This document summarizes a research paper that proposes a modified DNA computing approach to solve the graph coloring problem. The approach colors the vertices of a graph one by one, expanding DNA strands encoding promising solutions at each step and discarding infeasible solutions. This avoids an exponential number of DNA strands by not expanding the entire solution space at once. The approach is simulated on a sample 12-vertex graph, requiring fewer DNA strands than typical approaches. The modified approach provides an improvement over previous DNA computing methods for graph coloring by reducing the solution space in a step-wise manner.
A NEW ALGORITHM FOR SOLVING FULLY FUZZY BI-LEVEL QUADRATIC PROGRAMMING PROBLEMSorajjournal
This paper is concerned with new method to find the fuzzy optimal solution of fully fuzzy bi-level non-linear (quadratic) programming (FFBLQP) problems where all the coefficients and decision variables of both objective functions and the constraints are triangular fuzzy numbers (TFNs). A new method is based on decomposed the given problem into bi-level problem with three crisp quadratic objective functions and bounded variables constraints. In order to often a fuzzy optimal solution of the FFBLQP problems, the concept of tolerance membership function is used to develop a fuzzy max-min decision model for generating satisfactory fuzzy solution for FFBLQP problems in which the upper-level decision maker (ULDM) specifies his/her objective functions and decisions with possible tolerances which are described by membership functions of fuzzy set theory. Then, the lower-level decision maker (LLDM) uses this preference information for ULDM and solves his/her problem subject to the ULDMs restrictions. Finally, the decomposed method is illustrated by numerical example.
This work considers the multi-objective optimization problem constrained by a system of bipolar fuzzy relational equations with max-product composition. An integer optimization based technique for order of preference by similarity to the ideal solution is proposed for solving such a problem. Some critical features associated with the feasible domain and optimal solutions of the bipolar max-Tp equation constrained optimization problem are studied. An illustrative example verifying the idea of this paper is included. This
is the first attempt to study the bipolar max-T equation constrained multi-objective optimization problems
from an integer programming viewpoint.
Real interpolation method for transfer function approximation of distributed ...TELKOMNIKA JOURNAL
Distributed parameter system (DPS) presents one of the most complex systems in the control theory. The transfer function of a DPS possibly contents: rational, nonlinear and irrational components. This thing leads that studies of the transfer function of a DPS are difficult in the time domain and frequency domain. In this paper, a systematic approach is proposed for linearizing DPS. This approach is based on the real interpolation method (RIM) to approximate the transfer function of DPS by rational-order transfer function. The results of the numerical examples show that the method is simple, computationally efficient, and flexible.
Dictionary based Image Compression via Sparse Representation IJECEIAES
Nowadays image compression has become a necessity due to a large volume of images. For efficient use of storage space and data transmission, it becomes essential to compress the image. In this paper, we propose a dictionary based image compression framework via sparse representation, with the construction of a trained over-complete dictionary. The overcomplete dictionary is trained using the intra-prediction residuals obtained from different images and is applied for sparse representation. In this method, the current image block is first predicted from its spatially neighboring blocks, and then the prediction residuals are encoded via sparse representation. Sparse approximation algorithm and the trained overcomplete dictionary are applied for sparse representation of prediction residuals. The detail coefficients obtained from sparse representation are used for encoding. Experimental result shows that the proposed method yields both improved coding efficiency and image quality as compared to some state-of-the-art image compression methods.
Machine Learning Technique PCA Part's description in this research paper. Very good source for a clear understanding of how PCA i.e the Principal Component Analysis technique works while implementing machine learning techniques.
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 NEW ALGORITHM FOR SOLVING FULLY FUZZY BI-LEVEL QUADRATIC PROGRAMMING PROBLEMSorajjournal
This paper is concerned with new method to find the fuzzy optimal solution of fully fuzzy bi-level non-linear (quadratic) programming (FFBLQP) problems where all the coefficients and decision variables of both objective functions and the constraints are triangular fuzzy numbers (TFNs). A new method is based on decomposed the given problem into bi-level problem with three crisp quadratic objective functions and bounded variables constraints. In order to often a fuzzy optimal solution of the FFBLQP problems, the concept of tolerance membership function is used to develop a fuzzy max-min decision model for generating satisfactory fuzzy solution for FFBLQP problems in which the upper-level decision maker (ULDM) specifies his/her objective functions and decisions with possible tolerances which are described by membership functions of fuzzy set theory. Then, the lower-level decision maker (LLDM) uses this preference information for ULDM and solves his/her problem subject to the ULDMs restrictions. Finally, the decomposed method is illustrated by numerical example.
This work considers the multi-objective optimization problem constrained by a system of bipolar fuzzy relational equations with max-product composition. An integer optimization based technique for order of preference by similarity to the ideal solution is proposed for solving such a problem. Some critical features associated with the feasible domain and optimal solutions of the bipolar max-Tp equation constrained optimization problem are studied. An illustrative example verifying the idea of this paper is included. This
is the first attempt to study the bipolar max-T equation constrained multi-objective optimization problems
from an integer programming viewpoint.
Real interpolation method for transfer function approximation of distributed ...TELKOMNIKA JOURNAL
Distributed parameter system (DPS) presents one of the most complex systems in the control theory. The transfer function of a DPS possibly contents: rational, nonlinear and irrational components. This thing leads that studies of the transfer function of a DPS are difficult in the time domain and frequency domain. In this paper, a systematic approach is proposed for linearizing DPS. This approach is based on the real interpolation method (RIM) to approximate the transfer function of DPS by rational-order transfer function. The results of the numerical examples show that the method is simple, computationally efficient, and flexible.
Dictionary based Image Compression via Sparse Representation IJECEIAES
Nowadays image compression has become a necessity due to a large volume of images. For efficient use of storage space and data transmission, it becomes essential to compress the image. In this paper, we propose a dictionary based image compression framework via sparse representation, with the construction of a trained over-complete dictionary. The overcomplete dictionary is trained using the intra-prediction residuals obtained from different images and is applied for sparse representation. In this method, the current image block is first predicted from its spatially neighboring blocks, and then the prediction residuals are encoded via sparse representation. Sparse approximation algorithm and the trained overcomplete dictionary are applied for sparse representation of prediction residuals. The detail coefficients obtained from sparse representation are used for encoding. Experimental result shows that the proposed method yields both improved coding efficiency and image quality as compared to some state-of-the-art image compression methods.
Machine Learning Technique PCA Part's description in this research paper. Very good source for a clear understanding of how PCA i.e the Principal Component Analysis technique works while implementing machine learning techniques.
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.
Image Matting via LLE/iLLE Manifold LearningITIIIndustries
Accurately extracting foreground objects is the problem of isolating the foreground in images and video, called image matting which has wide applications in digital photography. This problem is severely ill-posed in the sense that, at each pixel, one must estimate the foreground and background pixels and the so-called alpha value from only pixel information. The most recent work in natural image matting rely on local smoothness assumptions about foreground and background colours on which a cost function has been established. In this paper, we propose an extension to the class of affinity based matting techniques by incorporating local manifold structural
information to produce both a smoother matte based on the socalled improved Locally Linear Embedding. We illustrate our new algorithm using the standard benchmark images and very comparable results have been obtained.
