We describe a new approach to solve the problem to find the maximum independent set in a given Graph, known also as Max-Stable set problem (MSSP). In this paper, we show how Max-Stable problem can be reformulated into a linear problem under quadratic constraints, and then we resolve the QP result by a hybrid approach based Continuous Hopfeild Neural Network (CHN) and Local Search. In a manner that the solution given by the CHN will be the starting point of the local search. The new approach showed a good performance than the original one which executes a suite of CHN runs, at each execution a new leaner constraint is added into the resolved model. To prove the efficiency of our approach, we present some computational experiments of solving random generated problem and typical MSSP instances of real life problem.
The numerical solution of Huxley equation by the use of two finite difference methods is done. The first one is the explicit scheme and the second one is the Crank-Nicholson scheme. The comparison between the two methods showed that the explicit scheme is easier and has faster convergence while the Crank-Nicholson scheme is more accurate. In addition, the stability analysis using Fourier (von Neumann) method of two schemes is investigated. The resulting analysis showed that the first scheme
is conditionally stable if, r ≤ 2 − aβ∆t , ∆t ≤ 2(∆x)2 and the second
scheme is unconditionally stable.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Computer Science
Active and Programmable Networks
Active safety systems
Ad Hoc & Sensor Network
Ad hoc networks for pervasive communications
Adaptive, autonomic and context-aware computing
Advance Computing technology and their application
Advanced Computing Architectures and New Programming Models
Advanced control and measurement
Aeronautical Engineering,
Agent-based middleware
Alert applications
Automotive, marine and aero-space control and all other control applications
Autonomic and self-managing middleware
Autonomous vehicle
Biochemistry
Bioinformatics
BioTechnology(Chemistry, Mathematics, Statistics, Geology)
Broadband and intelligent networks
Broadband wireless technologies
CAD/CAM/CAT/CIM
Call admission and flow/congestion control
Capacity planning and dimensioning
Changing Access to Patient Information
Channel capacity modelling and analysis
Civil Engineering,
Cloud Computing and Applications
Collaborative applications
Communication application
Communication architectures for pervasive computing
Communication systems
Computational intelligence
Computer and microprocessor-based control
Computer Architecture and Embedded Systems
Computer Business
Computer Sciences and Applications
Computer Vision
Computer-based information systems in health care
Computing Ethics
Computing Practices & Applications
Congestion and/or Flow Control
Content Distribution
Context-awareness and middleware
Creativity in Internet management and retailing
Cross-layer design and Physical layer based issue
Cryptography
Data Base Management
Data fusion
Data Mining
Data retrieval
Data Storage Management
Decision analysis methods
Decision making
Digital Economy and Digital Divide
Digital signal processing theory
Distributed Sensor Networks
Drives automation
Drug Design,
Drug Development
DSP implementation
E-Business
E-Commerce
E-Government
Electronic transceiver device for Retail Marketing Industries
Electronics Engineering,
Embeded Computer System
Emerging advances in business and its applications
Emerging signal processing areas
Enabling technologies for pervasive systems
Energy-efficient and green pervasive computing
Environmental Engineering,
Estimation and identification techniques
Evaluation techniques for middleware solutions
Event-based, publish/subscribe, and message-oriented middleware
Evolutionary computing and intelligent systems
Expert approaches
Facilities planning and management
Flexible manufacturing systems
Formal methods and tools for designing
Fuzzy algorithms
Fuzzy logics
GPS and location-based app
Min-based qualitative possibilistic networks are one of the effective tools for a compact representation of decision problems under uncertainty. The exact approaches for computing decision based on possibilistic networks are limited by the size of the possibility distributions.
Generally, these approaches are based on possibilistic propagation algorithms. An important step in the computation of the decision is the transformation of the DAG into a secondary structure, known as the junction trees. This transformation is known to be costly and represents a difficult problem. We propose in this paper a new approximate approach for the computation
of decision under uncertainty within possibilistic networks. The computing of the optimal optimistic decision no longer goes through the junction tree construction step. Instead, it is performed by calculating the degree of normalization in the moral graph resulting from the merging of the possibilistic network codifying knowledge of the agent and that codifying its preferences.
The numerical solution of Huxley equation by the use of two finite difference methods is done. The first one is the explicit scheme and the second one is the Crank-Nicholson scheme. The comparison between the two methods showed that the explicit scheme is easier and has faster convergence while the Crank-Nicholson scheme is more accurate. In addition, the stability analysis using Fourier (von Neumann) method of two schemes is investigated. The resulting analysis showed that the first scheme
is conditionally stable if, r ≤ 2 − aβ∆t , ∆t ≤ 2(∆x)2 and the second
scheme is unconditionally stable.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Computer Science
Active and Programmable Networks
Active safety systems
Ad Hoc & Sensor Network
Ad hoc networks for pervasive communications
Adaptive, autonomic and context-aware computing
Advance Computing technology and their application
Advanced Computing Architectures and New Programming Models
Advanced control and measurement
Aeronautical Engineering,
Agent-based middleware
Alert applications
Automotive, marine and aero-space control and all other control applications
Autonomic and self-managing middleware
Autonomous vehicle
Biochemistry
Bioinformatics
BioTechnology(Chemistry, Mathematics, Statistics, Geology)
Broadband and intelligent networks
Broadband wireless technologies
CAD/CAM/CAT/CIM
Call admission and flow/congestion control
Capacity planning and dimensioning
Changing Access to Patient Information
Channel capacity modelling and analysis
Civil Engineering,
Cloud Computing and Applications
Collaborative applications
Communication application
Communication architectures for pervasive computing
Communication systems
Computational intelligence
Computer and microprocessor-based control
Computer Architecture and Embedded Systems
Computer Business
Computer Sciences and Applications
Computer Vision
Computer-based information systems in health care
Computing Ethics
Computing Practices & Applications
Congestion and/or Flow Control
Content Distribution
Context-awareness and middleware
Creativity in Internet management and retailing
Cross-layer design and Physical layer based issue
Cryptography
Data Base Management
Data fusion
Data Mining
Data retrieval
Data Storage Management
Decision analysis methods
Decision making
Digital Economy and Digital Divide
Digital signal processing theory
Distributed Sensor Networks
Drives automation
Drug Design,
Drug Development
DSP implementation
E-Business
E-Commerce
E-Government
Electronic transceiver device for Retail Marketing Industries
Electronics Engineering,
Embeded Computer System
Emerging advances in business and its applications
Emerging signal processing areas
Enabling technologies for pervasive systems
Energy-efficient and green pervasive computing
Environmental Engineering,
Estimation and identification techniques
Evaluation techniques for middleware solutions
Event-based, publish/subscribe, and message-oriented middleware
Evolutionary computing and intelligent systems
Expert approaches
Facilities planning and management
Flexible manufacturing systems
Formal methods and tools for designing
Fuzzy algorithms
Fuzzy logics
GPS and location-based app
Min-based qualitative possibilistic networks are one of the effective tools for a compact representation of decision problems under uncertainty. The exact approaches for computing decision based on possibilistic networks are limited by the size of the possibility distributions.
