A growing body of data suggests that a significantly enhanced salivary cortisol response to waking may indicate
an enduring tendency to abnormal cortisol regulation. More methods have been proposed to deal with
forecasting problems using fuzzy time series. In this paper, our objective was to apply the response test to a
population already known to have long-term hypothalamo–pituitary–adrenocortical (HPA) axis dysregulation.
We hypothesized that the free cortisol response to waking, believed to be genetically influenced, would be
elevated in a significant percent age of cases, regard less of the afternoon Dexamethasone Suppression Test
(DST) value based on fuzzy time series and genetic algorithms. The proposed method adjusts the length of each
interval in the universe of discourse for forecasting the Longitudinal Dexamethasone Suppression Test (DST)
data on a fully remitted lithium responder for past 5 years who was asymptomatic and treated with lithium
throughout the experimental results show that the proposed method gets good forecasting results.
FUZZY ROUGH INFORMATION MEASURES AND THEIR APPLICATIONSijcsity
The degree of roughness characterizes the uncertainty contained in a rough set. The rough entropy was
defined to measure the roughness of a rough set. Though, it was effective and useful, but not accurate
enough. Some authors use information measure in place of entropy for better understanding which
measures the amount of uncertainty contained in fuzzy rough set .In this paper three new fuzzy rough
information measures are proposed and their validity is verified. The application of these proposed
information measures in decision making problems is studied and also compared with other existing
information measures.
In this paper, we define the new notion of interval-valued Pythagorean fuzzy ideals in semigroups and established the properties of its with suitable examples. Also, we introduce the concept of interval valued Pythagorean fuzzy sub-semigroup, interval valued Pythagorean fuzzy left (resp. right) ideal, interval valued Pythagorean fuzzy bi-ideal, interval valued Pythagorean fuzzy interior ideal and homomorphism of an interval valued Pythagorean fuzzy ideal in semigroups with suitable illustration. We show that every interval valued Pythagorean fuzzy left (resp. right) ideal is an interval valued Pythagorean fuzzy bi-ideal.
A FUZZY BASED APPROACH TO TEXT MINING AND DOCUMENT CLUSTERINGIJDKP
Fuzzy logic deals with degrees of truth. In this paper, we have shown how to apply fuzzy logic in text
mining in order to perform document clustering. We took an example of document clustering where the
documents had to be clustered into two categories. The method involved cleaning up the text and stemming
of words. Then, we chose ‘m’ features which differ significantly in their word frequencies (WF), normalized
by document length, between documents belonging to these two clusters. The documents to be clustered
were represented as a collection of ‘m’ normalized WF values. Fuzzy c-means (FCM) algorithm was used
to cluster these documents into two clusters. After the FCM execution finished, the documents in the two
clusters were analysed for the values of their respective ‘m’ features. It was known that documents
belonging to a document type ‘X’ tend to have higher WF values for some particular features. If the
documents belonging to a cluster had higher WF values for those same features, then that cluster was said
to represent ‘X’. By fuzzy logic, we not only get the cluster name, but also the degree to which a document
belongs to a cluster
MULTI PARENTS EXTENDED PRECEDENCE PRESERVATIVE CROSSOVER FOR JOB SHOP SCHEDUL...CHUNG SIN ONG
Job Shop Scheduling Problem (JSSP) is one of the hard combinatorial scheduling problems. This paper proposes a genetic algorithm with multi parents crossover called Extended Precedence Preservative Crossover (EPPX) that can be suitably modified and implemented with, in principal, unlimited number of parents which differ from conventional two parents crossover. JSSP representation encoded by using permutation with repetition guarantees the feasibility of chromosomes thus eliminates the legalization on children (offspring).The simulations are performed on a set of benchmark problems from the literatures and they indicate that the best solutions have the tendencies to be appeared by using 3-6 numbers of parents in the recombination. The comparison between the results of EPPX and other methodologies show the sustainability of multi parents recombination in producing competitive results to solve the JSSP.
AN ADVANCED TOOL FOR MANAGING FUZZY COMPLEX TEMPORAL INFORMATIONcsandit
Many real-life applications need to handle and manage time pieces of information. Allen temporal relations are one of the most used and known formalisms for modelling and handling
temporal data. This paper discusses a novel idea to introduce some kind of flexibility in defining such relations between two fuzzy time intervals. The key concept of this approach is a fuzzy tolerance relation conveniently modelled. Tolerant Allen temporal relations are then defined using the dilated and the eroded intervals of the initial fuzzy time intervals. By leveraging some particular fuzzy indices to compare two fuzzy time intervals, this extension of Allen relations is integrated in the Fuzz-TIME system developed in our previous works.
FUZZY ROUGH INFORMATION MEASURES AND THEIR APPLICATIONSijcsity
The degree of roughness characterizes the uncertainty contained in a rough set. The rough entropy was
defined to measure the roughness of a rough set. Though, it was effective and useful, but not accurate
enough. Some authors use information measure in place of entropy for better understanding which
measures the amount of uncertainty contained in fuzzy rough set .In this paper three new fuzzy rough
information measures are proposed and their validity is verified. The application of these proposed
information measures in decision making problems is studied and also compared with other existing
information measures.
In this paper, we define the new notion of interval-valued Pythagorean fuzzy ideals in semigroups and established the properties of its with suitable examples. Also, we introduce the concept of interval valued Pythagorean fuzzy sub-semigroup, interval valued Pythagorean fuzzy left (resp. right) ideal, interval valued Pythagorean fuzzy bi-ideal, interval valued Pythagorean fuzzy interior ideal and homomorphism of an interval valued Pythagorean fuzzy ideal in semigroups with suitable illustration. We show that every interval valued Pythagorean fuzzy left (resp. right) ideal is an interval valued Pythagorean fuzzy bi-ideal.