K-mer frequency statistics of biological sequences is a very important and important problem in biological information processing. This paper addresses the problem of index k-mer for large scale data reading DNA sequences in a limited memory space and time. Using the hash algorithm to establish index, the index model is set up to base pairing, and get the length of k-mer statistic information quickly, so as to avoid
searching all the sequences of the index. At the same time, the program uses hash table to establish index and build search model, and uses the zipper method to resolve the conflict in the case of address conflict.Algorithm of time complexity analysis and experimental results show that compared with the traditional indexing methods, the algorithm of the performance improvement is obvious, and very suitable for to be used in the k-mer length change with a wide range .
Duality in nonlinear fractional programming problem using fuzzy programming a...ijscmcj
In this paper we have considered nonlinear fractional programming problem with multiple constraints. A
pair of primal and dual for a special type of nonlinear fractional programming has been considered under
fuzzy environment. Exponential membership function has been used to deal with the fuzziness. Duality
results have been developed for the special type of nonlinear programming using exponential membership function. The method has been illustrated with numerical example. Genetic Algorithm as well as Fuzzy programming approach has been used to solve the problem.
Solving Linear Fractional Programming Problems Using a New Homotopy Perturbat...orajjournal
A new Homotopy Perturbation Method (HPM) is used to find exact solutions for the system of Linear Fractional Programming Problem (LFPP) with equality constraints. In best of my knowledge, first time we are going to introduce a new technique using Homotopy for solving LFP problem. The Homotopy Perturbation method (HPM) and factorization technique are used together to build a new method. A new
technique is also used to convert LFPP to Linear programming problem (LPP). The results betray that our proposed method is very easy and effective compare to the existing method for solving LFP problems with equality constraints applied in real life situations. To illustrate the proposed method numerical examples are solved and the obtained results are discussed.
Inventory Model with Price-Dependent Demand Rate and No Shortages: An Interva...orajjournal
In this paper, an interval-valued inventory optimization model is proposed. The model involves the price dependent
demand and no shortages. The input data for this model are not fixed, but vary in some real bounded intervals. The aim is to determine the optimal order quantity, maximizing the total profit and minimizing the holding cost subjecting to three constraints: budget constraint, space constraint, and
budgetary constraint on ordering cost of each item. We apply the linear fractional programming approach based on interval numbers. To apply this approach, a linear fractional programming problem is modeled with interval type uncertainty. This problem is further converted to an optimization problem with interval valued
objective function having its bounds as linear fractional functions. Two numerical examples in crisp
case and interval-valued case are solved to illustrate the proposed approach.
In the VLSI physical design, Floorplanning is the very crucial step as it optimizes the chip. The goal of
floorplanning is to find a floorplan such that no module overlaps with other, optimize the interconnection between
the modules, optimize the area of the floorplan and minimize the dead space. In this Paper, Simulated Annealing (SA)
algorithm has been employed to shrink dead space to optimize area and interconnect of VLSI floorplanning problem.
Sequence pair representation is employed to perturb the solution. The outcomes received after the application of SA
on different benchmark files are compared with the outcomes of different algorithms on same benchmark files and
the comparison suggests that the SA gives the better result. SA is effective and promising in VLSI floorplan design.
Matlab simulation results show that our approach can give better results and satisfy the fixed-outline and nonoverlapping
constraints while optimizing circuit performance.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
USING ADAPTIVE AUTOMATA IN GRAMMAR-BASED TEXT COMPRESSION TO IDENTIFY FREQUEN...ijcsit
Compression techniques allow reduction in the data storage space required by applications dealing with large amount of data by increasing the information entropy of its representation. This paper presents an adaptive rule-driven device - the adaptive automata - as the device to identify recurring sequences of symbols to be compressed in a grammar-based lossless data compression scheme.
Acredite: a negatividade é um vício. E você precisa se desintoxicar dele agora mesmo! Conheça 6 atitudes capazes de mudar sua percepção diária, ajudar a desenvolver sua inteligência emocional e, além de tudo isso, transformar as pessoas ao seu redor — mesmo as que você sequer conhece.
Image Matting via LLE/iLLE Manifold LearningITIIIndustries
Accurately extracting foreground objects is the problem of isolating the foreground in images and video, called image matting which has wide applications in digital photography. This problem is severely ill-posed in the sense that, at each pixel, one must estimate the foreground and background pixels and the so-called alpha value from only pixel information. The most recent work in natural image matting rely on local smoothness assumptions about foreground and background colours on which a cost function has been established. In this paper, we propose an extension to the class of affinity based matting techniques by incorporating local manifold structural
information to produce both a smoother matte based on the socalled improved Locally Linear Embedding. We illustrate our new algorithm using the standard benchmark images and very comparable results have been obtained.
K-mer frequency statistics of biological sequences is a very important and important problem in biological information processing. This paper addresses the problem of index k-mer for large scale data reading DNA sequences in a limited memory space and time. Using the hash algorithm to establish index, the index model is set up to base pairing, and get the length of k-mer statistic information quickly, so as to avoid
searching all the sequences of the index. At the same time, the program uses hash table to establish index and build search model, and uses the zipper method to resolve the conflict in the case of address conflict.Algorithm of time complexity analysis and experimental results show that compared with the traditional indexing methods, the algorithm of the performance improvement is obvious, and very suitable for to be used in the k-mer length change with a wide range .
Duality in nonlinear fractional programming problem using fuzzy programming a...ijscmcj
In this paper we have considered nonlinear fractional programming problem with multiple constraints. A
pair of primal and dual for a special type of nonlinear fractional programming has been considered under
fuzzy environment. Exponential membership function has been used to deal with the fuzziness. Duality
results have been developed for the special type of nonlinear programming using exponential membership function. The method has been illustrated with numerical example. Genetic Algorithm as well as Fuzzy programming approach has been used to solve the problem.
Solving Linear Fractional Programming Problems Using a New Homotopy Perturbat...orajjournal
A new Homotopy Perturbation Method (HPM) is used to find exact solutions for the system of Linear Fractional Programming Problem (LFPP) with equality constraints. In best of my knowledge, first time we are going to introduce a new technique using Homotopy for solving LFP problem. The Homotopy Perturbation method (HPM) and factorization technique are used together to build a new method. A new
technique is also used to convert LFPP to Linear programming problem (LPP). The results betray that our proposed method is very easy and effective compare to the existing method for solving LFP problems with equality constraints applied in real life situations. To illustrate the proposed method numerical examples are solved and the obtained results are discussed.
Inventory Model with Price-Dependent Demand Rate and No Shortages: An Interva...orajjournal
In this paper, an interval-valued inventory optimization model is proposed. The model involves the price dependent
demand and no shortages. The input data for this model are not fixed, but vary in some real bounded intervals. The aim is to determine the optimal order quantity, maximizing the total profit and minimizing the holding cost subjecting to three constraints: budget constraint, space constraint, and
budgetary constraint on ordering cost of each item. We apply the linear fractional programming approach based on interval numbers. To apply this approach, a linear fractional programming problem is modeled with interval type uncertainty. This problem is further converted to an optimization problem with interval valued
objective function having its bounds as linear fractional functions. Two numerical examples in crisp
case and interval-valued case are solved to illustrate the proposed approach.
In the VLSI physical design, Floorplanning is the very crucial step as it optimizes the chip. The goal of
floorplanning is to find a floorplan such that no module overlaps with other, optimize the interconnection between
the modules, optimize the area of the floorplan and minimize the dead space. In this Paper, Simulated Annealing (SA)
algorithm has been employed to shrink dead space to optimize area and interconnect of VLSI floorplanning problem.
Sequence pair representation is employed to perturb the solution. The outcomes received after the application of SA
on different benchmark files are compared with the outcomes of different algorithms on same benchmark files and
the comparison suggests that the SA gives the better result. SA is effective and promising in VLSI floorplan design.