Generally, these approaches are based on possibilistic propagation algorithms. An important step in the computation of the decision is the transformation of the DAG into a secondary structure, known as the junction trees. This transformation is known to be costly and represents a difficult problem. We propose in this paper a new approximate approach for the computation
of decision under uncertainty within possibilistic networks. The computing of the optimal optimistic decision no longer goes through the junction tree construction step. Instead, it is performed by calculating the degree of normalization in the moral graph resulting from the merging of the possibilistic network codifying knowledge of the agent and that codifying its preferences.
Limits of Local Algorithms for Randomly Generated Constraint Satisfaction Pro...Yandex
A major challenge in the field of random graphs is constructing fast algorithms for solving a variety of combinatorial optimization problems, such as finding largest independent set of a graph or finding a satisfying assignment in random instances of K-SAT problem. Most of the algorithms that have been successfully analyzed in the past are so-called local algorithms which rely on making decisions based on local information.
In this talk we will discuss fundamental barrier on the power of local algorithms to solve such problems, despite the conjectures put forward in the past. In particular, we refute a conjecture regarding the power of local algorithms to find nearly largest independent sets in random regular graphs. Similarly, we show that a broad class of local algorithms, including the so-called Belief Propagation and Survey Propagation algorithms, cannot find satisfying assignments in random NAE-K-SAT problem above a certain asymptotic threshold, below which even simple algorithms succeed with high probability. Our negative results exploit fascinating geometry of feasible solutions of random constraint satisfaction problems, which was first predicted by physicists heuristically and now confirmed by rigorous methods. According to this picture, the solution space exhibits a clustering property whereby the feasible solutions tend to cluster according to the underlying Hamming distance. We show that success of local algorithms would imply violation of such a clustering property thus leading to a contradiction.
Joint work with Madhu Sudan (Microsoft Research).
In conventional transportation problem (TP), supplies, demands and costs are always certain. This paper develops an approach to solve the unbalanced transportation problem where as all the parameters are not in deterministic numbers but imprecise ones. Here, all the parameters of the TP are considered to the triangular intuitionistic fuzzy numbers (TIFNs). The existing ranking procedure of Varghese and Kuriakose is used to transform the unbalanced intuitionistic fuzzy transportation problem (UIFTP) into a crisp one so that the conventional method may be applied to solve the TP. The occupied cells of unbalanced crisp TP that we obtained are as same as the occupied cells of UIFTP.
On the basis of this idea the solution procedure is differs from unbalanced crisp TP to UIFTP in allocation step only. Therefore, the new method and new multiplication operation on triangular intuitionistic fuzzy number (TIFN) is proposed to find the optimal solution in terms of TIFN. The main advantage of this method is computationally very simple, easy to understand and also the optimum objective value obtained by our method is physically meaningful.
A Generalized Sampling Theorem Over Galois Field Domains for Experimental Des...csandit
In this paper, the sampling theorem for bandlimited functions over
domains is
generalized to one over ∏
domains. The generalized theorem is applicable to the
experimental design model in which each factor has a different number of levels and enables us
to estimate the parameters in the model by using Fourier transforms. Moreover, the relationship
between the proposed sampling theorem and orthogonal arrays is also provided.
KEY
Kernal based speaker specific feature extraction and its applications in iTau...TELKOMNIKA JOURNAL
Extraction and classification algorithms based on kernel nonlinear features are popular in the new direction of research in machine learning. This research paper considers their practical application in the iTaukei automatic speaker recognition system (ASR) for cross-language speech recognition. Second, nonlinear speaker-specific extraction methods such as kernel principal component analysis (KPCA), kernel independent component analysis (KICA), and kernel linear discriminant analysis (KLDA) are summarized. The conversion effects on subsequent classifications were tested in conjunction with Gaussian mixture modeling (GMM) learning algorithms; in most cases, computations were found to have a beneficial effect on classification performance. Additionally, the best results were achieved by the Kernel linear discriminant analysis (KLDA) algorithm. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using ATR Japanese C language corpus and self-recorded iTaukei corpus. The ASR efficiency of KLDA, KICA, and KLDA technique for 6 sec of ATR Japanese C language corpus 99.7%, 99.6%, and 99.1% and equal error rate (EER) are 1.95%, 2.31%, and 3.41% respectively. The EER improvement of the KLDA technique-based ASR system compared with KICA and KPCA is 4.25% and 8.51% respectively.
Fractional pseudo-Newton method and its use in the solution of a nonlinear sy...mathsjournal
The following document presents a possible solution and a brief stability analysis for a nonlinear system,
which is obtained by studying the possibility of building a hybrid solar receiver; It is necessary to mention that
the solution of the aforementioned system is relatively difficult to obtain through iterative methods since the
system is apparently unstable. To find this possible solution is used a novel numerical method valid for one and
several variables, which using the fractional derivative, allows us to find solutions for some nonlinear systems in
the complex space using real initial conditions, this method is also valid for linear systems. The method described
above has an order of convergence (at least) linear, but it is easy to implement and it is not necessary to invert
some matrix for solving nonlinear systems and linear systems.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
AN EFFICIENT PARALLEL ALGORITHM FOR COMPUTING DETERMINANT OF NON-SQUARE MATRI...ijdpsjournal
One of the most significant challenges in Computing Determinant of Rectangular Matrices is high time
complexity of its algorithm. Among all definitions of determinant of rectangular matrices, Radic’s
definition has special features which make it more notable. But in this definition, C(N
M
) sub matrices of the
order m×m needed to be generated that put this problem in np-hard class. On the other hand, any row or
column reduction operation may hardly lead to diminish the volume of calculation. Therefore, in this paper
we try to present the parallel algorithm which can decrease the time complexity of computing the
determinant of non-square matrices to O(N).
NONSTATIONARY RELAXED MULTISPLITTING METHODS FOR SOLVING LINEAR COMPLEMENTARI...ijcsa
In this paper we consider some non stationary relaxed synchronous and asynchronous
multisplitting methods for solving the linear complementarity problems with their coefficient
matrices being H−matrices. The convergence theorems of the methods are given,and the efficiency
is shown by numerical tests.