A FUZZY BASED APPROACH TO TEXT MINING AND DOCUMENT CLUSTERINGIJDKP
Fuzzy logic deals with degrees of truth. In this paper, we have shown how to apply fuzzy logic in text
mining in order to perform document clustering. We took an example of document clustering where the
documents had to be clustered into two categories. The method involved cleaning up the text and stemming
of words. Then, we chose ‘m’ features which differ significantly in their word frequencies (WF), normalized
by document length, between documents belonging to these two clusters. The documents to be clustered
were represented as a collection of ‘m’ normalized WF values. Fuzzy c-means (FCM) algorithm was used
to cluster these documents into two clusters. After the FCM execution finished, the documents in the two
clusters were analysed for the values of their respective ‘m’ features. It was known that documents
belonging to a document type ‘X’ tend to have higher WF values for some particular features. If the
documents belonging to a cluster had higher WF values for those same features, then that cluster was said
to represent ‘X’. By fuzzy logic, we not only get the cluster name, but also the degree to which a document
belongs to a cluster
MULTI PARENTS EXTENDED PRECEDENCE PRESERVATIVE CROSSOVER FOR JOB SHOP SCHEDUL...CHUNG SIN ONG
Job Shop Scheduling Problem (JSSP) is one of the hard combinatorial scheduling problems. This paper proposes a genetic algorithm with multi parents crossover called Extended Precedence Preservative Crossover (EPPX) that can be suitably modified and implemented with, in principal, unlimited number of parents which differ from conventional two parents crossover. JSSP representation encoded by using permutation with repetition guarantees the feasibility of chromosomes thus eliminates the legalization on children (offspring).The simulations are performed on a set of benchmark problems from the literatures and they indicate that the best solutions have the tendencies to be appeared by using 3-6 numbers of parents in the recombination. The comparison between the results of EPPX and other methodologies show the sustainability of multi parents recombination in producing competitive results to solve the JSSP.
AN ADVANCED TOOL FOR MANAGING FUZZY COMPLEX TEMPORAL INFORMATIONcsandit
Many real-life applications need to handle and manage time pieces of information. Allen temporal relations are one of the most used and known formalisms for modelling and handling
temporal data. This paper discusses a novel idea to introduce some kind of flexibility in defining such relations between two fuzzy time intervals. The key concept of this approach is a fuzzy tolerance relation conveniently modelled. Tolerant Allen temporal relations are then defined using the dilated and the eroded intervals of the initial fuzzy time intervals. By leveraging some particular fuzzy indices to compare two fuzzy time intervals, this extension of Allen relations is integrated in the Fuzz-TIME system developed in our previous works.
The Analysis of Performance Measures of Generalized Trapezoidal Fuzzy Queuing...IJERA Editor
The purpose of this research paper was to propose a method which can be utilized to determine the different types of performance measures on the basis of the crisp values for the fuzzy queuing model which has an unreliable server and where the rate of arrival, the rate of service, the rate of breakdown and the rate of repair are all expressed as the fuzzy numbers. In this case the inter arrival time, the time of service, the rates of breakdown and the rates of repair are all triangular functions and are also expressed as the Trapezoidal fuzzy numbers. The main intent is to transform the fuzzy inter arrival time, the time of service, the rates of breakdown and the rates of repair into the crisp values by using the Ranking function method. Then the crisp values are applied in the classical formulas for the performance measure. In the fuzzy environment the ranking fuzzy numbers are very helpful in making the decisions. The ranking function method is one of the most reliable method, is simpler to apply in comparison to other methods and can be utilized to solve the different types of queuing problems. In this research paper a numerical example is also provided for both the triangular and the trapezoidal fuzzy number so that a practical insight into the problem can be provided.
Found this paper really interesting. It delves into the learning behaviors of Deep Learning Ensembles and compares them Bayesian Neural Networks, which theoretically does the same thing. This answers why Deep Ensembles Outperform
EFFICIENT KNOWLEDGE BASE MANAGEMENT IN DCSP ijasuc
DCSP (Distributed Constraint Satisfaction Problem) has been a very important research area in AI
(Artificial Intelligence). There are many application problems in distributed AI that can be formalized as
DSCPs. With the increasing complexity and problem size of the application problems in AI, the required
storage place in searching and the average searching time are increasing too. Thus, to use a limited
storage place efficiently in solving DCSP becomes a very important problem, and it can help to reduce
searching time as well. This paper provides an efficient knowledge base management approach based on
general usage of hyper-resolution-rule in consistence algorithm. The approach minimizes the increasing of
the knowledge base by eliminate sufficient constraint and false nogood. These eliminations do not change
the completeness of the original knowledge base increased. The proofs are given as well. The example
shows that this approach decrease both the new nogoods generated and the knowledge base greatly. Thus
it decreases the required storage place and simplify the searching process.
Soft Computing Techniques Based Image Classification using Support Vector Mac...ijtsrd
n this paper we compare different kernel had been developed for support vector machine based time series classification. Despite the better presentation of Support Vector Machine SVM on many concrete classification problems, the algorithm is not directly applicable to multi dimensional routes having different measurements. Training support vector machines SVM with indefinite kernels has just fascinated consideration in the machine learning public. This is moderately due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite. In this paper, by spreading the Gaussian RBF kernel by Gaussian elastic metric kernel. Gaussian elastic metric kernel is extended version of Gaussian RBF. The extended version divided in two ways time wrap distance and its real penalty. Experimental results on 17 datasets, time series data sets show that, in terms of classification accuracy, SVM with Gaussian elastic metric kernel is much superior to other kernels, and the ultramodern similarity measure methods. In this paper we used the indefinite resemblance function or distance directly without any conversion, and, hence, it always treats both training and test examples consistently. Finally, it achieves the highest accuracy of Gaussian elastic metric kernel among all methods that train SVM with kernels i.e. positive semi definite PSD and Non PSD, with a statistically significant evidence while also retaining sparsity of the support vector set. Tarun Jaiswal | Dr. S. Jaiswal | Dr. Ragini Shukla ""Soft Computing Techniques Based Image Classification using Support Vector Machine Performance"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23437.pdf
Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23437/soft-computing-techniques-based-image-classification-using-support-vector-machine-performance/tarun-jaiswal
OPTIMAL PREDICTION OF THE EXPECTED VALUE OF ASSETS UNDER FRACTAL SCALING EXPO...mathsjournal
In this paper, the optimal prediction of the expected value of assets under the fractal scaling exponent is
considered. We first obtain a fractal exponent, then derive a seemingly Black-Scholes parabolic equation.
