efficient (far more than the classical serial algorithm), and the third one has exceptionally low execution times.
These procedures are compared with the standard serial forward–backward procedure for performance comparison. The new algorithms are orders of magnitudes faster than the standard serial procedure, while (depending on the variant) using less memory.
keywords: Markov arrival process |Traffic model fitting |EM algorithm |Parallel computation |GPU |
مقاله انگلیسی |
10 |
Dynamic Adaptation to Environmental Changes of Optical Virtual Networking and Cloud Computing Systems for Tightly Coupling Big Data and Peripheral Computer Resources
سازگاری پویا با تغییرات محیطی شبکه های مجازی نوری و سیستم های محاسبات ابری برای جمع آوری داده های بزرگ و منابع کامپیوتری محیطی-2018 Recently, the use of big data has attracted attention as a profitable business strategy, and is expected to keep
increasing in the future. In contrast to existing ways of big
data analysis based on centralized computing environment such
on a few huge data centers, we advocate a distributed and
parallel computation environment aiming at fine-grained cloud
computing, which includes so-called edge computing. In the
advocated environment, it is assumed that many users, which
own big data to be analyzed, dynamically participate in the
network to request computer resources and leave after finishing
their analyses. In such a dynamic and realistic environment, this
paper improves the proximity of computer resources to big data
by applying virtualized network in which nodes with each big
data and the corresponding computer resources are mutually
connected by proper optical paths. Optical path arrangement
is periodically updated for new users, without affecting other
users currently using computer resources. Moreover, a resource
assignment algorithm suitable for such dynamic changes is also
proposed to achieve fairness in terms of the network distance
between big data and computer resources, and effective load
balancing among resource suppliers. We evaluate its effectiveness
by computer simulation.
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مقاله انگلیسی |