Transportation Problem with Pentagonal Intuitionistic Fuzzy Numbers Solved Us...IJERA Editor
This paper presents a solution methodology for transportation problem in an intuitionistic fuzzy environment in
which cost are represented by pentagonal intuitionistic fuzzy numbers. Transportation problem is a particular
class of linear programming, which is associated with day to day activities in our real life. It helps in solving
problems on distribution and transportation of resources from one place to another. The objective is to satisfy
the demand at destination from the supply constraints at the minimum transportation cost possible. The problem
is solved using a ranking technique called Accuracy function for pentagonal intuitionistic fuzzy numbers and
Russell’s Method
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A COMPREHENSIVE ANALYSIS OF QUANTUM CLUSTERING : FINDING ALL THE POTENTIAL MI...IJDKP
Quantum clustering (QC), is a data clustering algorithm based on quantum mechanics which is
accomplished by substituting each point in a given dataset with a Gaussian. The width of the Gaussian is a
σ value, a hyper-parameter which can be manually defined and manipulated to suit the application.
Numerical methods are used to find all the minima of the quantum potential as they correspond to cluster
centers. Herein, we investigate the mathematical task of expressing and finding all the roots of the
exponential polynomial corresponding to the minima of a two-dimensional quantum potential. This is an
outstanding task because normally such expressions are impossible to solve analytically. However, we
prove that if the points are all included in a square region of size σ, there is only one minimum. This bound
is not only useful in the number of solutions to look for, by numerical means, it allows to to propose a new
numerical approach “per block”. This technique decreases the number of particles by approximating some
groups of particles to weighted particles. These findings are not only useful to the quantum clustering
problem but also for the exponential polynomials encountered in quantum chemistry, Solid-state Physics
and other applications.
The aim of this research is to find accurate solution for the Troesch’s problem by using high performance technique based on parallel processing implementation.
Design/methodology/approach – Feed forward neural network is designed to solve important type of differential equations that arises in many applied sciences and engineering applications. The suitable designed based on choosing suitable learning rate, transfer function, and training algorithm. The authors used back propagation with new implement of Levenberg - Marquardt training algorithm. Also, the authors depend new idea for choosing the weights. The effectiveness of the suggested design for the network is shown by using it for solving Troesch problem in many cases.
Findings – New idea for choosing the weights of the neural network, new implement of Levenberg - Marquardt training algorithm which assist to speeding the convergence and the implementation of the suggested design demonstrates the usefulness in finding exact solutions.
Computing Maximum Entropy Densities: A Hybrid ApproachCSCJournals
This paper proposes a hybrid method to calculate the maximum entropy (MaxEnt) density subject to known moment constraints, which combines the linear equation (LE) method and Newton¡¯s method together. The new approach is more computationally efficient than ordinary Newton¡¯s method as it usually takes fewer Newton iterations to reach the final solution. Compared with the simple LE method, the hybrid algorithm will produce a more accurate solution. Numerical examples confirm the excellent performance of the proposed method.
Multi objective predictive control a solution using metaheuristicsijcsit
The application of multi objective model predictive control approaches is significantly limited with
computation time associated with optimization algorithms. Metaheuristics are general purpose heuristics
that have been successfully used in solving difficult optimization problems in a reasonable computation
time. In this work , we use and compare two multi objective metaheuristics, Multi-Objective Particle
swarm Optimization, MOPSO, and Multi-Objective Gravitational Search Algorithm, MOGSA, to generate
a set of approximately Pareto-optimal solutions in a single run. Two examples are studied, a nonlinear
system consisting of two mobile robots tracking trajectories and avoiding obstacles and a linear multi
variable system. The computation times and the quality of the solution in terms of the smoothness of the
control signals and precision of tracking show that MOPSO can be an alternative for real time
applications.
Limits of Local Algorithms for Randomly Generated Constraint Satisfaction Pro...Yandex
A major challenge in the field of random graphs is constructing fast algorithms for solving a variety of combinatorial optimization problems, such as finding largest independent set of a graph or finding a satisfying assignment in random instances of K-SAT problem. Most of the algorithms that have been successfully analyzed in the past are so-called local algorithms which rely on making decisions based on local information.
In this talk we will discuss fundamental barrier on the power of local algorithms to solve such problems, despite the conjectures put forward in the past. In particular, we refute a conjecture regarding the power of local algorithms to find nearly largest independent sets in random regular graphs. Similarly, we show that a broad class of local algorithms, including the so-called Belief Propagation and Survey Propagation algorithms, cannot find satisfying assignments in random NAE-K-SAT problem above a certain asymptotic threshold, below which even simple algorithms succeed with high probability. Our negative results exploit fascinating geometry of feasible solutions of random constraint satisfaction problems, which was first predicted by physicists heuristically and now confirmed by rigorous methods. According to this picture, the solution space exhibits a clustering property whereby the feasible solutions tend to cluster according to the underlying Hamming distance. We show that success of local algorithms would imply violation of such a clustering property thus leading to a contradiction.
Joint work with Madhu Sudan (Microsoft Research).
In conventional transportation problem (TP), supplies, demands and costs are always certain. This paper develops an approach to solve the unbalanced transportation problem where as all the parameters are not in deterministic numbers but imprecise ones. Here, all the parameters of the TP are considered to the triangular intuitionistic fuzzy numbers (TIFNs). The existing ranking procedure of Varghese and Kuriakose is used to transform the unbalanced intuitionistic fuzzy transportation problem (UIFTP) into a crisp one so that the conventional method may be applied to solve the TP. The occupied cells of unbalanced crisp TP that we obtained are as same as the occupied cells of UIFTP.
On the basis of this idea the solution procedure is differs from unbalanced crisp TP to UIFTP in allocation step only. Therefore, the new method and new multiplication operation on triangular intuitionistic fuzzy number (TIFN) is proposed to find the optimal solution in terms of TIFN. The main advantage of this method is computationally very simple, easy to understand and also the optimum objective value obtained by our method is physically meaningful.
A Generalized Sampling Theorem Over Galois Field Domains for Experimental Des...csandit
In this paper, the sampling theorem for bandlimited functions over
domains is
generalized to one over ∏
domains. The generalized theorem is applicable to the
experimental design model in which each factor has a different number of levels and enables us
to estimate the parameters in the model by using Fourier transforms. Moreover, the relationship
between the proposed sampling theorem and orthogonal arrays is also provided.