Matlab simulation results show that our approach can give better results and satisfy the fixed-outline and nonoverlapping
constraints while optimizing circuit performance.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
USING ADAPTIVE AUTOMATA IN GRAMMAR-BASED TEXT COMPRESSION TO IDENTIFY FREQUEN...ijcsit
Compression techniques allow reduction in the data storage space required by applications dealing with large amount of data by increasing the information entropy of its representation. This paper presents an adaptive rule-driven device - the adaptive automata - as the device to identify recurring sequences of symbols to be compressed in a grammar-based lossless data compression scheme.
Acredite: a negatividade é um vício. E você precisa se desintoxicar dele agora mesmo! Conheça 6 atitudes capazes de mudar sua percepção diária, ajudar a desenvolver sua inteligência emocional e, além de tudo isso, transformar as pessoas ao seu redor — mesmo as que você sequer conhece.
Distribution of maximal clique size underijfcstjournal
In this paper, we analyze the evolution of a small-world network and its subsequent transformation to a
random network using the idea of link rewiring under the well-known Watts-Strogatz model for complex
networks. Every link u-v in the regular network is considered for rewiring with a certain probability and if
chosen for rewiring, the link u-v is removed from the network and the node u is connected to a randomly
chosen node w (other than nodes u and v). Our objective in this paper is to analyze the distribution of the
maximal clique size per node by varying the probability of link rewiring and the degree per node (number
of links incident on a node) in the initial regular network. For a given probability of rewiring and initial
number of links per node, we observe the distribution of the maximal clique per node to follow a Poisson
distribution. We also observe the maximal clique size per node in the small-world network to be very close
to that of the average value and close to that of the maximal clique size in a regular network. There is no
appreciable decrease in the maximal clique size per node when the network transforms from a regular
network to a small-world network. On the other hand, when the network transforms from a small-world
network to a random network, the average maximal clique size value decreases significantly.
Heb jij een statische mindset of een mindset die op groei is gebaseerd? In deze nieuwe tijd is het verschil tussen succes en falen gelegen in, hoe jij tegen de wereld aankijkt
Leer hoe je jouw mindset kunt veranderen.
O termo MOBILE tem se tornado um grande hype não só no mercado de tecnologia mas no mundo moderno. Exponho nesta apresentação a minha visão sobre como mobile deveria ser tratado pelos desenvolvedores de soluções inovadoras.
Although DNA computing has emerged as a new computing paradigm with its massive parallel computing
capabilities, the large number of DNA required for larger size of computational problems still remain as a
stumbling block to its development as practical computing. In this paper, we propose a modification to
implement a physical experimentation of two Boolean matrices multiplication problem with DNA
computing. The Truncated Matrices reduces the number of DNA sequences and lengths utilized to compute
the problem with DNA computing.
Truncated Boolean Matrices for DNA ComputationIJCSEA Journal
Although DNA computing has emerged as a new computing paradigm with its massive parallel computing capabilities, the large number of DNA required for larger size of computational problems still remain as a stumbling block to its development as practical computing. In this paper, we propose a modification to implement a physical experimentation of two Boolean matrices multiplication problem with DNA computing. The Truncated Matrices reduces the number of DNA sequences and lengths utilized to compute the problem with DNA computing.
A Design and Solving LPP Method for Binary Linear Programming Problem Using D...ijistjournal
Molecular computing is a discipline that aims at harnessing individual molecules for computational purposes. This paper presents the applied Mathematical sciences using DNA molecules. The Major achievements are outlined the potential advances and the challenges for the practitioners in the foreseeable future. The Binary Optimization in Linear Programming is an intensive research area in the field of DNA Computing. This paper presents a research on design and implementation method to solve an Binary Linear Programming Problem using DNA computing. The DNA sequences of length directly represent all possible combinations in different boxes. An Hybridization is performed to form double strand molecules according to its length to visualize the optimal solution based on fluorescent material . Here Maximization Problem is converted into DNA computable form and a complementary are found to solve the problem and the optimal solution is suggested as per the constraints stipulated by the problem.
A Design and Solving LPP Method for Binary Linear Programming Problem Using D...ijistjournal
Molecular computing is a discipline that aims at harnessing individual molecules for computational purposes. This paper presents the applied Mathematical sciences using DNA molecules. The Major achievements are outlined the potential advances and the challenges for the practitioners in the foreseeable future. The Binary Optimization in Linear Programming is an intensive research area in the field of DNA Computing. This paper presents a research on design and implementation method to solve an Binary Linear Programming Problem using DNA computing. The DNA sequences of length directly represent all possible combinations in different boxes. An Hybridization is performed to form double strand molecules according to its length to visualize the optimal solution based on fluorescent material . Here Maximization Problem is converted into DNA computable form and a complementary are found to solve the problem and the optimal solution is suggested as per the constraints stipulated by the problem.
Performance of Matching Algorithmsfor Signal Approximationiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
BEHAVIOR STUDY OF ENTROPY IN A DIGITAL IMAGE THROUGH AN ITERATIVE ALGORITHM O...ijscmcj
Image segmentation is a critical step in computer vision tasks constituting an essential issue for pattern
recognition and visual interpretation. In this paper, we study the behavior of entropy in digital images
through an iterative algorithm of mean shift filtering. The order of a digital image in gray levels is defined.
The behavior of Shannon entropy is analyzed and then compared, taking into account the number of
iterations of our algorithm, with the maximum entropy that could be achieved under the same order. The
use of equivalence classes it induced, which allow us to interpret entropy as a hyper-surface in real m-
dimensional space. The difference of the maximum entropy of order n and the entropy of the image is used
to group the the iterations, in order to caractrizes the performance of the algorithm
An analysis between different algorithms for the graph vertex coloring problem IJECEIAES
This research focuses on an analysis of different algorithms for the graph vertex coloring problem. Some approaches to solving the problem are discussed. Moreover, some studies for the problem and several methods for its solution are analyzed as well. An exact algorithm (using the backtracking method) is presented. The complexity analysis of the algorithm is discussed. Determining the average execution time of the exact algorithm is consistent with the multitasking mode of the operating system. This algorithm generates optimal solutions for all studied graphs. In addition, two heuristic algorithms for solving the graph vertex coloring problem are used as well. The results show that the exact algorithm can be used to solve the graph vertex coloring problem for small graphs with 30-35 vertices. For half of the graphs, all three algorithms have found the optimal solutions. The suboptimal solutions generated by the approximate algorithms are identical in terms of the number of colors needed to color the corresponding graphs. The results show that the linear increase in the number of vertices and edges of the analyzed graphs causes a linear increase in the number of colors needed to color these graphs.
Investigation on the Pattern Synthesis of Subarray Weights for Low EMI Applic...IOSRJECE
In modern radar applications, it is frequently required to produce sum and difference patterns sequentially. The sum pattern amplitude coefficients are obtained by using Dolph-Chebyshev synthesis method where as the difference pattern excitation coefficients will be optimized in this present work. For this purpose optimal group weights will be introduced to the different array elements to obtain any type of beam depending on the application. Optimization of excitation to the array elements is the main objective so in this process a subarray configuration is adopted. However, Differential Evolution Algorithm is applied for optimization method. The proposed method is reliable and accurate. It is superior to other methods in terms of convergence speed and robustness. Numerical and simulation results are presented.