We further obtain its solutions under given conditions for the prediction of expected value of assets given
the fractal exponent.
Hybridization of Bat and Genetic Algorithm to Solve N-Queens ProblemjournalBEEI
In this paper, a hybrid of Bat-Inspired Algorithm (BA) and Genetic Algorithm (GA) is proposed to solve N-queens problem. The proposed algorithm executes the behavior of microbats with changing pulse rates of emissions and loudness to final all the possible solutions in the initialization and moving phases. This dataset applied two metaheuristic algorithms (BA and GA) and the hybrid to solve N-queens problem by finding all the possible solutions in the instance with the input sizes of area 8*8, 20*20, 50*50, 100*100 and 500*500 on a chessboard. To find the optimal solution, consistently, ten run have been set with 100 iterations for all the input sizes. The hybrid algorithm obtained substantially better results than BA and GA because both algorithms were inferior in discovering the optimal solutions than the proposed randomization method. It also has been discovered that BA outperformed GA because it requires a reduced amount of steps in determining the solutions.
FINE GRAIN PARALLEL CONSTRUCTION OF NEIGHBOUR-JOINING PHYLOGENETIC TREES WITH...ijdpsjournal
In biological research, scientists often need to use the information of the species to infer the evolutionary relationship among them. The evolutionary relationships are generally represented by a labeled binary tree, called the evolutionary tree (or phylogenetic tree). The phylogeny problem is computationally intensive, and thus it is suitable for parallel computing environment. In this paper, a fast algorithm for
constructing Neighbor-Joining phylogenetic trees has been developed. The CPU time is drastically reduced as compared with sequential algorithms. The new algorithm includes three techniques: Firstly, a linear array A[N] is introduced to store the sum of every row of the distance matrix (the same as SK),
which can eliminate many repeated (redundancy) computations, and the value of A[i] are computed only once at the beginning of the algorithm, and are updated by three elements in the iteration. Secondly, a very compact formula for the sum of all the branch lengths of OTUs (Operational Taxonomic Units) i and
j has been designed. Thirdly, multiple parallel threads are used for computation of nearest neighboring pair.
Zadeh conceptualized the theory of fuzzy set to provide a tool for the basis of the theory of possibility. Atanassov extended this theory with the introduction of intuitionistic fuzzy set. Smarandache introduced the concept of refined intuitionistic fuzzy set by further subdivision of membership and non-membership value. The meagerness regarding the allocation of a single membership and non-membership value to any object under consideration is addressed with this novel refinement. In this study, this novel idea is utilized to characterize the essential elements e.g. subset, equal set, null set, and complement set, for refined intuitionistic fuzzy set. Moreover, their basic set theoretic operations like union, intersection, extended intersection, restricted union, restricted intersection, and restricted difference, are conceptualized. Furthermore, some basic laws are also discussed with the help of an illustrative example in each case for vivid understanding.
Hypersoft set is an extension of the soft set where there is more than one set of attributes occur and it is very much helpful in multi-criteria group decision making problem. In a hypersoft set, the function F is a multi-argument function. In this paper, we have used the notion of Fuzzy Hypersoft Set (FHSS), which is a combination of fuzzy set and hypersoft set. In earlier research works the concept of Fuzzy Soft Set (FSS) was introduced and it was applied successfully in various fields. The FHSS theory gives more flexibility as compared to FSS to tackle the parameterized problems of uncertainty. To overcome the issue where FSS failed to explain uncertainty and incompleteness there is a dire need for another environment which is known as FHSS. It works well when there is more complexity involved in the parametric data i.e the data that involves vague concepts. This work includes some basic set-theoretic operations on FHSSs and for the reliability and the authenticity of these operations, we have shown its application with the help of a suitable example. This example shows that how FHSS theory plays its role to solve real decision-making problems.
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.
Lagrangian Relaxation And Danzig Wolfe Scheduling Problemmrwalker7
My term project report for my Applied Optimization course. I proposed to examine the application of Lagrangian Relaxation and Danzig-Wolfe Decomposition techniques to the Generalized Assignment Problem. I both presented the formulations and compared the different methods in terms of the solve time metric for cases of varying complexity.
The Generalized Assignment Problem is a mixed-integer problem and a superset of the Scheduling Problem.
Sensitivity Analysis of GRA Method for Interval Valued Intuitionistic Fuzzy M...ijsrd.com
The aim of this paper is to investigate the multiple attribute decision making problems with intuitionistic fuzzy information, in which the information about attribute weights are incompletely known, and the attribute values take the form of intuitionistic fuzzy numbers. In order to get the weight vector of the attribute, we establish an optimization model based on the basic ideal of traditional gray relational analysis (GRA) method, by which the attribute weights can be determined. For the special situations where the information about attribute weights are completely unknown, we establish another optimization model. By solving this model, we get a simple and exact formula, which can be used to determine the attribute weights. Then, based on the traditional GRA method, calculation steps for solving an interval-valued intuitionistic fuzzy environment and developed modified GRA method for interval-valued intuitionistic fuzzy multiple attributes decision-making with incompletely known attribute weight information. This paper provides a new method for sensitivity analysis of MADM problems so that by sing it and changing the weights of attributes, one can determine changes in the final results for a decision making problem. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.
Sequence Similarity between Genetic Codes using Improved Longest Common Subse...rahulmonikasharma
Finding the sequence similarity between two genetic codes is an important problem in computational biology. In this paper, we developed an efficient algorithm to find sequence similarity between genetic codes using longest common subsequence algorithm. The algorithm takes the advantages over the edit distance algorithm and improves the performance. The proposed algorithm is tested on randomly generated DNA sequence and finding the exact DNA sequence comparison. The DNA genetic code sequence comparison can be used to discover information such as evolutionary divergence and ways to apply genetic codes from one DNA sequence to another sequence.