KEY
Kernal based speaker specific feature extraction and its applications in iTau...TELKOMNIKA JOURNAL
Extraction and classification algorithms based on kernel nonlinear features are popular in the new direction of research in machine learning. This research paper considers their practical application in the iTaukei automatic speaker recognition system (ASR) for cross-language speech recognition. Second, nonlinear speaker-specific extraction methods such as kernel principal component analysis (KPCA), kernel independent component analysis (KICA), and kernel linear discriminant analysis (KLDA) are summarized. The conversion effects on subsequent classifications were tested in conjunction with Gaussian mixture modeling (GMM) learning algorithms; in most cases, computations were found to have a beneficial effect on classification performance. Additionally, the best results were achieved by the Kernel linear discriminant analysis (KLDA) algorithm. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using ATR Japanese C language corpus and self-recorded iTaukei corpus. The ASR efficiency of KLDA, KICA, and KLDA technique for 6 sec of ATR Japanese C language corpus 99.7%, 99.6%, and 99.1% and equal error rate (EER) are 1.95%, 2.31%, and 3.41% respectively. The EER improvement of the KLDA technique-based ASR system compared with KICA and KPCA is 4.25% and 8.51% respectively.
Fractional pseudo-Newton method and its use in the solution of a nonlinear sy...mathsjournal
The following document presents a possible solution and a brief stability analysis for a nonlinear system,
which is obtained by studying the possibility of building a hybrid solar receiver; It is necessary to mention that
the solution of the aforementioned system is relatively difficult to obtain through iterative methods since the
system is apparently unstable. To find this possible solution is used a novel numerical method valid for one and
several variables, which using the fractional derivative, allows us to find solutions for some nonlinear systems in
the complex space using real initial conditions, this method is also valid for linear systems. The method described
above has an order of convergence (at least) linear, but it is easy to implement and it is not necessary to invert
some matrix for solving nonlinear systems and linear systems.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
AN EFFICIENT PARALLEL ALGORITHM FOR COMPUTING DETERMINANT OF NON-SQUARE MATRI...ijdpsjournal
One of the most significant challenges in Computing Determinant of Rectangular Matrices is high time
complexity of its algorithm. Among all definitions of determinant of rectangular matrices, Radic’s
definition has special features which make it more notable. But in this definition, C(N
M
) sub matrices of the
order m×m needed to be generated that put this problem in np-hard class. On the other hand, any row or
column reduction operation may hardly lead to diminish the volume of calculation. Therefore, in this paper
we try to present the parallel algorithm which can decrease the time complexity of computing the
determinant of non-square matrices to O(N).
NONSTATIONARY RELAXED MULTISPLITTING METHODS FOR SOLVING LINEAR COMPLEMENTARI...ijcsa
In this paper we consider some non stationary relaxed synchronous and asynchronous
multisplitting methods for solving the linear complementarity problems with their coefficient
matrices being H−matrices. The convergence theorems of the methods are given,and the efficiency
is shown by numerical tests.
Transportation Problem with Pentagonal Intuitionistic Fuzzy Numbers Solved Us...IJERA Editor
This paper presents a solution methodology for transportation problem in an intuitionistic fuzzy environment in
which cost are represented by pentagonal intuitionistic fuzzy numbers. Transportation problem is a particular
class of linear programming, which is associated with day to day activities in our real life. It helps in solving
problems on distribution and transportation of resources from one place to another. The objective is to satisfy
the demand at destination from the supply constraints at the minimum transportation cost possible. The problem
is solved using a ranking technique called Accuracy function for pentagonal intuitionistic fuzzy numbers and
Russell’s Method
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A COMPREHENSIVE ANALYSIS OF QUANTUM CLUSTERING : FINDING ALL THE POTENTIAL MI...IJDKP
Quantum clustering (QC), is a data clustering algorithm based on quantum mechanics which is
accomplished by substituting each point in a given dataset with a Gaussian. The width of the Gaussian is a
σ value, a hyper-parameter which can be manually defined and manipulated to suit the application.
Numerical methods are used to find all the minima of the quantum potential as they correspond to cluster
centers. Herein, we investigate the mathematical task of expressing and finding all the roots of the
exponential polynomial corresponding to the minima of a two-dimensional quantum potential. This is an
outstanding task because normally such expressions are impossible to solve analytically. However, we
prove that if the points are all included in a square region of size σ, there is only one minimum. This bound
is not only useful in the number of solutions to look for, by numerical means, it allows to to propose a new
numerical approach “per block”. This technique decreases the number of particles by approximating some
groups of particles to weighted particles. These findings are not only useful to the quantum clustering
problem but also for the exponential polynomials encountered in quantum chemistry, Solid-state Physics
and other applications.
The aim of this research is to find accurate solution for the Troesch’s problem by using high performance technique based on parallel processing implementation.
Design/methodology/approach – Feed forward neural network is designed to solve important type of differential equations that arises in many applied sciences and engineering applications. The suitable designed based on choosing suitable learning rate, transfer function, and training algorithm. The authors used back propagation with new implement of Levenberg - Marquardt training algorithm. Also, the authors depend new idea for choosing the weights. The effectiveness of the suggested design for the network is shown by using it for solving Troesch problem in many cases.
Findings – New idea for choosing the weights of the neural network, new implement of Levenberg - Marquardt training algorithm which assist to speeding the convergence and the implementation of the suggested design demonstrates the usefulness in finding exact solutions.
Computing Maximum Entropy Densities: A Hybrid ApproachCSCJournals
This paper proposes a hybrid method to calculate the maximum entropy (MaxEnt) density subject to known moment constraints, which combines the linear equation (LE) method and Newton¡¯s method together. The new approach is more computationally efficient than ordinary Newton¡¯s method as it usually takes fewer Newton iterations to reach the final solution. Compared with the simple LE method, the hybrid algorithm will produce a more accurate solution. Numerical examples confirm the excellent performance of the proposed method.
Multi objective predictive control a solution using metaheuristicsijcsit
The application of multi objective model predictive control approaches is significantly limited with
computation time associated with optimization algorithms. Metaheuristics are general purpose heuristics
that have been successfully used in solving difficult optimization problems in a reasonable computation
time. In this work , we use and compare two multi objective metaheuristics, Multi-Objective Particle
swarm Optimization, MOPSO, and Multi-Objective Gravitational Search Algorithm, MOGSA, to generate
a set of approximately Pareto-optimal solutions in a single run. Two examples are studied, a nonlinear
system consisting of two mobile robots tracking trajectories and avoiding obstacles and a linear multi
variable system. The computation times and the quality of the solution in terms of the smoothness of the
control signals and precision of tracking show that MOPSO can be an alternative for real time
applications.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A DERIVATIVE FREE HIGH ORDERED HYBRID EQUATION SOLVERZac Darcy
Generally a range of equation solvers for estimating the solution of an equation contain the derivative of
first or higher order. Such solvers are difficult to apply in the instances of complicated functional
relationship. The equation solver proposed in this paper meant to solve many of the involved complicated
problems and establishing a process tending towards a higher ordered by alloying the already proved
conventional methods like Newton-Raphson method (N-R), Regula Falsi method (R-F) & Bisection method
(BIS). The present method is good to solve those nonlinear and transcendental equations that cannot be
solved by the basic algebra. Comparative analysis are also made with the other racing formulas of this
group and the result shows that present method is faster than all such methods of the class.