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
Diagnosis of Faulty Sensors in Antenna Array using Hybrid Differential Evolut...IJECEIAES
In this work, differential evolution based compressive sensing technique for detection of faulty sensors in linear arrays has been presented. This algorithm starts from taking the linear measurements of the power pattern generated by the array under test. The difference between the collected compressive measurements and measured healthy array field pattern is minimized using a hybrid differential evolution (DE). In the proposed method, the slow convergence of DE based compressed sensing technique is accelerated with the help of parallel coordinate decent algorithm (PCD). The combination of DE with PCD makes the minimization faster and precise. Simulation results validate the performance to detect faulty sensors from a small number of measurements.
SATISFIABILITY METHODS FOR COLOURING GRAPHScscpconf
The graph colouring problem can be solved using methods based on Satisfiability (SAT). An instance of the problem is defined by a Boolean expression written using Boolean variables and the logical connectives AND, OR and NOT. It has to be determined whether there is an assignment of TRUE and FALSE values to the variables that makes the entire expression true.A SAT problem is syntactically and semantically quite simple. Many Constraint Satisfaction Problems (CSPs)in AI and OR can be formulated in SAT. These make use of two kinds of
searchalgorithms: Deterministic and Randomized.It has been found that deterministic methods when run on hard CSP instances are frequently very slow in execution.A deterministic method always outputs a solution in the end, but it can take an enormous amount of time to do so.This has led to the development of randomized search algorithms like GSAT, which are typically based on local (i.e., neighbourhood) search. Such methodshave been applied very successfully to find good solutions to hard decision problems
Traveling Salesman Problem in Distributed Environmentcsandit
In this paper, we focus on developing parallel algorithms for solving the traveling salesman problem (TSP) based on Nicos Christofides algorithm released in 1976. The parallel algorithm
is built in the distributed environment with multi-processors (Master-Slave). The algorithm is installed on the computer cluster system of National University of Education in Hanoi,
Vietnam (ccs1.hnue.edu.vn) and uses the library PJ (Parallel Java). The results are evaluated and compared with other works.
TRAVELING SALESMAN PROBLEM IN DISTRIBUTED ENVIRONMENTcscpconf
In this paper, we focus on developing parallel algorithms for solving the traveling salesman
problem (TSP) based on Nicos Christofides algorithm released in 1976. The parallel algorithm
is built in the distributed environment with multi-processors (Master-Slave). The algorithm is
installed on the computer cluster system of National University of Education in Hanoi,
Vietnam (ccs1.hnue.edu.vn) and uses the library PJ (Parallel Java). The results are evaluated
and compared with other works.
A New Method Based on MDA to Enhance the Face Recognition PerformanceCSCJournals
A novel tensor based method is prepared to solve the supervised dimensionality reduction problem. In this paper a multilinear principal component analysis(MPCA) is utilized to reduce the tensor object dimension then a multilinear discriminant analysis(MDA), is applied to find the best subspaces. Because the number of possible subspace dimensions for any kind of tensor objects is extremely high, so testing all of them for finding the best one is not feasible. So this paper also presented a method to solve that problem, The main criterion of algorithm is not similar to Sequential mode truncation(SMT) and full projection is used to initialize the iterative solution and find the best dimension for MDA. This paper is saving the extra times that we should spend to find the best dimension. So the execution time will be decreasing so much. It should be noted that both of the algorithms work with tensor objects with the same order so the structure of the objects has been never broken. Therefore the performance of this method is getting better. The advantage of these algorithms is avoiding the curse of dimensionality and having a better performance in the cases with small sample sizes. Finally, some experiments on ORL and CMPU-PIE databases is provided.
Behavior study of entropy in a digital image through an iterative algorithmijscmcj
Image segmentation is a critical step in computer vision tasks constituting an essential issue for pattern recognition and visual interpretation. In this paper, we study the behavior of entropy in digital images through an iterative algorithm of mean shift filtering. The order of a digital image in gray levels is defined. The behavior of Shannon entropy is analyzed and then compared, taking into account the number of iterations of our algorithm, with the maximum entropy that could be achieved under the same order. The use of equivalence classes it induced, which allow us to interpret entropy as a hyper-surface in real m dimensional space. The difference of the maximum entropy of order n and the entropy of the image is used to group the the iterations, in order to caractrizes the performance of the algorithm.
Exact network reconstruction from consensus signals and one eigen valueIJCNCJournal
The basic inverse problem in spectral graph theory consists in determining the graph given its eigenvalue
spectrum. In this paper, we are interested in a network of technological agents whose graph is unknown,
communicating by means of a consensus protocol. Recently, the use of artificial noise added to consensus
signals has been proposed to reconstruct the unknown graph, although errors are possible. On the other
hand, some methodologies have been devised to estimate the eigenvalue spectrum, but noise could interfere
with the elaborations. We combine these two techniques in order to simplify calculations and avoid
topological reconstruction errors, using only one eigenvalue. Moreover, we use an high frequency noise to
reconstruct the network, thus it is easy to filter the control signals after the graph identification. Numerical
simulations of several topologies show an exact and robust reconstruction of the graphs.
Similar to A Modified Dna Computing Approach To Tackle The Exponential Solution Space Of The Graph Coloring Problem (20)
ENHANCING ENGLISH WRITING SKILLS THROUGH INTERNET-PLUS TOOLS IN THE PERSPECTI...ijfcstjournal
This investigation delves into incorporating a hybridized memetic strategy within the framework of English
composition pedagogy, leveraging Internet Plus resources. The study aims to provide an in-depth analysis
of how this method influences students’ writing competence, their perceptions of writing, and their
enthusiasm for English acquisition. Employing an explanatory research design that combines qualitative
and quantitative methods, the study collects data through surveys, interviews, and observations of students’
writing performance before and after the intervention. Findings demonstrate a beneficial impact of
integrating the memetic approach alongside Internet Plus tools on the writing aptitude of English as a
Foreign Language (EFL) learners. Students reported increased engagement with writing, attributing it to
the use of Internet plus tools. They also expressed that the memetic approach facilitated a deeper
understanding of cultural and social contexts in writing. Furthermore, the findings highlight a significant
improvement in students’ writing skills following the intervention. This study provides significant insights
into the practical implementation of the memetic approach within English writing education, highlighting
the beneficial contribution of Internet Plus tools in enriching students' learning journeys.
A SURVEY TO REAL-TIME MESSAGE-ROUTING NETWORK SYSTEM WITH KLA MODELLINGijfcstjournal
Messages routing over a network is one of the most fundamental concept in communication which requires
simultaneous transmission of messages from a source to a destination. In terms of Real-Time Routing, it
refers to the addition of a timing constraint in which messages should be received within a specified time
delay. This study involves Scheduling, Algorithm Design and Graph Theory which are essential parts of
the Computer Science (CS) discipline. Our goal is to investigate an innovative and efficient way to present
these concepts in the context of CS Education. In this paper, we will explore the fundamental modelling of
routing real-time messages on networks. We study whether it is possible to have an optimal on-line
algorithm for the Arbitrary Directed Graph network topology. In addition, we will examine the message
routing’s algorithmic complexity by breaking down the complex mathematical proofs into concrete, visual
examples. Next, we explore the Unidirectional Ring topology in finding the transmission’s
“makespan”.Lastly, we propose the same network modelling through the technique of Kinesthetic Learning
Activity (KLA). We will analyse the data collected and present the results in a case study to evaluate the
effectiveness of the KLA approach compared to the traditional teaching method.
