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Register a new userA New Hybrid Digital Signature Algorithm with high Security and Performance
خوارزمية هجينة جديدة للتوقيع الرقمي عالية الأمان و الأداء
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تعتمد معظم خوارزميات التوقيع الرقمي الحالية في بنيتها على مفاهيم رياضية معقدة يتطلب تنفيذها وقتاً طويلاً و جيداً حسابياً كبيرا. و كمحاولة للتخفيف من هذه المشاكل اقترح بعض الباحثون خوارزميات توقيع رقمي تعتمد على توابع و عمليات حسابية بسيطة سريعة التنفيذ، إلا أن ذلك كان على حساب الأمان.
The majority of recent digital signature algorithms depend, in their structure, on complicated mathematical concepts that require a long time and a significant computational effort to be executed. As a trial to reduce these problems, some researchers have proposed digital signature algorithms which depend on simple arithmetic functions and operations that are executed quickly, but that was at the expense of the security of algorithms.
References used
ALESE, B, PHILEMON, E, FALAKI, S 2012- Comparative analysis of Public key encryption scheme. International Journal of Engineering and Technology, Vol. 2, No. 9
BOUFTAS, S 2014- On a new fast public key cryptosystem. Retrieved Mars 2015 from http://eprint.iacr.org/2014/946
DENNING, D 1984- Digital Signatures with RSA and Other Public Key Cryptosystems. Communications of the ACM, Vol. 27, No.4
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Multi-objective evolutionary algorithms are used in a wide range
of fields to solve the issues of optimization, which require several
conflicting objectives to be considered together. Basic evolutionary
algorithm algorithms have several drawbacks,
such as lack of a
good criterion for termination, and lack of evidence of good
convergence. A multi-objective hybrid evolutionary algorithm is
often used to overcome these defects.
optimization
الأمثلة
الأمثلة متعددة الأهداف
الخوارزميات التطورية
الخوارزميات التطورية المتعددة الأهداف
الخوارزميات التطورية عديدة الأهداف
(Multi-Objective Optimization (MO
Evolutionary Algorithms
(Multi-Objective Evolutionary Algorithms (MOEAs
(Many-Objective Evolutionary Algorithms (MaOEAs
المزيد..
In the Multi-objective Traveling Salesman Problem (moTSP)
simultaneous optimization of more than one objective functions is
required. This paper proposes hybrid algorithm to solve the multiobjectives
Traveling Salesman problem through the integration of
the ant colony optimization algorithm with the Genetic algorithm.
This paper introduces a new algorithm to solve some problems
that data clustering algorithms such as K-Means suffer from.
This new algorithm by itself is able to cluster data without the
need of other clustering algorithms.
Nonlinear conjugate gradient (CG) method holds an important role in solving large-scale unconstrained optimization problems. In this paper, we suggest a new modification of CG coefficient �� that satisfies sufficient descent condition and possesses global convergence property under strong Wolfe line search. The numerical results show that our new method is more efficient compared with other CG formulas tested.
This Paper Attempts to study the latest advancements in High Performance Computing Technologies, Which Provides suitable environments, Solid infrastructure, Software and Hardware Components, allowing Scientists and Researchers to solve Math, Biology,
Machine Learning, Physics Simulations, and numerous other problems, Allowing significant breakthroughs in these fields.
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