A stochastic algorithm for solving the posterior inference problem in topic m...TELKOMNIKA JOURNAL
Latent Dirichlet allocation (LDA) is an important probabilistic generative model and has usually used in many domains such as text mining, retrieving information, or natural language processing domains. The posterior inference is the important problem in deciding the quality of the LDA model, but it is usually non-deterministic polynomial (NP)-hard and often intractable, especially in the worst case. For individual texts, some proposed methods such as variational Bayesian (VB), collapsed variational Bayesian (CVB), collapsed Gibb’s sampling (CGS), and online maximum a posteriori estimation (OPE) to avoid solving this problem directly, but they usually do not have any guarantee of convergence rate or quality of learned models excepting variants of OPE. Based on OPE and using the Bernoulli distribution combined, we design an algorithm namely general online maximum a posteriori estimation using two stochastic bounds (GOPE2) for solving the posterior inference problem in LDA model. It also is the NP-hard non-convex optimization problem. Via proof of theory and experimental results on the large datasets, we realize that GOPE2 is performed to develop the efficient method for learning topic models from big text collections especially massive/streaming texts, and more efficient than previous methods.
Selecting the best stochastic systems for large scale engineering problemsIJECEIAES
Selecting a subset of the best solutions among large-scale problems is an important area of research. When the alternative solutions are stochastic in nature, then it puts more burden on the problem. The objective of this paper is to select a set that is likely to contain the actual best solutions with high probability. If the selected set contains all the best solutions, then the selection is denoted as correct selection. We are interested in maximizing the probability of this selection; P(CS). In many cases, the available computation budget for simulating the solution set in order to maximize P(CS) is limited. Therefore, instead of distributing these computational efforts equally likely among the alternatives, the optimal computing budget allocation (OCBA) procedure came to put more effort on the solutions that have more impact on the selected set. In this paper, we derive formulas of how to distribute the available budget asymptotically to find the approximation of P(CS). We then present a procedure that uses OCBA with the ordinal optimization (OO) in order to select the set of best solutions. The properties and performance of the proposed procedure are illustrated through a numerical example. Overall results indicate that the procedure is able to select a subset of the best systems with high probability of correct selection using small number of simulation samples under different parameter settings.
A Derivative Free High Ordered Hybrid Equation Solver Zac Darcy
Generally a range of equation solvers for estimating the solution of an equation contain the derivative of
first or higher order. Such solvers are difficult to apply in the instances of complicated functional
relationship. The equation solver proposed in this paper meant to solve many of the involved complicated
problems and establishing a process tending towards a higher ordered by alloying the already proved
conventional methods like Newton-Raphson method (N-R), Regula Falsi method (R-F) & Bisection method
(BIS). The present method is good to solve those nonlinear and transcendental equations that cannot be
solved by the basic algebra. Comparative analysis are also made with the other racing formulas of this
group and the result shows that present method is faster than all such methods of the class.
Duality Theory in Multi Objective Linear Programming Problemstheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
Regularized Compression of A Noisy Blurred Image ijcsa
Both regularization and compression are important issues in image processing and have been widely
approached in the literature. The usual procedure to obtain the compression of an image given through a
noisy blur requires two steps: first a deblurring step of the image and then a factorization step of the
regularized image to get an approximation in terms of low rank nonnegative factors. We examine here the
possibility of swapping the two steps by deblurring directly the noisy factors or partially denoised factors.
The experimentation shows that in this way images with comparable regularized compression can be
obtained with a lower computational cost.
Similar to CHN and Swap Heuristic to Solve the Maximum Independent Set Problem (20)
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
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CHN and Swap Heuristic to Solve the Maximum Independent Set Problem
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 6, December 2017, pp. 3583 – 3592
ISSN: 2088-8708 3583
Institute of Advanced Engineering and Science
w w w . i a e s j o u r n a l . c o m
CHN and Swap Heuristic to Solve the Maximum
Independent Set Problem
Bouhouch Adil1
, Loqman Chakir2
, and El Qadi Abderrahime3
1
Team TIM, High School of Technology, CEDoc-SFA faculty of sciences, Moulay Ismail University, Meknes, Morocco
2
department of informatics, Sciences Faculty, Dhar Mehraz, Sidi Mohammed Ben Abdellah University,Fez, Morocco
3
LASTIMI, High School of Technology - Mohammed V University of Rabat, Morocco
Article Info
Article history:
Received: Jan 9, 2017
Revised: Jun 14, 2017
Accepted: Jul 3, 2017
Keyword:
Max-Stable problem
Independent set
CHN
Local search
Combinatory problems
Graph
ABSTRACT
We describe a new approach to solve the problem to find the maximum independent
set in a given Graph, known also as Max-Stable set problem (MSSP). In this paper,
we show how Max-Stable problem can be reformulated into a linear problem under
quadratic constraints, and then we resolve the QP result by a hybrid approach based
Continuous Hopfeild Neural Network (CHN) and Local Search. In a manner that the
solution given by the CHN will be the starting point of the local search. The new
approach showed a good performance than the original one which executes a suite of
CHN runs, at each execution a new leaner constraint is added into the resolved model.
To prove the efficiency of our approach, we present some computational experiments
of solving random generated problem and typical MSSP instances of real life problem.
Copyright c 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Name Bouhouch Adil
Affiliation Team TIM, High School of Technology Meknes
Address High School of Technology Meknes Moulay Ismail university Meknes Morocco
Phone +212664786422
Email bouhouch.adil@gmail.com
1. INTRODUCTION
The Max-Stable Set Problem (MSSP) is a problem that attempts to find the largest independent set
at a given graph. All the nodes included in the independent set must respect the condition that they are not
pairwise connected by an arc. Max-table is largely applied in many areas: case-based reasoning [1], com-
puter vision [2], scheduling, ... . Max-Stableis a Strong NP-Hard problem while it’s hard to be approximate.
Therefore, solving MSSP in polynomial time for arbitrary graph case is unlikely. For arbitrary graph there
are many exact algorithm which enumerating all cliques and select the one with the maximal cardinality. To
our knowledge Harary [3] was the first one who introduced in the literature an exact method. Loukakis [4]
generated all maximal independent sets lexicographically introduced by a depth-first enumerative algorithm.