Critical Paths Identification on Fuzzy Network Projectiosrjce
In this paper, a new approach for identifying fuzzy critical path is presented, based on converting the
fuzzy network project into deterministic network project, by transforming the parameters set of the fuzzy
activities into the time probability density function PDF of each fuzzy time activity. A case study is considered as
a numerical tested problem to demonstrate our approach.
A recently updated portfolio of my work produced for up and coming job interviews and to decorate coffee tables. Mainly focusing on branding, layout and illustrations.
The Analysis of Performance Measures of Generalized Trapezoidal Fuzzy Queuing...IJERA Editor
The purpose of this research paper was to propose a method which can be utilized to determine the different types of performance measures on the basis of the crisp values for the fuzzy queuing model which has an unreliable server and where the rate of arrival, the rate of service, the rate of breakdown and the rate of repair are all expressed as the fuzzy numbers. In this case the inter arrival time, the time of service, the rates of breakdown and the rates of repair are all triangular functions and are also expressed as the Trapezoidal fuzzy numbers. The main intent is to transform the fuzzy inter arrival time, the time of service, the rates of breakdown and the rates of repair into the crisp values by using the Ranking function method. Then the crisp values are applied in the classical formulas for the performance measure. In the fuzzy environment the ranking fuzzy numbers are very helpful in making the decisions. The ranking function method is one of the most reliable method, is simpler to apply in comparison to other methods and can be utilized to solve the different types of queuing problems. In this research paper a numerical example is also provided for both the triangular and the trapezoidal fuzzy number so that a practical insight into the problem can be provided.
Found this paper really interesting. It delves into the learning behaviors of Deep Learning Ensembles and compares them Bayesian Neural Networks, which theoretically does the same thing. This answers why Deep Ensembles Outperform
EFFICIENT KNOWLEDGE BASE MANAGEMENT IN DCSP ijasuc
DCSP (Distributed Constraint Satisfaction Problem) has been a very important research area in AI
(Artificial Intelligence). There are many application problems in distributed AI that can be formalized as
DSCPs. With the increasing complexity and problem size of the application problems in AI, the required
storage place in searching and the average searching time are increasing too. Thus, to use a limited
storage place efficiently in solving DCSP becomes a very important problem, and it can help to reduce
searching time as well. This paper provides an efficient knowledge base management approach based on
general usage of hyper-resolution-rule in consistence algorithm. The approach minimizes the increasing of
the knowledge base by eliminate sufficient constraint and false nogood. These eliminations do not change
the completeness of the original knowledge base increased. The proofs are given as well. The example
shows that this approach decrease both the new nogoods generated and the knowledge base greatly. Thus
it decreases the required storage place and simplify the searching process.
Soft Computing Techniques Based Image Classification using Support Vector Mac...ijtsrd
n this paper we compare different kernel had been developed for support vector machine based time series classification. Despite the better presentation of Support Vector Machine SVM on many concrete classification problems, the algorithm is not directly applicable to multi dimensional routes having different measurements. Training support vector machines SVM with indefinite kernels has just fascinated consideration in the machine learning public. This is moderately due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite. In this paper, by spreading the Gaussian RBF kernel by Gaussian elastic metric kernel. Gaussian elastic metric kernel is extended version of Gaussian RBF. The extended version divided in two ways time wrap distance and its real penalty. Experimental results on 17 datasets, time series data sets show that, in terms of classification accuracy, SVM with Gaussian elastic metric kernel is much superior to other kernels, and the ultramodern similarity measure methods. In this paper we used the indefinite resemblance function or distance directly without any conversion, and, hence, it always treats both training and test examples consistently. Finally, it achieves the highest accuracy of Gaussian elastic metric kernel among all methods that train SVM with kernels i.e. positive semi definite PSD and Non PSD, with a statistically significant evidence while also retaining sparsity of the support vector set. Tarun Jaiswal | Dr. S. Jaiswal | Dr. Ragini Shukla ""Soft Computing Techniques Based Image Classification using Support Vector Machine Performance"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23437.pdf
Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23437/soft-computing-techniques-based-image-classification-using-support-vector-machine-performance/tarun-jaiswal
OPTIMAL PREDICTION OF THE EXPECTED VALUE OF ASSETS UNDER FRACTAL SCALING EXPO...mathsjournal
In this paper, the optimal prediction of the expected value of assets under the fractal scaling exponent is
considered. We first obtain a fractal exponent, then derive a seemingly Black-Scholes parabolic equation.
We further obtain its solutions under given conditions for the prediction of expected value of assets given
the fractal exponent.
Hybridization of Bat and Genetic Algorithm to Solve N-Queens ProblemjournalBEEI
In this paper, a hybrid of Bat-Inspired Algorithm (BA) and Genetic Algorithm (GA) is proposed to solve N-queens problem. The proposed algorithm executes the behavior of microbats with changing pulse rates of emissions and loudness to final all the possible solutions in the initialization and moving phases. This dataset applied two metaheuristic algorithms (BA and GA) and the hybrid to solve N-queens problem by finding all the possible solutions in the instance with the input sizes of area 8*8, 20*20, 50*50, 100*100 and 500*500 on a chessboard. To find the optimal solution, consistently, ten run have been set with 100 iterations for all the input sizes. The hybrid algorithm obtained substantially better results than BA and GA because both algorithms were inferior in discovering the optimal solutions than the proposed randomization method. It also has been discovered that BA outperformed GA because it requires a reduced amount of steps in determining the solutions.
FINE GRAIN PARALLEL CONSTRUCTION OF NEIGHBOUR-JOINING PHYLOGENETIC TREES WITH...ijdpsjournal
In biological research, scientists often need to use the information of the species to infer the evolutionary relationship among them. The evolutionary relationships are generally represented by a labeled binary tree, called the evolutionary tree (or phylogenetic tree). The phylogeny problem is computationally intensive, and thus it is suitable for parallel computing environment. In this paper, a fast algorithm for
constructing Neighbor-Joining phylogenetic trees has been developed. The CPU time is drastically reduced as compared with sequential algorithms. The new algorithm includes three techniques: Firstly, a linear array A[N] is introduced to store the sum of every row of the distance matrix (the same as SK),
which can eliminate many repeated (redundancy) computations, and the value of A[i] are computed only once at the beginning of the algorithm, and are updated by three elements in the iteration. Secondly, a very compact formula for the sum of all the branch lengths of OTUs (Operational Taxonomic Units) i and
j has been designed. Thirdly, multiple parallel threads are used for computation of nearest neighboring pair.