A COMPARATIVE ANALYSIS ON SOFTWARE ARCHITECTURE STYLESijfcstjournal
Software architecture is the structural solution that achieves the overall technical and operational
requirements for software developments. Software engineers applied software architectures for their
software system developments; however, they worry the basic benchmarks in order to select software
architecture styles, possible components, integration methods (connectors) and the exact application of
each style.
The objective of this research work was a comparative analysis of software architecture styles by its
weakness and benefits in order to select by the programmer during their design time. Finally, in this study,
the researcher has been identified architectural styles, weakness, and Strength and application areas with
its component, connector and Interface for the selected architectural styles.
SYSTEM ANALYSIS AND DESIGN FOR A BUSINESS DEVELOPMENT MANAGEMENT SYSTEM BASED...ijfcstjournal
A design of a sales system for professional services requires a comprehensive understanding of the
dynamics of sale cycles and how key knowledge for completing sales is managed. This research describes
a design model of a business development (sales) system for professional service firms based on the Saudi
Arabian commercial market, which takes into account the new advances in technology while preserving
unique or cultural practices that are an important part of the Saudi Arabian commercial market. The
design model has combined a number of key technologies, such as cloud computing and mobility, as an
integral part of the proposed system. An adaptive development process has also been used in implementing
the proposed design model.
AN ALGORITHM FOR SOLVING LINEAR OPTIMIZATION PROBLEMS SUBJECTED TO THE INTERS...ijfcstjournal
Frank t-norms are parametric family of continuous Archimedean t-norms whose members are also strict
functions. Very often, this family of t-norms is also called the family of fundamental t-norms because of the
role it plays in several applications. In this paper, optimization of a linear objective function with fuzzy
relational inequality constraints is investigated. The feasible region is formed as the intersection of two
inequality fuzzy systems defined by frank family of t-norms is considered as fuzzy composition. First, the
resolution of the feasible solutions set is studied where the two fuzzy inequality systems are defined with
max-Frank composition. Second, some related basic and theoretical properties are derived. Then, a
necessary and sufficient condition and three other necessary conditions are presented to conceptualize the
feasibility of the problem. Subsequently, it is shown that a lower bound is always attainable for the optimal
objective value. Also, it is proved that the optimal solution of the problem is always resulted from the
unique maximum solution and a minimal solution of the feasible region. Finally, an algorithm is presented
to solve the problem and an example is described to illustrate the algorithm. Additionally, a method is
proposed to generate random feasible max-Frank fuzzy relational inequalities. By this method, we can
easily generate a feasible test problem and employ our algorithm to it.
LBRP: A RESILIENT ENERGY HARVESTING NOISE AWARE ROUTING PROTOCOL FOR UNDER WA...ijfcstjournal
Underwater detector network is one amongst the foremost difficult and fascinating analysis arenas that
open the door of pleasing plenty of researchers during this field of study. In several under water based
sensor applications, nodes are square measured and through this the energy is affected. Thus, the mobility
of each sensor nodes are measured through the water atmosphere from the water flow for sensor based
protocol formations. Researchers have developed many routing protocols. However, those lost their charm
with the time. This can be the demand of the age to supply associate degree upon energy-efficient and
ascendable strong routing protocol for under water actuator networks. During this work, the authors tend
to propose a customary routing protocol named level primarily based routing protocol (LBRP), reaching to
offer strong, ascendable and energy economical routing. LBRP conjointly guarantees the most effective use
of total energy consumption and ensures packet transmission which redirects as an additional reliability in
compare to different routing protocols. In this work, the authors have used the level of forwarding node,
residual energy and distance from the forwarding node to the causing node as a proof in multicasting
technique comparisons. Throughout this work, the authors have got a recognition result concerning about
86.35% on the average in node multicasting performances. Simulation has been experienced each in a
wheezy and quiet atmosphere which represents the endorsement of higher performance for the planned
protocol.
STRUCTURAL DYNAMICS AND EVOLUTION OF CAPSULE ENDOSCOPY (PILL CAMERA) TECHNOLO...ijfcstjournal
This research paper examined and re-evaluates the technological innovation, theory, structural dynamics
and evolution of Pill Camera(Capsule Endoscopy) technology in redirecting the response manner of small
bowel (intestine) examination in human. The Pill Camera (Endoscopy Capsule) is made up of sealed
biocompatible material to withstand acid, enzymes and other antibody chemicals in the stomach is a
technology that helps the medical practitioners especially the general physicians and the
gastroenterologists to examine and re-examine the intestine for possible bleeding or infection. Before the
advent of the Pill camera (Endoscopy Capsule) the colonoscopy was the local method used but research
showed that some parts (bowel) of the intestine can’t be reach by mere traditional method hence the need
for Pill Camera. Countless number of deaths from stomach disease such as polyps, inflammatory bowel
(Crohn”s diseases), Cancers, Ulcer, anaemia and tumours of small intestines which ordinary would have
been detected by sophisticated technology like Pill Camera has become norm in the developing nations.
Nevertheless, not only will this paper examine and re-evaluate the Pill Camera Innovation, theory,
Structural dynamics and evolution it unravelled and aimed to create awareness for both medical
practitioners and the public.
AN OPTIMIZED HYBRID APPROACH FOR PATH FINDINGijfcstjournal
Path finding algorithm addresses problem of finding shortest path from source to destination avoiding
obstacles. There exist various search algorithms namely A*, Dijkstra's and ant colony optimization. Unlike
most path finding algorithms which require destination co-ordinates to compute path, the proposed
algorithm comprises of a new method which finds path using backtracking without requiring destination
co-ordinates. Moreover, in existing path finding algorithm, the number of iterations required to find path is
large. Hence, to overcome this, an algorithm is proposed which reduces number of iterations required to
traverse the path. The proposed algorithm is hybrid of backtracking and a new technique(modified 8-
neighbor approach). The proposed algorithm can become essential part in location based, network, gaming
applications. grid traversal, navigation, gaming applications, mobile robot and Artificial Intelligence.
EAGRO CROP MARKETING FOR FARMING COMMUNITYijfcstjournal
The Major Occupation in India is the Agriculture; the people involved in the Agriculture belong to the poor
class and category. The people of the farming community are unaware of the new techniques and Agromachines, which would direct the world to greater heights in the field of agriculture. Though the farmers
work hard, they are cheated by agents in today’s market. This serves as a opportunity to solve
all the problems that farmers face in the current world. The eAgro crop marketing will serve as a better
way for the farmers to sell their products within the country with some mediocre knowledge about using
the website. This would provide information to the farmers about current market rate of agro-products,
their sale history and profits earned in a sale. This site will also help the farmers to know about the market
information and to view agricultural schemes of the Government provided to farmers.
EDGE-TENACITY IN CYCLES AND COMPLETE GRAPHSijfcstjournal
It is well known that the tenacity is a proper measure for studying vulnerability and reliability in graphs.
Here, a modified edge-tenacity of a graph is introduced based on the classical definition of tenacity.
Properties and bounds for this measure are introduced; meanwhile edge-tenacity is calculated for cycle
graphs and also for complete graphs.