Their study includes a comparison against Regneri [5] and Tsukiyama [6] algorithmes. The theoretical superior
efficiency of their algorithm is also reinforced by computational results, moreover there method was largely
faster than that proposed by Tsukiyama [6] and that introduced by Bron [7]. After two years, Loukakis [8]
imported additional change to their previous work [4] and improve it by three speed-up. In the years 1988s,
Johnson [9] introduced an exact approach which determines all maximal stable sets in lexicographic order. The
approach get each independent set by times complexity of order O(n3
). Chiba [10] proposed an algorithm to
the maximal cliques on the order O(a(G)mµ) of times complexity -where a(G) is the arboricity of graph G- ,
this is over the time complexity of [6]. Based on the Born [7] work, Tomita [11] proposed an improved variant
having complexity equal to O(3n/3
). Intuitively, it seems that exacts approaches are better to solve Max-stable
problem. They consist to enumerate all possible independent set and then select the one which have the maxi-
mum of cardinality [12]. However, the analysis of the complexity discouraged this idea for large graph. In this
Journal Homepage: http://iaesjournal.com/online/index.php/IJECE
Institute of Advanced Engineering and Science
w w w . i a e s j o u r n a l . c o m
, DOI: 10.11591/ijece.v7i6.pp3583-3592
2. 3584 ISSN: 2088-8708
case heuristic methods proved to be an efficient alternative away to solve large problem instances. The second
categories contains Meta-heuristic methods which explore the search space, local search methods and hybrid
methods. In [13] show how to build a good hybrid strategies. Recently, many approach based neural network
was developed to solve combinatorial and hard optimisation problem. The authors of [14]investigate Continu-
ous Hopfield Neural Network (CHN) to solve largess instances of Max-Stable problems. The mean idea is to
execute the CHN many times. The role of first run is to find a valid initial solution of MSSP by CHN and a
quadratic reformulation. In the second step the cardinality of the solution given by first run is added as linear
constraint to the resolution model, then they run CHN again. The second step can be repeated until to have no
solution improvement. In this paper we propose new hybrid approach, in order to take advantage of he faster
convergence of CHN in the one hand. In the second hand, the Local Search which look through neighborhods
to find the best one. To solve MSSP by LS, There are many successful heuristic [15, 16, 17, 18, 19, 20] . The
common among them is The start with a random solution and improve it regularly by very simple operations
like deletions of nodes which don’t meet the adjacent condition , insertions of new nodes or swaps (case when
current node succeed by its neighbors).
This paper is structured as follows: In section 2. we present the resolution model bested CHN which
is divided into many steps:we introduce the continuous Hopfeild network, next we give the reformulation of
maximum stable set problem as a 0-1 quadratic program, then we build an adapted energy function of CHN.
Section 3. is devoted to improve the solution by LS. Experimental results are presented in the last section.
2. CONTINUOUS HOPFEILD NEURAL NETWORK TO SOLVE MSSP
In this section we present an overview of [14] approach which implement CHN to solve a quadratic
model of MSSP.
2.1. The continuous Hopfield neural network
CHN is a fully connected neural Model with one layer. It was Introduced by Hopfield in the year 1980
to solve combinatorial problems. Further, Hopfield [21] proposed an energy functions to solve many opti-
mization problems as linear programming problems, analog to digital conversion, graph coloring problem,TSP,
processing image and . The evolution of CHN dynamic is controlled by The following differential equation:
dy
dt
= −
x
τ
+ T x + ib
(1)
where
x : vector of neurons input
y : vector of output
T : the Matrix of weight between each neurones pairs
Neurons output is governed by function:
xi = g(yi) =
1
2
(1 + tanh(
yi
u0
)) with u0 > 0 and i = 1, ..., n
The g function is bounded (g(u) ∈ [0, 1]) and u0 is a parameter to estimate the gain (or slope) of
the activation function. The limit point of the CHN ue
by this differential system exists such that u(t) = ue
∀t ≥ te (and te ≥ 0), this point is called an equilibrium point The energy of CHN is a Lyapunov function
defined as:
E(x) = −
1
2
xt
Tx − (ib
)t
x +
n
i=1
1
τi
xi
0
g−1
(v)dv
If T is symmetric then the equilibrium points existence is guaranteed. So, any combinatorial problems formu-
late as the following expression can be solved by The CHN:
E(x) = −
1
2
xt
Tx − (ib
)t
x (2)
IJECE Vol. 7, No. 6, December 2017: 3583 – 3592
3. IJECE ISSN: 2088-8708 3585
The matrix T is called also matrix of weights connections and just inhibitory connection is allowed on this
symmetric matrix. E(x) evaluate at the hypercube [0, 1]n
and converge to the corners of this n-dimensional
space. To solve a combinatorial problem by CHN we need just to associate the energy function of CHN with the
objective function of problem to be minimized, so that the minimum of E(x) coincide with the combinatorial
problem solution. Implicitly, the inputs of network outputs represent the problem solution. To show how we
can mapping a combinatorial problem to be associated with it neural model we take the assignment problem.
It is a easier and direct model to be mapped. So, we consider the following model of assignment problem with
n variables m linear constraint:
(QP)
Min 1
2 xt
Qx + qt
x
Subjectto
Ax = b
xi ∈ {0, 1} i = 1, .....n
Furthermore, we he need to define the following set to solve this QP:
• H ≡ {x ∈ [0, 1]n
}: the Hamming hypercube
• Hc ≡ {x ∈ {0, 1}n
}: the corners of the Hamming hypercube.
• Hf ≡ {x ∈ Hc : Ax = b}: feasible solution set.
To map the QP bellow, we must respect some conditions so that local minimums of the QP to be
associated with the CHN limit. The energy can also be defined by two terms:
E(x) = E0
(x) + ER
(x) ∀x ∈ H
Where:
• the term E0
(x) is associated with the problem objective function.
• the quadratic function ER
(x) have two objectives. The first one, is to penalizes the connections which
violated at last one constraint. The second one, concerns to guarantees that the CHN converge to a valid
solution. To perform a good mapping, this function must be constant in FxH and ding a well choice.
For this problem an adapted generalized function is proposed in [22]:
E(x) =
α
2
xt
Qx +
1
2
(Ax)t
φ(Ax) + xt
diag(γ)(1 − x) + βt
Ax ∀x ∈ H (3)
With parameters α ∈ R+
, γ ∈ Rn
, β ∈ RN
and a m × m matrix φ. The goal of this work is to solve the
maximum cardinality of the independent set by improving the previous approach based on the CHN proposed
by [14]. The main idea of last work is to convert the MSSP as CHN energy function. Before mapping problem
used a quadratic 0-1 model to represent the MSSP. In the section 2.2., we describe the reformulation of MSSP,
then we present the solving approach in the section 2.3..