Zadeh conceptualized the theory of fuzzy set to provide a tool for the basis of the theory of possibility. Atanassov extended this theory with the introduction of intuitionistic fuzzy set. Smarandache introduced the concept of refined intuitionistic fuzzy set by further subdivision of membership and non-membership value. The meagerness regarding the allocation of a single membership and non-membership value to any object under consideration is addressed with this novel refinement. In this study, this novel idea is utilized to characterize the essential elements e.g. subset, equal set, null set, and complement set, for refined intuitionistic fuzzy set. Moreover, their basic set theoretic operations like union, intersection, extended intersection, restricted union, restricted intersection, and restricted difference, are conceptualized. Furthermore, some basic laws are also discussed with the help of an illustrative example in each case for vivid understanding.
Hypersoft set is an extension of the soft set where there is more than one set of attributes occur and it is very much helpful in multi-criteria group decision making problem. In a hypersoft set, the function F is a multi-argument function. In this paper, we have used the notion of Fuzzy Hypersoft Set (FHSS), which is a combination of fuzzy set and hypersoft set. In earlier research works the concept of Fuzzy Soft Set (FSS) was introduced and it was applied successfully in various fields. The FHSS theory gives more flexibility as compared to FSS to tackle the parameterized problems of uncertainty. To overcome the issue where FSS failed to explain uncertainty and incompleteness there is a dire need for another environment which is known as FHSS. It works well when there is more complexity involved in the parametric data i.e the data that involves vague concepts. This work includes some basic set-theoretic operations on FHSSs and for the reliability and the authenticity of these operations, we have shown its application with the help of a suitable example. This example shows that how FHSS theory plays its role to solve real decision-making problems.
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.
Lagrangian Relaxation And Danzig Wolfe Scheduling Problemmrwalker7
My term project report for my Applied Optimization course. I proposed to examine the application of Lagrangian Relaxation and Danzig-Wolfe Decomposition techniques to the Generalized Assignment Problem. I both presented the formulations and compared the different methods in terms of the solve time metric for cases of varying complexity.
The Generalized Assignment Problem is a mixed-integer problem and a superset of the Scheduling Problem.
Sensitivity Analysis of GRA Method for Interval Valued Intuitionistic Fuzzy M...ijsrd.com
The aim of this paper is to investigate the multiple attribute decision making problems with intuitionistic fuzzy information, in which the information about attribute weights are incompletely known, and the attribute values take the form of intuitionistic fuzzy numbers. In order to get the weight vector of the attribute, we establish an optimization model based on the basic ideal of traditional gray relational analysis (GRA) method, by which the attribute weights can be determined. For the special situations where the information about attribute weights are completely unknown, we establish another optimization model. By solving this model, we get a simple and exact formula, which can be used to determine the attribute weights. Then, based on the traditional GRA method, calculation steps for solving an interval-valued intuitionistic fuzzy environment and developed modified GRA method for interval-valued intuitionistic fuzzy multiple attributes decision-making with incompletely known attribute weight information. This paper provides a new method for sensitivity analysis of MADM problems so that by sing it and changing the weights of attributes, one can determine changes in the final results for a decision making problem. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.
Sequence Similarity between Genetic Codes using Improved Longest Common Subse...rahulmonikasharma
Finding the sequence similarity between two genetic codes is an important problem in computational biology. In this paper, we developed an efficient algorithm to find sequence similarity between genetic codes using longest common subsequence algorithm. The algorithm takes the advantages over the edit distance algorithm and improves the performance. The proposed algorithm is tested on randomly generated DNA sequence and finding the exact DNA sequence comparison. The DNA genetic code sequence comparison can be used to discover information such as evolutionary divergence and ways to apply genetic codes from one DNA sequence to another sequence.
Critical Paths Identification on Fuzzy Network Projectiosrjce
In this paper, a new approach for identifying fuzzy critical path is presented, based on converting the
fuzzy network project into deterministic network project, by transforming the parameters set of the fuzzy
activities into the time probability density function PDF of each fuzzy time activity. A case study is considered as
a numerical tested problem to demonstrate our approach.
A recently updated portfolio of my work produced for up and coming job interviews and to decorate coffee tables. Mainly focusing on branding, layout and illustrations.
Considering all aspects, clinical and administrative, of the surgical process at OSNC/Rex, identify and explore areas for improvement; Design a quality improvement initiative addressing a specific developmental opportunity. This should be a complete design/presentation with area of need, options, SWOT analysis, recommendations and action steps.
The Cortisol Awakening Response for Modified Method for Higher Order Logical ...IJERA Editor
We hypothesized that the free cortisol response to waking, believed to be genetically influenced, would be
elevated in a significant percent age of cases, regard less of the afternoon Dexamethasone Suppression Test
(DST) value based on high-order fuzzy logical relationships. First, the proposed method fuzzifies the historical
data into fuzzy sets to form high-order fuzzy logical relationships. Then, it calculates the value of the variable
between the subscripts of adjacent fuzzy sets appearing in the antecedents of high-order fuzzy logical
relationships. Finally, it chooses a modified high-order fuzzy logical relationships group to forecast the free
cortisol response to walking and the short day time profile using various mean techniques like, Arithmetic
Mean, Geometric Mean, Heronian Mean, Root Mean Square and Harmonic Mean.