COMPARATIVE STUDY OF DIFFERENT ALGORITHMS TO SOLVE N QUEENS PROBLEMijfcstjournal
This Paper provides a brief description of the Genetic Algorithm (GA), the Simulated Annealing (SA)
Algorithm, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm and attempts to
explain the way as how the Proposed Genetic Algorithm (GA), the Proposed Simulated Annealing (SA)
Algorithm using GA, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm can be
employed in finding the best solution of N Queens Problem and also, makes a comparison between these
four algorithms. It is entirely a review based work. The four algorithms were written as well as
implemented. From the Results, it was found that, the Proposed Genetic Algorithm (GA) performed better
than the Proposed Simulated Annealing (SA) Algorithm using GA, the Backtracking (BT) Algorithm and
the Brute Force (BF) Search Algorithm and it also provided better fitness value (solution) than the
Proposed Simulated Annealing Algorithm (SA) using GA, the Backtracking (BT) Algorithm and the Brute
Force (BF) Search Algorithm, for different N values. Also, it was noticed that, the Proposed GA took more
time to provide result than the Proposed SA using GA.
PSTECEQL: A NOVEL EVENT QUERY LANGUAGE FOR VANET’S UNCERTAIN EVENT STREAMSijfcstjournal
In recent years, the complex event processing technology has been used to process the VANET’s temporal
and spatial event streams. However, we usually cannot get the accurate data because the device sensing
accuracy limitations of the system. We only can get the uncertain data from the complex and limited
environment of the VANET. Because the VANET’s event streams are consist of the uncertain data, so they
are also uncertain. How effective to express and process these uncertain event streams has become the core
issue for the VANET system. To solve this problem, we propose a novel complex event query language
PSTeCEQL (probabilistic spatio-temporal constraint event query language). Firstly, we give the definition
of the possible world model of VANET’s uncertain event streams. Secondly, we propose an event query
language PSTeCEQL and give the syntax and the operational semantics of the language. Finally, we
illustrate the validity of the PSTeCEQL by an example.
CLUSTBIGFIM-FREQUENT ITEMSET MINING OF BIG DATA USING PRE-PROCESSING BASED ON...ijfcstjournal
Now a day enormous amount of data is getting explored through Internet of Things (IoT) as technologies
are advancing and people uses these technologies in day to day activities, this data is termed as Big Data
having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose
frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by
traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets
but it has large communication cost which reduces execution efficiency. This proposed new pre-processed
k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using kmeans algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets
from generated clusters using MapReduce programming model. Results shown that execution efficiency of
ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as
one of the pre-processing technique.
A MUTATION TESTING ANALYSIS AND REGRESSION TESTINGijfcstjournal
Software testing is a testing which conducted a test to provide information to client about the quality of the
product under test. Software testing can also provide an objective, independent view of the software to
allow the business to appreciate and understand the risks of software implementation. In this paper we
focused on two main software testing –mutation testing and mutation testing. Mutation testing is a
procedural testing method, i.e. we use the structure of the code to guide the test program, A mutation is a
little change in a program. Such changes are applied to model low level defects that obtain in the process
of coding systems. Ideally mutations should model low-level defect creation. Mutation testing is a process
of testing in which code is modified then mutated code is tested against test suites. The mutations used in
source code are planned to include in common programming errors. A good unit test typically detects the
program mutations and fails automatically. Mutation testing is used on many different platforms, including
Java, C++, C# and Ruby. Regression testing is a type of software testing that seeks to uncover
new software bugs, or regressions, in existing functional and non-functional areas of a system after
changes such as enhancements, patches or configuration changes, have been made to them. When defects
are found during testing, the defect got fixed and that part of the software started working as needed. But
there may be a case that the defects that fixed have introduced or uncovered a different defect in the
software. The way to detect these unexpected bugs and to fix them used regression testing. The main focus
of regression testing is to verify that changes in the software or program have not made any adverse side
effects and that the software still meets its need. Regression tests are done when there are any changes
made on software, because of modified functions.
GREEN WSN- OPTIMIZATION OF ENERGY USE THROUGH REDUCTION IN COMMUNICATION WORK...ijfcstjournal
Advances in micro fabrication and communication techniques have led to unimaginable proliferation of
WSN applications. Research is focussed on reduction of setup operational energy costs. Bulk of operational
energy costs are linked to communication activities of WSN. Any progress towards energy efficiency has a
potential of huge savings globally. Therefore, every energy efficient step is an endeavour to cut costs and
‘Go Green’. In this paper, we have proposed a framework to reduce communication workload through: Innetwork compression and multiple query synthesis at the base-station and modification of query syntax
through introduction of Static Variables. These approaches are general approaches which can be used in
any WSN irrespective of application.
A NEW MODEL FOR SOFTWARE COSTESTIMATION USING HARMONY SEARCHijfcstjournal
Accurate and realistic estimation is always considered to be a great challenge in software industry.
Software Cost Estimation (SCE) is the standard application used to manage software projects. Determining
the amount of estimation in the initial stages of the project depends on planning other activities of the
project. In fact, the estimation is confronted with a number of uncertainties and barriers’, yet assessing the
previous projects is essential to solve this problem. Several models have been developed for the analysis of
software projects. But the classical reference method is the COCOMO model, there are other methods
which are also applied such as Function Point (FP), Line of Code(LOC); meanwhile, the expert`s opinions
matter in this regard. In recent years, the growth and the combination of meta-heuristic algorithms with
high accuracy have brought about a great achievement in software engineering. Meta-heuristic algorithms
which can analyze data from multiple dimensions and identify the optimum solution between them are
analytical tools for the analysis of data. In this paper, we have used the Harmony Search (HS)algorithm for
SCE. The proposed model which is a collection of 60 standard projects from Dataset NASA60 has been
assessed.The experimental results show that HS algorithm is a good way for determining the weight
similarity measures factors of software effort, and reducing the error of MRE.
AGENT ENABLED MINING OF DISTRIBUTED PROTEIN DATA BANKSijfcstjournal
Mining biological data is an emergent area at the intersection between bioinformatics and data mining
(DM). The intelligent agent based model is a popular approach in constructing Distributed Data Mining
(DDM) systems to address scalable mining over large scale distributed data. The nature of associations
between different amino acids in proteins has also been a subject of great anxiety. There is a strong need to
develop new models and exploit and analyze the available distributed biological data sources. In this study,
we have designed and implemented a multi-agent system (MAS) called Agent enriched Quantitative
Association Rules Mining for Amino Acids in distributed Protein Data Banks (AeQARM-AAPDB). Such
globally strong association rules enhance understanding of protein composition and are desirable for
synthesis of artificial proteins. A real protein data bank is used to validate the system.
International Journal on Foundations of Computer Science & Technology (IJFCST)ijfcstjournal
International Journal on Foundations of Computer Science & Technology (IJFCST) is a Bi-monthly peer-reviewed and refereed open access journal that publishes articles which contribute new results in all areas of the Foundations of Computer Science & Technology. Over the last decade, there has been an explosion in the field of computer science to solve various problems from mathematics to engineering. This journal aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals. Topics of interest include, but are not limited to the following:
Because the technology is used largely in the last decades; cybercrimes have become a significant
international issue as a result of the huge damage that it causes to the business and even to the ordinary
users of technology. The main aims of this paper is to shed light on digital crimes and gives overview about
what a person who is related to computer science has to know about this new type of crimes. The paper has
three sections: Introduction to Digital Crime which gives fundamental information about digital crimes,
Digital Crime Investigation which presents different investigation models and the third section is about
Cybercrime Law.