2.2. Formulation of the Maximum Stable Set Problem
Let G = (V, E) an undirected graph with V a set of n nodes and E the set of m edges. An independent
set of a graph G is a set of nodes S with the property that any node nodes in S is not connected by a direct
edges to others nodes of S. The MSSP consist to determine the independent set which have the cardinality
maximum α(G).
So, the objective function to maximize is the number of nodes which are pairwise independent, rather we re-
formulate the connection penalty by a quadratic constraint which represent direct edge connection. To perform
the reformulation, we define the binary variables xi such that:
xi =
1 if vi ∈ S
0 Otherwise
Let S ⊂ V be a stable set of nodes. For each node vi of the graph G, we have the following relations:
CHN and Swap Heuristic to Solve the Maximum Independent Set ... (Bouhouch adil)
4. 3586 ISSN: 2088-8708
• To be valid S must don’t contains two adjacent node. So, for two node vi and vj in S: there corresponding
neurones xi and xj we have: let pij a connection penalty between vi and vj.
(vi, vj) ∈ S =⇒ xi = 1andxj = 1
(vi, vj) ∈ E =⇒ pijxixj = 0 =⇒ pij = 0
(vi, vj) /∈ E =⇒ pijxixj = 1 =⇒ pij = 1
Now we can define the quantity:
P(x) =
n
i=1
n
j=1
pijxixj (4)
Referring to all connections constraints imposed in 2.2.. We have P(h) = 0 when all nodes in S are
pairwise not adjacent. With
pij =
1 if (vi, vj) ∈ E
0 Otherwise
(5)
• The objective function to be minimised is :
f(x) = −
n
i=1
xi
Finally, the QP of the MSSP problem can be formulated as:
(QP)
Min f(x) = −
n
i=1
xi
Subject to
P(x) =
n
i=1
n
j=1
pijxixj = 0
x ∈ {0, 1}n
After the reformulation of MSSP problem as a quadratic 0-1 programming, it can be solved by any
adapted approach by minimizing the linear function under quadratic constraints (QP), such as interior point,
semidefinite relaxations [23] or lagrangian relaxations[24]. In this paper we are interested in a very different
approach based on CHN [14]. In the last approach authors propose to solve the MSSP into two phases. First,
solving the QP of MSSP by CHN. We note by Γ be the value found by CHN. Second, changing QP model
formulation by adding a new constraint to the objective function such as:
(NQP)
Min f(x) = −
n
i=1
xi
Subject to
P(x) =
n
i=1
n
j=1
pijxixj = 0
n
i=1
xi ≥ Γ
x ∈ {0, 1}n
Then they solve the new quadratic problem(NQP) by a second CHN associated to this new reformulation
(CHN2
) . In this work we propose to replace the second run CHN2
by the local search heuristic. On other
words, the solution given by the first run on the QP will be the starting point of the local search.
IJECE Vol. 7, No. 6, December 2017: 3583 – 3592
5. IJECE ISSN: 2088-8708 3587
2.3. A continuous Hopfield network to solve MSSP
Now The MSSP was reformulate as QP, the next step is to find an adapted energy function , we choose
the same one introduced by [14]:
E(x) = −α
n
i=1
xi +
1
2
φ
n
i=1
n
j=1
bijxixj + γ
n
i=1
xi(1 − xi) (6)
The advantage of this energy expression is more associated with the objective function, it’s relaxed by the
quadratic constraint.
by corresponding the Equation (2) with Equation (6), we deduce the weights and thresholds:
Ti,j = −φpij + 2δi,jγ
ib
i = α − γ
(7)
With δij represent the Kronecker symbol.
δij =
1 if i = j
0 if i = j
Matrix p is given by Equation (5), the values of parameters φ, γ and α are critical to reach the CHN
equilibrium points into a valid solution of MSSP.
The study of partial derivatives of the generalized energy function led to control those parameters to
guide the convergence to a feasible solution:
∂E(x)
∂xi
= Ei(x) = −α + φ
n
j=1
bijxj + γ(1 − 2xi)
To determine the parameters-setting, Talavan [25] used the hyperplane method analyse to study ∂E(x).
This procedure consist of partition the Hamming hypercube H by a hyperplane containing all feasible
solutions so that two properties must be respected by the CHN evolution: first, adding any solution not belong-
ing to this hyperplane, second, dropping out any infeasible solution belonging to the hyperplane.
Also, some conditions are imposed to determine these parameters-setting easily:
φ > 0, γ ≥ 0
• To minimize the objective function, we fixed the following constraint:
α > 0
• To escape the stability of the interior points x ∈ H − HC, the next constraint is necessary:
Ti,i = 2γ ≥ 0
While MSSP model contains just one constraint then we have:
HC − HF = {x ∈ HC/h(x) > 0}
Let x ∈ HC −HF , so for two adjacents nodes xi and xj are in the stable set S, then xi = xj = 1 and therefore
the activation of xi will be discouraged if E0
i (x) ≥ where > 0.
Based to this study the parameter settings are restricted by the following condition:
−α + φ − γ ≥
A feasible solution can be reached if the following conditions are respected:
α > 0, φ > 0, γ ≥ 0
−α + φ − γ =
(8)
The question then is about making good choice of these parameter under the condition bellow to calculate the
weights and thresholds of the CHN.,The advantage of resolving MSSP by Continuous dynamic of Hopfield
neural network is that CHN are very fast but always converge to a local minima, in [14] authors insert some
linear constraint to perturb the network weights and consequently escaping from all local minima less than
threshold Γ. This method appear efficient, but the control of CHN convergence become difficult. .
CHN and Swap Heuristic to Solve the Maximum Independent Set ... (Bouhouch adil)
6. 3588 ISSN: 2088-8708
3. PROPOSED APPROACH
Hopfeild neural network converge faster, but it converges closely to the nearest local minima of the
starting point. So many time the network gives a no good solution. To overcome this weakness many solution
was proposed to escape from local minima. In [14], authors propose to add a linear constraint which restrict a
lower bound of the minimum cardinality of the solution, as shown in [14], the insertion of new constraint to the
QP leads to improve the solution, but in contrast of obtained numerical results, we remark that network failed
to give a valid solution many time. To improve this meta-heuristic approach, we propose to use a hybridization
with other heuristic. It seems beneficial to combine CHN with an adapted local search. In [16] authors prove
that swap(1,2) give a good performance. Algorithm (1) show the proposed local search based Swap heuristic.