A Systematic Overview of Underwater Wireless Sensor Networks: Applications, Challenge and Research Perspectives
Establishing the Forecasting Model with Time Series Data Based on Graph and Particle Swarm Optimization
Comparison of Websites Employing Search Engine Optimization and Live Data
Inquiring Natural Language Processing Capabilities on Robotic Systems through Virtual Assistants: A Systemic Approach
SGT: Session-based Recommendation with GRU and Transformer
RESIDUALS AND INFLUENCE IN NONLINEAR REGRESSION FOR REPEATED MEASUREMENT DATAorajjournal
All observations don’t have equal significance in regression analysis. Diagnostics of observations is an important aspect of model building. In this paper, we use diagnostics method to detect residuals and influential points in nonlinear regression for repeated measurement data. Cook distance and Gauss newton method have been proposed to identify the outliers in nonlinear regression analysis and parameter estimation. Most of these techniques based on graphical representations of residuals, hat matrix and case deletion measures. The results
show us detection of single and multiple outliers cases in repeated measurement data. We use these techniques
to explore performance of residuals and influence in nonlinear regression model.
AN ADVANCED TOOL FOR MANAGING FUZZY COMPLEX TEMPORAL INFORMATIONcscpconf
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The Cortisol Awakening Response Using Modified Proposed Method of Forecasting Based on Fuzzy Time Series
1. Dr. P. Senthil Kumar et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 5, Issue 10, (Part - 3) October 2015, pp.63-70
www.ijera.com 63 | P a g e
The Cortisol Awakening Response Using Modified Proposed
Method of Forecasting Based on Fuzzy Time Series
Dr. P. Senthil Kumar*, B. Mohamed Harif ** & A. Nithya***
*Assistant professor of Mathematics, Rajah Serofji Government College. Thanjavur. (T.N)
**Assistant professor of Mathematics, Rajah Serofji Government College. Thanjavur. (T.N)
***Research Scholar, Department of Mathematics, Rajah Serofji Government College. Thanjavur. (T.N)
ABSTRACT
A growing body of data suggests that a significantly enhanced salivary cortisol response to waking may indicate
an enduring tendency to abnormal cortisol regulation. More methods have been proposed to deal with
forecasting problems using fuzzy time series. In this paper, our objective was to apply the response test to a
population already known to have long-term hypothalamo–pituitary–adrenocortical (HPA) axis dysregulation.
We hypothesized that the free cortisol response to waking, believed to be genetically influenced, would be
elevated in a significant percent age of cases, regard less of the afternoon Dexamethasone Suppression Test
(DST) value based on fuzzy time series and genetic algorithms. The proposed method adjusts the length of each
interval in the universe of discourse for forecasting the Longitudinal Dexamethasone Suppression Test (DST)
data on a fully remitted lithium responder for past 5 years who was asymptomatic and treated with lithium
throughout the experimental results show that the proposed method gets good forecasting results.
Keywords: Fuzzy Time Series, Fuzzy Logical Relationship, Mean Square Error, glucocorticoids, salivary
cortisol, bipolar disorder, lithium, Dexamethasone Suppression Test, DST.
I. INTRODUCTION
Forecasting activities play an important role in
our daily life. In order to solve the forecasting
problems, many researchers have proposed many
different forecasting methods [1], [2], [3], [5]. In
[21], Song et al. proposed the definition of fuzzy time
series. They also proposed the time-invariant model
[22] and the time-variant model [23] of fuzzy time
series to forecast enrollments of the University of
Alabama. Both the time-invariant model and time-
variant model used Max-Min Composition
operations. Huarng [15] in another work is used a
heuristic function to present a method for forecasting
the enrollments of the university of Alabama based
on chen‟s method [2]. Hurang and yu [16] presented
a method for dealing with forecasting problems using
ratio-based lengths of intervals to improve the
forecasting accuracy rate. Chen et-al.[8] presented a
method for forecasting enrollments using automatic
clustering technique and fuzzy logical relationships.
Kuo et al. [17] presented a method for forecasting
enrollments using fuzzy time series and particles
warm optimization techniques. Chen and chen [10]
presented online fuzzy time series analysis based on
entropy discretization and fast fourier transform.
Erolegrioglu [12] presented a method in fuzzy time
series which is based on particle swarm optimization
technique to handle the high order fuzzy time series
model. A growing body of literature points to
hypothalamo–pituitary–adrenocortical (HPA) axis
dysregulation as a critical factor in the development
of mood disorders. Long-term enhanced cortisol
secretion may have important health ramifications in
addition to its contribution to mood syndromes. The
free cortisol response to waking is a promising series
of salivary tests that may provide a useful and non-
invasive measure of HPA functioning in high-risk
studies. The small sample size limits generalizability
of our findings. Because interrupted sleep may
interfere with the waking cortisol rise, we may have
underestimated the proportion of our population with
enhanced cortisol secretion. Highly cooperative
participants are required [11].
II. FUZZY TIME SERIES
In this session, we brief review the concept of
fuzzy time series from [21], [22], [23]. The main
difference of fuzzy time series and traditional time
series is that the values of fuzzy
time series are represented by fuzzy sets [27] rather
than real values.
Let D be the universe of discourse, where
n
iidD 1
. A fuzzy set iA in the universe of
discourse D is defined as follows:
n
i i
iA
i
d
df
A i
1
)(
, Where iAf is the membership
function of the fuzzy set iA , iAf : D 1,0 ,
RESEARCH ARTICLE OPEN ACCESS
2. Dr. P. Senthil Kumar et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 5, Issue 10, (Part - 3) October 2015, pp.63-70
www.ijera.com 64 | P a g e
)( jA df i
is the degree of membership of jd in the
fuzzy set iA , 1,0)( jA df i
and nj 1 .
Recently, interest has turned to more refined
testing and the probability that HPA dysregulation
may even predate the onset of clinical illness [9].
Preliminary data suggest that this dysregulation may
be concentrated within the families of individuals
with mood disorders [10], suggesting the hypothesis
that early abnormalities in cortisol regulation may
confer a risk for the future development of mood
disorders. To understand the temporal relation
between HPA dysregulation and the onset of bipolar
disorder (BD), it is essential to have a reliable and
non-invasive test that can be repeatedly administered
prospectively and is acceptable to high-risk
populations. Promising candidates for such a test
include the salivary free cortisol response to waking
and the short day time profile, a test that adds
afternoon and evening measurements to the waking
values[9].
Let .....)2,1,0....,()( ttY be the universe of
discourse in which fuzzy sets ......)2,1()( itfi
are defined in the universe of discourse )(tY .