DISTRIBUTION OF MAXIMAL CLIQUE SIZE UNDER THE WATTS-STROGATZ MODEL OF EVOLUTI...ijfcstjournal
In this paper, we analyze the evolution of a small-world network and its subsequent transformation to a
random network using the idea of link rewiring under the well-known Watts-Strogatz model for complex
networks. Every link u-v in the regular network is considered for rewiring with a certain probability and if
chosen for rewiring, the link u-v is removed from the network and the node u is connected to a randomly
chosen node w (other than nodes u and v). Our objective in this paper is to analyze the distribution of the
maximal clique size per node by varying the probability of link rewiring and the degree per node (number
of links incident on a node) in the initial regular network. For a given probability of rewiring and initial
number of links per node, we observe the distribution of the maximal clique per node to follow a Poisson
distribution. We also observe the maximal clique size per node in the small-world network to be very close
to that of the average value and close to that of the maximal clique size in a regular network. There is no
appreciable decrease in the maximal clique size per node when the network transforms from a regular
network to a small-world network. On the other hand, when the network transforms from a small-world
network to a random network, the average maximal clique size value decreases significantly
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf
A Modified Dna Computing Approach To Tackle The Exponential Solution Space Of The Graph Coloring Problem
1. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol. 3, No.2, March 2013
A MODIFIED DNA COMPUTING APPROACH TO
TACKLE THE EXPONENTIAL SOLUTION SPACE OF
THE GRAPH COLORING PROBLEM
Ramin Maazallahi1 and Aliakbar Niknafs2
1
Department of Computer Engineering, Shahid Bahonar University of Kerman, Iran
ramin.mz66@gmail.com
2
Department of Computer Engineering, Shahid Bahonar University of Kerman, Iran
niknafs@uk.ac.ir
ABSTRACT
Although it has been evidenced that DNA computing is able to solve the graph coloring problem in a
polynomial time complexity, but the exponential solution space is still a restrictive factor in applying this
technique for solving really large problems. In this paper a modified DNA computing approach based on
Adleman-Lipton model is proposed which tackles the mentioned restriction by coloring the vertices one by
one. In each step, it expands the DNA strands encoding promising solutions and discards those which
encode infeasible ones. A sample graph is colored by simulating the proposed approach and shows a
notable reduction in the number of DNA strands used.
KEYWORDS
DNA Computing, Graph Coloring, Exponential Solution Space, Adleman-Lipton, NP-Complete
1. INTRODUCTION
The graph coloring problem is defined as assigning colors to the vertices of a graph such a way
that no two adjacent vertices have the same color and a minimum number of colors are used. This
problem has many applications such as fault diagnosis [1], functional compression [2], broadcast
scheduling [3], resource allocation [4] and biological networks [5].
The graph coloring problem is one of NP-complete problems, where the number of possible color
assignments increases exponentially with respect to the number of vertices. This makes the
solution space of the problem exponential; therefore a brute force algorithm fails to check all the
assignments to find the solutions. In situation when the exact algorithms are not applicable,
heuristic methods are used which give satisfactory solutions but do not always guaranty the
optimality of solutions.
The largest saturation degree (DSATUR) [6] and the recursive largest first (RLF) [7] are of the
first heuristic approaches proposed to solve the graph coloring problem. They operate greedy and
color the vertices of the graph one by one. DSATUR algorithm evaluates a degree of saturation
for each vertex of the graph and starts by assigning the first color to a vertex of maximal degree.
The degree of saturation is reevaluated for the remaining vertices and the next vertex to be
colored is selected. The RLF algorithm selects the vertex with the largest number of uncolored
neighbors and colors that vertex. It then removes that vertex and its neighbors and selects the next
DOI:10.5121/ijfcst.2013.3201 1
2. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol. 3, No.2, March 2013
uncolored vertex. The process is repeated until all of the vertices of the graph are colored.
Although these approaches are fast, but they usually yield the locally optimal solution.
Genetic algorithm (GA) as a search heuristic which mimics the process of natural evolution has
been used in [8] to solve the graph coloring problem. The authors claim that GA outperforms
DSATUR on the hardest graphs, although for small and large graphs DSATUR performs better.
Also the time complexity of GA is lower than DSATUR.
In some researches the use of local search algorithms have been proposed for solving the graph
coloring problem. Some examples are Tabu Search [9], Simulated Annealing [10] and Variable
Space Search [11]. Also neural network approach for graph coloring is proposed by Rahman [12].
Dorrigiv [13] proposes a hybrid algorithm based on Artificial Bee Colony (ABC) algorithm and
RLF algorithm. In their approach, first the ABC algorithm is used to generate a sequence of nodes
and then the RLF algorithm is applied for coloring the vertices of the graph.
In recent decades, the efficiency of using DNA computing for solving NP-complete problems has
been demonstrated (see for example [14-17]). DNA computing has the advantage of searching the
whole exponential solution space of the problem by applying polynomial number of biological
operations with respect to the problem’s input size. DNA computing has already been used for
solving the graph coloring problem in some researches [18-21]; But the main problem of applying
DNA computing for large graphs is the exponential number of DNA strands needed for
computation.
In this paper we propose a modified DNA computing approach for solving the graph coloring
problem which does not need the initial exponential space of solutions. We start with the first
vertex and generate DNA strands coloring that vertex correctly. Then we add the second vertex
and expand the previous DNA strands to correctly color both the vertices. This process is
repeated until the whole of vertices are colored. Expanding the promising solutions and
discarding the infeasible ones, makes this possible to solve the graph coloring problem for larger
graphs than before.
The rest of this paper is as follows. Section 2 briefly describes DNA computing and the Adleman-
Lipton model which has been used in this paper. In Section 3, we present our approach in details
and simulate it for solving a sample graph in Section 4. Finally, in Section 5 we draw conclusions
and present suggestions for future work.
2. DNA COMPUTING
DNA (deoxyribonucleic acid) is a polymer made up of a linear arrangement of monomers called
nucleotides. Distinct nucleotides are only detected with their bases, where there are four different
bases known as Adenine, Thymine, Cytosine and Guanine, abbreviated as A, T, C and G
respectively. Nucleotides are simply referred to as A, T, C and G nucleotides, depending on the
kind of base that they have. Therefore we can represent DNA strands as strings over the alphabet
{A,T,C,G}.
DNA computing is a novel computational paradigm that uses DNA molecules for storing
information and biological operations, such as polymerase chain reaction (PCR), ligation and gel
electrophoresis, for acting on the information stored. Considering that a test tube can contain up
to 1018 DNA molecules [22] and a biological operation will act on all of the molecules
simultaneously, the power of DNA computing becomes apparent. It resembles a system with 10 18
processors running in parallel. In every DNA computing experiment, a test tube is used for
storing the initial DNA strands which each of the strands encodes a candidate solution to the
2
3. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol. 3, No.2, March 2013
problem. The computation then carries on by applying a sequence of biological operations which
filter out infeasible candidate solutions.
The vast parallel processing ability of DNA molecules makes DNA computing a powerful tool
for solving intractable problems. Adleman [23] was the first who used DNA molecules and
biological operations for solving an instance of the Hamiltonian path problem in laboratory.
Following Adleman’s success, Lipton used the same technique to solve the satisfiability problem
[24]. The biological operations proposed by Adleman and Lipton constructed the Adleman-
Lipton model. Several other models have also been proposed for DNA computing, such as the
sticker model [25], parallel filtering model [26], split-and-merge filtering [27] and filtering-by-
blocking model [28].
The Adleman-Lipton model is constructed upon the following biological operations:
Append (T, S): Given a tube T and a string S, this operation appends S to the end of all
the DNA strands of T.
Copy (T, T1, T2, …, Tn): Given a tube T, this operation creates tubes T1 to Tn which are
identical copies of T. (T1 to Tn will have the same DNA strands as T.)
Merge (T, T1, T2, …, Tn): Given tubes T1 to Tn, this operation pours the contents of T1 to
Tn into tube T; therefore T contains all the DNA strands of T1 to Tn.