As described, this process by replacing each node n in the the given initial solution by two nodes u and v. The
chosen u and v must be neighbors of n. Therefore, the solution is improved at each time by adding one node.
This last search is down in linear times. This is possible by adding a collection of neighborhods set V (n) which
store for each graph node the set of it adjacent nodes. The Local search work directly on the solution given
by the CHN. We note that to check if a node n of the graph is in the solution, we verify if its associated CHN
node is active. Also, to reduce the time to find the neighbors of the candidate node which can be replaced,
we maintaining a stack To visit of valid candidate to visit. After examine each node, we removed it from the
candidate list, and go to the next until the list To visit become empty. Although, we added to the candidates
list To visit any new insert node in the solution S.
In this work, we implement the direct and simple local search based on swap heuristic. For this, to replace
the current candidate node n, we consider just the first pair of node u, v founded, which meet conditions.
Nevertheless, we can add more improvement to this LS, like sorting V(n) by number adjacent neighbor in the
solution [26] or plateau search [16].
Function Swap 1 2( S,T : ) : Solution
In :
• T o visit : Stack of candidate nodes
• V (i) : Set of neighborhood of the nodei
Out : Improved Solution
T o visit = S
While (T o visit is not empty) do
n = pop(T o visit)
For (u, v) ∈ V (n) × V (n) do
Have Adjacent in S = false
If (u /∈ S and u /∈ S and T (u, v) = −φ ) then
If (is marked(u) = false and is marked(v) = false) then
For (a = n) ∈ S do
If (T (u, a) = −φ or T (v, a) = −φ ) then
Have Adjacent in S = true
Break
end If
end For
If (Have Adjacent in S = false ) then
replace n by u and v
add u and v T o visit
Break
end If
end If
end If
end For
mark(n)
done
End
Algorithm 1. Local search algorithm
Naturally, CHN attempted to minimize the number conflicted constraints, therefore, some times network can
stabilise with not null Energy E = 0, and gives an invalid solution. To overcome this problem, we add a local
heuristic called Remove Process. It delete nodes from the solution until to being valid.
IJECE Vol. 7, No. 6, December 2017: 3583 – 3592
7. IJECE ISSN: 2088-8708 3589
Sort S by number of Adjacent
For each v ∈ S do
if(∃u ∈ S / (u, v) ∈ E) delate v
end For
Algorithm 2. Delate Process
4. NUMERICAL RESULT
To show the efficiency of our approach, we present a computational results. First, we run the solver
on the random indirected generated graphs. The used generator return a Gn,p random graph, also known as an
Erd˜os-R´enyi graph or a binomial graph [27], where n the number of nodes and the graph density which can
be determinate by the probability for edge creation p. So we generated five classes classified by the number
of graph nodes from 100 to 500, and for each class, the density p varied from 10% to 100%. For a given p
we generate random 100 instances. Figure 1 plot the CPU time tacked by CHN, CHN2
-the improved variant
[14]- and CHNSwap. The comparison is down under fixing the density D = 50% and varying the numbers of
nodes. Practically, the curves of CHN and CHNSwap are superposed.
Figure 1. the CPU time as a function of number of nodes.
Figure 2 give the evolution of times of CHNSwap approach as function of the graph density (D),
respectively, for classes V=100, V=300 and V=500. We note that left vertical axis is the times performed by
CHN and the right for LS. It is clear that the problem difficulty increases as the number of nodes increases. The
same remark can be seen at all tested graph classes.
To study the solution quality we run our approach on selected instance from the benchmark DIMACS[28].
CHN and Swap Heuristic to Solve the Maximum Independent Set ... (Bouhouch adil)
9. IJECE ISSN: 2088-8708 3591
Graphs E V CHN(ms) Swap(ms)
johnson8-2-4.clq 210 28 2,12 0,01
MANN a9.clq 918 45 3,18 0,06
hamming6-4.clq 704 64 1,62 0,18
johnson8-4-4.clq 1855 70 9,53 0,12
johnson16-2-4.clq 5460 120 25,65 0,32
DSJC125.9.col 6961 125 7,14 0,32
C125.9.clq 6963 125 9,88 0,08
C125.9.clq 6963 125 9,44 0,06
keller4.clq 9435 171 41,06 0,31
c-fat200-1.clq 1534 200 77,86 1,17
c-fat200-2.clq 3235 200 61,01 0,95
c-fat200-5.clq 8473 200 34,64 0,61
brock200 2.clq 9876 200 76,04 0,72
brock200 4.clq 13089 200 65,54 0,4
san200 0.7 1.clq 13930 200 16,09 0,5
gen200 p0.9 44.clq 17910 200 4,58 0,44
san200 0.9 3.clq 17910 200 2,5 0,68
C250.9.clq 27984 250 8,92 0,71
p hat300-1.clq 10933 300 163,3 2,38
p hat300-2.clq 21928 300 159,36 2,43
p hat300-3.clq 33390 300 139,28 1,07
MANN a27.clq 70551 378 160,81 0,82
san400 0.9 1.clq 71820 400 115,3 1,82
frb30-15-1.clq 83198 450 362,35 2,31
Table 2. Times performance
The efficiency of the proposed approach is reinforced by the experimental results. Table 1 shows that
our method gives good result then used CHN alone. Table 2 show the time added by the improvement process
with local heuristics against the time needed by CHN. It’s clear that improvement is down with adding non-
significant time.
Our approach is very powerful from a theoretical point of view. This approach gives a good performance
compared with the improved approach CHN2
proposed by [14] which crush often with no valid solution.
5. CONCLUSION
In the last decade several areas have applied MSSP problem and Max-Clique problem. So, many
heuristic were developed. In this sense we directed our contribution. In this work, a new approach has been
proposed based CHN and swap local heuristic to solve the maximal cardinality of independent sets. The MSSP
is solved into three step: First, it is reformulate as quadratic problem. Second, the QP is associated with CHN
energy function, then we stabilise CHN to converge at the solution. Third, the local search process performed
by starting from the solution given in the second step. It same difficult to integrating the local heuristic in the
main loop of CHN stabilisation for this we have chosen the collaborative hybridisation between CHN and LS.
We executed a series of experiments in order to prove the efficiency regarding the time complexity and the
solution of quality. The time allowed to LS is very small so that we can assert that our approach is able to find
the maximal independent set for very large graph. Furthermore, the rate of success to give a valid solution is
up to 100% in our approach. We note that this approach can be used to solve the Max-Clique problem special
for perfect graph by solving the graph dual.
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