Assume that )(tF is a collection of
......)2,1()( itfi , then )(tF is called a fuzzy
time series of .....)2,1,0....,()( ttY .
Assume that there is a fuzzy relationship
),1( ttR , such that
),1()1()( ttRotFtF , where the symbol “
o ” represents the max-min composition operator,
then )(tF is called caused by )1( tF .
Let iAtF )1( and let jAtF )( , where
iA and jA are fuzzy sets, then the fuzzy logical
relationship (FLR) between )1( tF and )(tF can
be denoted by ji AA , where iA and jA are
called the left-hand side(LHS) and the right hand side
(RHS) of the fuzzy logical relationship, respectively.
Fuzzy logical relationships having the same
left-hand side can be grouped into a fuzzy logical
relationship group(FLRG). For example, assume that
the following fuzzy logical relationships exist:
,...,,, jmijcijbijai AAAAAAAA
III. A MODOFIED PROPOSED
METHOD FOR FUZZY TIME
SERIES FORECASTING
In this session, we present a new method to
forecast the Longitudinal Dexamethasone
Suppression Test (DST) [10] data on a fully remitted
lithium responder for past 5 years who was
asymptomatic and treated with lithium throughout,
based on fuzzy time series and genetic algorithms.
Step 1: In many of the exiting algorithms, the
universe of discourse is considered as
2max1min , BBBBD into intervals of
equal length, where minB and maxB are the
minimum value and the maximum value of the
historical data, respectively, and 1B and 2B are two
proper positive real values to divide the universe of
discourse D into n intervals nddd ,.....,, 21 of
equal length. Here we considered the universe of
discourse using normal distribution range based
definition, i.e., D = [𝜇 - 3σ, 𝜇 + 3σ] where 𝜇 and σ are
mean and standard deviation values of the data,
respectively. Also, in the exiting method [9], the
forecasted variable is calculated by taking into
account all the values including the repeated values
are considered as single value. We call the forecasted
value is modified forecasted variable, because of
these modifications, the root mean square error of the
modified method is minimum composed to the
existing method.
Step 2: Here 𝜇 = 216.61, σ = 89.03, 𝜇 - 3σ = -50.48
and 𝜇 + 3σ = 483.7 the universe of discourse D = [-
50.48, 483.7] ≃ [-50, 480]. But 821 ,....., andAAA
are linguistic terms represented by fuzzy sets.
Therefore, the universe of the discourse D = [50,
450]. Firstly, divide the universe of discourse D into
Eight intervals d1, d2, d3, d4, d5, d6, d7 and d8, where
d1 = [50, x1], d2 = [x1, x2], d3 = [x2, x3], d4 = [x3, x4],
d5 = [x4, x5], d6 = [x5, x6], d7 = [x6, x7] and d8 = [x7;
450]; x1, x2, x3, x4, x5, x6 and x7 are integer variables
and x1 < x2 < x3 < x4 < x5 < x6 < x7. We can see that
the universe discourse D = [50, 450] into Eight
intervals d1, d2, d3, d4, d5, d6, d7 and u8, where d1 =
[50, 100], d2 = [100, 150], d3 = [150, 200], d4 = [200,
250], d5 = [250, 300], d6 = [300, 350], d7 = [350, 400]
and d8 = [400; 450];
Step 3: Define the linguistic terms iA represented
by fuzzy sets, shown as follows
,0000
005.01
8765
4321
1
dddd
dddd
A
,0000
05.015.0
8765
4321
2
dddd
dddd
A
3. Dr. P. Senthil Kumar et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 5, Issue 10, (Part - 3) October 2015, pp.63-70
www.ijera.com 65 | P a g e
,0000
5.015.00
8765
4321
3
dddd
dddd
A
,0005.0
15.000
8765
4321
4
dddd
dddd
A
8765
4321
5
005.01
5.0000
dddd
dddd
A
,05.015.0
0000
8765
4321
6
dddd
dddd
A
,5.015.00
0000
8765
4321
7
dddd
dddd
A
,15.000
0000
8765
4321
8
dddd
dddd
A
Where nandAAA ,....., 21 are linguistic terms
represented by fuzzy sets. Then, we can fuzzify the
Longitudinal Dexamethasone Suppression Test
(DST)[10] data on a fully remitted lithium responder
for past 5 years who was asymptomatic and treated
with lithium throughout, shown in Table 1, as shown
in Table 3. Furthermore, we can get the fuzzy logical
relationship groups as shown in Table 4, where the
ith fuzzy logical relationship group contains fuzzy
logical relationships whose current state is Ai, where
1 ≤ i ≤8. Then, apply the following forecasting
method to forecast the data [1]:
Step 4: Assume that the fuzzified data of the ith year
is Aj and assume that there is only one fuzzy logical
relationship in the fuzzy logical relationship groups
in which the current state of the fuzzy logical
relationship is Aj , shown as follows:
“Aj → Ak”
where Aj and Ak are fuzzy sets and the
maximum membership value of Ak occurs at interval
dk, then the forecasted data of the i + 1th year is the
midpoint mk of the interval dk.