Extract (T, S, T+, T-): Given a tube T and a string S, this operation produces two tubes
T+ and T- as follows: All the DNA strands of T having S in their sequence are extracted
and poured into T+ and the remained DNA strands are poured into T-.
Detect (T): Given a tube T, this operation returns true if there is at least one DNA strand
in T, otherwise it returns false.
Discard (T): Given a tube T, this operation ignores T.
3. PROPOSED APPROACH
In this section, we propose our approach for coloring the graph by three colors (3-vertex coloring
problem) and the generalization of our approach for k-vertex coloring problem is straightforward.
Let G (V , E ) be an undirected graph. V {v1 , v 2 ,..., v n } is the set of vertices and
E {e1 , e 2 ,..., e m } is the set of edges. For each ei E , ei (v j , v k ) where v j and v k are two
vertices from V and are said to be adjacent vertices. The goal is to assign three colors of red,
green and blue to the vertices in V , such a way that no two adjacent vertices share the same
color.
To solve the problem by DNA computing, we need three distinct DNA strands for encoding the
three possible color assignments for each vertex. Let ri be a DNA strand encoding the red color
for i th vertex, vi . In the same way, g i and bi are DNA strands which encode the green and blue
colors for vi , respectively. If these strands are concatenated to each other, they form longer DNA
strands encoding a candidate solution for the graph coloring problem. For a graph with n number
of vertices, a candidate solution is represented by a DNA strand as follows:
c1c 2 ...c n (1)
where ci {ri , g i , bi } is a DNA strand encoding the color assigned to vi . In all DNA computing
experiments, we start with a test tube containing all possible candidate solutions to the problem
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and then filter out infeasible solutions to get a test tube of strands, each encoding a possible
solution. But this approach fails for large problems, because the number of DNA strands in the
initial test tube grows exponentially with respect to the input size of the problem. We propose to
make the solution space of the problem, step by step, to avoid the huge number of DNA strands
needed.
We color the vertices of the graph, one by one. Initially DNA strands are generated to color only
the first vertex of the graph. Then these strands are expanded to color the second vertex too. In
this step all the DNA strands that encode an infeasible solution to the problem are poured out and
are not expanded in the rest of the algorithm. The remaining DNA strands are then expanded to
color the third vertex too. Again infeasible solutions are discarded to avoid exponential growth of
solution space. This process is repeated until all of the vertices of the graph are colored. Figure 1
shows our proposed algorithm based on Adleman-Lipton model.
Figure 1. Proposed algorithm
We start with an empty tube denoted by T0 . In lines 3 to 6, vi is colored by red, green and blue
colors. Then the inner for loop extracts DNA strands encoding an infeasible solution until now by
checking the previously colored vertices. If v j is adjacent to v i , then v j and v i cannot have the
same color. So the DNA strands that assign red color to both of these vertices are extracted and
poured into tube Tred _ bad to denote illegal color assignments (line 9). Lines 10 and 11 do the same
thing for green and blue colors. After the inner loop finishes, the DNA strands remained in Tred ,
Tgreen and Tblue are legal color assignments. These tubes are merged into tube T0 in line 14. By
finishing the i th cycle of the outer for loop, T0 contains DNA strands that correctly color all the
vertices from v1 to v i . The illegal color assignments are discarded in lines 15 to 17 to avoid
exponential growth of solution space. After the algorithm finishes, T0 contains DNA strands
which correctly color all the vertices from v1 to v n .
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4. EXPERIMENTAL RESULTS
We have verified the correctness of our approach through extensive simulation results but to have
a comparison with previous works, we present the results for the graph used in [21].
Figure 2 shows this graph which consists of 12 vertices. The DNA strands used in our simulation
are shown in Table 1.
Figure 2. A sample graph with 12 vertices from [21]
Table 1. DNA strands used for solving the graph coloring problem shown in Figure 2
Color DNA strand Color DNA strand
r1 AAGGCAGGAACAGATCAACC r7 TCGCTGCGATTCGATTTGTG
g1 CGTTCTAAATAGGGTCGTGT g7 CCTCAGCGCCTCCGCGTAGC
b1 GATTAGACTTAGCTCGTCCG b7 GCTCATCGTCGAAGCGTAGA
r2 CCACAATGTTATAATACCAC r8 GTTCAATCCTTGCAGCCTCG
g2 ATCTTAGCACGATTCTCCTG g8 CGTATAGAGCTGCACCATAC
b2 GTATATTCAAGTCTCGAGCC b8 CGCAGGCAATAAGGGATTTG
r3 TTTAGATGAAACTCGCGTTC r9 CTCCGATTAATGCACATTTA
g3 TGGCACTCTTAAATCGAATA g9 GTTCGCGGATAAGAAGTCGA
b3 TTGACAAGGAGGAGGATCCA b9 GCGTCCTAGGATCGTTCATT
r4 TCGGGGTAAAGTGATTACTG r10 TTCCCTTTCCGGACTCTTCG
g4 ACCGATCAGTAACTAAATTC g10 GGCTACTTCTTGTTACTCCA
b4 CGATGAGCGCCCTGAGGGGC b10 TAACTGAATCGTCCAATCAC
r5 CGCCGCGTAAGGAGCCCGGT r11 CAAACTGCTACGTCGCCAAT
g5 ACTTATCTTATAAGCGCCGG g11 GGCTCCGAAACGATGGAAGT
b5 GGTCCAGCCTAACTTTTCAT b11 TTCTTGGGGCTTGGGCTATA
r6 ATCTTGACCGCCAATATAAG r12 CTCACAGAATGCTGCGCAAA
g6 CCAATTGTGCCAGCACGTTA g12 TAAATTTACTTCGGGACACC
b6 AGATACCCGTCTGGTTCACC b12 TCTCAACAGCGTCTGGAAGT
A typical DNA computing approach for coloring the graph shown in Figure 2 needs an initial test
tube of 312 different DNA strands to encode all the solution space; but in our approach the
solution space was searched step by step and the number of different DNA strands in a test never
exceeded 180.
The idea of reducing the number of DNA strands was already proposed by Xu et. al [21]. It is
reported in [21] that based on their approach, the initial solution space included 238 DNA strands,
so the effectiveness of our approach is demonstrated. This reduction in the number of DNA
strands is because our approach prunes infeasible solutions and does not expand them; therefore a
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huge part of the solution space is not searched at all. Figure 3 shows the six possible color
assignments obtained by our approach.
Figure 3. The six possible color assignments obtained by simulating our approach
for the graph shown in Figure 2
5. CONCLUDING REMARKS
It has been evidenced that DNA computing is able to solve NP-complete problems in a
polynomial time complexity. By using the inherent parallel processing ability of DNA molecules,
we can encode all the solution space of the problem and extract feasible solutions by applying
polynomial number of biological operations; but the exponential number of DNA strands for
encoding all the solution space is still a major problem.
In this paper we proposed an approach based on Adleman Lipton DNA computing model which
is able to yield all the solutions to the graph coloring problem without needing the exponential
solution space. Our proposed approach has the advantage of discarding a DNA strand before
expanding it, when it turns out to be an infeasible solution. This make a remarkable reduction in
the number of DNA strands used and makes it possible to apply DNA computing approach for
larger graphs than before. We solved a sample graph with 12 vertices by our approach which
needed at most 180 number of different DNA strands instead of 312 strands. Applying the same
technique for solving other NP-complete problems is our future work.
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