Step 5: Rules For Forecasting
LVj – lower value of the interval dj
UVj – upper value of the interval dj
Lj – length of the interval dj
The midpoint mk of the interval dk
The fuzzified data of the jth
year is Aj in which the
current state of the fuzzy logical relationship is Ak ,
shown as follows:
“Aj → Ak”
Gn – Given value of state „n‟
Gn-1 – Given value of state „n‟
Gn-2 – Given value of state „n‟
Fj – forecasted value of the current state „j‟
Computational Algorithms
For i = 3, 4, 5, ......(end of time series data)
Obtained fuzzy logical relation for “Aj → Ak”
V = 0 and x =0
1.Dn = |( Gn - 2Gn-1 + Gn-2)|
2. a) if mj + Dn/4 ≥ LVk & mj + Dn/4 ≤ UVk
then V = V + mj + Dn/4, x = x + 1
b) if mj - Dn/4 ≥ LVk & mj - Dn/4 ≤ UVk
then V = V + mj - Dn/4, x = x + 1
c) if mj + Dn/2 ≥ LVk & mj + Dn/2 ≤ UVk
then V = V + mj + Dn/2, x = x + 1
d) if mj - Dn/2 ≥ LVk & mj - Dn/2 ≤ UVk
then V = V + mj - Dn/2, x = x + 1
e) if mj + Dn≥ LVk & mj + Dn≤ UVk
then V = V + mj + Dn, x = x + 1
f) if mj - Dn≥ LVk & mj - Dn≤ UVk
then V = V + mj - Dn, x = x + 1
g) if mj + Dn ≥ LVk & mj + Dn ≤ UVk
then V = V + mj + Dn, x = x + 1
h) if mj - 2Dn ≥ LVk & mj - 2Dn ≤ UVk
then V = V + mj - 2Dn, x = x + 1
3. Fk = (V + mk) / (x + 1)
Next i
4. Dr. P. Senthil Kumar et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 5, Issue 10, (Part - 3) October 2015, pp.63-70
www.ijera.com 66 | P a g e
Example
Figure 1: The Longitudinal Dexamethasone Suppression Test (DST) data on a fully remitted lithium responder
for past 5 years who was asymptomatic and treated with lithium throughout.
Table 1: Fuzzified value and Fuzzy logical relationships for Medical data
S. No Actual Value Fuzzy set Fuzzy logical relationships
1 225 A4 -
2 190 A3 A4→A3
3 395 A7 A3→A7
4 140 A2 A7→A2
5 90 A1 A2→A1
6 120 A2 A1→A2
7 180 A3 A2→A3
8 110 A2 A3→A2
9 210 A4 A2→A4
10 145 A2 A4→A2
11 190 A3 A2→A3
12 185 A3 A3→A3
13 260 A5 A3→A5
14 210 A4 A5→A4
15 430 A8 A4→A8
16 430 A8 A8→A8
17 420 A8 A8→A8
18 190 A3 A8→A3
19 260 A5 A3→A5
20 190 A3 A5→A3
21 295 A5 A3→A5
22 270 A5 A5→A5
23 230 A4 A5→A4
24 140 A2 A4→A2
25 199 A2 A2→A2
26 120 A2 A2→A2
5. Dr. P. Senthil Kumar et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 5, Issue 10, (Part - 3) October 2015, pp.63-70
www.ijera.com 67 | P a g e
27 315 A6 A2→A6
28 390 A7 A6→A7
29 145 A2 A7→A2
30 210 A4 A2→A4
31 135 A2 A4→A2
32 140 A2 A2→A2
33 140 A2 A2→A2
34 310 A6 A2→A6
35 210 A4 A6→A4
36 180 A3 A4→A3
37 195 A3 A3→A3
38 175 A3 A3→A3
39 190 A3 A3→A3
40 210 A4 A3→A4
41 135 A2 A4→A2
42 175 A3 A2→A3
43 195 A3 A3→A3
44 210 A4 A3→A4
45 120 A2 A4→A2
46 385 A7 A2→A7
47 290 A5 A7→A5
48 195 A3 A5→A3
49 140 A2 A3→A2
Mean square error =
n
valueacutual
n
i
i
1
2
i |valueforecasted|
MSE
Forecasted Error = |Forecasted value – Actual value|/Actual value
Average Forecasting Error = sum of forecasting error / number of errors
Table 2: Forecasted Value and MSE
S. No Actual Value Forecasted Value Forecasted Error
1 225 - -
2 190 - -
3 395 335 0.151899
4 140 135 0.035714
5 90 74.38 0.173556
6 120 120 0
7 180 375 1.083333
8 110 120 0.090909
9 210 217.5 0.035714
10 145 133.75 0.077586
11 190 169.17 0.109632
12 185 175 0.054054
13 260 265 0.019231
14 210 227.08 0.081333
15 430 425 0.011628
16 430 425 0.011628
7. Dr. P. Senthil Kumar et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 5, Issue 10, (Part - 3) October 2015, pp.63-70
www.ijera.com 69 | P a g e
Figure 2: Comparison of actual data forecasted value
IV. Experimental Results
There was a significant difference between BD
patients and our control subjects in the maximum
percentage rise of salivary cortisol response to
awakening. Those showing a waking response also
had significantly higher mean cortisol values at 30
minutes after waking, compared with 509 normal
subjects described in Wust‟s and others study. Base
line values at time zero, immediately upon waking,
did not differ significantly between our sample and
Wust‟s control subjects. Patients and our 5 control
subjects did not differ significantly in the percent age
decline from the peak morning value to the evening
values. In this section we apply the proposed for
forecasting the Longitudinal Dexamethasone
Suppression Test (DST) data on a fully remitted
lithium responder for past 5 years who was
asymptomatic and treated with lithium throughout.
It means that the modified proposed method gets
a higher average forecasting accuracy rate than other
existing methods to forecast the maximum
percentage rise of salivary cortisol response to
awakening. We can see that the modified proposed
method get the smallest Mean square error.
V. CONCLUSION
In this paper, Our dysregulation, even when
lithium-responsive BD patients are clinically well
and their DSTs are observations support the
hypothesis that the free cortisol response to waking
can reflect relatively enduring HPA normal. Because
the test is easy to administer, the free cortisol
response to waking may hold promise as a marker in
studies of high-risk families predisposed to, or at risk
for, mood disorders, we have presented a new
method for forecasting the Longitudinal
Dexamethasone Suppression Test (DST) data on a
fully remitted lithium responder for past 5 years who
was asymptomatic and treated with lithium
throughout based on fuzzy time series and genetic
algorithms. We also make a comparison of the MSE
of the forecasted medical data for different methods.
In this paper, we use the MSE to compare the
performance of prediction of students' enrollment.
However, how to narrow the maximum deviation of
predicted value from the actual one is more important
than the MSE. Therefore, in the future, we will
develop a new method to deal with a more accurate
prediction by narrowing the maximum deviation of
predicted value from the actual one.
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8. Dr. P. Senthil Kumar et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 5, Issue 10, (Part - 3) October 2015, pp.63-70
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